We aimed to evaluate the acute effect of musical auditory stimulation on heart rate autonomic regulation during endodontic treatment. The study included 50 subjects from either gender between 18 and 40 years old, diagnosed with irreversible pulpitis or pulp necrosis of the upper front teeth and endodontic treatment indication. HRV was recorded 10 minutes before (T1), during (T2), and immediately (T3 and T4) after endodontic treatment. The volunteers were randomly divided into two equal groups: exposed to music  or not. We found no difference regarding salivary cortisol and anxiety score. In the group with musical stimulation heart rate decreased in T3 compared to T1 and mean RR interval increased in T2 and T3 compared to T1. SDNN and TINN indices decreased in T3 compared to T4, the RMSSD and SD1 increased in T4 compared to T1, the SD2 increased compared to T3, and LF (low frequency band) increased in T4 compared to T1 and T3. In the control group, only RMSSD and SD1 increased in T3 compared to T1. Musical auditory stimulation enhanced heart rate autonomic modulation during endodontic treatment. Dental treatment has been reported by many patients as a situation generating stress and anxiety. One of the main elements that seem to interfere with the behavior of most individuals seeking dental care is the acceptance that they will be subjected to some kind of discomfort during the treatment –4.In this context, the literature has investigated alternative therapies to improve stressful situations , 6. AmonStress and anxiety are associated with the regulation of cardiac function by the autonomic nervous system (ANS), which allows us to evaluate the balance between the sympathetic and parasympathetic systems. In the heart, the ANS specifically influences heart rate and cardiac contractility. Furthermore, to increase or decrease of heart rate, ANS also regularly controls heart beats. The fluctuation of consecutive heart beats is analyzed through heart rate variability (HRV) –12.Along these lines, the development of technical, scientific, and biological procedures in endodontic treatment achieves highly successful results. However, in spite of this, the treatment involves several stages ranging from anesthesia until obliteration of the root canal system. Also, the technique can be accomplished by basic technical operative steps which are prone to failure and various complications in its clinical progression .Consequently, we believe that, during endodontic treatment, musical auditory stimulation may improve HRV. Knowledge of physiological responses involved in endodontic treatment is imperative because it provides a more appropriate treatment plan to the patient and provides greater safety clinically for the endodontist. Analysis of the ANS oscillations during endodontic procedure provides information, which can cause potential risks for cardiovascular system. Therefore, we investigated the acute effects of musical auditory stimulation on heart rate autonomic regulation during endodontic treatment.Fifty subjects participated in this study with 13 females and 12 males in the music group and 12 females and 13 males in the control group. All participants aged between 18 and 40 years were examined. Everyone was diagnosed with irreversible pulpitis or pulp necrosis of the upper front teeth and endodontic treatment indication. The patients were divided into two equal groups: control group (patients who were submitted to standardized endodontic treatment) and music group . The sample was selected from endodontic treated patients undergoing the root canal procedure. All volunteers were informed about the procedures and objectives of the study and, after agreeing, they signed an informed consent form which was confidential. The study was approved by the Ethics Committee in Research of our University (Protocol number 993.350).We considered the following as exclusion criteria: smokers; individuals with cardiorespiratory, neurological disorders and other reported impairments that prevented the subject from performing the procedures and treatment with drugs that influence cardiac autonomic regulation; patients who have undergone previous root canal treatment; women between 10–15 and 20–25 days after the first day of the menstrual cycle in order to avoid influence of luteal and follicular phase; pregnant women; and subjects who reported hearing disorders.2.Prior to the endodontic treatment the following information was collected: age, gender, mass, height, and body mass index (BMI). Anthropometric measurements were obtained according to the recommendations described by Lohman . Mass waData collection was undertaken in a room with temperature between 21°C and 25°C and humidity between 50% and 60%. The volunteers were instructed to not ingest alcohol, caffeine, or other ANS stimulants for 24 hours before the evaluation. The volunteers were instructed to remain at rest and avoid talking during the collection.Primarily, the blood pressure (BP) measurement was implemented. Subsequently, the saliva collection for salivary cortisol measurement was undertaken. Following this the analysis of the protocol of cardiac modulation during endodontic treatment was assumed.Systolic (SBP) and Diastolic Blood Pressure (DBP) measurements were performed using an aneroid sphygmomanometer (Tycos®) and stethoscope (Sprague®). BP was measured before and after the endodontic treatment.We did not induce salivation to collect saliva samples; we collected salivary cortisol samples before and after endodontic treatment between 14:00 and 17:00 by the same researcher. The volunteers were told to not eat or drink one hour before collecting saliva and wash their mouths with water. The Salivette® tubes were refrigerated until analysis of the material.Data assembly was performed using a Salivette tube consisting of a plastic tube that contains a highly absorbent cotton roll, maintained in the mouth for about three minutes. Then, it was placed inside the collector plastic tube. After collecting all the Salivette tubes, samples were centrifuged for 20 minutes at 2800 rpm at a temperature of 4°C. During centrifugation, saliva passed the cylindrical swab medium through the cavity in the bottom of the drop tube to the clean centrifuge tube. Mucus and airborne particles were captured in the conical tip of the tube, allowing for easy decanting of clarified saliva.Following centrifugation, the saliva was transferred to an Eppendorf type tube with lid and stored in a freezer at −20°C until the time when it was inspected. The material was cooled, ensured by the use of dry ice, Styrofoam, and a thermal bag. The samples were processed using the electrochemiluminescence method.10 minutes before the beginning of root canal therapy session (T1)The initial 10 minutes after the application of local anesthetic (T2)The total duration of endodontic treatment (T3)30 minutes after the endodontic treatment termination (T4)HRV was recorded in the first session of endodontic treatment at four intervals:After the initial assessment, we placed the heart rate receiver Polar RS800CX  on the chest of the volunteers at the distal third of the sternum region and strapped at the wrist. After placing the strap and the monitor, the protocol was initiated.Inferior alveolar nerve anesthesia (2% DFL® ALPHACAINE)Decayed tissue removal and access to the pulp chamber  compatible with the size of the pulp chamber, mounted on high speed turbine, air-cooled water, and Endo Z bur (Les Fils d' August Mailefer SA)The operating field isolation with rubber damChemical-mechanical preparation of root canals (Hand files Flexofile Dentsply® and sodium hypochlorite 2.5%)Channel drying (Dentsply paper cones)Intracanal medication (SS White®)Cotton ball laying on channel inputInterim sealing (IRM®)The endodontic treatment was performed by the same dentist. We describe below all the steps of endodontic treatment:n = 25), in which a group performed endodontic treatment while listened to music during T2, T3, and T4 periods and the other group was not exposed to musical auditory stimulation. The music played was “Träumerei” of Kinderszenen Op. 15-7 and was conveyed through a mp4 player (Sony® NWZ-E473) to an earphone. The control group remained with the earphone turned-off.The volunteers were randomly divided into two equal groups .Digital filtering was based on an algorithm in the Polar Protrainer software . The algorithm intends to detect all measurement errors through analyzing median and moving average based filtering methods. The criteria adapt to the RR interval to be verified; if RR interval is very stable, relatively small peaks will be interpreted as errors. However, if RR interval includes very large variability, the criteria will be looser. Very small errors are not corrected. In the correction phase, the algorithm computes several more matching “candidates” to substitute the detected errors. The algorithm identifies the difference between previous and the next accepted RR interval and then makes the series of corrected values to follow the same differential coefficient. The special property of the error correction algorithm maintains the total time of the recording: that is, the sum of RR intervals is exactly the same as the elapsed time  and time domain RRtri (triangular index) and TINN  indices. Significance of the abovementioned indices was previously described , 18.To determine the sample size a priori knowledge was needed, based on Moreno et al.  findings t-test and ANOVA for repeated measures followed by Bonferroni posttest. In circumstances where the normal distribution was unaccepted, the Mann–Whitney test and the Friedman test followed by Dunn's posttest was applied. Dissimilarities in these tests were considered statistically significant when the value of “p” is less than 0.05. The statistical program applied was GraphPad Software StatMate 2:00 version for Windows, GraphPad Software, San Diego, California, USA.For comparison between individuals of the same group, it was necessary to assess data normality by the Shapiro-Wilk test. When normal distribution was achieved, we applied the unpaired Student'sAfter applying the normality test we checked that all distributions evaluated were parametric. In this sense, data were presented as mean and standard deviation.In the control group, we noted that RR interval was increased during endodontic treatment (T3) compared to before endodontic procedures (T1). RMSSD  and SD1 indices were also increased during endodontic treatment (T3) compared to before endodontic procedures (T1) .Regarding the groups subjected to musical auditory stimulation during the endodontic treatment, we observed that DAP increased after the procedures (T4). HR decreased while RR interval decreased during endodontic treatment (T3) compared to before treatment (T1) .Regarding HRV analysis, SDNN, TINN, and SD2 indices were weakened during endodontic treatment (T3) compared to after treatment (T4). Moreover, RMSSD and SD1 were increased after treatment (T4) compared to before treatment (T1) .Regarding spectral analysis, LF in absolute units was increased after treatment (T4) compared to before (T1) and during treatment (T3) .In our study, we found a decrease in the SDNN, SD2, and LF indices during endodontic treatment with musical stimulation compared to after treatment. Those indices correspond to overall HR modulation .p = 0.01), TINN (p = 0.01), and SD2 (p = 0.002) were increased in the control group during T3 with no changes in T1, T2, and T4. TINN was higher in T1 (p = 0.04) and T3 (p = 0.001) in the control group with no changes in T2 and T4. The LF/HF ratio was increased in T1 (p = 0.04) and T2 (p = 0.02) in the control group with no changes in T3 and T4.We also compared HRV indices between both groups at all instants. SDNN (2), LF (nu), HF (ms2), and HF (nu) LF/HF ratio during T1, T2, T3, and T4.There was no significant difference between groups for mean RR interval, RMSSD, pNN40, SD1, LF  and music , 29. SupAlso, reduction of the LF index was observed in the study of da Silva et al. , which eWe reported that the TINN index was also decreased during endodontic treatment in the music group. Previously a study  illustra2) after stimulation with heavy metal music in healthy females. The authors also found that SDNN, LF (ms2 and nu), and the LF/HF ratio increased while the HF index (nu) decreased after exposure to classical baroque music. The abovementioned study supports our results.Founded on our data, RMSSD and SD1 indices increased after endodontic treatment compared to before treatment. Our data points to a beneficial elevation in parasympathetic heart rate regulation induced by music during endodontic treatment. Ferreira et al.  testifiep = 0.0577) to reduce cortisol levels in the music group. Hence, we suggest that a more prolonged time of exposure to music may influence cortisol levels.In this study, there was deficiency of significance of salivary cortisol before and after endodontic treatment. In this context, we indicate that music operated through ANS and not via hypothalamic-pituitary axis due to the short period of endodontic treatment. Nonetheless, there was a trend . Conversely, exclusion criteria helped to ensure regulatory standards regarding the sample selection. Factors such as age, gender, anthropometric characteristics, and health conditions have been shown to influence physiological reactions . We inveMusical auditory stimulation acutely improved heart rate autonomic regulation during endodontic treatment. Our results encourage the use of relaxant music during endodontic treatment in order to lessen cardiovascular responses.


The aim of this work was to investigate if the content of glutamyl aminopeptidase (GluAp) in microvesicular and exosomal fractions of urine is related with renal dysfunction in cisplatin-treated rats.g to obtain microvesicular and exosomal fractions. GluAp was measured with a commercial ELISA kit in both fractions. Serum creatinine (SCr) and body weight were measured 15 days after treatment. We analyzed if early excretion of GluAp in microsomal and exosomal fractions was correlated with final SCr and body weight increase. In a second experiment, enzymatic activities of GluAp and alanyl aminopeptidase (AlaAp) in urine, microvesicular and exosomal fractions were measured three days after injection. We analyzed the correlation of both markers with SCr determined at this point. Finally, we studied the expression of GluAp and extracellular vesicles markers Alix and tumor susceptibility gene (TSG101) in both fractions by immunoblotting.Urine samples were collected 24 hours after injection of cisplatin  or saline serum (n = 10), and they were subjected to differential centrifugation at 1.000, 17.000 and 200.000 GluAp excretion was increased in all fractions of urine after cisplatin treatment, even if data were normalized per mg of creatinine, per body weight or per total protein content of each fraction. We found significant predictive correlations with SCr concentration, and inverse correlations with body weight increase determined 15 days later. Three days after injection, aminopeptidasic activities were markedly increased in all fractions of urine in cisplatin-treated rats. The highest correlation coefficient with SCr was found for GluAp in microvesicular fraction. Increase of GluAp in microvesicular and exosomal fractions from cisplatin-treated rats was confirmed by immunoblotting. Alix and TSG101 showed different patterns of expression in each fraction.Determination of GluAp content or its enzymatic activity in microvesicular and exosomal fractions of urine is an early and predictive biomarker of renal dysfunction in cisplatin-induced nephrotoxicity. Measurement of GluAp in these fractions can serve to detect proximal tubular damage independently of glomerular filtration status. Early diagnosis of acute kidney injury remains to be a challenge nowadays. Traditional biomarkers, like serum blood urea nitrogen (BUN) or creatinine (SCr) start to rise when kidney has lost at least a 50% of its function ,2 and thConsequently, several biomarkers such as N-acetyl-β-D-glucosaminidase (NAG), kidney injury molecule-1 (KIM-1) or neutrophil gelatinase-associated lipocalin (NGAL) have been proposed during the last years as early markers of renal dysfunction in different pathologies, and urine is the most promising source of substances that can be related with the extent of kidney injury .Cisplatin is an antineoplasic drug that induces nephrotoxicity as a collateral effect ,6, and iUrine can contain extracellular vesicles (EVs) that are released from renal epithelium ,11. AlthGluAp was identified in urinary exosomes using proteomic analysis , but theg during 15 minutes at 4°C, and frozen at -80°C until analysis.Twenty male Wistar rats with a weight from 175 to 234 g were purchased from Harlan Laboratories  and distributed in two groups: control and cisplatin (n = 10 each group). Cisplatin  was dissolved in sterile saline solution (3.5 mg/ml). Animals of Cisplatin group were subcutaneously injected with 2 ml/kg of this solution, receiving a dose of 7 mg/kg of cisplatin, while animals of Control group were injected with 2 ml/kg of sterile saline solution. Urine samples were taken in a metabolic cage during the next 24 h. 15 days after injection, blood samples were obtained from left ventricle under anesthesia , centrifugated at 1000 In a second experiment, twenty male Wistar rats purchased from Envigo  weighing from 360 to 469 g were distributed in Control and Cisplatin groups (n = 10 each group). Cisplatin group was injected with 7 mg/kg of cisplatin and Control group was injected with saline serum as described before. Urine and blood samples were collected three days after treatment, at the peak of cisplatin nephrotoxicity, and processed as described.All experimental procedures were performed in strict accordance with the European Union Guidelines to the Care and Use of Laboratory Animals. This study was approved by the Ethical Committee of the Universidad de Jaén and Junta de Andalucía with the approval number 450–5297. All surgery was performed under sodium pentobarbital anesthesia, and all efforts were made to minimize suffering.g, 10 minutes, 4°C in order to separate whole cells, bacteria, cellular debris and other substances present in urine. Precipitates were discarded. Some aliquots of supernatants were frozen at -80°C to determine creatinine. The rest of supernatant was treated with protease inhibitors. 1 μl of 1 mM leupeptin and 50 μl of 10 mM phenylmethylsulfonyl fluoride (PMSF) were added to each ml of supernatant.Isolation method of microsomal and exosomal fractions from urine was modified from Zhou et al. . Urine sg, 15 minutes, 4°C. Precipitates including microsomal fraction  were redissolved in 0.5 ml of 20 mM HCl-Tris, pH 8.6 and frozen at -80°C until processing.Supernatants containing protease inhibitors were subjected to a second centrifugation step at 17000 g, 1 hour, 4°C. Precipitates containing exosomal fraction were redissolved in 0.3 ml of 20 mM HCl-Tris, pH 8.6, and frozen at -80°C.Finally, supernatants were subjected to ultracentrifugation at 200000 In the second experiment, urine samples were collected three days after injection and processed as described. Protease inhibitors were not added in order to determine GluAp and AlaAp enzymatic activities in supernatant, microvesicular and exosomal fractions.GluAp was measured in microsomal and exosomal fractions obtained from urine 24 hours after injection as previously described, using an ELISA kit from Sunred Biotechnologies . All samples and standards were measured in duplicate. Absorbances of all samples were inside the range of the standard curve (from 4 to 64 ng/ml). Concentration of GluAp in microsomal and exosomal fractions was divided by the corresponding factor and referred to the starting volume of urine.GluAp and AlaAp fluorimetric activities were determined three days after injection in a kinetic fluorimetric assay using L-glutamic acid γ-2-naphthylamide or alanyl-2-naphthylamide  as substrates, respectively. 20 μl of urine were incubated during 30 min at 37°C with 80 μl of substrate solution . Substrates had been previously dissolved in 1 ml of dimethyl sulfoxide and stored at -20°C. The amount of 2-naphthylamine released as a result of the aminopeptidase activities was measured fluorimetrically at an emission wavelength of 412 nm with an excitation wavelength of 345 nm, and quantified using a standard curve of 2-naphthylamine (0–200 nmol/ml). Fluorimetric data from samples and standard curve were taken each minute. Specific aminopeptidase activities were calculated from the slope of the linear portion of enzymatic assay, and expressed as nanomol of substrate hydrolyzed per minute per mg of urine creatinine (mU).Creatinine and proteinuria was measured in supernatants from all samples of urine in a Spin120 autoanalyzer using reagents purchased from Spinreact .Protein concentration in microsomal and exosomal fractions was measured in a 96-well microplate using a DC Protein Bioassay kit from Biorad Laboratories, Madrid, Spain.50 ml of urine samples from control rats and 200 ml from cisplatin-treated rats obtained 3 days after treatment with cisplatin were pooled and subjected to the two-steps centrifugation at 17.000 and 200.000 g. Pellets were dissolved in 50 mM Tris-HCl (pH 6.8) containing 1.5% SDS. Total protein content was measured with DC Protein Bioassay kit from Biorad Laboratories, Madrid, Spain. 30 μg of protein were subjected to SDS-PAGE, transferred to a nitrocellulose membrane, blocked with 1% casein (Bio-Rad Laboratories) and probed overnight at 4°C with 1μg/ml of goat anti-GluAp-antibody , 1 μg/ml of rabbit anti-Alix-antibody  or 1 μg/ml of rabbit anti-TSG101-antibody . After washing, membranes were probed with 0.04 μg/ml rabbit anti-goat horseradish peroxidase-linked IgG antibody  or 0.04 μg/ml mouse anti-rabbit horseradish peroxidase-linked IgG antibody  as secondary antibodies. Bands were visualized with ECL  in a CCD camera image system.We used t test for the analysis of variables with normal distribution and equal variances, Welch modification of t test was used for data with normal distribution and unequal variances and Mann–Whitney W (Wilcoxon) test was used to analyze the differences when data did not correspond to a normal distribution. Shapiro–Wilk test was used to analyze the normality of distributions. Differences were considered statistically significant at p < 0.05 level.Linear regressions were made with StatGraphics software to establish the correlation of GluAp or AlaAp in the different fractions with SCr and body weight increase.Cisplatin treatment induced a significant increase in GluAp excreted in microvesicular  and exosUrinary markers are usually normalized per mg of urine creatinine, useful for spot samples, or per daily total excretion when data of diuresis are available. Nevertheless, alterations in glomerular filtration are accompanied with a low excretion of urinary creatinine that could falsely increase the levels of urinary markers. Besides, alterations in diuresis can also influence the quantification of urinary markers, because differences in water content can affect biochemical determinations. In our study, cisplatin induced an increase in microvesicular and exosomal GluAp content normalized per mg of total protein in each fraction, implicating that nephrotoxic effect of cisplatin over tubular epithelia could be assessed independently of creatinine status or diuresis.Besides, augmentation in GluAp excretion were not influenced in a high extent by creatinine excretion or total protein content in each fraction, because there was not any difference in these variables between both groups of rats, although diuresis was significantly higher in cisplatin-treated rats .i.e. disorders that affect the function of renal tubule epithelia [The high content of GluAp in exosomal and microsomal fractions that we have found in this study could be due to a direct effect of cisplatin over proximal tubule that would evoke a higher release of microvesicles and exosomes from this section of nephron where aminopeptidasic enzymes are mainly expressed ,20. Thispithelia .vs control group, and body weight was decreased  showed a very strong correlation with SCr and body weight decrease . Correlaous work , and theIn previous works, we found an early high increase in the excretion  or activGluAp activity was highly increased three days after injection in urine supernatant, microvesicular and exosomal fractions from cisplatin-treated rats when data were normalized by urinary creatinine, body weight or total protein content of each fraction, and it was even increased per ml of urine . This laInterestingly, in this experiment, urinary creatinine excretion was decreased in cisplatin-treated group because of decreased glomerular filtration . TherefoFurthermore, cisplatin-treated rats displayed high proteinuria at this point , and quaIt is also remarkable that GluAp remained increased in microvesicular and exosomal fractions of cisplatin-treated rats, although protein content was higher in both fractions in this group .AlaAp activity, which has been described as an exosomal marker of proximal tubular damage , 16 showAlaAp and GluAp activities were highly correlated in supernatant, microvesicular and exosomal fractions . These dThree days after injection, SCr was very increased and body weight was decreased , probablExosomal fraction has been widely used for biomarkers research studies, and there are evidences of physiological and pathological roles of exosomes in the kidney, where regulate the co-functioning between different parts of the nephron, through secretion and reuptake of their contents such as mRNAs and miRNAs that can affect the function of the recipient cell ,21. NeveImmunoblotting also demonstrated that GluAp was increased per mg of protein in microvesicular and exosomal fractions of urine from cisplatin-treated rats , left.In order to test the enrichment in extracellular vesicles of these fractions, we analyzed the expression of Alix and TSG101, accesory proteins that are required for the formation of multivesicular bodies  and are In our study, we found a higher expression of Alix at 96 kDa , middle Interestingly, both TSG101 and Alix were also detected in microvesicular fraction, but the pattern of expression was completely different to that of exosomal fraction. These differences in electrophoretic migration of both proteins can be due to the fact that microvesicles carry more proteins with posttranslational modifications when compared with exosomes . In the It is also remarkable that the expression of Alix and TSG101 was very similar in cisplatin than in control group, and GluAp was the only marker that was clearly overexpressed in cisplatin-treated rats in both fractions, strengthening the role of the content of GluAp in these fractions as a marker of nephrotoxicity.GluAp content or its enzymatic activity in microvesicular and exosomal fractions of urine has been shown to be an early and predictive biomarker of renal dysfunction in cisplatin-induced nephrotoxicity.These variables are useful to evaluate proximal tubular damage regardless of glomerular filtration rate.S1 FileIndividual data of body weight, diuresis and biochemical measurements in samples collected 24 hours (Experiment 1) and 3 days (Experiment 2) after cisplatin injection.(XLS)Click here for additional data file.


R0, is estimated at 3.0 (standard deviation 0.6) across all seasons and locations. The peak magnitude of RSV outbreaks is forecast with nearly 70% accuracy , four weeks before the predicted peak. This work represents a first step in the development of a real-time RSV prediction system.Recent studies have shown that systems combining mathematical modeling and Bayesian inference methods can be used to generate real-time forecasts of future infectious disease incidence. Here we develop such a system to study and forecast respiratory syncytial virus (RSV). RSV is the most common cause of acute lower respiratory infection and bronchiolitis. Advanced warning of the epidemic timing and volume of RSV patient surges has the potential to reduce well-documented delays of treatment in emergency departments. We use a susceptible-infectious-recovered (SIR) model in conjunction with an ensemble adjustment Kalman filter (EAKF) and ten years of regional U.S. specimen data provided by the Centers for Disease Control and Prevention. The data and EAKF are used to optimize the SIR model and i) estimate critical epidemiological parameters over the course of each outbreak and ii) generate retrospective forecasts. The basic reproductive number,  Respiratory syncytial virus (RSV) is the most common cause of acute lower respiratory infection and bronchiolitis. Prompt treatment of RSV is necessary to prevent damage to lung tissue, complications from prolonged oxygen deprivation, and the potential development of reactive airway disorders. Another respiratory disease, influenza, has been simulated and forecast with high accuracy, using an epidemiological model and data assimilation methods. Here, we adapt such a model-filter system to simulate and forecast RSV epidemics in the United States. We find that the timing and volume of RSV epidemics can be forecast with high accuracy. Advance warning of the epidemic timing and volume of RSV patients has the potential to help medical centers prepare for a surge in infected patients and thus reduce delays to treatment in emergency departments. Respiratory Syncytial Virus (RSV) infects nearly all children within the first two years of life, and is the most common cause of respiratory infection and bronchiolitis requiring hospitalization for infants under the age of one . The rat2) recommended for children varies from 90%–95% [2, due to a variety of causes, were found to be at risk for long-lasting cognitive damage and associated behavioral issues [The toll of RSV on the elderly is also high. RSV hospitalization rates in New York City are 1.5 times greater for individuals 75 and older than for 1-4-year-olds , and as  90%–95% –13. Chill issues –16. The l issues ,18. Accul issues ,20. Herea priori based on clinical and laboratory estimates, or estimated from historical incidence, using a compartmental model structure and transmission dynamics. RSV parameter estimates derived using these different methods or data, however, can vary widely. For example, the parameter that represents the duration of infection, D, has been identified variously through antigen detection as 6.7 days [R0, one study in Florida found mean R0 values of 1.7 and 7.4 using an SIRS model and modified SEIRS model, respectively [R0 above 9 using a SIRS that accounted for repeated infections, waning immunity, and/or age structure [The core component of our forecasting system is a dynamic model describing the propagation of RSV through a simulated population. Many of the dynamic models previously developed for simulation of RSV are compartmental constructs that divide a population into the different states associated with the chain of infection: susceptible, infected, and recovered (SIR), as well as constructs additionally accounting for partial immunity (SIRS) and exposure (SEIRS) –23. Epid6.7 days , using R6.7 days  and as n6.7 days . Similarectively , while otructure ,23. MuchHere, the same model-inference system developed for the generation of RSV forecasts is also used to infer critical RSV epidemiological parameters. We apply this system at a broad scale using regional RSV data from the U.S. and in so doing attempt to further resolve RSV parameter estimates. To perform this work, and as has been performed for other infectious disease systems –29, we cth) through week 26 of the following year, with at least 20 tests per week during this season. Over the first ten weeks of the RSV season, incidence is low and testing is often limited; consequently, we omitted these first ten weeks and began all simulations on week 37, in mid-September, when the number of RSV tests administered typically begins to rise. To scale the number of positive tests to regions with very different reporting magnitudes, we divide by the number of laboratories reporting to each region each season. With these data, we estimated RSV incidence over nine census divisions, ten HHS regions, and ten RSV seasons. Census division RSV is presented in RSV specimen data were provided by the Centers for Disease Control and Prevention (CDC). These data, sampled via antigen detection, viral isolation, and polymerase chain reaction, are collected and administered by laboratories and other medical facilities that participate in the National Respiratory and Enteric Virus Surveillance System (NREVSS). The number of samples tested for RSV each week and the number of positive results were recorded, since July 2004 at the census division and Health and Human Services (HHS) region geographic scales . For each ensemble member, the initial combination of model state variables and parameters was randomly selected from prescribed ranges using Latin hypercube-generated uniform distribution sampling  is the probability of an RSV infection (RSV incidence in the SIR model), p(g) is the probability of access to RSV testing among the entire population, p(RSV|g) is the probability that someone with access to RSV testing is infected with RSV, and p(g|RSV) is the probability of access to a RSV testing facility if a person is infected with RSV. As shown in γ is defined as p(g)/ p(g|RSV). We experimented with a range of possible values and selected a single value for simplicity, γ = 0.001, that produces RSV forecasts with the lowest RMSE errors.Here, We used the ensemble adjustment Kalman filter (EAKF) –34 to asThe random initialization of state variables and parameters for each 300-member ensemble simulation adds an element of stochastic variation to the otherwise deterministic EAKF. To account for any effects of initialization, each 300-member ensemble simulation was repeated ten times, in each instance with a new random selection of initial state variables and parameters. We explored the sensitivity of the model-inference system to other components of the system, including the scaling factor γ (as described above), observational error variance (OEV), the use of inflation, and range of initial state variables.OEV0 is a minimum OEV and a is a constant. This form represents the increasing uncertainty of clinical data as epidemic infectiousness increases [OEV, and based on lowest RMSE, selected OEV0 and a, respectively, as 104 and 50.The EAKF uses OEV to describe uncertainty in the observed data, and weighs it against the variance of the SIR model simulations, which is estimated directly from the spread of the 300 ensemble simulations. More specifically, OEV is used by the EAKF to weigh the observations (RSV) and model prior estimation of RSV incidence, and to produce the model posterior estimation of both the state variables and parameters. As for influenza, the OEV for RSV was assumed to be small when observations over the prior 3 weeks  for each location and year by integrating the latest posterior of updated variables and parameters through to the end of the season. Forecasts were evaluated relative to lead week, defined here as the week of forecast minus the predicted peak week of the ensemble mean trajectory (i.e. negative lead weeks indicate that incidence will peak in the future).We present We quantified forecast accuracy using four metrics: i) prediction of RSV outbreak peak timing, i.e. whether the ensemble mean trajectory prediction of peak timing is within ±1 week of the actual peak, ii) prediction of RSV outbreak peak intensity, i.e. whether the ensemble mean trajectory predicted peak outbreak incidence is within ±15% of observed peak incidence (with the observed as the denominator), iii) prediction of total RSV attack rate, i.e. whether the ensemble mean trajectory prediction of the seasonal attack rate, defined as the total number of cases per year, is within ±15% of observation, and iv) prediction of the onset of the RSV epidemic. We define the RSV onset at a threshold of γΝ for two consecutive weeks, which roughly corresponds to the CDC defined onset of 10% percent positive RSV cases for two consecutive weeks. For sensitivity, timing and onset each within ±2 weeks and magnitude and attack rate each within ±25% are also presented.These prediction metrics were also evaluated to determine how forecast accuracy varied as a function of the ensemble variance ,35. HereOur simulations of RSV time series using the model-filter system replicated the historical data well. D and R0 for RSV at outbreak peak are shown in D and R0 were 6.4 days and 3.0, respectively. During the period of forecast, there is little variation of these estimates, though the minimum, 5th percentile, 95th percentile, and maximum parameter values vary more (R0 shows some geographic variability and is consistently lower in Census Division 5 (mean R0 = 2.5), which stretches from Florida to Maryland . Here we paired an SIR model with the EAKF to predict RSV incidence. We have shown that the model-filter forecasts are generally more accurate than historical expectance Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigOEV0 and a equal to 104 and 50, respectively.The variable OEV was computed with (TIF)Click here for additional data file.S4 FigThe red line is 1:1.(TIF)Click here for additional data file.S5 FigAll regions and seasons are shown.(TIF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 FigR0 is 2.5, in Census Division 5, which contains Florida, and the largest mean R0 is 3.4 in Census Division 4.These estimates remain similar from year to year and location. Overall, the smallest mean (TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 FigEach forecast shown is the mean of 300 ensemble members. The thick black line is observed RSV. The thin colored lines represent the ensemble mean trajectories of successive weekly forecasts, beginning Week 43 through Week 10.(TIF)Click here for additional data file.S10 FigEach forecast shown is the mean of 300 ensemble members. The thick black line is observed RSV. The thin colored lines represent the ensemble mean trajectories of successive weekly forecasts, beginning Week 43 through Week 10.(TIF)Click here for additional data file.S11 FigAll forecasts, regardless of ensemble variance, are shown.(TIF)Click here for additional data file.S12 FigBlanks indicate week and lead week combinations without forecasts. Error decreases as both week of forecast and lead week increase. Including week of forecast along with ensemble variance and lead week could refine prediction calibration.(TIF)Click here for additional data file.S13 Figth percentile, taken over the entire forecast period for each region, are shown; during some lead weeks, there are no forecasts in the bottom 50th percentile, hence some regions do not have all four lead week groupings (e.g. 7–8 weeks before the predicted onset). Positive significant linear correlations  between difference in error and observed standard deviation are found for each forecast criterion at least four weeks in advance of the predicted peak or onset. Scatter points above the zero line represent forecasts that outperform the historical mean. Both census division (CD) and HHS regions are plotted. Only forecasts with ensemble variance in the bottom 50(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableOnly forecasts with ensemble variance in the lowest 50th percentile were included.(DOCX)Click here for additional data file.


Physical inactivity and low consumption of fruits and vegetables (FV) during adolescence may persist through adulthood, putting adolescents at risk of developing chronic diseases. Although studies from high-income countries have reported differences in FV consumption and physical activity (PA) between adolescent boys and girls, few exist from low-and-middle-income countries (LMICs). In this study, we examined patterns of FV consumption and PA among adolescent boys and girls in LMICs. Country selection was based on availability of Global School-Based Student Health Survey (GSHS) data from 2004 to 2013. The total analytic sample was 164,771 adolescents from 49 LMICs. Descriptive statistics were generated to determine adolescents meeting the World Health Organization (WHO) recommendations for FV and PA. A Rao-Scott adjusted chi-square statistic was computed to assess gender differences. Less than 30% of adolescents across all countries met the WHO guidelines for FV consumption or PA. Morocco (29.5%) and India (29.5%) however had the highest percentage of adolescents meeting recommendations for FV and PA, respectively. Adolescent boys were more active than girls, and this difference was more notable in the Middle East and North African region. Adolescents achieving the WHO recommendations for daily consumption of FV and PA were consistently low in all countries. Currently, NCDs are responsible for about 70% of deaths1. NCDs are especially important in low- and-middle-income countries, where more than three-quarters of NCD deaths occur1. It is well known that risk of NCDs is primarily related to four behavioral risk factors: tobacco use, physical inactivity, an unhealthy diet, and excessive alcohol use3. It is a public health priority to promote healthy lifestyles as well as monitor levels of behavioral risk factors in all age groups, including children and adolescents. For this purpose, the WHO has established recommendations for adolescents for minimal levels of physical activity (PA) and dietary intake of fruits and vegetables (FV). Currently, the WHO recommends at least 60 minutes of moderate-to-vigorous physical activity each day4, and a dietary intake of at least 400 grams of FV each day 5.Non-communicable diseases (NCDs), such as cancer, diabetes, and cardiovascular disease, are the leading cause of death worldwide6 in seven African countries  found that most adolescents (77.5%) did not meet the recommended daily servings of FV. Similarly, Doku et al.7 found that 56% and 48% of adolescents in Ghana rarely (≤3 days/week) consumed fruits and vegetables respectively. In China, Shi et al.8 found that less than 50% of school children ate fruits daily. Also, in the North Gaza strip of Palestinian Territories, Abudayya et al.9 found that only 11.6% of boys and 16.2% of girls consumed fruits daily. This shows that FV consumption is consistently low in many LMICs.Despite these WHO recommendations, current evidence indicates that adolescents eat far less than the recommended five daily servings of FV. For example, a study by Peltzer & Pengpidet al.10 found that of the 49 studies reviewed, 27 studies found that girls had a higher consumption of fruits and vegetables compared to boys. In 18 studies, there were no differences in the intake of FV between adolescents’ boys and girls. Lastly, in four studies, the authors found that boys had a higher FV intake compared to girls10.Studies have also shown gender differences concerning the daily intake of FV. For example, in a review of determinants of FV intake among children and adolescents, Rasmussen 4. Moreover, studies have shown that PA declines during adolescence, especially among girls12. In an examination of PA patterns among adolescents using the Global School-based Student Health Survey (GSHS) and the Health Behavior in School-aged Children (HBSC) survey, Hallal et al.13 found that 80.3% of adolescents between 13–15 years from 105 countries did not meet the recommended daily PA. The authors also found that girls were less active than boys13.Also, it appears children and youth between the ages of 5–17 commonly do not engage in at least 60 minutes of moderate- to vigorous-intensity PA daily14. The study results showed that adolescent girls recorded a higher percentage (84%) of insufficient PA compared to adolescent boys (78%)14. When disaggregating the PA data by WHO regions, the Eastern Mediterranean Region (88%), the African Region (85%) and the Western Pacific Region (85%) were ranked highest among regions reporting insufficient PA among adolescents. In all WHO regions, adolescent girls were less active than boys14. Thus, the adolescence represents a critical period to encourage and create a conducive environment to be active, thereby minimizing the risk of NCDs, especially among girls.Similarly, in 2010, the WHO found that globally 81% of adolescents aged 11−17 did not meet the daily physical activity recommendationHowever, there are few cross-national studies from LMICs examining patterns of FV consumption and physical activity among adolescent boys and girls. The purpose of this study was to compare FV consumption and PA behavior patterns among adolescent girls and boys in 49 LMICs. We hypothesize that there are differences between the FV consumption and PA behavior patterns among adolescent girls and boys in LMICs.15. The study sample of LMICs included countries from the regions demarcated by the World Bank if they had (1) publicly available data on FV intake and PA behaviors), and (2) data available to allow the assessment of differences between adolescent boys and girls for these health behaviors.This study utilizes an ecologic study design to compare patterns of FV consumption and PA among adolescents from countries and regions demarcated by the World Bank as LMICs. The World Bank regions are Sub-Saharan Africa, Middle East and North Africa, South Asia, Latin America and the Caribbean, Europe and Central Asia and East Asia and the Pacific16.The primary data source is the Global School-based Student Health Survey (GSHS). The GSHS is a standardized survey developed by the WHO in collaboration with the United Nations International Children’s Emergency Fund, the United Nations Educational, Scientific and Cultural Organization, and the United Nations Program on HIV/AIDS, with technical assistance provided by the Centers for Disease Control and Prevention. The GSHS is a collaborative surveillance project designed to help countries measure and assess health behaviors and protective factors among students between the ages of 13 to 17 years. It allows countries to develop priorities, establish programs, and advocate for resources for youth health programs and policies16. GSHS data from 2004 to 2013 that were publicly available for LMICs15 were used. According to the World Bank, these are countries that had a gross national income (GNI) of less than $12,616 in 201215. A total of 49 countries with publicly available data on dietary behaviors and PA for adolescents met this definition for inclusion in our study. Figure As of December 2013, 90 countries (mostly LMICs) had completed the GSHS, and representatives from over 120 countries have been trained to administer the GSHS17:The survey measures used were based on the following GSHS questionsFruit; i) “During the past 30 days, how many times per day did you usually eat fruits, such as COUNTRY SPECIFIC EXAMPLES?” Response options were; (A) I did not eat fruit during the past 30 days (B) Less than one time per day (C) 1 time per day (D) 2 times per day (E) 3 times per day (F) 4 times per day (G) 5 or more times per day.Vegetables; ii) “During the past 30 days, how many times per day did you usually eat vegetables, such as COUNTRY SPECIFIC EXAMPLES?”The response options were (A) I did not eat vegetables during the past 30 days (B) Less than one time per day (C)1 time per day (D) 2 times per day (E) 3 times per day (F) 4 times per day (G)5 or more times per day.Physical Activity; iii) “During the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day?” The response options; A) 0 days B) 1 day C) 2 days D) 3 days E) 4 days F) 5 days G) 6 days H) 7 days.18.The data were analyzed using SPSS version 21 software . The GSHS employs a complex sampling design, and the data were analyzed taking into account this sampling design. The weighting process was also used. This process adds weights, stratum and primary sampling unit (PSU) to every student record in the GSHS data file to reflect the weighting process and the 2-staged sampling design. The weight variable allows generalizability of the GSHS results to the entire students’ population whereas the stratum and PSU account for the 2-stage sample design used for the GSHS. Specifically, the stratum reflects the GSHS sampling process at the first level, which is done in schools and the PSU reflects the second level of the GSHS sampling process conducted in classrooms19.Descriptive statistics were generated to determine the number and percentage of adolescents in each of the LMICs meeting the WHO recommendations of daily intake of FV and PA. In generating the descriptive statistics, the variables took values of 1 (two otherwise) for i) consumption of fruits: adequate (2 or more/day), ii) consumption of vegetables: adequate (3 or more/day), iii) consumption of FV: adequate (adequate on both FV), iv) PA: adequate (60 minutes/day).The Rao-Scott adjusted chi-square statistic was used to test if there was a statistically significant difference between adolescent boys and girls in meeting the WHO recommendations of daily fruit servings, daily vegetable servings, daily FV servings and daily PA for each country and odds ratios were also calculated. The Rao-Scott adjusted chi-square statistic takes into consideration the complex sample design utilized in the GSHS data collection. The p-value of the adjusted F- statistics generated was used in determining statistical significance because it provides a better approximation of the significancehttp://www.who.int/chp/gshs/datasets/en/.The datasets generated and/or analyzed during the current study are available at Figure Differences in the outcomes of interest between adolescent boys and girls were observed in some but not all countries. In the sub-Saharan African region, only two countries  showed girls more likely to meet the daily recommended intake of fruits compared to boys. Similarly, in two other countries (Seychelles and Tanzania) girls were more likely (p < 0.05) to meet the daily recommended intake of vegetables  out of the five countries, adolescent girls were more likely to meet the daily recommended servings of fruit as compared to boys. In Lebanon, however, adolescent girls were less likely to consume two servings of fruits daily compared to adolescent boys {OR = 0.74; 95%CI (0.63–0.88); p-value = 0.002} Table .With regards to daily servings of vegetables (≥3 per day), a statistically significant difference between adolescent girls and boys was seen in three countries in the Middle East and North Africa. In two of the three countries (Lebanon and Tunisia), adolescent girls were less likely to consume ≥3 servings of vegetables per day than boys. Nonetheless, in Morocco {OR = 1.28; 95%CI (1.07–1.53); p-value = 0.01}, girls were more likely to consume the daily recommended intake of vegetables compared to boys  between adolescent boys and girls in terms of FV intake, particularly in Costa Rica, Dominica, Jamaica, and Peru. More boys met the recommendations for daily fruit intake than the girls in Costa Rica, Dominica and Jamaica. Adolescent girls in Peru on the other hand, were more likely to meet the ≥2 servings of fruit {OR = 1.26; 95%CI (1.05–1.51); p-value = 0.014} than adolescent boys. In Argentina, girls were more likely to have ≥5 servings of FV daily compared to boys {OR = 1.43; 95%CI (1.02–2.00); p-value = 0.037} ; p-value = 0.007} compared to boys Tables  and 2.Regarding the daily WHO recommendations for daily PA, differences were observed between adolescent boys and girls in 50% of countries examined in the sub-Saharan African region. In all five countries , boys were more likely to meet the recommended daily PA guidelines compared to adolescent girls Table .Table 3DIn the Middle East and North African region, almost all the countries recorded a significant difference between boys and girls except for Yemen. In all the countries that showed a statistically significant difference, adolescent boys were more active and were more likely to meet the recommended daily PA guidelines than girls. Also, in the South Asian region, adolescent boys in the Maldives and Sri Lanka were more active than the girls Table .In the Latin American and the Caribbean region, adolescent boys were more likely to have 60 minutes of daily PA compared to girls, especially in Argentina, Belize, Bolivia, Costa Rica, Guatemala, Honduras, St. Vincent and the Grenadines, and Suriname. In the Former Yugoslav Republic of Macedonia, boys were more active than girls as the results showed boys as being more likely to have 60 minutes of daily PA {OR = 1.52; 95%CI (1.21–1.89); p-value = 0.001} compared to girls Table .Six of the eight countries (75%) in the East Asian and Pacific region recorded a significant difference between adolescent boys and girls in meeting the daily recommended PA. Five of the six countries 83%) showed that adolescent boys were more likely to have 60 minutes of daily PA for seven days compared to adolescent girls, especially in Fiji, Malaysia, Mongolia, Myanmar, and Thailand. However, in Tonga, adolescent boys were less active, rather the girls were more likely to have 60 minutes of daily PA for seven days {OR = 0.70; 95%CI (0.52–0.94); p-value = 0.018} compared to the boys  showed gender differences in which girls had a higher intake of fruits and/or vegetables. Studies have attributed this gender difference to a preference for FV. It has also been reported that boys do not eat FV because they do not like them as much as girls do22. In addition to preference, Bere et al.21 found that the gender differences could also be attributed to the perceived accessibility of FV at home. They found that girls have a greater knowledge, and stronger intentions and self-efficacy compared to boys21. Lastly, according to Cooke et al.22, girls liked fruits and vegetables more than boys. The boys preferred energy-dense foods such as fatty and sugary foods to adapt to their higher energy requirement more so than girls.The results showed a clear, although variable trend in the FV intake between boys and girls, in which girls were more likely to reach the WHO recommendations. In the case of fruit, 58% of the countries where sex differences were noted showed that girls were more likely to consume ≥2 servings of fruit daily than adolescent boys. On the contrary, for vegetables, 67% of the countries that recorded a sex difference showed that boys were more likely to consume ≥3 servings of vegetables daily than girls. Rasmussen 2, the prevalence of physical inactivity is increasing among LMICs and it is already one of the leading causes of death. Among LMICs, India showed the highest proportion of adolescents who engaged in PA 60 minutes daily. The results from this study are consistent with those from Hallal et al.13, who found that 80.3% of adolescents between 13–15 years from 105 countries did not meet the recommended daily PA.According to the WHO12. A notable statistical difference between adolescent boys and girls with respect to PA was seen predominantly in the Middle East and North African region. These differences may be attributable to cultural and gender norms. Evidence from the Middle East and North Africa suggest that a barrier for women to engage in PA among women is the lack of facilities separated by gender, where women feel more comfortable being physically active24.The results showed that physical inactivity was more common among adolescent girls in LMICs. In almost all the countries (30 out of 31), boys were more likely to meet the WHO recommendations for PA daily compared to girls. This result is consistent with studies showing that PA declines during adolescence, especially among girlsDespite its design and results, the study had a few limitations. The study included the use of the GSHS data, which surveys adolescents in schools. Thus the results might not necessarily reflect the FV consumption pattern and PA behavior among all adolescents in the respective countries. Specifically, information from adolescents who are unable to attend school or have dropped out would not be captured. Second, in some countries, FV are seasonal staples. Hence their consumption might be high or low in a country based on the season in which the data were collected. As stated earlier, PA patterns may also be affected by cultural perspectives resulting in the differences observed between countries and regions.Third, adolescents had to recall and self-report their FV consumption and PA behavior during the past 30 days and week, respectively. This approach may result in either overestimating or underestimating intake behaviors, leading to information bias. Finally, we cannot be certain that the 5 servings reported by the adolescents were equivalent to 400 grams since we could not quantify the servings from the GSHS data.The WHO recommendations for daily consumption of FV and PA were not consistently met in all countries. All the countries had less than 50% of their adolescents consuming ≥5 servings of FV daily. In general, the study found adolescent boys to be more active than girls, and this difference was more notable in the Middle East and North African region.


Squalius carolitertii and the critically endangered S. torgalensis that inhabit in the Atlantic-type northern and in the Mediterranean-type southwestern regions, respectively. We performed protein structure modeling of fourteen genes linked to protein folding, energy metabolism, circadian rhythms and immune responses. Structural differences in proteins between the two species were found for HSC70, FKBP52, HIF1α and GPB1. For S. torgalensis, besides structural differences, we found higher thermostability for two proteins (HSP90 and GBP1), which can be advantageous in a warmer environment. Additionally, we investigated how these species might respond to projected scenarios of 3° climate change warming, acidification (ΔpH = -0.4), and their combined effects. Significant changes in gene expression were observed in response to all treatments, particularly under the combined warming and acidification. While S. carolitertii presented changes in gene expression for multiple proteins related to folding , only one such gene was altered in S. torgalensis (stip1). However, S. torgalensis showed a greater capacity for energy production under both the acidification and combined scenarios by increasing cs gene expression and maintaining ldha gene expression in muscle. Overall, these findings suggest that S. torgalensis is better prepared to cope with projected climate change. Worryingly, under the simulated scenarios, disturbances to circadian rhythm and immune system genes  raise concerns for the persistence of both species, highlighting the need to consider multi-stressor effects when evaluating climate change impacts upon fish. This work also highlights that assessments of the potential of endangered freshwater species to cope with environmental change are crucial to help decision-makers adopt future conservation strategies.Current knowledge on the biological responses of freshwater fish under projected scenarios of climate change remains limited. Here, we examine differences in the protein configuration of two endemic Iberian freshwater fish species,  Then, fish were slowly acclimated to the control experimental conditions, in eight 200 L tanks (four per species), for 2 weeks, mimicking summer average values for temperature . Overhead tank illumination was provided, according to prevailing natural light conditions, under a 12:12 (day: night) light regime. Ammonia, nitrite and nitrate levels were monitored daily  and kept always below detectable levels. Normoxic conditions were maintained and pH values were monitored and adjusted automatically by means of a computerized controlling system  connected to individual oxygen and pH probes , respectively. Monitoring was performed every 2 seconds and pH values were adjusted through injection of N2/CO2  and upregulated by aeration with CO2 filtered air . Conductivity was individually monitored  and kept between 400–500 μS/cm. Automatic dosing systems , linked to the Profilux system, enabled inflow of freshwater (300 or 600 μS/cm), in order to lower or raise conductivity values (culture tanks), within desired interval (400–500 μS/cm). After 30 days of experimental exposure, five to seven individuals of each treatment and species were euthanized , during early morning period. Experimental procedures used in this research were in accordance with the requirements imposed by the Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes .After laboratory acclimation, four different groups  of nditions : i) contLiver and muscle tissue samples were immediately collected from fish and stored using RNAlater , following the manufacturer’s instructions. For ribonucleic acid (RNA) extraction, TRI Reagent  was added to liver and muscle samples. After homogenization with a Tissue Ruptor , RNA was extracted according to the TRI Reagent manufacturers protocol. TURBO DNase  was employed to degrade any remaining genomic contaminants, followed by phenol/chloroform purification and LiCl precipitation . Sample S. carolitertii and S. torgalensis  (for details on primer conditions see 2(Fold Change) < 0.58), in order to assure that they are highly expressed, but not differentially expressed. Furthermore, reference genes stability was also verified in Squalius pyrenaicus transcriptome  and checked for normality (Shapiro-Wilk’s test) and homoscedasticity (Levene’s test). A two-way analysis of variance (ANOVA) was performed to identify statistical differences in transcript expression patterns across the experimental conditions for all genes independently, for each tissue. Post-hoc tests for multiple comparisons (Tukey tests) were applied whenever significant differences across treatments were observed. All statistical analyses were performed using a significance level of 0.05, using a custom python script and the program STATISTICA v.12 .Four of fourteen target proteins, showed alterations in their predicted tertiary structure between the two species , and twoThe physical and chemical parameters of the selected proteins were similar between species, with GBP1 and HSC70 presenting small changes in their theoretical isoelectric point (pI) and GBP1 and HSP90 having 1 unit differences in their respective aliphatic indexes (fkbp4 gene), HIF1α and GBP1 showed differences between species. For HSC70, there were 11 noncontiguous aminoacids (a.a.) different between the two species , Per-ARNT-Sim (PAS) and DNA-binding domains. The HIF1α transcription factor presented two non-synonymous substitutions between species  . These stability values are inferior to those observed by  mediates the transfer of proteins from HSP70 to HSP90, through the formation of an “intermediate complex” composed of these three proteins and the substrate protein (mainly steroid hormone receptors) [stip1 gene transcription under the combined warming and acidification condition in liver tissues of both species highlights the importance of synergistic effects in climate change studies.HSP70 and HSP90 proteins usually form a complex of chaperones that help in the correct folding of important proteins for cell functioning. However, both proteins are capable of independent activity. While HSP70 is responsible for the folding of nascent proteins and other important cell processes (e.g. trafficking of proteins across membranes), the most common client proteins of HSP90 are regulators of transcription or protein kinases (see  for furtactivity  and whosactivity . Therefounctions . Stress-ceptors) . The sevhif1a induces many genes during hypoxia, but also participates in other pathways such as glucose metabolism, with ldha being a target gene of this transcription factor [hif1a and ldha genes was observed in liver. Thus, the induction of hif1a gene expression seems to be more related to glucose metabolism rather than hypoxia. Importantly, the liver is capable of catabolism and anabolism at the same time; an ability not shared by any other organ or tissue [The genes involved in energy metabolism presented an intricate and interconnected response  plays a key role in nitrogen metabolism, catalyzing the conversion of ammonia and glutamate to glutamine, a less toxic compound that is used in the production of several other metabolites [S. carolitertii muscle. Though we did not feed our experimental groups of fish differently, demand for nitrogen compounds is expected to decrease under increased temperatures and so herbivory is increased, which has been reported in omnivorous copepods and fish [glula gene might be a suitable biomarker for the usage of nitrogen in omnivorous fish undergoing climate change.Glutamine ammonia ligase or glutamine synthetase  catalyzes the formation of lysine-derived cross-links in collagen and elastin and it is involved in several other biological functions  [lox gene is vital during development and in atypical cell functioning [We did not find significant differences for the escence) . Therefoctioning .cry1aa and per1a, presented significant changes under both warming and acidification conditions. Expression was increased for cry1aa and decreased for per1a in both species. The cry1aa gene is known to be induced in fish during the early morning, whereas per1a has higher expression late at night (end of the dark period) [cry1aa, per1a gene does not exhibit light-dependent expression [cry and per genes that are involved in clock regulation.Warming and acidification have impacts on many biological processes that occur in vertebrate cells. Despite limited evidence that circadian clock genes may be directly impacted by climate change, temperature may trigger the responses of such genes , 76, as  period) . Contrarpression . Therefogpb1 gene for the combined warming and acidification condition. Although we analyzed only one gene related to the immune system, the combination of these two environmental factors severely decreased its expression, putatively leading to its suppression. Therefore, further attention should be paid to the effects and interactions of the multiple environmental factors involved in climate change on genes involved in the immune response.There is growing concern about the effects of environmental change on the immune system of vertebrates ,10. SomeS. carolitertii in the Atlantic-type northern region and S. torgalensis in the Mediterranean-type southwestern region. We observed protein structural differences between the two species for HSC70, FKBP52 and HIF1α and higher thermostability for HSP90 and GBP1 in S. torgalensis. Most of the changes in gene expression were observed for S. carolitertii, whereas S. torgalensis showed no major changes in the heat shock response or in respiratory capacity. Taken together, these results suggest that S. torgalensis, which lives in a warmer environment, is less impacted by temperature increases and acidification. Consequently, our results suggest that S. torgalensis could be capable of dealing with the IPCC projections of warming and/or acidification at the end of this century. Our study highlights the importance of assessing the potential of endangered freshwater species to cope with projected climate change conditions for the proper implementation of conservation strategies.Climate change projections for freshwater ecosystems are scarce and may be worse than we simulated here, particularly for the acidification of these ecosystems, where organic matter content may be extremely variable between water bodies and seasons, contrary to what is observed in oceanic waters ,41. In tS1 FigS. carolitertii and S. torgalensis for that specific region and yellow represents the amino acids which correspond to non-synonymous substitutions.Regions in light grey have no differences between species, blue and red indicate the conformation of (PDF)Click here for additional data file.S2 FigThe lower the stability value the better the reference gene and thus less variable across the experimental conditions.(PDF)Click here for additional data file.S3 Fighif1a (represented with a rectangle with two curved sides), which is a key gene in the regulation of many gene involved in these pathways. Circles indicate genes which regulate relevant pathways but that are not target genes and polygons symbolize the substrates.Doted arrows indicate gene expression regulation from the source to the sink gene; dashed arrows represent a source gene that encodes a protein is responsible for substrate conversion; and full arrows indicate a direct conversion. Target genes are represented with squares, except for (PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableReal-time PCRs were done in a final volume of 10 μL, containing 5 μL of Sso Advanced universal SYBR® Green supermix (2x)  and 0.4 μL of each primer (with a concentration of 0.4 μM). The assay conditions included an initial denaturation step at 95°C for 30 s, followed by 40 cycles at 95°C for 10 s and 60°C for 30 s.(DOCX)Click here for additional data file.S3 TableS. pyrenaicus males and females from . Non-DE and N/A stands for genes that are not significantly differentially expression and not applicable, respectively.Reference genes have a column for the differential gene expression value between (DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Cell growth is a complex phenotype widely used in systems biology to gauge the impact of genetic and environmental perturbations. Due to the magnitude of genome-wide studies, resolution is often sacrificed in favor of throughput, creating a demand for scalable, time-resolved, quantitative methods of growth assessment. We present ODELAY , an automated and scalable growth analysis platform. High measurement density and single-cell resolution provide a powerful tool for large-scale multiparameter growth analysis based on the modeling of microcolony expansion on solid media. Pioneered in yeast but applicable to other colony forming organisms, ODELAY extracts the three key growth parameters  that define microcolony expansion from single cells, simultaneously permitting the assessment of population heterogeneity. The utility of ODELAY is illustrated using yeast mutants, revealing a spectrum of phenotypes arising from single and combinatorial growth parameter perturbations. Lag time is the period up to the attainment of linearity of the log-plot, doubling time is inversely proportional to the slope of the linear region of the log-plot, and carrying capacity is the maximum population size when the slope of the log plot approaches zero.Growth is a well-established, sensitive metric of cellular fitness that is widely used to interrogate genetic and environmental interactions. The most basic models of microorganism population expansion over time consist of three distinct phases: lag phase, log phase, and stationary phase . Each phSaccharomyces cerevisiae, is a model organism commonly exploited to elucidate genetic and environmental interactions on a genome-wide scale. Many methods of assessing yeast strain growth characteristics have been described. One of the oldest and most common methods to quantify yeast growth is to measure the increase of turbidity in liquid culture over time  cameras have been used to periodically image pinned arrays of growing colonies , 2% w/v peptone (BD), and 2% w/v dextrose (BD)]. Galactose growth media contained 2% w/v galactose (Acros) in place of glucose and solid media contained 2% w/v agar (BD) for cell spotting assays or 1.0% w/v agarose (Invitrogen) for ODELAY analyses. First, 220 μl yeast cultures were inoculated in 96-well flat bottom plates (Corning Costar) and grown overnight. Cultures were then diluted 1:11 and optical densities read using a Synergy H4 plate reader. Individual wells were then diluted to a density of 0.09 OD and the culture grown for 6 hr to ensure all strains were in exponential phase. The cultures were again measured using the plate reader and then diluted to 0.01 OD. The 96-well plate containing the prepared cultures was then sonicated for 30 sec in an ice-cold water ultrasonic bath to dissociate cell clusters.600 of ∼0.01, were spotted onto agarose slabs using a Matrix Hydra DT fluidics robot (Thermo Scientific). Slides were air dried for ∼3–5 min and then placed inside a microscope equipped with a humidified environmental chamber maintained at 30°. This yields approximately 300 single cells per 9 mm2. Spotting at higher density leads to merging of colonies and fewer measurements overall. The stage was then leveled so that cells remained in focus across the entire 8 × 12 spotted array.It is noteworthy that the sensitivity of ODELAY makes the method sensitive to growth conditions and so reproducible slide preparation is important. Growth media was prepared as a 1:1:8 mixture of the following sterile stock solutions, respectively: 10 × YEP (10% w/v yeast extract and 20% w/v peptone), 20% w/v carbon source , and 1.33% w/v agarose in water. Typically, a 150 ml volume of 1.33% agarose stock was prepared, divided into 15 ml aliquots in 50 ml conical bottom tubes, and stored at 4° until use. Agarose aliquots with 2 ml 10 × YEP and 2 ml 20% carbon source were placed in rapidly boiling water for 20 min to completely melt the agarose gel. Water lost to evaporation was replaced by weighing the conical tube before and after boiling, yielding a final growth substrate containing 1% yeast extract, 2% peptone, 2% carbon source, and 1% agarose. The molten solution was poured into custom molds that formed 2 mm slabs of agar supported by 50 mm by 75 mm by 0.1 inch glass slides (Fisher Scientific). The apparatus was allowed to cool to room temperature and, after separation of the glass slides, the agar plates were equilibrated overnight in a humidified chamber at 4°. Careful separation of the glass slides was critical as any mechanical deformation of the agar altered the lag time and doubling time of cultures in the regions deformed. The following day, yeast cells in exponential liquid culture, diluted to an OD2 area of the agar. Since the stage was leveled before image collection, the focus did not drift appreciably across the tiled images. These steps were repeated on each of the 96 spotted strains in either 30 min or 1 hr increments for 48 hr.Bright field images were captured using a Leica DMI6000 microscope (Leica) equipped with a 10 × objective. Images were recorded by a Hammamasu ORCA Flash 4.0 camera. The microscope stage movements and camera were controlled by a custom MATLAB graphical user interface using the Micromanager Core API version 1.4 , reaches maximum (achieved at f″(t) = 0). Lag time  is greatest  = 0). The carrying capacity (K), in pixel area, represents the cross-sectional area of the base of the modeled microcolony projected to stationary phase (f(t) as t→∞) and is calculated as follows:Image acquisition and panorama stitching were performed using MATLAB scripts. Briefly, images were stitched using a method based on FFT phases  using the manufacturer’s suggested protocols, with the exception that culture volume was reduced to 200 μl to prevent artifacts arising from liquid splashing onto the plate lid during maximal agitation. A starting ODhttp://aitchisonlab.com/ODELAY.The authors state that all data necessary for confirming the conclusions presented in the article are represented fully within the article. Datasets and files to analyze them can be found at 5 and 106 individual microcolonies per experiment.An ideal solid-phase, time-resolved, growth analysis platform would allow for high sample density and be amenable to automated data acquisition and processing. The optimized method, which we have termed ODELAY, is depicted schematically  and consFile S1 and File S2).ODELAY consists of an automated pipeline that encompasses acquisition and processing of images, identification and measurement of microcolonies at each time point, matching of microcolonies through time, and extrapolation of growth parameters from growth curves. This current platform employs theoretical approximation of ODELAY growth curves using the Gompertz function as an unsupervised method to extract growth parameters , the dynamic range of ODELAY is 8–12 doublings, from a single cell up to 250 or as many as 4000 cells, which compares favorably to a dynamic range of 3–5 doublings attainable by most currently available technologies.Microcolony convergence is the limiting factor of ODELAY’s dynamic range, which can be controlled by altering the initial cell density obtained when spotting yeast cultures. Increased cell density decreases the time it takes for growing colonies to converge. In contrast, the dynamic range of liquid culture measurements is limited by either the nutrient capacity of the media or the linear range of the density sensor. Due to differences in strain doubling times, the dynamic range is best defined by the total number of doublings required to reach the upper limit starting from a single cell. At optimal seed density of ∼200–500 cells per spot , as well as relative lag between tested strains. To demonstrate the quantification of lag time by ODELAY, we exploited the well-studied and highly regulated response of yeast to a carbon source shift from its preferred source, glucose, to an alternative source, galactose . Cells pAs with heterogeneity of doubling times, ODELAY enables the detection of heterogeneity in lag times. To demonstrate the utility of ODELAY in assessing population heterogeneity of lag times, we compared growth parameters of a control yeast strain (BY4742) in galactose-containing medium after pregrowth in glucose media for differing amounts of time . We stagA strength of the ODELAY platform is to extract doubling times and lag times for populations of cells growing on solid media in a high-throughput manner. To demonstrate multiparameter growth rate analysis by ODELAY, we assayed a collection of 140 strains that contained gene deletions of transcription factors, transcriptional regulators, and nuclear transport factors including nucleoporins and karyopherins. The genes selected were previously associated with regulating the response to a carbon source shift . While ODELAY is similar to previously published methods that observe colonies forming from single cells in liquid media . Each colony has the potential to be a unique strain, allowing complete screening of yeast libraries within a short period of time. However, arraying colonies at higher densities limits the total observation time from when a colony can be clearly resolved by the imaging methods to when the colonies merge (Periodically imaged colony pinning assays, such as ultrahigh-density omics (600 analysis when applied to flocculent mutant strains, such exceptions may yield informative phenotypic information. Furthermore, ODELAY could be adapted to analyze the 3D volume of the growing microcolonies; however, this would trade off time for collecting images or limit the total area interrogated. Lag time measurements were also observed to have local variations, which are also commonly observed in other solid phase growth assays (Mycobacterium tuberculosis, Pseudomonas aeruginosa, Staphylococcus aureus, and others.Similar to other growth assays, there are caveats associated with ODELAY. Extraction of cell doubling time by ODELAY relies on the assumption that microcolony cross-sectional area is directly proportional to the volume of cells in a given colony and that this relationship between volume and area is unaffected by changes in growth condition and/or genetic background. There will certainly be exceptions to this assumption in yeast and other colony forming micro-organisms; however, similar to the limitations in liquid culture ODIn summary, ODELAY is a quantitative tool capable of multiparameter growth analysis based on time resolved microcolony expansion on solid media. The unique features of ODELAY include its relatively large dynamic range, when compared to other available methods, which enables quantitative measurement of doubling time, lag time, and carrying capacity in a single experiment. Additionally, ODELAY has the ability to assess population heterogeneity, including viability, through the analysis of single microcolonies.www.g3journal.org/lookup/suppl/doi:10.1534/g3.116.037044/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Changes in chromatin structure regulate gene expression and genome maintenance. Molecules that bind to the nucleosome, the complex of DNA and histone proteins, are key modulators of chromatin structure. Previous work indicated that cholesterol, a ubiquitous cellular lipid, may bind to chromatin in vivo, suggesting a potential function for lipids in modulating chromatin architecture. However, the molecular mechanisms of cholesterol’s action on chromatin structure have remained unclear. Here, we explored the biophysical impact of cholesterol on nucleosome and chromatin fibers reconstituted in vitro and characterized in silico the cholesterol binding to the nucleosome. Our findings support that cholesterol assists 10 and 30 nm chromatin formation and induces folding of long chromatin fibers as a result of direct interaction of the cholesterol to six nucleosomal binding sites. Chromatin is a macromolecular complex composed of distinct molecules. The nucleosome is the repetitive basic unit of chromatin. Nucleosomes arise from the precise stoichiometric interaction of proteins and DNA. Positively charged histone proteins interact with the DNA and provide an optimal strategy to functionally pack the genetic code inside a cell.Changes in chromatin architecture are decisive for regulating the access of transcription factors, coregulators, and the basic transcription machinery to specific enhancers in target genes [reviewed in ]. Thus, 2+ or Ca2+, on the nucleosome and chromatin structure has been well-characterized in vitro  and the state of the histone H4 tail , the nucThe characterization of endonuclear lipids has motivated a growing number of studies to hypothesize that this class of molecules may have an important and functional role on chromatin structure and gene expression .Cholesterol is a ubiquitous cellular lipid, which is essential for steroid hormone production, the structural integrity of cellular membranes, and cell growth. Seminal studies in rats have shown that labeled cholesterol administered by gavage was detected within 2 h in the nuclei, associated with chromatin fibers purified from rats . MoreoveHerein, motivated by the biophysical and biochemical aspects of chromatin interactions, we investigated the mechanism of cholesterol action on chromatin structure. In vitro, low concentration of cholesterol assisted 10 and 30 nm fibers and mononucleosome formation. Moreover, micrococcal nuclease (MNase) digestion, which targets nucleosome-free regions of long chromatin fibers, was inhibited in the presence of cholesterol. Finally, we used docking and molecular dynamics (MD) simulations to show that cholesterol interacts with nucleosomes through multiple binding sites.2. Soluble cholesterol  was added to the binding buffer or after dialysis when fibers or mononucleosomes were already formed. The analyses of reconstitution and folding were verified by electrophoresis in native agarose gels (0.8%) for long chromatin fibers and native bis-acrylamide gels (6%) for mononucleosomes. Electrophoresis was carried out with 0.2× TBE [18 mM Tris-borate (pH 8) and 0.4 mM EDTA) electrophoresis buffer at 20 mA. For folded chromatin, the samples were first fixed with 0.1% (v/v) glutaraldehyde on ice for 20 min. Densitometry was performed using ImageJ  version 1.49.The histone octamer (HO) and linker histone (H5) were purified from chicken erythrocyte nuclei as described in Ref. Reconstituted long chromatin fibers (60 nM), in presence or absence of soluble cholesterol, were digested for 8 min with 0.08 u of MNase (New England Biolabs) at 37°C. Aliquots of samples were collected at 0, 1, 2, 4, and 8 min of digestion and stopped with 10 mM EDTA (pH 8.0). The digestion was analyzed by electrophoresis in native agarose gels (0.8%), carried out with 0.2× TBE electrophoresis buffer at 20 mA.2+ was adapted from Rhodes laboratory protocol . The supernatants and pellets were verified by electrophoresis in native 0.8% agarose gels, carried out with 0.2× TBE electrophoresis buffer at 20 mA. Typhoon 9200 was used with 532 nm green laser excitation to visualize cholesterol fluorescence.Chromatin compaction by Mgprotocol . BrieflyAtomistic models were built using high-resolution X-ray structure of the nucleosome core particle (NCP), Protein Data Bank code 1KX5 , and choAll images created and computational analyses were performed using visual MD . More deTo better understand the effects of cholesterol on chromatin, we reconstituted long chromatin fibers and mononucleosomes in vitro using HOs purified from chicken erythrocytes  on a 601 bp DNA array. As a quality control of the chromatin reconstitution system, we analyzed open and compacted fibers by electron microscopy showing that this system was successful .We used soluble cholesterol  with proven physiological relevance , 28. IniA similar phenomenon was observed when cholesterol was present during the long fiber reconstitution in vitro. In this case, DNA saturation was reached at lower HO concentrations. In the presence of cholesterol, the array 177.36 was saturated at 2.0 μM HO , lane 1 2+, which is a divalent ion with characterized properties to induce chromatin folding  from chicken erythrocytes  in the presence of MgBecause we observed that chromatin folding was facilitated by cholesterol, we wondered whether the effect of cholesterol on chromatin formation might have structural consequences. To evaluate this possibility, we performed MNase digestion of chromatin fibers formed in the presence or absence of cholesterol. We found that MNase digestion of long chromatin fibers was inhibited in the presence 100 μM of cholesterol . Importa2+, which is known for promoting nucleosome-nucleosome interactions , labeled fibers moved from the supernatant to the pellet in the presence of Mg2+ .−1). For every site, the estimated absolute free energies favor cholesterol binding, rationalizing the stability of the molecular complex observed in the equilibrium MD simulations.The close interaction between cholesterol and nucleosome suggests favorable binding affinities of the ligand to the receptor sites. To quantitatively evaluate this, we proceeded with a series of independent free energy calculations to estimate the per-site affinity of cholesterol to nucleosome. As shown in Cholesterol, an abundant cellular lipid, is among the molecules interacting with eukaryotic chromatin in vivo. However, very little is known about the mechanism and biological role of cholesterol-chromatin interactions.Herein, we showed, in vitro and in silico, that cholesterol has an anticipated role in modulating chromatin structure and provided atomistic insights into the impact of cholesterol on the nucleosome structure. Specifically, cholesterol appears to facilitate chromatin folding, verified by its effect on compacted chromatin fiber formation and by the inhibition of MNase digestion of long chromatin fibers. Complementary computational studies suggested direct molecular interaction of cholesterol with the nucleosome through six binding sites nearby important interacting regions for NBPs. Thus, our results support a model in which cholesterol induces chromatin formation and folding via direct interaction with nucleosomes through multiple binding sites.Within this scenario, we speculate that the observed macroscopic effects on chromatin structure and stability might result from dewetting of the nucleosome, triggered by cholesterol binding. Specifically, dewetting transitions may impact core-to-core interactions at hydrophobic regions, including the docking domain and the H3-H3 four helix bundle, with global impact on chromatin. Consistent with this hypothesis, we found that cholesterol dehydrates the nucleosome surface in ∼17 (±3) water molecules per binding site , which lIt is known that some forms of transcriptionally silenced heterochromatin are localized adjacent to the nuclear lamina . NeverthLipid microdomains, containing sphingomyelin and cholesterol in the inner nuclear membrane, have been suggested to provide a resting place for active chromatin and transcription factors . In addi