In this review we integrate the scientific literature and results-proven practice and outline a novel framework for understanding the training and development of elite long-distance performance. Herein, we describe how fundamental training characteristics and well-known training principles are applied. World-leading track runners  and marathon specialists participate in 9 ± 3 and 6 ± 2 (mean ± SD) annual competitions, respectively. The weekly running distance in the mid-preparation period is in the range 160–220 km for marathoners and 130–190 km for track runners. These differences are mainly explained by more running kilometers on each session for marathon runners. Both groups perform 11–14 sessions per week, and ≥ 80% of the total running volume is performed at low intensity throughout the training year. The training intensity distribution vary across mesocycles and differ between marathon and track runners, but common for both groups is that volume of race-pace running increases as the main competition approaches. The tapering process starts 7–10 days prior to the main competition. While the African runners live and train at high altitude (2000–2500 m above sea level) most of the year, most lowland athletes apply relatively long altitude camps during the preparation period. Overall, this review offers unique insights into the training characteristics of world-class distance runners by integrating scientific literature and results-proven practice, providing a point of departure for future studies related to the training and development in the Olympic long-distance events. This review bridges the gap between science and results-proven practice regarding how training principles and training methods should be applied for the Olympic long-distance events and identified clear distinctions in training organization between track runners and marathon specialistsThe weekly running distance is in the range 160–220 km for marathoners and 130–190 km for track runners, with both groups performing 11–14 sessions per week, and ≥ 80% of the total running volume at low intensityTraining intensity distribution varies across mesocycles and differs between marathon and track runners, but common for both groups is that volume of race-pace running increases as the main competition approaches2max; the highest rate at which the body can take up and utilize oxygen during severe exercise), fractional utilization (the ability to sustain a high percentage of VO2max when running), and running economy . Together, these variables integrate the sustained ability to produce adenosine triphosphate (ATP) aerobically and convert muscular work to power/speed [2max remained essentially stable at ~ 70 ml kg−1 min−1 [Training for long-distance running (LDR) aims to improve the “big three” performance-determining variables: maximum oxygen uptake  will be highlighted since they organize their training year differently. Although anchored in the standard Olympic running distances, this review is also relevant for other endurance sports.The scientific literature supporting this narrative review was obtained from PubMed, using varying combinations of the search terms “endurance,” “long distance,” “marathon,” “training,” “conditioning,” “running,” “elite,” “high performing,” world-class,” “runners, ” and “athletes.” In addition, we searched for non-scientific, publicly available, and English-language training information related to podium contestants from international championships  and world marathon majors. Most of the training data were obtained from websites  dedicated to providing the athletics community an expansive library of information written by top athletes and coaches. Within these websites, all relevant training logs and coaching philosophies were purchased/downloaded and reviewed. Training information from doping-banned athletes or coaches were excluded. Moreover, a Google Search for podium contestants (using athlete name and “training” as search terms) and LDR books was performed. Although we cannot guarantee that relevant data have not been overlooked, the search revealed training logs/information from 59 world-leading athletes and 16 coaches of podium contestants , 37–112 Several limitations to our approach must be acknowledged. Firstly, the inclusion of results-proven training information can be discussed since it is not based on peer-reviewed research. However, elite athletes are systematic in their collection of training “data” and report their training accurately , 113, juInformation about the periodization pattern of LDR training over the course of a year is scarce in the scientific literature. Since Arthur Lydiard introduced his periodization system in the late 1950s –48, leadgeneral and specific preparation. In the general preparation period, the focus is high volume to build an aerobic foundation. From the specific preparation period onward, the focus gradually shifts toward higher volume of specific race-pace intensity [At least three phases are typically organized within a macrocycle for track runners: a preparation period, a competition period, and a transition period. The transition period begins immediately after the conclusion of the outdoor competition season, typically consisting of 1–2 weeks with rest or recreational training/low-intensive running , 87, 94,ntensity –94, 112.ntensity  and bearntensity . While tntensity .Some track runners apply double periodization , consisting of a preparation phase, an indoor or cross-country season, a new preparation phase, and finally an outdoor track competition season  , 57, 68.Marathon runners periodize their training year differently. The marathon runners listed in Table The underlying mechanisms for the superiority of specific periodization models in LDR remain unclear, and there is no direct evidence enabling us to compare outcomes across various periodization methodologies . AlthougThe specific training methods for LDR consist of varying forms of continuous long runs and interval training Table . These t2max are not consistently observed after periods of increased HIT [The historical view is that, compared to a high frequency of LIT bouts, high-intensity training (HIT) stimulates central adaptations to a larger degree  –129. Howased HIT –132. Howased HIT , 134. Inased HIT , 135–137ased HIT , 38. ThiSeveral successful long-distance runners have supplemented their sport-specific training with alternative locomotion modalities, so-called cross-training, including swimming, biking, cross-country skiing, and workouts on elliptical machines , 57, 94.Less specific training forms such as strength, power and plyometric training in small doses (relative to running training dosage) are commonly applied by world-leading long-distance runners , 111. Ev−1, while most corresponding track runners are in the range 450–600 h year−1 [Most world-leading marathon runners train 500–700 h yearh year−1 , 87, 94.h year−1 , 144–153h year−1 , 28, 34,h year−1 , 144–153h year−1 . To obtah year−1 , 83–112.Many long-distance runners accumulate much of their running kilometers on dirt roads/forest paths instead of paved roads to reduce mechanical loading and maximize training volume. This indicates that the running movement per se is not the main contributor to limited training tolerance, but rather the leg-surface interaction and resulting forces . RunningA discussion of training volume and the constraints created by mechanical interactions between runner and running surface would be incomplete without mentioning running shoes. Recent developments on the footwear front have received massive attention in the LDR community. The “super-shoe” was introduced to road running in 2016 and to track running in 2019, chronologically coincident with a wave of LDR records. These shoes are now subject to strict guidelines and testing . The fooWhile most scientific studies tend to only report training volume across macro- and mesocycles e.g., , 27, 28,−1; however, a considerably higher proportion of their volume (25–30%) is at or near marathon race pace, compared to others who cover 220–240 km wk−1, with only 15–20% at or near marathon pace [Typical weekly running volume in the mid-preparation period is ~ 160–220 km for marathon runners , 112 andhon pace –107, 112hon pace , 53, 54.−1 longer than males [One could argue that the ~ 10% slower running velocity in women  should ban males –107. We −1), seemingly experiencing more challenges related to injury management and fatigue [Overall, total running volume has remained relatively constant among world-leading long-distance runners since the 1950–1960s , 80–82.  fatigue . Based u fatigue .While training volume in endurance sports is straightforward to quantify, training intensity quantification is more complicated. The preponderance of scientific and results-proven practice recommends that intensity scales/zones/domains in LDR should be based on physiological parameters , external work rates (running pace or types of training), or perceived exertion , 163–165Endurance athletes employ varying methods of intensity distribution quantification. These are anchored around blood lactate ranges, running pace references, “time-in-zone” heart rate analysis calibrated against preliminary threshold testing, or the “session goal” approach where each training session is nominally allocated to an intensity zone based on the intensity of the main workout part , 170. Th2max during MIT-sessions [The description of training intensity distribution in previous studies of long-distance runners can mainly be categorized into the following three models: (1) The pyramidal model, characterized by a large volume of LIT combined with a small volume of moderate-intensity training (MIT) and an even smaller volume of HIT, (2) the polarized model, where the same large volume of LIT is combined with less MIT and more HIT, and (3) the threshold model, where a relatively larger proportion of training is performed in the threshold intensity range demarcated by lactate/ventilatory thresholds 1 (LT1/VT1) and 2 LT2/VT2 , 170–172sessions .The most consistent training intensity characteristic of elite distance runners is that most of the running distance (≥ 80%) is performed at low intensity throughout the training year  , 37–112,Training in zone 3  represents 5–15% of the total running volume in elite long-distance runners –112. HowInterval training in zone 4–5 also represents 5–15% of the total running volume, but this proportion is inversely related to zone 3-training. That is, marathon runners perform most training in zones 4–5 in the early-to-mid preparation period before replacing such training with more extensive bouts of zone-3 and upper end of zone-2 training as the major competition approaches –97, 100.During the pre-competition and competition period, most world-class 5000-m runners perform 1–2 weekly interval training sessions in zone 6 or in combination with zone 5 , 72–76. Distance runners perform sprint training (zone 7 in our model) regularly during the annual cycle, although this accounts for less than 1% of the total running volume , 109–111In summary, the annual training intensity distribution is very similar for track runners and marathon specialists, as low intensity volume dominates. However, substantial differences may be present within each mesocycle. Both groups increase the volume of race-pace running as the main competition approaches. Table Tapering in elite sports refers to the marked reduction of total training load prior to important competition(s). This is a short-term balancing act, as tapering strategies are intended to decrease the cumulative effects of fatigue while maintaining fitness , 180. BeIn cases where major competitions are arranged in warm and/or humid cities, and perhaps also many time zones away from the athletes’ regular location, tapering is integrated with time-, heat-, and humidity-acclimatization processes. For more details related to these topics, we refer readers to previously published reviews –188.The general scientific guidelines for effective tapering in endurance sports include a 2- to 3-week period with 40–60% reduction in training volume adopting a progressive nonlinear format, while training intensity and frequency are maintained –182. HowA review of the competition schedule for the athletes listed in Table The LDR community became aware of the impact of altitude on endurance performance in the late 1960s and particularly in connection with the 1968 Olympics in Mexico City (2300 m above sea level). Clearly, sufficient altitude acclimatization ahead of endurance competitions at altitudes 1000 m above sea level is required to perform optimally , 190. HoThe potential effect of altitude training is influenced by the hypoxic dose, which is a function of the duration of the stay and the altitude . Most woIt has been questioned whether altitude training has positive effects on endurance capacity and sea-level performance beyond the effects achieved with similar training performed at sea level. Here, high-quality scientific evidence is limited, and researchers interpret the current scientific data differently , 196. AlThis review integrates the scientific literature and results-proven practice regarding the training and development of world-class LDR performance. Herein, we have outlined a framework for specific characteristics  and identified the training organization differences between track runners and marathon specialists. Marathon and track runners participate in 6 ± 2 and 9 ± 3 (mean ± SD) annual competitions, respectively. Typical weekly running volume in the mid-preparation period is in the range 160–220 km for marathon runners and 130–190 km for track runners. These differences are mainly explained by fewer running kilometers for each session for track runners, as training frequency (11–14 sessions per week) is equal for both groups. Moreover, ≥ 80% of total running distance is performed at low intensity throughout the training year. In the general preparation period, the focus is to build an aerobic foundation by a large total running volume. From the specific preparation period onward, the volume of race-pace running increases as the main competition approaches. Hence, training intensity distribution models vary across mesocycles and differ between marathon and track runners. While the African runners live and train at high altitude (2000–2500 m above sea level), most lowland athletes apply relatively long altitude camps during the preparation period. The tapering process starts 7–10 days prior to the main competition, typically preceded by a 2–4-week altitude camp. Overall, this review offers novel insights into areas of LDR training that formerly have been scarcely studied in the scientific literature, providing a point of departure for future studies and may serve as a position statement related to the training and development in the Olympic long-distance events.


The worldwide spreading pandemic, COVID-19, has caused hostile ageism toward older adults. We adopted a new intervention to reduce such hostile ageism. “Imagine that they were Young” referred to the imagination of what an older adult might look like, think, and behave when they were once young, which was a reversed but refined intervention of the widely-used method of “Imagine that you were old.” In the present study, intergenerational tension was primed, and then 205 younger adults in China aged 18–37 were randomly assigned to 3 different conditions , asking them to imagine (and then write down) once older adults were young, or a future aging self, or read an unrelated essay respectively as experimental manipulations. Then they should distribute medical funds worthy of Chinese 1 million to two patients with COVID-19 of 25 and 85 years old indicating their attitudes toward older adults (or hostile ageism). Finally, we measured their general attitudes and stereotypes toward older adults. Results verified the effectiveness of both interventions, such that younger adults who took either intervention distributed more medical funds and showed more positive aging attitudes toward older adults than those in the control group. Moreover, “Imagine that they were Young” was tested to be even more effective than “Imagine that you were old.” A series of relative mediation models revealed that the stereotype of warmth mediated the effect for both interventions on decreased hostile ageism behaviors, compared with the control condition. While “Imagine that they were Young” could additionally reduce hostile ageism through a higher level of “including the older adults in their self-group (the young).” This new intervention might be a good alternative to eliminate hostile ageism. Ageism, a form of discrimination against older adults, was first conceptualized by Butler  and is tThe worldwide spreading pandemic, COVID-19, has directly brought ageism to our society  older adults as a burden to society  were once as young as themselves (young participants). Compared with “Imagine that you were old,” imagining a rejuvenated older adult involved less about aging stereotypes and younger adults were much more familiar with younger than older people's lives. Such intervention is in line with the perspective of “Inclusion of Outgroups in the Self”  and the level of IOS.In the present study, we measured not only general ageist attitudes with self-report scales but also using a hypothetical dictator game with contextual information available  for two reasons. First of all, most previous studies only used self-report scales to represent explicit ageist attitudes which could induce severe social desirability effect, making participants more likely to conceal their ageist attitudes (instead of behaviors) toward older adults  on hostile ageism by resource distribution tasks .Hypothesis 4: Compared with the control condition, the “Imagine that they were young” intervention could reduce hostile ageism through an increased level of IOS.https://www.credamo.com/home.html#/). The platform allowed us to display videos online and participants were restricted to those who could respond with computers rather than smartphones. After we had uploaded our experiment on this platform, 250 participants participated and finished our experiment. They were paid 8 RMB  as a reward. Of the 250 participants, 45 responses  over 40 years old, or who failed the attention check questions were discarded. A total of 205 responses aged 19 to 37 entered the final analysis. .Before data collection, the power analysis was conducted for sample size determination, and G*power software recommended 207 participants on conducting one-way ANOVA under 3 conditions . Participants were recruited through an online platform named Credamo . Participants were also informed that the basic probability of cure was 25% for the young patient and 0 for the old, with every 10k medical funds allocated contributing to a 1% increase in the probability of cure. For younger patients, 750k medical funds would guarantee a 100% cure (and the remaining funds would only increase the probability of cure for the 85-year-old adult to 25%), while for older adults, only if they received the whole 1 million funds would guarantee a 100% cure . Such a scenario creates a sense of resources scarcity, which would lead to intergenerational tension (as well as competition). Their decisions of allocation would certainly reflect their attitudes toward older (vs. younger) adults, or in other words, the (hostile) ageism.Ageism attitude. We used the intergenerational tension ageism scale . After that, they looked the same image with 4 particular strangers. Then they have to imagine that when the older adults as what appeared in video and image were as young as the younger participants. They were asked to write down what the strangers would look like, what their characteristics would be, and what would happen in their life at that young .Control condition. Participants in the control group read an article named “Sauropod, the Titan on our planet” derived mainly from Wikipedia and were rewrote by researchers. After reading that, participants should answer four questions about the main content of the passage.Next, we measured their hostile ageism behaviors , level of IOS, aging stereotype, ageism attitude and recorded their age and gender in order. Finally, we thanked their participation and presented anti-ageism material for debriefing.2 = 0.389, p = 0.823) and age  = 0.296, p = 0.744) across three conditions.In Hostile ageism behavior. A one-way ANOVA with conditions  as a between-subject factor on medical funds allocated to older patients was conducted. Results of one-way ANOVA revealed significant differences across the three conditions  = 6.191, p = 0.002, partial η2 = 0.058) as post-hoc test showed that only the participants who “imagined that they were young” (p = 0.002) allocated more medical funds to older adults than those in the control condition. Instead, participants who “imagined that you were old” did not outperform those in the control condition (p = 0.292). However, no significant difference emerged between participants who took “Imagine that they were young” and “Imagine that you were old” intervention (p = 0.187).Ageism attitudes. A 3 *3 mixed model ANOVA showed only one significant main effect of dimensions on ageism attitudes  = 203.39, p < 0.001, partial η2= 0.502). The post-hoc test revealed that participants were more likely to perform ageism of succession than consumption (p < 0.001) or identity (p < 0.001), and perform ageism of consumption than identity (p < 0.001). However, no significant differences across conditions  = 0.32, p = 0.73, partial η2= 0.003) and interaction  = 1.95, p = 0.102, partial η2= 0.019) emerged.Aging stereotypes. A 3 *2 mixed-model ANOVA showed significant main effect for both factors but insignificant interaction = 0.18, p = 0.84, partial η2 = 0.002). For stereotypes, participants were more likely to rate older adults as warmer rather than more competent  = 397.05, p < 0.001, partial η2 = 0.66). For different conditions  = 4.91, p = 0.008, partial η2= 0.046), post-hoc test further revealed that participants in both “imagined that they were young” (p = 0.019) and “imagined that you were old” (p = 0.033) conditions perceived older adults as more positive  than those in the control condition.Level of IOS. A one-way ANOVA with conditions  as a between-subject factor on the level of IOS was conducted. Results found significant differences across the three conditions  = 3.16, p = 0.045, partial η2 = 0.030). The post-hoc test revealed that only participants who took he “Imagine that they were young” intervention showed a higher level of IOS than those under the control group (p = 0.038). While participants who took the “Imagine that you were old” intervention showed no significant difference in the level of IOS compared with participants under either control (p = 0.88) or “Imagine that they were young” (p = 0.44) condition.relative mediation models with different mediators (M)  were analyzed separately  and D2  were generated to represent different interventions. Medical funds allocated to older adults (Y) were used as the dependent variable, which reflected non-hostile ageism. For hostile ageism attitudes, no significant differences emerged across conditions as we found above. Therefore, we did not conduct the relative mediation analysis using hostile ageism attitudes as dependent variables. We then used the PROCESS macro developed by Hayes and Preacher , three Preacher  in SPSS D1  and marginal significance for variable D2 . Omnibus test of total effect of conditions on ageism behaviors showed significant  = 6.19, p = 0.003, partial η2= 0.058).First of all, for all three relative mediation models, relative total effects reached significance for variable F = 4.28, p = 0.015, partial η2= 0.041). Only the indirect effect of variable D1  but not D2  reached significant, which indicated that “Imagine that they were young” condition could reduce hostile ageism (b = 3.75) by including older adults more (a1 = 0.48) into their self-group.  = 4.55, p = 0.012, partial η2 = 0.043). Significant relative indirect effects emerged for both variable D1  and variable D2 . To be specific, participants who took either “Imagine that they were young” or “Imagine that you were old” intervention rated older adults as 0.50(a1) or 0.45(a2) higher on warmth, respectively, than the control condition, which lead to a significantly reduced hostile ageism (b = 2.63).  = 5.02, p = 0.008, partial η2= 0.048). However, no significant relative indirect effect emerged for either variable D1  or variable D2 , which indicated stereotype of competence did not serve as mediator on reducing hostile ageism.  and resulted in lower hostile ageism behaviors as Hypothesis 3 predicted. In addition, as Hypothesis 4 expected, participants in the “Imagine that they were young” condition (but not in the “Imagine that you were old” condition), compared to those in the control condition, showed a significantly higher level of IOS  as well, which also lead to reduced hostile ageism.per se.Although both interventions could successfully reduce hostile ageism to some extent, our newly developed, “Imagine that they were young” intervention outperformed “imagine that you were old” in terms of effectiveness. From our mediation analysis, it is revealed that the perceived warmth of older adults could lead to reduced hostile ageism for both “Imagine that you were old” and “Imagine that they were young.” Indeed, as suggested by the BIAS map  eventually into an out-group” was in direct contrast to “Including an older adult into the in-group” by definition. Besides, as terror management theory  could predict less hostile ageism. Cadieux et al.  found th Becker,  claimed, Becker, . Further Becker, . Ritteno Becker,  found th Becker, ]. Our daAlthough, warmth ratings were confirmed to be an important antecedent in affecting participants' hostile ageism. However, the stereotype of competence (particularly incompetence) failed to have any effect on reducing hostile ageism in our study, which is contrary to the BIAS map. For example, Lytle et al.  found thOur new interventions provided new insights into the practical issue of how to reduce hostile ageism. For instance, old photographs or sundries may help portray a more detailed and vivid image of a rejuvenated older adult. Older adults could also be invited to take part in this “Imagine that they were young” by providing their own real-life stories or autobiographies which could hugely contribute to facilitating intergenerational understanding. For example, Fruhauf et al.  invited It still deserved attention on how older adults would be portrayed negatively in COVID-19  failed to emerge significant differences across three conditions in any analysis. One possibility could be potential social desirability, such that behaviors might be more implicit, while the SIC scale as an explicit measure would be prone to reach a higher level during the pandemic as a social norm in China  more and also resulted in lower hostile ageism. Our research shed light on how we could encourage younger adults to take a different perspective to perceive younger and older people as continuity belonging to a uniform society and, hence, avoid so-called “calculated ageism” .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The patient died 6 days later.A 55‐year‐old male with comorbidities was diagnosed positive to a highly transmissible SARS‐COV‐2 variant (with the D614G substitution) and admitted to our hospital with moderate COVID‐19. His respiratory function progressively worsened and he was admitted to the ICU (12 days later) under mechanical ventilation. Green‐blue inclusions were observed in a few neutrophils at his blood smear a few days later, as well as vacuolated neutrophils, dysplasia of granulocytes, apoptotic cells, echinocytes, and nucleated red blood cells. At that time the patient was hemodynamically unstable, oliguric, and with signs of metabolic acidosis. He had elevated LDH (56.94 ukat/L) and ferritin (33511 μg/L), moderately increased AST (4.48 ukat/L) and ALT (1.74 ukat/L), thrombocytopenia (10“Green bodies” are rare cytoplasmic inclusions of high lipid content in neutrophils and monocytes of peripheral blood, which have been observed in critically ill patients suffering from liver necrosis, severe tissue damage, lactic acidosis, and more recently from COVID‐19. Usually underreported, they seem to be an ominous prognostic sign, as in this case, and their early detection may be of clinical importance.


Nature Communications 10.1038/s41467-020-16168-x, published online 27 May 2020.Correction to: 68”. Additionally, the original version of this Article omitted the reference 68 to the previous work in “Leventoux N, Augustus M, Azar S, et al. Transformation Foci in IDH1-mutated Gliomas Show STAT3 Phosphorylation and Downregulate the Metabolic Enzyme ETNPPL, a Negative Regulator of Glioma Growth. Sci Rep. 2020;10(1):5504. Published 2020 Mar 26. 10.1038/s41598-020-62145-1”. This has been added as a reference 68 in the newly corrected paragraph in the “Methods” section under “GBM tumour sample”. These have been corrected in both the PDF and HTML versions of the Article.The original version of this Article contained an error in the “Methods” section, which incorrectly omitted at the start of the “GBM tumour sample” paragraph the following information and reference to previous work: “The used protein extracts derived from both low and high grade gliomas were originally processed and used in the article from from Pr JP Hugnot’s lab


Human biomonitoring (HBM) data provide information on total exposure regardless of the route and sources of exposure. HBM studies have been applied to quantify human exposure to contaminants and environmental/occupational pollutants by determining the parent compounds, their metabolites or even their reaction products in biological matrices. HBM studies performed among the Portuguese population are disperse and limited. To overcome this knowledge gap, this review gathers, for the first time, the published Portuguese HBM information concerning polycyclic aromatic hydrocarbons (PAHs), metals, metalloids, and pesticides concentrations detected in the urine, serum, milk, hair, and nails of different groups of the Portuguese population. This integrative insight of available HBM data allows the analysis of the main determinants and patterns of exposure of the Portuguese population to these selected hazardous compounds, as well as assessment of the potential health risks. Identification of the main difficulties and challenges of HBM through analysis of the enrolled studies was also an aim. Ultimately, this study aimed to support national and European policies promoting human health and summarizes the most important outcomes and lessons learned through the HBM studies carried out in Portugal. It has defined a list of priority hazardous substances including, but not limited to, emerging substances, flame retardants, phthalates, polycyclic aromatic hydrocarbons (PAHs), pesticides, benzophenones, mycotoxins, and some heavy metals and metalloids [In everyday life, humans are exposed to a broad range of hazardous substances and their mixtures, present in air, soil, water, and food. It is of utmost importance to ensure scientific evidence in order to allow early protection of human health, since some of these chemicals cause deleterious effects, and prolonged human exposure, even at low doses, can be linked with chronic diseases and cancer . Differetalloids . SeveralThe available scientific literature was searched on Thomson Reuters ISI Web of Knowledge, PubMed, Science Direct, and Google Scholar databases. Combinations of at least two of the following keywords were used: “Portugal”, “Portuguese”, “human biomonitoring”, “biomarkers of exposure”, “polycyclic aromatic hydrocarbons”, “PAH”, “pesticides”, “heavy metals”, “cadmium”, “chromium”, “arsenic”, and “lead”. All the HBM studies assessing exposures to PAHs, pesticides, heavy metals and metalloids within the Portuguese population were selected.The inclusion criteria for the selected studies were the determination of at least one of the selected pollutants and/or its biomarkers of exposure in biological fluids and to have full access to the study; studies not reporting original data or surveyed in populations not including Portuguese subjects were excluded. Overall, the literature search identified a total of 25 HBM studies published between 2003 and 2021 and assessing the Portuguese population exposure to PAHs , heavy metals and metalloids , and pesticides .PAHs are organic pollutants released from petrogenic sources and by reactions of incomplete combustion of organic materials and pyrolysis. The production of energy from petroleum and its derivatives , coal tar, and wood, as well as emissions from the commercial, institutional, and household sector, agricultural activities, and from road transports constitute the major sources of ambient PAHs ,14,15. TRegarding the Portuguese population, there are 10 HBM studies assessing the environmental and/or occupational exposure to PAHs. Recently, some authors  assessedp ≤ 0.05) in subjects participating in firefighting comparatively with non-exposed firefighters. Moreover, those authors also found increased levels of oxidative stress in the blood cells of some exposed firefighters [−2 µmol/mol creatinine in French non-smoking adults [−1 in Belgian adolescents [−1 in German smoking adults [−1 in Slovakian steel workers from a control group [−1 in Polish coke-oven smoking workers [PAH metabolites were also determined in the urine of Portuguese schoolchildren, grill workers, and firefighters ; no datafighters . Availabfighters . Most ofg adults  to 13.8 g adults ; 0.180 µlescents  to 12.2 g adults ) and occg adults  to 28.6 g adults ; 6.41 µgol group  to 155.1 workers ). PeopleAmong the compounds under study within the Portuguese population, 1-hydroxynapthalene and 1-hydroxyacenaphthene were by far the most predominant metabolites, being followed by 2-hydroxyfluorene, 1-hydroxyphenanthrene, and 1-hydroxypyrene . Indeed,−2 to 0.184 µmol/mol creatinine and reached maximum concentrations of 0.941 µmol/mol creatinine in children attending a preschool situated directly next to a mall and a gas station and in a major entrance road of a city in the north of the country  of 1-hydroxypyrene as indicative of occupational exposure to PAHs [−1 [Median concentrations of 1-hydroxypyrene, (considered the biomarker of exposure to PAHs) among the Portuguese preschool children varied between 5.72 × 10 country . The ran country . Grill w workers  to 15.4  workers ,41. A masubjects ,56,57,58subjects ,43,47,59subjects  proposed to PAHs . Regardi to PAHs . This prPAHs [−1 ,41,56), PAHs [−1 ). More rPAHs [−1  proposedPAHs [−1 .−6 to 1.1 × 10−3 µmol/mol creatinine for non-smokers versus 2.3 × 10−5 to 2.3 × 10−2 µmol/mol creatinine for smokers) and Italian citizens . Barbeau et al. [−3 to 0.74 × 10−3 µmol/mol creatinine. Authors reported that workers in anode production presented a greater exposure to 3-hydroxybenzo(a)pyrene and 1-hydroxypyrene than other metallurgic workers. More HBM studies are necessary to better evaluate occupational exposure to PAHs and the associated health risks, which will contribute to the establishment of reference values.The metabolite 3-hydroxybenzo(a)pyrene, a PAH biomarker of carcinogenicity, was only found in the urine of some grill workers . This bu et al.  performe−1 in non-smoking Italian adults [−1 in Belgian adolescents [−1 in a Polish control group of coke-oven workers [−1 in smoking coke-oven workers [−1 in Belgian adolescents [−1 in Italian adults exposed to solid waste incinerator emissions  [−1 in Polish coke-oven workers [−1 in the end-shift urine of Italian road paving workers [−1 in Polish smoking coke-oven workers [−1) [−1 [−1  [An alternative to assess environmental and/or occupational exposure to PAHs is through the assessment of unmetabolized compounds in biological samples. So far there are only seven studies regarding the urinary levels of PAHs among the European population: three were conducted in Poland, another three in Italy, and one in Belgium. Overall, urinary levels of total PAHs among the general population ranged from 38.6 ng Ln adults  to 98.8 lescents . Occupat workers  to 1998  workers . As obselescents  to 1.6 n ng L−1) . Regardi workers  to 54 ng workers  and reac workers . Benzo(aers [−1)  and in P[−1) [−1 ,64). Urial PAHs) .Exposure to PAHs has been directly associated with many potential health risks . Due to −1) with maximum values reaching 1295.5 µg L−1  has been forbidden in industrialized countries, and restricted in several others. However, these compounds endure until today in the environment. In Portugal, the use of several OCPs has been prohibited since 1988; in 2003 the use of lindane in agriculture was also prohibited . Aside f5 µg L−1 . Among Din males . Cruz etin males  evaluateal areas . The hig5 µg L−1 . The com−1 and 0.85 ± 0.50 µg L−1 for maternal and cord serum, respectively, with significant correlations being observed when compared (p < 0.05)  . A multi−1) and HCB  the most prevalent pesticides; significant associations were found between p,p′–DDE and HCB concentrations and the age of the participants [−1 lipid, respectively [−1 lipid for HCB, 0.74 and 27.0 ng g−1 lipid, respectively, for α- and β -HCH, and 3.8 and 104.6 ng g−1 lipid, respectively, for DDT and DDE. Lindane (γ-HCH) was found in roughly 10% of the subjects [Several European HBM programs and regional studies have shown that OCPs, namely DDE and HCB, are found in higher values in older people comparatively with other age ranges . In Italicipants . A HBM sicipants ). Overalectively . Each OCectively . No correctively . The disectively . Median subjects .Glyphosate is a broad-spectrum non-selective herbicide with increasing use, being nowadays one of the most commonly used herbicides at a global scale, as recently reviewed . The broIn Portugal, there is a lack of biomonitoring studies confirming exposure to glyphosate, with only two single studies published in 2020  and 2021−1) and AMPA in 50% (at an average level of 0.16 µg L−1). In the second round, glyphosate was determined in 73% of the participants (at an average level of 0.13 µg L−1) and AMPA in 97% (at an average level of 0.10 µg L−1). The frequency of contamination was comparable to that found in similar studies carried out in adult populations in Germany [−1) [In an adult biomonitoring pilot-study , 79 Port Germany  and Irel Germany , althougany [−1) .n = 39) of the urine samples analyzed at an average level of 1.77 ± 0.86 µg/L. The number of positive samples was comparable to the results of the scarce previous studies carried out in other countries. Nevertheless, it is noteworthy that the maximum value of glyphosate determined in the urine of Portuguese children (4.35 µg L−1) was higher than the values previously reported, such as in Denmark (3.31 µg L−1) [−1) [−1) [The exposure of Portuguese children to glyphosate is of particular concern, due to their higher susceptibility owing to physiological immaturity and higher consumption per kilogram of body weight ,104. In  µg L−1) , Mexico −1) [−1) , and the−1) [−1) .Heavy metals and metalloids can originate from natural and anthropogenic sources, such as volcanic activities, industrial activities, road traffic, the use of fertilizers and pesticides, among others ,108. Som−1) and Ni (1.82 µg g−1), while the lowest average concentration was found for Cd (0.05 µg g−1). The occurrence of trace elements in the hair of the Portuguese population was lower than the values found in nails, with the highest average concentration found for Zn (281.9 ng g−1)  and Cu ; the lowest average values were described for Be (0.027 µg L−1) in breast milk and for Co (0.34 µg L−1) in blood (−1), Pb (90 µg L−1) and Hg (2.0 µg L−1) [−1 creatinine, respectively) and Se  while the lowest levels were reported for Cd (0.6 µg g−1 creatinine)  A. Regardin blood B. Compar µg L−1) , it was  µg L−1) ,116,117.atinine) B.Portuguese history is linked to mining activities, which have positively affected the regional and national economies; however, they promote environmental contamination, even after mines closure ,114. Thi−1) and Hg (11 µg L−1) were higher than the available reference values . Additionally, blood concentrations of Cd, Hg, and Pb among the Portuguese population were higher than those reported by HBM studies performed in Germany, Saudi Arabia, and Canada [Incinerators can potentially release heavy metals and metalloids into the environment; their influence on the populations residing in the vicinity has been raising public and scientific concern ,124,125.d Canada ,122,123.d Canada .−1) were higher than the limits established by the Center for Disease Control and Prevention (100 μg L−1) [−1. These values concur with the concentrations obtained in general blood and are also much higher than the levels observed in populations from Canada, the Czech Republic, and Germany, in which 95% confidence intervals were below 45 μg L−1 [Regarding the studies performed with maternal and cord blood, reduced concentrations of Pb have been described over time, probably due to the use of unleaded gasoline, however some detected levels (1–229 μg L μg L−1) . In chil μg L−1) . About 25 μg L−1 ,122.Volcanic emissions may represent a significant risk to human health due to the exposure to trace elements, namely through contaminated soils . A HBM sTrace elements can be transferred from both the body reserves and the blood into the breast milk of a lactating mother ,120. Two−1 creatinine; [This review collects, for the first time, the existing information related to the contribution of HBM to the characterization of the  exposure to PAHs, metals and metalloids, and pesticides in the Portuguese population. Regarding exposure to PAHs, it was found that the main metabolites of low molecular weight compounds were the most abundant. Urinary 1-hydroxypyrene, the PAH biomarker of exposure to PAHs, was found at levels that were predominantly lower than the proposed benchmarks for non-smoking and non-occupationally exposed individuals  and foratinine; . UrinaryDespite the Portuguese participation in the European Human Biomonitoring Program-HBM4EU, the present study revealed the existence of limited information regarding the evaluation of Portuguese exposure to the selected hazardous substances and the lack of standardization in the different methodologies applied. The difficulty in mobilizing a representative sample  to study a wide range of health indicators and obtain more robust results can be also identified as a limitation. This not only hindered an integrated view of the problem but also hampered consistent comparison between the obtained results, ultimately resulting in difficulty in implementing policies based on scientific evidence. Additionally, available information is insufficient to explore temporal and geographical trends across the Portuguese and European populations. Therefore, more HBM studies are needed to better characterize Portuguese exposure to the selected health-hazardous contaminants/pollutants and compare it with total exposures determined in other European populations. To overcome this gap of knowledge, a regular HBM surveillance program should be performed among different age groups of the European population, which would allow a more comprehensive temporal and geographical evaluation of environmental and occupational exposure to the selected health-relevant pollutants. Other health-relevant pollutants  should be included in future HBM studies to perform a more complete exposure assessment that would support a more realistic health risk assessment  of the population. The paucity of specific and properly validated biomarkers, as well as the lack of information on the toxicokinetics that persist for many chemicals, hinders objective risk assessments. In addition, for many chemicals, the lifetime health impacts associated with exposure remain unknown and guidance is largely missing. These limitations were in line with the main hurdles and challenges of HBM considering risk assessment of chemicals identified by EU and extra-EU regulators . In spit


Glycyrrhiza uralensis Fisch. Ex DC.), ‘Ju’ (Dendranthema morifolium (Ramat.) Tzvelev), ‘Bu’ (Microcos paniculata L.), ‘Jin’ (Lonicera japonica Thunb.), ‘Xia’ (Prunella vulgaris L.), and ‘Ji’ (Plumeria rubra L.), the long-term jargon is connected with the inflammation of modern immunology through a few pro-inflammatory markers. In vitro studies have indicated that cellular inflammation is lowered by Ju and Jin either individually or synergistically with Gan. Among all herbs, only Gan detoxicated cellular toxicity of Bu in a dose dependent manner. The synergistic formulation of Ju and Gan, or Jin and Gan, in a reduction of Shang Huo, was tested in vivo. Both combinations exhibited a lower percentage of neutrophils, monocytes, and CD4+/CD8+ ratio in the blood, as well as inflammatory cytokines. Furthermore, body weight in the combinatory groups was more stable than treatments using single herbs. The combination of old traditional oriental methods with Western science logistics, has resulted in the formulation of different herbs into one concoction for the use of detoxification and synergism.Herbal teas or herbal drinks are traditional beverages that are prevalent in many cultures around the world. In Traditional Chinese Medicine, an herbal drink infused with different types of medicinal plants is believed to reduce the ‘Shang Huo’, or excessive body heat, a status of sub-optimal health. Although it is widely accepted and has a very large market, the underlying science for herbal drinks remains elusive. By studying a group of herbs for drinks, including ‘Gan’ ( Hordeum vulgare L.) in East Asia  Dahlg] in South Africa (Cymbopogon citratus (DC.) Stapf] in India and Pakistan (Thymus vulgaris L.) in Germany (Bergenia crassifolia (L.) Fritsch] and Ivan-tea (Epilobium angustifolium L.) in Russia (Ilex paraguariensis A.St.-Hil.) in South America , which is contemporary of the “Huang-di Nei-jin.” The SBJ states that an ideal formula should contain a group of harmonious herbs, each playing unique roles, mimicking a well-organized society with a monarch, a minister, an assistant, and a guide . HoweverGlycyrrhiza uralensis Fisch. Ex DC. (Leguminosae), Dendranthema morifolium (Ramat.) Tzvelev (Asteraceae), Microcos paniculata L. , Lonicera japonica Thunb. (Caprifoliaceae), Prunella vulgaris L. (Lamiaceae), and Plumeria rubra L. (Apocynaceae). Each herb’s ability to reduce Shang-Huo is indicated and then quantified by inflammatory protein markers. With the quantification, each herb’s contribution to reducing the Shang-Huo was compared in parallel. A combination of different herbs in one pot also showed synergistic efficacy. Some herbs were not beneficial to reducing Shang-huo, but it did alleviate cytotoxicity. The findings in cells were also verified in vivo. Mice studies also showed that a combination of different herbs is favorable for maintaining immune biochemical profiles and keeping a steady body weight while ingesting a single type of herb led to weight loss.In the current study, Shang-huo, which is linked to inflammation through an inflammatory endothelial cell model was studied. A collection of six herbal materials were investigated in this study: Glycyrrhiza uralensis Fisch. Ex DC.), “Ju” (The flower of Dendranthema morifolium (Ramat.) Tzvelev), “Bu” (The leaf of Microcos paniculata L.), “Jin” (The flower of Lonicera japonica Thunb.), “Xia” (The fruit body of Prunella vulgaris L.), and “Ji” (The flower of Plumeria rubra L.)  . All herubra L.) . Each he2 humidity incubator.Human microvascular endothelial cells (HMEC-1) were gifted from Dr. Moonsoo M Jin (Weill Cornell Medicine). HMEC-1 cells were cultured in MCDB 131 medium (Sigma) supplemented with 10% FBS , 1 mg/ml hydrocortisone , and 10 ng/ml recombinant human epidermal growth factor . For induction of inflammation, HMEC-1 cells were treated with 1 mg/ml of LPS 3 h after the treatment with herbal extracts. The mammalian cells were maintained at 37°C in a 5% CO4 cells/well) were cultured in MCDB131 media in a 96-well plate at 37°C and exposed to herbal extract with different concentrations for 24 h. Cells treated with medium concentration were only used as a negative control. After removing the supernatant of each well and followed by PBS washing, cells were added with 20 µL of MTT solution (5 mg/ml in PBS) and 80 µL culture medium. The cells were then incubated for another 4 h until the formazan crystals were dissolved in dimethyl sulfoxide (100 µL). The absorbance intensity was measured by a microplate reader  at 570 nm with a reference wavelength of 630 nm. All experiments were performed in triplicate, and the relative cell viability (%) was calculated as a percentage relative to the untreated control.To determine cell viability, the colorimetric MTT assay was used. HMEC-1 cells (1 × 104 cells/well) were cultured in MCDB131 media in a 96-well plate at 37°C, and then exposed to extracts with different concentrations for 24 h. Cells treated with a medium concentration only served as a negative control. After removing the supernatant of each well, PBS washing was done, before adding cells with 10 µL of CCK-8 solution (5 mg/ml in PBS) and 90 µL of culture medium. The cells were then incubated for another 4 h, and the absorbance intensity was measured by a microplate reader  at 450 nm. All experiments were performed in triplicate, and the relative cell viability (%) was calculated as a percentage relative to the untreated control.To determine cell viability under a different treatment, the colorimetric CCK-8 metabolic activity assay was used. HMEC-1 cells . Mice peripheral blood was treated with lysis buffer  and washed by PBS. The cells were then permeabilized and stained with fluorescent-conjugated monoclonal antibodies to CD3, CD4, CD8, CD14, CD11b, and Ly-6G/Ly-6C . After washing, the cells were analyzed on a FACSCalibur .Immunofluorescence flow cytometry for ICAM-1 analysis on the HMEC-1 surface was conducted according to the method described previously . Flow cyRNA extraction was conducted according to a previous method . Then 50Escherichia coli. 026B6, Sigma, United States) in 300 μL saline buffer. The control was administered the same volume of saline buffer. The body weight of the mice was recorded every 24 h for 10 days.The animal experiments were conducted according to the guidelines for the care of animals of Huazhong Agricultural University , and were approved by the Institutional Animal Care and Ethics Committee (HZAUMO-2018-057). C57BL/6 mice  was purchased from the Hubei Provincial Center for disease Control and Prevention and maintained in the animal facility at Huazhong Agriculture University . Standard guidelines for laboratory animal care were followed. The study protocol was approved by the Huazhong Agricultural University Animal Care Committee. The mice were randomly divided into different groups as indicated, each with six to eight mice. Lyophilized herbal extracts of different amounts were resuspended in saline buffer and orally administered to mice (200 μL) twice daily for 4 days. On day 4, 3 h after the administration of the herbal extracts, the mice were intraperitoneally injected with 10 mg/kg lipopolysaccharide  was added to disrupt red blood cells. After sitting on a bench for 10 min, the sample was centrifuged, and the pellet was washed again by saline buffer. The pelleted lymphocytes were examined by a cytometer .Data was expressed as mean ± standard deviation (SD) of at least four identical samples. The statistical analysis of the data was carried out using GraphPad Prism 7. The unpaired student’s t-test was used to determine statistical significance in comparison to the matched controls , 3. One-Glycyrrhiza uralensis Fisch.) or “Gancao” in Chinese , chrysanthemum, “Juhua” or “Ju” (Chrysanthemum morifolium Ramat.), honeysuckle, “Jinyinhua” or “Jin” (Lonicera japonica Thunb.), frangipani, “Jidanhua” or “Ji” (Plumeria rubra L.), shiral, “Buzhaye” or “Bu” (Microcos paniculata L.), and self-heal, “Xiakucao” or “Xia” (Prunella vulgaris L.), to test their effectiveness against Shang-huo (Pharmacopoeia (version 2015), one to two chemicals from each herb were quantified by HPLC for quality control and standardization -induced human microvascular endothelial cells (HMEC-1), an inflammatory cell mode was applied as the cellular model  to test We then focused on interpreting the rationality of mixing different herbs into a one-pot drink, since the anti-inflammatory strength of each herb is different. Because Bu exhibited a concentration-dependent toxicity, we tested the cellular viability with a combination of Bu and the other five herbal extracts, respectively. It showed that only Gan exhibited a concentration-dependent rescue of the cellular viability . Gan is Besides neutralizing the toxicity of other herbs at higher concentrations, we wonder if Gan also contributes to the efficacy if the concentration of the herbal drink is lower. To further clarify the difference that Gan makes in an herbal extract, we selected Ju, Jin, and Xia, which all have a cytotoxicity over 0.1 mg/ml . The res+ to CD8+ T cells was investigated during the treatment. T cells play a major role in cell-mediated immunity. The ratio of CD4+ to CD8+ cells is a marker of HIV progression since it accurately indicates the overall immunity dysfunction imposed by the virus  and 3 (luteolin-7-O-glucoside) in Ju was less than Jin; while a relatively high content of compound 2  was not found in Jin (Similar to single chemicals, the capability of Shang Huo clearing herbs were also identified and ranked using this method. Most of the herbs tested in this study are used in TCM to clear Huo . Howeverd in Jin . Althougd in Jin , 5. It iGan is very particular in TCM because it is used in most herbal formulas. Thus, Gan is called the prime minister of TCM . GlycyrrIn an effort to explore the formulation principle, the mouse study was conducted. The percentage of neutrophils, monocytes, and CD4/CD8 ratio are all related to inflammation. To check the immune cellular change, mice were fed Gan, Ju, or both. The results clearly showed that Gan or Ju alone did not lower any of these factors but a combination of these two significantly induced a lower ratio, meaning less stress on the immune system.To our surprise, after herbal extract treatment, all the cytokines were increased when mice were treated by a single herb . ObviousThere have been reports of adverse effect due to ingestion of various herbal teas . It has in vitro and in vivo. The synergy principles for herbal mixtures are common for adaptogens (In TCM theory it is difficult to discriminate the difference of Huo-clearing capability and all so-called Huo-clearing herbs. In the current study, linking inflammation in immunology and ‘Huo’ of TCM enabled comparable quantification of Huo and thus the herbs’ contribution to Huo-clearing could be screened and selected. The rationality of mixing different herbs with a certain ratio was also verified with statistical significance aptogens  and tradaptogens .There are many kinds of herbal drinks on the market, with various herbs, and different formulas. Although all of these drinks claim a function of Huo-clearing, the lack of an evaluation system makes it difficult for customers to select the correct drink. Our method may aid in future herbs selection and the tea formulations.


O(n2) or O(n3) to O(n) in various steps including numerical integration, risk set calculation, and standard error estimation, where n is the number of subjects. Using both simulated and real-world biobank data, we demonstrate that these linear scan algorithms can speed up the existing methods by a factor of up to hundreds of thousands when n > 104, often reducing the runtime from days to minutes. We have developed an R package, FastJM, based on the proposed algorithms for joint modeling of longitudinal and competing risk time-to-event data and made it publicly available on the Comprehensive R Archive Network (CRAN).Semiparametric joint models of longitudinal and competing risk data are computationally costly, and their current implementations do not scale well to massive biobank data. This paper identifies and addresses some key computational barriers in a semiparametric joint model for longitudinal and competing risk survival data. By developing and implementing customized linear scan algorithms, we reduce the computational complexities from  Joint models of longitudinal and survival data have been widely used to mitigate incorrect estimation and statistical inferences associated with separate analysis of each endpoint , 2. For sumption –5. Jointsumption –9 and fosumption –14. Compsumption , Rizopousumption , Sudell sumption  and the https://www.optum.com/EHR) includes 80+ millions patients with longitudinal lab measures. The Million Veteran Project [5~108 patients. However, current implementations of many joint models are inefficient and not scalable to the size of biobank data as demonstrated later in Sections Despite the explosive growth of literature on joint models for longitudinal and time-to-event data during the past three decades, efficient implementation of joint models has lagged behind, which limits the application of joint models to only small to moderate studies. Recently, massive sample size data collected from electronic health records (EHRs) and insurance claim databases warrants great opportunities to conduct clinical studies in a real-world setting. For example, the UK Biobank is a prospective cohort study with approximately 500,000 individuals, aged 37-73 years, from the general population between 2006 and 2010 in the United Kingdom , 19. Agg Project  are someO(n2), O(n2), and O(n3), respectively, where n is the number of subjects. Consequently, current implementation grinds to a halt as n becomes large . We further develop tailored linear scan algorithms to reduce the computational complexity to O(n) in each of the aforementioned components. We illustrate by simulation and real-world data that the linearization algorithms can result in a drastic speed-up by a factor of many thousands when n > 104, reducing the runtime from days to minutes for big data. Finally, we have developed a user-friendly R package FastJM to fit the shared parameter semiparametric joint model using the proposed efficient algorithms and made it publicly available on the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=FastJM.The purpose of this paper is to develop and implement efficient algorithms for a semiparametric joint model of longitudinal and competing risk time-to-event data. As specified in The rest of the paper is organized as follows. Yi(t) be the longitudinal outcome at time t for subject i, i = 1, 2, ⋯, n, and n is the total number of subjects. Suppose the longitudinal outcome Yi(t) is observed at time points tij, j = 1, 2, ⋯, ni, and denote Yi = . Let Ci =  be the competing risk data on subject i, where Ti is the observed time to either failure or censoring and Di takes value in {0, 1, 2, ⋯, K}, with Di = 0 indicating a censored event and Di = k implying the kth type of failure is observed on subject i, k = 1, 2, ⋯, K. The censoring mechanism is assumed to be independent of the failure time.Let Yi(t) is characterized by a linear mixed effects model:Consider the following joint model in which the longitudinal outcome mi(t) is the mean of the longitudinal outcome at time t, Xi(1)(t) and Yi(t), β represents a p × 1 vector of fixed effects of Xi(1)(t), bi ~ Nq denotes a q × 1 vector of random effects for εi(t) ~ N is the measurement error independent of bi, and εi(t1) is independent of εi(t2) for any t1 ≠ t2. In the competing risk survival submodel (λk(t | Xi(2), bi, γk, νk) is the conditional cause-specific hazard rate for type k failure at time t, given time-invariant covariates Xi(2) and the shared random effects bi, and λk0(t) is a completely unspecified baseline cause-specific hazard function for type k failure. The two submodels are linked together by the shared random effects bi, and the strength of association is quantified by the association parameters ν1, ⋯, νK.In the longitudinal submodel , mi(t) i) ~ N0, σ is the mβ, σ2, γ, ν, Σ, λ01(·), ⋯, λK0(·)} denote all unknown parameters for the joint models T and ν = T. Let I(Di = k) be the competing event indicator for type k failure, which takes the value 1 if the condition Di = k is satisfied and 0 otherwise. The observed-data likelihood for Ψ is then given byYi and Ci are independent conditional on the covariates and the random effects.Let Ψ = {K unknown hazard functions, finding its maximum likelihood estimate by maximizing the above observed-data likelihood is nontrivial. However, a customized EM algorithm can be derived to compute the maximum likelihood estimate of Ψ by regarding the latent random effects bi as missing data [Y, C, b) isΛk0(.) is the cumulative baseline hazard function for type k failure and ΔΛk0(Ti) = Λk0(Ti) − Λk0(Ti−).Because Ψ involves ing data . The comm)( is the estimate of Ψ from the mth iteration. The E-step (Em)((bi), Em)((bibiT), Em)({exp(νkbi)}, Em)({biexp(νkbi)}, and Em)({bibiTexp(νkbi)}, whereh(·). Furthermore, it can be shown that the M-step , and that the other parameters γ and ν can be updated using the one-step Newton-Raphson method. Details are provided in equations (7.1)–(7.8) of the supplementary materials :QΨ;Ψm≡Eb M-step):Ψm+1=argmn is large. Here, we focus on the profiled likelihood-based method and show that its computation can be linearized with respect to n.As discussed in Elashoff et al.  Section, p.72, sΩ =  denote the parametric component of Ψ and Ωli) is the observed score vector from the profiled likelihood li) of Ω on the ith subject by profiling out the baseline hazards. Details of the observed score vector for each parametric component are provided in equations (7.9)–(7.13) of the supplementary materials.Let ormation –25:(8)∑O(n2) or O(n3) operations, which become computationally prohibitive at large sample size n. Below we identify these bottlenecks and discuss appropriate linear scan algorithms to reduce their computational complexities to O(n).With naive implementation, multiple quantities in the above E-step, M-step, and standard error estimation will involve At each EM iteration, the E-step  requires(1) Standard Gauss-Hermite Quadrature Rule for Numerical Integration. A commonly used method for numerical evaluation of integral (Λk0m)((.) the right-continuous and nondecreasing cumulative baseline hazard function for type k failure at the mth EM iteration as defined in Section 7.1 (equation (7.4)) of the supplementary material and ΔΛk0m)((Ti) the jump size of Λk0m)((.) at Ti, q the dimension of the random effects vector, ∑t1,t2,⋯,tq  the shorthand for ∑t1=1nq  ⋯ ∑tq=1nq , nq the number of quadrature points, ct = T the abscissas with corresponding weights πt, q because the Cartesian product of the abscissas is used to evaluate the integrand with respect to each random effect. Lastly, the alternative abscissas integral  is basedt of Σ∧m . However(2) Pseudo-Adaptive Gauss-Hermite Quadrature Rule for Numerical Integration. When the number of longitudinal measurements per subject is relatively large, Rizopoulos [nq) of quadrature points and is thus computationally more efficient. The pseudo-adaptive Gauss-Hermite quadrature rule proceeds as follows. First, fit the linear mixed effects model (Xi(1) = (Xi(1)(ti1), ⋯, Xi(1)(tini)), Vi=iT(1)Σi(1) + σ2I. Then, define the alternative abscissas Em)({h(bi)} byzopoulos  introducts model  to extraEquation .nq = 6 quadrature points produces almost identical results to the standard Gauss-Hermite quadrature rule with nq = 20 quadrature points.A derivation of  is proviaccuracy . For exaO(n2) operations since each n subjects. However, because the same quantity O(n) operations. Our simulation study in the supplementary material  Linear Scan Algorithm for Calculating . Both the standard Gauss-Hermite quadrature rule  at their prespecified abscissas across all subjects (see Equations (Λk0m)((Ti) across all subjects for each k. We observe from equation (7.4) that for each k, {Λk0m)((tk1), ⋯, Λk0m)((tkqk)} have already been calculated from the mth EM iteration, where tk1 > ⋯>tkqk are qk distinct observed type k event times. For each i, calculating Λk0m)((Ti) would involve O(n) operations if a global search is performed to find the interval of two adjacent type k event times containing Ti. Consequently, calculating {Λk0m)((T1), ⋯, Λk0m)((Tn)} would require O(n2) operations. However, by taking advantage of the fact that Λk0m)((t) is a right-continuous and nondecreasing step function, one can obtain {Λk0m)((T1), ⋯, Λk0m)((Tn)} from {Λk0m)((tk1), ⋯, Λk0m)((tkqk)} in O(n) operations using the following linear scan algorithm. First, sort the observation times Ti, i = 1, ⋯, n, in descending order. Denote by (i) the ranked index of a subject. Then, define a mappingtk1, ⋯, tkqk are scanned forward from the largest to the smallest, and for each tkj, only a subset of the ranked observation times Ti)((Ti) as follows:ure rule  and the ure rule  require quations  and 14)(3) Lineatk1 and scan through the first set of observation times T(1) ≥ ⋯≥Tik1)( where Tik1)( ≥ tk1 > Tik1 + 1)'s take the value Λk0(tk1). Next, move forward to tk2 and scan through the second set Tik1 + 1)( ≥ ⋯≥Tik2)( ≥ tk2 > Tik2)+1's take the value evaluated at Λk0(tk2). Repeat the same process until Tn)( is scanned. Because the scanned Ti) operations.Specifically, start with γk and νk , involve aggregating information over the risk set R(tkj) = {r : Tr ≥ tkj} at each uncensored event time tkj. These quantities are further aggregated across all tkj's. If all subjects are scanned to determine the risk set R(tkj) at each tkj, then aggregating information over the risk set for all uncensored event times would obviously require O(n2) operations. Below we explain how to linearize the number of operations for risk set calculations across all uncensored event times by taking advantage of the fact that the risk set is decreasing over time for right censored data.In the M-step, multiple quantities in equations (A.4)–(A.8), such as the cumulative baseline hazard functions and the Hessian matrix and score vector for Λk0m + 1)((tkj), j = 1, ⋯, qk, one needs to compute ∑r∈R(tkj) exp(XrT(2)γkm)E{exp(νkm)(br)}, j = 1, ⋯, qk. Because the distinct uncensored event times tk1 > ⋯>tkqk are arranged in decreasing order, the risk set R(tk(j + 1)) can be decomposed into two disjoint sets: R(tk(j + 1)) = R(tkj) ∪ {r : Tr)( ∈ [tk(j + 1), tkj)}, and consequently,a1, ⋯, an. It follows from the recursive formula (R(tkj) do not need to be scanned to calculate the second term of (r∈R(tkj) ar,  j = 1, ⋯qk, in O(n) operations when Tr)( for γk in (A.5), we first rewrite it asar = ΔΛk0(tkj)m + 1)(exp(XrT(2)γkm)E{exp(νkm)T(br)}Xr(2)XrT(2) similar to the linear scan algorithm for Λk0m + 1)((tkj)'s.Next, to calculate the Hessian matrix based on  with ar γk and νk in equations (A.6)-(A.8) in O(n) operations in a similar fashion.Finally, the above linear risk set scan algorithm can be adapted to calculate the Hessian matrix and score vector for i, two components of the score vector, r ∈ R(Ti)} or over both {r ∈ R(tkj)} and {j : tkj ≤ Ti}. If implemented naively, the aggregation can take either O(n) or O(n2) operations, respectively. As a result, the observed information matrix can take O(n3) operation as it requires summing up the information across all subjects. Below we describe a sequential linear scan algorithm to reduce the computational complexity from O(n3) to O(n).The standard error estimation formula in  relies oOur algorithm can be easily explained by considering the calculation of the following expression in the second term of B(Ti) for i = 1, ⋯, n, where B(t) ≡ ∑j:tkj≤t bkj andIn other words, we need to compute tk1 > ⋯>tkqk are in descending order and that the subjects are sorted so that the observation times Ti's are in descending order.Before going further, we recall that the distinct uncensored event times B(tk1), ⋯, B(tkqk) can be computed in O(n) operations as one scans through tk1, ⋯, tkqk backward in time. Second, analogous to (B(T(1)), B(T(2)), ⋯, B(Tn)} from {B(tk1), ⋯, B(tkqk)}:tk1, ⋯, tkqk are scanned forward from the largest to the smallest, and for each tkj, only a subset of the ranked observation times Ti)'s as follows:First of all, because the risk set is decreasing over time for right censored data, it follows from Equation  that B and 16 GB RAM running MacOS.We present a simulation study to illustrate the computational speed-up rendered by the proposed linear algorithms as the sample size Yij fromXi(1)(tij)T =  and bi = T. In the above joint model, tij = 0, 1, ⋯ represent scheduled visit times, Xi1 follows N, Xi2 ~ Bernoulli(0.5) is a binary covariate, the random effects bi = T follows a N2 distribution with Σ11 = 0.5, Σ22 = 0.25, and Σ12 = 0, the measurement errors εij are iid N and independent of bi, and the baseline hazards λ01(t) and λ01(t) are constants 0.05 and 0.1, respectively. We simulated noninformative censoring time Vi following exp (20) and let Ti = min{Ti1∗, Ti2∗, Vi} be the observed time (possibly censored) for subject i. The longitudinal measurements for subject i at tij are assumed missing after Ti.We generated longitudinal measurements Yij fromYij=β0+β12i+ν1Tbi,λ2t;X1i,XMethod 1. This method is a standard implementation of the EM algorithm using the standard Gauss-Hermite quadrature rule in the E-step (Equation (nq = 20) without any linear computation.Equation  with nq Method 2. This is a standard implementation of the EM algorithm using the pseudo-adaptive quadrature rule in the E-step (Equation (nq = 6) with the linear calculation of Equation  with nq Method 3. This is a method 2+linear scan for calculating The number of quadrature points nq for methods 1 and 2 was determined by first trying different values, {10, 20, 30} for method 1 and {6, 9, 12, 15} for method 2, and then choosing the smallest value for which the estimation results are stabilized and similar between the two implementation methods. For comparison purposes, we have also included the runtime of an established joint model R package joineR, which uses a similar EM algorithm for parameter estimation to fit a semiparametric joint model with a slightly different latent association structure in the competing risk submodel [We first compared the runtime between three different implementations of the EM algorithm for fitting the joint models  and 2)  2) as desubmodel . The resn = 105, and the speed-up is expected to increase exponentially as n increases  during a 5-year follow-up period. Patients' FEV1 values were collected annually upon recruitment into the study. FEV1 was chosen as the primary outcome since its trajectory is an indicator of a patient's natural loss of lung function during the progression of COPD. Since not all patients completed the whole study period, about 9.47% of longitudinal measurements were missing. One of the possible reasons for dropout is that treatment was not effective, and hence, missing longitudinal measurements after dropout are nonignorable.The lung health study (LHS) data were collected from a ten-center randomized clinical trial on 5,887 middle-aged smokers with mild to moderate chronic obstructive pulmonary disease (COPD) . Patient1 together with the possible informative dropout time provides an attractive approach to deal with nonignorable missing longitudinal data due to dropout. Based on previous findings, we considered the following covariates when characterizing the trajectory of Y = FEV1: time (year), sex, age, body mass index (BMI), baseline number of cigarettes smoked per day, and the logarithm of two-point methacholine concentration-FEV1 O'Connor slope (logslope) [1 between SIP (or SIA) and usual care can be evaluated by testing if the interactions are zero or not. Specifically, we considered the following linear mixed effects model:Xi(1)(tij)T =  and bi = T are assumed normally distributed with zero mean and a covariance matrix Σ. For the dropout time Ti (possibly censored at the end of the study), we assume the Cox proportional hazard submodel. λ0(t) denotes the baseline hazard function and Wi(t) is a latent association structure that links the two submodels.Joint modeling of FEVogslope) . We alsoλ0(t) and Wi(t).a, and JSMb), FastJM finished in 0.3 minutes while other methods took 20.4 minutes to 111 minutes. As a matter of fact, the runtime of FastJM was even shorter than those of some parametric joint models (JSMa and JSMb). We also observed that JMbayes based on a Bayesian MCMC framework is considerably slower than its frequentist counterpart JM. Finally, the parameter estimates and inference results for the longitudinal outcome were almost identical between all packages, but slightly different for the survival submodel because of their slightly different latent structure Wi(t). Detailed analysis results are summarized in Section A.7 of the supplementary materials.Among all the semiparametric models .The UK Biobank (UKB) is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals, aged 37-73 years, from the general population between 2006 and 2010 in the United Kingdom , 19. ParXi(1)(tij)T =  and k = 1, 2. In Model-L, the random error term bi = T are assumed normally distributed with zero mean and covariance matrix Σ. In Model-PCH, k = 1 denotes type 2 diabetes and k = 2 stroke, λk0(t) denotes the baseline cause-specific hazard function for cause k, and Wik(t) denotes the latent association structure of SBP with cause k risk.In this example, we consider a joint model of longitudinal systolic blood pressure (SBP) measurements and a competing risk event time defined as age-at-onset of type 2 diabetes (T2D) as the first risk and age-at-onset of stroke, myocardial infarction (MI), or all-cause death as the second risk, whichever occurred first. Age-at-onset of outcomes were based on participants' primary care or hospital records, whichever occurred first. Follow-up was censored at the primary care data end date for the relevant country or the date of outcomes, if this occurred earlier. SBP measures were extracted from either baseline assessment visit or primary care data. Covariates include sex, ethnicity, and BMI measured during baseline visit. However, considering the imbalanced racial distribution in this case study, we only considered white vs. non-white ethnicity groups. Specifically, the joint model consists of a linear mixed effects model for the longitudinal outcome (SBP),To our knowledge, besides our FastJM package, joineR and JM are two other current joint model R packages that are capable of handling competing risk event outcomes. However, because JM encountered convergence issues, we will focus on FastJM and joineR in this case study. Wi(t) between two packages. Detailed analysis results are provided in Section A.8 of the supplementary materials.Finally, the analysis results produced by FastJM and joineR are similar for the longitudinal submodel for the UKB-PC subset of 5,000 and 20,000 participants and for UKB-PC full data. For the survival submodel, the analysis results are also similar for most parameters except for the association parameters due to the different latent structure O(n2) or O(n3) to O(n) within each iteration of the EM algorithm and in the standard error estimation step for a semiparametric joint model of a longitudinal biomarker and a competing risk event time outcome. Through simulation and case studies, we have demonstrated that the efficient implementation can generate a speed-up by a factor of up to hundreds of thousands and oftentimes reduce the runtime from days to minutes when the sample size is large (n > 104), making it feasible to fit a joint model on a large data in real time.We have developed customized linear scan algorithms to reduce the computational complexity from The ideas and techniques of this paper can potentially be adapted to improve computational efficiency for other joint models. For instance, the linear computational algorithm in This paper has focused on linearizing the computation with respect to the sample size within the framework of classical EM algorithm that is coupled with the pseudo-adaptive quadrature rule for numerical integration in the E-step. It would be interesting to investigate if coupling our algorithms with other numerical integration methods such as quasi-Monte Carlo method  in the Ehttps://CRAN.R-project.org/package=FastJM.A user-friendly R package FastJM  has been


L2-regularization term. For this problem, we derive a mixed-integer quadratic optimization (MIQO) formulation by applying a piecewise-linear approximation to the log-likelihood function. Optimization software can solve this MIQO problem to optimality. Moreover, we propose two methods for selecting a limited number of tangent lines effective for piecewise-linear approximations. We assess the efficacy of our method through computational experiments using synthetic and real-world datasets. Our methods provide better log-likelihood values than do conventional greedy algorithms in selecting tangent lines. In addition, our MIQO formulation delivers better out-of-sample prediction performance than do forward stepwise selection and L1-regularized estimation, especially in low-noise situations.We present a mixed-integer optimization (MIO) approach to sparse Poisson regression. The MIO approach to sparse linear regression was first proposed in the 1970s, but has recently received renewed attention due to advances in optimization algorithms and computer hardware. In contrast to many sparse estimation algorithms, the MIO approach has the advantage of finding the best subset of explanatory variables with respect to various criterion functions. In this paper, we focus on a sparse Poisson regression that maximizes the weighted sum of the log-likelihood function and the  A count variable, which takes only on nonnegative integer values, reflects the number of occurrences of an event during a fixed time period. Count regression models such as Poisson, overdispersed Poisson, and negative binomial regression are standard methods for predicting such count variables –3. In paThe aim of sparse estimation is to decrease the number of nonzero estimates of regression coefficients. This method is often used for selecting a significant subset of explanatory variables –12. Subsdata collection and storage costs can be reduced,computational load of estimating regression coefficients can be reduced,interpretability of regression analysis can be increased, andgeneralization performance of a regression model can be improved.best sparse estimation involves evaluating all possible subset regression models. However, the exhaustive search method , where xi ≔ ⊤ is a vector composed of p explanatory variables, and yi ∈ {0}∪[m] is a count variable to be predicted for each instance i ∈ [n]. We define binary labels asSuppose we are given a sample of Y is assumed to follow the Poisson distributionw ≔ ⊤ is a vector of regression coefficients, and b is an intercept term. Then, the occurrence probability of the given sample is expressed asThe random count variable b, w) are estimated by maximizing the log-likelihood functionfk(u) is a nonlinear function defined asThe regression parameters (fk(u) for k ∈ {0, 5, 10, 15, 20}. Since its second derivative fk(u) is a nonlinear concave function.fk(u).The following theorem gives an asymptote of Theorem 1. When u goes to −∞, fk(u) has the asymptoteProof. We havez ≔ ⊤ be a vector composed of binary decision variables for subset selection, namely,Before deriving our desired formulation, we introduce a mixed-integer nonlinear optimization (MINLO) formulation for sparse Poisson regression. Let L2-regularization term wα⊤w to be minimized, where L2-regularization term. This sparse Poisson regression can be formulated as the MINLO problemθ ∈ [p] is a user-defined parameter of the subset size. If zj = 0, then the jth coefficient must be zero by logical implication of To improve the generalization performance of a resultant regression model, we also introduce the arameter . We therThe logical implication of It is very difficult to handle the MINLO problem by Eqs ukℓ, fk(ukℓ))∣ℓ ∈ [h]} be a set of h tangent points for the function fk(u), the corresponding tangent lines arefk(u).Letting {(fk(u) can be approximated by the pointwise minimum of a set of h tangent lines. For each u, we approximate fk(u) byAs k; therefore, we can employ the same set {uℓ∣ℓ ∈ [h]} for all k ∈ {0}∪[m] when selecting tangent points for piecewise-linear approximations.We next focus on the approximation gap Theorem 2. k ∈ {0}∪[m].Proof. We haveT ≔ (tik)i, k)∈[n] × ({0}∪[m]))∣ℓ ∈ [h]} as a set of h tangent points for the function fk(u). Consequently, the MINLO problem by Eqs We are now ready to present our desired formulation for sparse Poisson regression. Let The accuracy of piecewise-linear approximations depends on the associated set of tangent lines. It is clear that with increasingly many appropriate tangent lines, the MIQO problem by Eqs Sato et al.  developeThese previous algorithms have two limitations addressed in this paper. First, they totally ignore the properties of the sample distribution. Second, tangent lines are determined one at a time, so the resultant set of tangent lines is not necessarily optimal. In the following sections, we propose two methods, namely the adaptive greedy algorithm and the simultaneous optimization method, to resolve the first and second limitations, respectively.adaptive greedy algorithm, selects tangent lines depending on the sample distribution.Our first method, the i ∈ [n]. Our algorithm aims to minimize the sum of squared approximation gaps in response to this empirical distribution. Although the previous algorithms compute a set of tangent lines independent of datasets, our algorithm can adapt a set of tangent lines to each dataset.Suppose we are given h tangent points sth tangent point Gks(u) = min{gk(u∣uℓ)∣ℓ∈[s]}, and  is an input interval of the nonlinear function of k ∈ {0}∪[m].We select simultaneous optimization method, selects a set of h tangent lines simultaneously, not sequentially.Our second method, the ℓth and (ℓ + 1)th tangent lines is specified by ck, meaning gk(u∣uℓ) = gk(u∣uℓ+1) holds when u = ck. It follows from Suppose the intersection between the h tangent points minimizing the total approximation gap  = L and ck = U are fixed, and ck is defined by ℓ ∈ [h − 1]. NLO software can handle this problem, yielding a locally optimal set of tangent points. This method also provides the same set of tangent lines for all k ∈ {0}∪[m].We then simultaneously determine a set of This section describes computational experiments for evaluating the effectiveness of our method for sparse Poisson regression.h is the number of tangent lines to be selected.We investigate the performance of our MIQO formulation by Eqs EqlSpc(h): setting equally spaced tangent pointsAreaGrd(h): the greedy algorithm developed by Sato et al.  =  and use the asymptote of statsmodels module to perform maximum likelihood estimation of the full model of us ∈ {−5.00, −4.99, −4.98, …, 4.99, 5.00} for s ∈ [h]. We use the Python scipy.optimize module (method=’SLSQP’) to solve the NLO problem by Eqs https://www.gurobi.com/) to solve the MIQO problem by Eqs L2-regularization parameter to α = 0 in Tables We implemented these algorithms in the Python programming language. We set the input interval  by roundingρ ∈ {0.35, 0.70} and σ2 ∈ {0.01, 0.10, 1.00} in the experiments.We next sampled explanatory variables from a normal distribution as We trained sparse Poisson regression models with 100 training instances. We estimated prediction performance by applying the trained regression model to sufficiently many test instances. The tables show average values for 10 repetitions, with standard errors in parentheses.θ = 5 and 10, respectively. The column labeled “LogLkl” shows the log-likelihood value of σ2, ρ) are shown in bold. The columns labeled “Time (s)” show computation times in seconds required for solving the MIQO problem (MIQO) and for selecting tangent lines (TngLine).Tables Our adaptive greedy algorithm (AdpGrd) attained the largest log-likelihood values for most problem instances but required long computation times to select tangent lines. This result implies that effective sets of tangent lines are different depending on the dataset, so the adaptive greedy algorithm, which computes a different set of tangent lines suitable for each dataset, can perform well. Our simultaneous optimization method (SmlOpt), on the other hand, selected tangent lines very quickly and also provided the second-best log-likelihood values for a majority of problem instances. These results clearly show that our AdpGrd and SmlOpt methods can find sparse regression models of better quality than do the conventional AreaGrd and GapGrd methods.θ = 5 and 10, respectively. The best RMSE, accuracy, and recall values for each problem instance  are shown in bold.Tables σ2 ∈ {0.01, 0.10}, our AdpGrd and SmlOpt methods delivered better prediction performance than did the FwdStep and L1-Rgl algorithms for all problem instances. In contrast, L1-Rgl algorithm performed very well when  =  in L1-regularized estimation. This observation is consistent with the simulation results reported by Hastie et al. , tuning tangent points more finely will probably make marginal improvements in the prediction performance. In addition, to upgrade the prediction performance in high-noise situations, we should adopt the Lp-regularization term α‖w‖p with finely tuned parameters α and p in our MIQO formulation [A future direction of study will be to develop an efficient algorithm specialized for solving our MIQO problem. We are now working on extending several MIO-based high-performance algorithms , 48, 49 mulation .


Long non-coding RNA β2.7 is the most highly transcribed viral gene during latent human cytomegalovirus (HCMV) infection. However, as yet, no function has ever been ascribed to β2.7 during HCMV latency. Here we show that β2.7 protects against apoptosis induced by high levels of reactive oxygen species (ROS) in infected monocytes, which routinely support latent HCMV infection. Monocytes infected with a wild-type (WT) virus, but not virus deleted for the β2.7 gene (Δβ2.7), are protected against mitochondrial stress and subsequent apoptosis. Protected monocytes display lower levels of ROS and additionally, stress-induced death in the absence of β2.7 can be reversed by an antioxidant which reduces ROS levels. Furthermore, we show that infection with WT but not Δβ2.7 virus results in strong upregulation of a cellular antioxidant enzyme, superoxide dismutase 2 (SOD2) in CD14+ monocytes. These observations identify a role for the β2.7 viral transcript, the most abundantly expressed viral RNA during latency but for which no latency-associated function has ever been ascribed, and demonstrate a novel way in which HCMV protects infected monocytes from pro-death signals to optimise latent carriage. Human cytomegalovirus (HCMV) is a widespread beta-herpesvirus that infects 40–100% of populations worldwide ,3,4,5,6.It is becoming increasingly clear that latency-associated gene expression is more complex than first thought ,9,10. TrDuring a lytic infection, where β2.7 is also the most highly transcribed viral gene, accounting for approximately 46.8% of polyadenylated viral RNA at 72 h post infection (h.p.i) , it is kConsequently, we have analysed the role of β2.7 expression in monocytes, in which HCMV is known to undergo a latent infection. We find that β2.7 does indeed protect infected monocytes from apoptosis and that this is mediated by preventing the accumulation of high levels of ROS, likely at least in part via upregulation of superoxide dismutase 2 (SOD2). 2 atmosphere. Where stated, monocytes were treated with 50 μM cadmium chloride , N-acetyl cysteine  or MitoTEMPO . Primary CD14+ monocytes were isolated from the peripheral blood of healthy donors or from apheresis cones  as described in Poole et al. . In brieWT Toledo and Δβ2.7 Toledo viruses were kind gifts from Gavin Wilkinson and are described in McSharry et al. . WT TB40In brief, the sequence from nt 35,220 up to the TATA box at nt 37,726 within the TB40/E BAC4 sequence listed in EF999921 was deleted by homologous recombination with the galK gene. The galK gene was subsequently removed from the BAC, again by homologous recombination, using the reversion primers listed in g, before being frozen at −80 °C. To concentrate virus, supernatants were defrosted and spun for 2 h at 18 °C at 14,500× g in an Avanti-J25 centrifuge with a JLA-16.250 rotor (Beckman Coulter). The pellet was resuspended in X-Vivo 15 media, aliquoted, and then stored at −80 °C. Prior to use, concentrated virus was defrosted and spun for 2 min at 800× g to further remove any cellular debris.Viruses were propagated by inoculating HFFs at an MOI of 0.1. At 90% infection, the supernatant was harvested every 2–3 days and centrifuged for 10 min at 1500× Monocytes were inoculated with virus at a multiplicity of infection (MOI) of 3 as based on titration on RPE-1 cells, and incubated for 3 h, before being washed twice with PBS and given fresh X-Vivo15 media. Ultra-violet light (UV) inactivation of virus was conducted by placing samples in a tissue culture plate within 10 cm of a UV germicidal (254 nm) lamp for 30 mins as described in . Mitochondrial membrane potential was assayed using tetramethylrhodamine, ethyl ester . Cells were stained with 50 nM TMRE  and Hoechst 33,342 stain  for 30 min at 37 °C in the dark, prior to being washed once with PBS and imaged on a widefield Nikon TE200 microscope. Percentage of TMRE-stained cells was determined from 3 fields of view per replicate over at least 3 independent biological repeats. Superoxide was stained with MitoSOX™ Red . MitoSOX reagent was added to cells to a final concentration of 1 μM for 10 min at 37 °C in the dark. Cells were then washed three times with warm Hank’s buffered saline solution , Hoeschst stained and imaged on a widefield Nikon TE200 microscope. Quantification of MitoSOX fluorescence was performed using ImageJ software, analysing a minimum of 400 cells per replicate. Samples were directly lysed in Laemmli Buffer and separated by SDS-PAGE on a 12% polyacrylamide gel. Separated proteins were transferred to Hybond nitrocellulose membranes  and blocked in 5% milk in Tris-buffered saline (milk-TBS). Primary antibodies were diluted in milk-TBS as follows: cleaved caspase-3 , PARP , β-actin , SOD2 , and GPX-1 , and incubated with membranes overnight at 4 °C. Secondary antibody  was incubated with membrane for 1 hr at room temperature. Blots were incubated with Amersham ECL Western Blotting detection reagent  for 5 min and then exposed to autoradiography film . All densitometry analysis was performed using ImageJ software. 2), which is an established way of causing apoptosis in primary lymphocytes and monocytes by inducing loss of mitochondrial membrane potential (MMP) [To test if β2.7 has a protective role during latent infection, we stressed infected monocytes with cadmium chloride  ,23; simial (MMP) ,25. The al (MMP)  while otal (MMP) . Howeveral (MMP) ,25,26. CWe started by analysing mitochondrial function in infected monocytes using the fluorescent dye, TMRE, a positively charged, cell permeant molecule which accumulates in the mitochondrial matrix of actively respiring mitochondria. It therefore serves as a measure of MMP, such that mitochondrial oxidative phosphorylation uncouplers such as FCCP, which disrupts ATP synthesis by depolarising mitochondrial membrane potential, cause dye dispersal and loss of TMRE fluorescence  virus or virus with a β2.7 gene deletion (Δβ2.7) in the Toledo strain of HCMV. Monocytes infected with HCMV are known to undergo latent infection which can be maintained in long-term culture ,30,31,32To ensure this effect was not virus isolate-specific and to specifically analyse infected cells in the cell population (which could not be tracked in untagged WT Toledo virus infections), we repeated this analysis at earlier timepoints (3 d.p.i and 5 d.p.i) with GFP-tagged WT or GFP-tagged Δβ2.7 TB40/E viruses C,D. WT i2 and 24 h post treatment, protein was harvested and assayed for levels of cleaved caspase-3, a hallmark of apoptosis (2 to mock infected cells resulted in detectable cleaved caspase-3 levels which was not observed in WT virus-infected cells treated with cadmium (A loss in MMP is known to either stimulate apoptosis or be a consequence of it . Therefopoptosis . Additio cadmium . By cont2*-) and hydroxyl radicals (HO*-), as well as their derivatives, e.g., hydrogen peroxide (H2O2), peroxynitrite (ONOO-), and hypochlorous acid (HOCl) [HCMV infection protects monocytes from cadmium-induced cell death . As cadmd (HOCl) . At highd (HOCl) ,42. The d (HOCl) .To test whether β2.7 modulates ROS levels during a latent infection, we mock infected or infected CD14+ monocytes with WT or Δβ2.7 Toledo virus for 6 days and then treated them with cadmium chloride. Twenty-four hours post treatment, cells were stained with Hoechst and MitoSOX, a mitochondrial superoxide dye . Consistent with β2.7 protecting cells from superoxide induction during infection, we observed increased MitoSOX staining in Δβ2.7 infected cells even in the absence of exogenous ROS induction, indicating that infection in the absence of β2.7 increases ROS levels A. This dAlthough our observations clearly showed that β2.7 expression results in lower ROS levels, as well as protection from apoptosis in exogenously stressed cells, it was important to determine whether the high ROS levels we observed in the absence of β2.7 were driving cell death rather than simply being a side effect of it. To address this, we first tested whether ROS were causing mitochondrial stress by adding cadmium to CD14+ monocytes following treatment with increasing concentrations of antioxidants to lower ROS. We then measured MMP to test whether lowering ROS levels with an antioxidant would rescue mitochondrial activity. As can be seen in We then wished to investigate whether the antioxidant, NAC, would also rescue Δβ2.7 infected cells from cadmium-induced apoptosis. We treated mock, WT or Δβ2.7 virus-infected monocytes with cadmium, with or without NAC, and immunoblotted protein lysates from these cells for cleaved caspase-3 and cleaved PARP (both indicative of apoptosis). As expected, treatment of monocytes with cadmium induced detectable levels of cleaved caspase-3 and cleaved PARP which was substantially increased in Δβ2.7 infected cells C. HoweveOur data, so far, indicated that β2.7 protects infected cells from the effects of high ROS levels; however, the mechanism for this was unclear. One important regulator of ROS is the mitochondrial enzyme superoxide dismutase 2 (SOD2) . ElectroWe reasoned, therefore, that a possible mechanism of action of β2.7 could be through modulation of SOD2 protein levels. Consequently, we analysed cellular SOD2 levels during infection in the presence or absence of β2.7. As the cellular enzyme, GPX-1, also functions in the detoxification of ROS see A, we alsFinally, we were surprised that despite only ~10% of monocytic cells establishing a latent infection ,49, we oβ2.7 is an HCMV-encoded long non-coding RNA that is the most abundant latent viral transcript. Despite this, its role during latency remains has remained undefined and no latency-associated function for it has ever been described. We now show that β2.7 protects infected monocytes from apoptosis by preventing increases in ROS levels and by upregulating expression of the antioxidant enzymes, SOD2 and GPX-1. Reactive oxygen species pose a significant problem for all cells: whilst at low levels, they have important roles in cell signalling, high levels of ROS lead to irreversible oxidation of proteins and DNA, lipid peroxidation, inhibition of MMP and ATP production, and, ultimately, apoptosis or necrosis. In this paper, we have observed that HCMV protects against ROS during infection of monocytes and prevents apoptosis of the infected host cell, likely optimising maintenance of the latent reservoir to enable efficient reactivation and dissemination of reactivated virus. We recognise that in our analyses, we generated ROS artificially with cadmium chloride in order to address the ability of HCMV to combat ROS induction. However, infected monocytes are likely to face many natural ROS-inducing insults during latent carriage in vivo. Firstly, monocytes are drawn to sites of inflammation, in which ROS levels are high . ProinflFurthermore, we hypothesize that latent infection itself raises ROS levels in the cell, given that we saw higher ROS levels in Δβ2.7 infected monocytes compared to mock infected, even in the absence of cadmium ion treatment. Indeed, binding of HCMV virions to cells has been shown to generate ROS , and it In this study, we found that as well as protecting infected monocytes from high ROS levels, β2.7 also prevents high ROS levels and upregulates SOD2 in uninfected bystander cells by means of a secreted factor. Preventing ROS-induced cell death in surrounding cells may indirectly protect the infected cell; death of myeloid cells is thought to mediate further local inflammation and associated tissue damage . FurtherWe are, as yet, uncertain of the identity of the secreted factor responsible for SOD2 upregulation. SOD2 is often upregulated by proinflammatory cytokines , howeverOur analysis has focussed on mitochondrial ROS: the ROS dye used herein (MitoSOX) localises to the mitochondria, as does one of the antioxidants we used to protect against cadmium toxicity (MitoTEMPO). However, other sources of ROS exist within monocytes. For example, inflammation can drive neutrophils and monocytes to produce extracellular ROS via NADPH oxidase 2 (NOX2), which can diffuse back into cells . MonocytFinally, whilst it is known that expression of β2.7 in isolation can protect cells from rotenone-induced apoptosis during permissive infection , it is nIn conclusion, we have shown that the HCMV long non-coding RNA, β2.7, which is the most abundant latent viral transcript but previously had no defined role during latency, protects against ROS-induced apoptosis and upregulates antioxidant enzymes in infected and bystander monocytes. This function of β2.7 likely plays a key role in the maintenance and dissemination of latent HCMV infection in the host.