The hematopoietic stem cell (HSC) niche is a crucial driver of regeneration and malignancy. Its interaction with hematopoietic and malignant stem cells is highly complex and direct experimental observations are challenging. We here develop a mathematical model which helps relate processes in the niche to measurable changes of stem and non-stem cell counts. HSC attached to the niche are assumed to be quiescent. After detachment HSC become activated and divide or differentiate. To maintain their stemness, the progeny originating from division must reattach to the niche. We use mouse data from literature to parametrize the model. By combining mathematical analysis and computer simulations, we systematically investigate the impact of stem cell proliferation, differentiation, niche attachment, and detachment on clinically relevant scenarios. These include bone marrow transplantation, clonal competition, and eradication of malignant cells. According to our model, sampling of blood or bulk marrow provides only limited information about cellular interactions in the niche and the clonal composition of the stem cell population. Furthermore, we investigate how interference with processes in the stem cell niche could help to increase the effect of low-dose chemotherapy or to improve the homing of genetically engineered cells.  The hematopoietic stem cell (HSC) niche is involved in progression of myeloid malignancies, therapy-induced malignant cell eradication, HSC mobilization, and engraftment of genetically engineered cells. We present a mathematical modeling approach for studying the interactions between stem cells and niche cells  and for simulating novel treatment ideas. This allows for quantification of HSC fitness based on interactions within the HSC niche. Simulations imply that sampling of blood, whole marrow, or primitive progenitors may not reflect the cellular composition of the stem cell compartment and thus not correlate with disease progression or therapy outcome.1 To preserve their functionality, HSC require complex interactions with their micro-environment, the so-called stem cell niche.1Adult stem cells drive tissue maintenance and regeneration. A paradigmatic example is the blood-forming (hematopoietic) system. In a multi-step process, hematopoietic stem cells (HSC) give rise to all types of mature blood cells.2-5 Animal models and clinical data support the concept that CSC impair the stem cell niche and out-compete HSC.5-8 A similar mechanism underlies clonal hematopoiesis of indeterminate potential (CHIP) where mutated stem and progenitor cell clones expand over time.9Similar to adult stem cells, cancer stem cells (cancer initiating cells) give rise to a heterogeneous malignant cell bulk and are required for persistence and expansion of the cancer. Key diseases of the hematopoietic system, such as chronic myeloid leukemia (CML), acute myeloid leukemia (AML), or the Philadelphia-chromosome negative myeloproliferative neoplasms (MPN), are triggered by cancer stem cells (CSC).10 Cancer therapies attempt to eradicate CSC from the niche. Agents which detach stem cells from the niche are used for HSC mobilization.11 HSC transplantation (HSCT)11 and HSC-based gene therapy12 aim at sustainable colonization of the niche by donor-related or genetically engineered stem cell clones.Due to its involvement in various diseases, the stem cell niche is the target of several clinical interventions.13 In practice, MRD is quantified using peripheral blood or unsorted marrow samples. To assess if such samples can provide reliable information about the CSC burden, detailed insights in niche-mediated processes and their relation to the time evolution of precursor, progenitor, and mature cell counts are required.9Stem cell dynamics also play a role in diagnostics and follow-up of malignant diseases. Minimal residual disease (MRD) is a well-established approach to monitor treatment response and progression of different hematological cancers.10 transplantation experiments,5 time-lapse imaging,14 and cell-tracking.16 However, ethical and technical issues, especially the current inability to expand human HSC in vitro,10 limit our understanding of human HSC dynamics. Mechanistic mathematical and computational models can help close this gap, as they relate processes in the HSC niche to measurable quantities such as mature blood cell counts or disease progression.17-24Experimental approaches which have substantially contributed to our knowledge of the HSC niche are coculture studies,26 and has led to novel quantitative information about stem cell dynamics in health28 and disease.29-43Mechanistic modeling is a potent approach for studying the properties of complex systems and to select the best fitting out of multiple competing hypotheses. It has a long tradition in hematologyHere, we present a novel mechanistic mathematical model of the HSC niche. The model is parameterized based on the results of mouse experiments described in literature and its dynamics are related to clinical observations. It accounts for HSC detachment from the niche, attachment to the niche as well as stem cell proliferation, self-renewal, and differentiation. The focus of this study is to understand how these processes affect the time evolution of healthy and cancerous cell populations. We use the model to address the following specific questions, which relate closely to the clinical aspects mentioned above:What are the prerequisites for the long-term persistence of a stem cell population in the niche? Which mechanisms mediate the fitness of a stem cell clone?What determines if two or more stem cell clones can coexist in the niche?Which therapeutic approaches facilitate the eradication of stem cell clones from the niche?Can we use peripheral blood or bulk marrow samples to infer the dominant clones in the stem cell niche?How can donor-derived or genetically engineered stem cell clones be introduced to the niche and persist for long times?Our study reveals a complex impact of processes in the HSC niche on cell population dynamics. We discuss potential pitfalls related to the intuitive interpretation of clinical or experimental data.Specifically, our model suggests that the abundance of a specific mutation (or cell clone) in the peripheral blood and in the marrow cell bulk may considerably differ from the abundance of the respective mutation (or clone) in the stem cell population. This may impact the interpretation and clinical use of measurements obtained from peripheral blood or unsorted marrow samples.Based on model analysis and simulation, we identify which stem cell properties imply selective advantages. Moreover, we ask whether low-dose preconditioning combined with mobilizing agents and HSCT could serve as an alternative to conventional AML therapy in patients with multi-morbidity.The proposed model accounts for interactions of stem cells with the HSC niche. It allows us to infer how these interactions impact blood cell formation and progression of stem cell-driven diseases. The model is a 6-dimensional system of ordinary differential equations with 11 non-negative parameters. The model is illustrated in 44 The capacity is defined as the maximal number of stem cells that can reside in the niche. In the model, stem cells detach from the niche with a constant probability per unit of time. During detachment stem cells get activated.45-47 Activated HSC either divide or differentiate. Upon division stem cells produce progeny which are again stem cells, upon differentiation stem cells irreversibly adopt the progenitor fate, whereas stem cells can self-renew indefinitely, progenitors have a reduced self-renewal potential and further differentiate after a finite number of divisions.We assume that the capacity of the HSC niche is finite and constant in time.25 and the mechanisms underlying loss of stemness48-51 have been modeled extensively in the literature. Our distinction between different states of non-quiescent stem cells, ie, active and inactive, is new and has been introduced as a hypothetical mechanism behind the observation that stem cells cannot persist in absence of an appropriate niche.10 A similar mechanism has been considered previously in the literature by introducing a niche affinity which decreases in dividing cells.22 Pioneering modeling works accounting for the transition of HSC between active and resting states are the works of Mackey25 and of Rubinow and Lebowitz.52We assume that upon division the stem cells enter a state which we denote as inactive. The inactive cells either reattach to the niche or differentiate. The inactive state is required to reproduce the observation that stem cells decline in absence of the niche, see 53The following parameters quantify the processes accounted for by our model. The detachment rate is defined as the proportion of niche-bound stem cells detaching per unit of time. The attachment rate determines which fraction of unbound stem cells attaches to the niche per unit of time. The division frequency of active stem cells is quantified by the proliferation rate, the differentiation of stem cells per unit of time is determined by the differentiation rate. In summary, our model considers 3 different stem cell subpopulations, namely, active stem cells (not attached to the niche), inactive stem cells (not attached to the niche) originating from divisions of active stem cells and quiescent stem cells (attached to the niche). Furthermore, it accounts for empty niche spaces. We used published data from mouse experiments to fit the parameters of HSC. This was accomplished using unbounded simulated annealing, in which 5 pairs of parameters are assumed to be pairwise equal. As cost function we used the sum of squared residuals. Details are provided in 10 When the cell counts of the homeostatic state are perturbed, eg, due to blood loss, the system recovers and the healthy equilibrium is re-established /dI /dI .Model simulations show that an increased rate of stem cell attachment results in higher homeostatic stem cell counts inside and outside the bone marrow niche . This isModel analysis implies that the homeostatic number of empty niches is independent of the HSC detachment rate . The mec45 an increase of the HSC detachment rate is also linked to an increased progenitor production.Since non-stem cells can divide only a finite number of times, the mature cell production scales with the production of progenitors. The latter corresponds to the number of stem cells that differentiate into progenitors per unit of time. For this reason, conditions which increase the number of stem cells in homeostasis also increase the progenitor production in the model. Examples for this are increased niche capacity or increased proliferation rate . Since i48In case of a persistent increase of HSC differentiation rate, homeostatic progenitor production decreases . Transie4-7 Systematic model analysis enables us to identify which cell properties confer a competitive advantage to a stem cell clone. Assuming that disease-related genetic, epigenetic, or metabolic alterations impact cell properties, this approach provides insights into disease dynamics. According to our model, competitive advantages result from reduced differentiation, increased proliferation, or increased binding to the niche. This is in line with previous results.53Stem cell-driven malignant or pre-malignant diseases result from the growth of a mutated stem cell clone which interferes with healthy cells.Since disadvantageous alterations of a specific cell trait can be compensated by advantageous alterations of other traits, it is a complex task to predict whether certain cell properties imply a growth advantage compared with the wild type or not. For this reason, we derived a mathematical expression that quantifies the fitness of a clone. We define the fitness of a clone as its ability to out-compete wild-type cells or other clones from the niche. The fitness is calculated asFH increases. This is in line with the intuition that the stronger the binding of a cell to the niche, the more difficult it is to out-compete the cell from the niche. Similarly, if the proliferation rate FH increases. This is intuitive, since the faster stem cells produce offspring, the more difficult it is to drive their population to extinction. If the differentiation rates FH decreases, since the more stem cells are lost per unit of time due to differentiation, the slower the growth of the stem cell population and the easier it is out-competed by a fast growing clone. The term (rH − dAH) is proportional to the number of progeny stem cells arising from activated HSC. The term (rH + dAH) is proportional to the loss of active stem cells per unit of time due to differentiation or inactivation. The term bH/dIH quantifies which fraction of the inactive stem cells is lost due to differentiation. Therefore, the interpretation of the formula is intuitive: The more the production of new stem cells outweighs the loss of stem cells due to differentiation, the higher is the fitness of the clone.Here, ar model , Fig. S9The formula quantifies how an advantage in one property can counterbalance a disadvantage in other properties. For example, if the differentiation rate Whenever 2 clones differ with respect to their fitness, the clone with superior fitness will eventually out-compete the clone with inferior fitness. However, it depends on the specific parameters how long it takes for the inferior clone to decline and whether this happens within the life-time of the host. A scenario where the fitness of all clones is equal corresponds to neutral competition.The fitness is independent of the detachment rate. Therefore, mobilization of malignant cells from the niche is not sufficient to eradicate a disease. However, if a given clone already has a competitive advantage, an increase of its detachment rate accelerates the decline of the inferior clone .in vitro niches or xeno-transplantation models could be used to determine clonal fitness and to predict the dynamics of clonal competition.Mathematical analysis reveals an interesting relation between the fitness of a clone and the number of vacant niche spaces. In a setting where a single stem cell clone populates the niche, the homeostatic number of empty niche spaces is given by 1/F, where F denotes the clonal fitness defined above. Consequently, the more empty niche spaces exist in a monoclonal equilibrium, the lower the clonal fitness of the respective clone. The derivation of this result is presented in 45 In the model, stem cells divide after detachment from the niche, as experimentally observed.45 The progeny originating after division differentiate or reattach to the niche. Computer simulations indicate that the malignant cell burden in the stem cell niche may differ considerably from the malignant cell burden in more mature cell fractions  HSC increases, whereas the number of niche-bound cells remains unchanged. These observations suggest that we cannot infer the number of niche-bound HSC based on circulating HSC counts. Similar findings hold for progenitor and precursor cells. Consequently, peripheral blood or bulk marrow samples might provide only insufficient information about the stem cell compartment.Disease-related or experimentally induced alterations of HSC properties may lead to a contrary effect on the short compared with the long time scale: A persistent increase of the HSC attachment rate triggers a transient decline of circulating HSC followed by a durable increase. The initial decline can be explained by an increased niche attachment of circulating cells. Since niche attachment reduces the probability of HSC death or differentiation, the total number of HSC increases over time. In case of unchanged detachment rate, the number of HSC leaving the niche per unit of time increases if the number of niche-bound cells increases. This drives the system to a new equilibrium with increased niche-bound and circulating HSC. Analogously, in response to a persistent increase of the detachment rate, the homeostatic counts of unbound/circulating  but also of unsorted bone marrow samples, which are part of the state-of-the-art diagnostics of many diseases. Since processes in the HSC niche can be modified by mobilizing agents, their quantitative understanding may have implications for future treatment approaches.Stem Cells online.Supplementary material is available at sxac079_suppl_Supplementary_MaterialsClick here for additional data file.


This paper examines the perception of risk associated with the presence of coronavirus in food purchased online and online vs. offline food shopping during the COVID-19 epidemic. The influence of COVID-19 status on risk perception was tested using the data collected from 742 consumers between December 2021 and January 2022. The empirical approach distinguished between the epidemic’s status in a province (or region), city, and other areas of the country and applied the ordered logit technique. The regional and citywide epidemic increased the perception that online purchases carry the virus and are riskier than those made offline. Further examination showed that the regional/provincial epidemic created the perception that packaging or social media use were risk factors when purchasing food online. Heterogeneity analysis showed that risk perception was significantly higher in affected cities than in non-affected provinces or other provinces. Risk perception differed across five online food categories, with the highest levels for online-ordered meals and fresh products. Strengthening COVID-19 prevention and control in cities and the province, managing risk due to the handling of food purchased online, and government monitoring of social media use will lessen consumers’ risk perceptions and encourage the use of online food offers during epidemics. Between 2019 and 2021, the average annual growth rate of online catering service revenues exceeded 15%, a higher rate than for the catering industry in general . In the The coronavirus epidemic has shown typical regional characteristics. The gradual spread of the virus was mainly from large urban centers and gateway cities to lower-level node cities in China . The pubThe contributions of this paper to the existing literature are to: (1) deepen understanding of risk perceptions of online food purchases caused by coronavirus during regional public health emergencies; and (2) ascertain ways to reduce the effect of pandemics on safety perceptions of online-purchased food. The knowledge gained will help to shape policies and public education programs to assure consumer safety perceptions when relying on online food purchases during public health emergencies, including planning for future pandemics.The 21st century epidemics prior to COVID-19 included severe acute respiratory syndrome (SARS), influenza A H1N1 (swine flu), and Middle East respiratory syndrome (MERS) . HoweverA two-factor model and a multidimensional scale have been used to measure consumer perceptions of food safety risk. Gao et al. (2020)  showed tThe existing research on the risk perception of online food purchased during COVID-19 presents two views. First, the risk of online food containing the coronavirus is low for two reasons. In the early stages of the COVID-19 outbreak, the virus may have been present in other purchasing environments compared with offline purchasing . The supThe opposite view is that the risk of eating online food containing coronavirus remains high because COVID-19 has a strong mutation ability and a high transmission rate ,28. The Joint regional pandemic prevention and control regimes continually evolve, and their effectiveness still needs to be studied in light of the dynamic epidemic situation. Ali and Keil (2006)  report tStudies of the COVID-19 pandemic focusing on the risk perception and safety of online-purchased food are limited. The existing studies have focused on three aspects of COVID-19, among them the severity of the pandemic. Liu Lingzhi (2021)  classifiHowever, there is a lack of empirical studies considering a regional dimension. Currently, the epidemic in many areas of China is small-scale and involves sporadic regional characteristics. The practical significance of such regional variation needs more research from the perspective of prevention and control measures coordinated among regions. To address the regional variation, this paper proposes the following hypothesis:Hypothesis 1 (H1).Regional COVID-19 severity increases consumer risk perception of becoming infected by online-purchased food.The COVID-19 pandemic has exposed the fragility of the fresh food supply chain, exacerbating information asymmetry and affecting consumer perceptions of the safety risks of online-purchased food. Production, processing, logistics, and sales are viewed as hazards because they can facilitate infecting consumers . The epiHypothesis 2 (H2).Regional COVID-19 severity indirectly influences consumer risk perception of online-purchased food as a source of infection.The accuracy of social media may be difficult to assess. Social media operators may spend a limited amount of resources on content verification. In the face of a large amount of information, the audience’s ability to discriminate among multiple messages is uneven, and inaccurate information could influence consumer behavior. The outbreak of COVID-19 brought uncertainty. Consumers, regardless of the severity of the lockdown, searched for epidemic-related information using social media platforms. The opinions expressed through social media might have contributed to anxiety, strengthened risk perceptions of the pandemic , and indHypothesis 3 (H3).The regional impact of COVID-19 enhances consumer risk perception of online-purchased food containing coronavirus through social media use.The focus on regional COVID-19 impact guided the construction of the questionnaire and included the design, a pilot test, and questionnaire revisions following the pilot. The final questionnaire consisted of three parts, corresponding to http://www.nhc.gov.cn/xcs/yqtb/list_gzbd_15.shtml (accessed on 25 August 2022).The survey was conducted between December 2021 and January 2022. Following the National Health Commission of China’s report that the vast majority of confirmed cases at that time (December 2021–January 2022) were in Xi’an, Shaanxi Province, that province is defined as the epidemic province, or P province, and Xi’an City is defined as the epidemic city, or C city. At the time, there were few confirmed cases in other regions or provinces, which served as a control. The confirmed COVID-19 cases from December 2021 to January 2022 can be found at: The questionnaire was distributed through online network channels in two stages between January 12 and January 22, 2022. In the first stage, the research team distributed the questionnaires through WeChat and a mobile phone network. To encourage participation in the survey, a respondent was offered a gift once the questionnaire was completed and met the quality screening. A total of 216 valid questionnaires were collected, including eight in C cities in P province, four in non-C cities in P province, and 204 in other regions. In the second stage of the survey, 526 valid questionnaires were collected with the help of professional survey companies, including 113 in the C city of P province, 103 in non-C cities of P province, and 310 in other regions. In both stages, a total of 121 questionnaires were collected from the C city of P province, 107 from non-C cities of P province, and 514 from other regions, for a total of 742 questionnaires. Two explanatory variables capture consumer perceptions. Specifically, the perception that infection is spread by viruses present on packaging or food purchased online and the use of social media to learn about COVID-19. Social media postings include unverified information, which, in the case of searching for information about COVID-19, is of importance to disoriented consumers. Survey respondents indicated their perceptions by selecting one option along the five-step scale .The sample includes slightly more females (54.31%) than males. The majority of respondents were 23–40 years of age (67.12%). Most respondents received university education corresponding to a bachelor’s degree (58.63%). The monthly after-tax income, measured by seven categories, is relatively high and exceeds 6000 yuan (mean 4.14), but the large standard deviation (1.63) suggests wide dispersion. The majority of respondents reported a family size of three (44.2%). Among the vulnerable respondents were a relatively small number of pregnant women (1.35%), but the share of elderly people was substantial (24.80%). Overall, the sample includes relatively young individuals, well-educated respondents, and relatively high-income respondents, all features common to large agglomeration populations.The respondent’s personal characteristics , family demographic characteristics, and the identification of vulnerable groups  are control variables in the study . The expThe ordinal nature of the dependent variables suggests an ordered logit as the estimation technique. The technique is applied in the estimation of two empirical equations modeling the effect of the regional COVID-19 epidemic on the perception of risk because of the purchase of food online and the perception that online food purchases represent a higher risk of becoming infected than buying food offline at conventional retail outlets. Empirical analysis is performed using Stata software .Next, the study employs a mediation effect model to test the effect of two variables of interest: the transmission of the virus by the packaging of food purchased online and the use of social media by the respondent to seek COVID-19 relevant information. To test the mediation effect, the study uses the Karlson, Holm, and Breen (KHB) method. The method permits the calculation of the total effect, direct effect, and indirect effect using the logit or probit technique. The KHB model can decompose the total effect into direct and indirect effects by comparing coefficients in the model . KHB is To assure the robustness of the estimated equations modeling the perceptions of the risk of becoming infected by a virus present on the packaging of food bought online and the online purchase being riskier than buying offline, both equations were subjected to a test. The test involved two regression techniques to verify the directional effects and statistical significance of coefficients. The baseline model is robust, proving that the region and city affected by the epidemic increase consumer perceptions of safety hazards associated with online food purchases.The heterogeneity analysis further identified the effects of selected explanatory variables on consumer perceptions of hazards involving the purchase of food online. In particular, the analysis distinguished between the influence of the location, i.e., residing in a non-affected region, an affected province, or an affected city. Additionally, the heterogeneity analysis discerned the effects with regard to five food groups purchased online, namely aquatic products, fresh agricultural products, fruits and vegetables, online-ordered meals, and shelf-stable foods.The empirical specification is as follows:In the empirical relationship, ariables , θi is tFurther examination of whether a regional/province epidemic can indirectly affect consumer risk perception of viruses present on online-purchased food packaging or in its contents, as well as any social media use, is based on the KHB decomposition (Karlson 2012) . The medRegion R and City C—see As for the mediation effect of the COVID-19 infection from online-purchased food, testing involves the following steps :Equation (2) shows the regional COVID-19 direct influence on consumer risk perceptions of food purchased online. Equation (3) show the regional COVID-19 influence on the risk perception of the coronavirus presence on packaging or food purchased online. Equation (4) show the total effect of regional COVID-19 and the perceived risk of the coronavirus’s presence on the packaging of food purchased online on the dependent variable. Similarly, the mediation effect equation examines the influence of social media use.The indirect effects of virus infection from the packaging of food purchased online are statistically significant . The posSeveral reports indicated that the surface of frozen food packaging contained virus from virus-infected handlers, causing the spread of the epidemic ,48. ConsThe indirect effect of social media use is statistically significant and positive in two equations testing the effect of the regional/province epidemic . The indThe pandemic reduces consumers’ sense of security and trust in online-purchased food safety. The closer consumers are to the pandemic-affected region/province or city, the more frequently they search to obtain information through convenient, accessible social media, likely to alleviate the fear of the unknown heightened by the information asymmetry. The multi-modal presentations on social media platforms  that areIn summary, the regional/province epidemic effect not only directly influences consumer perceptions of the safety of online-purchased food but also indirectly affects consumer perceptions through the effect of social media use. The indirect effect of the presence of coronavirus on online-purchased food is greater. Both hypotheses 2 and 3 have been supported.The robustness of the empirical results is checked using the replacement model and replacement key variables. The ordered probit and OLS regression models were estimated using the same sample . The useRegional factors associated with the epidemic significantly influence consumer perceptions of the safety of online food purchases. The earlier results show only the average effect using the whole sample and do not consider the heterogeneity of the effect of regional epidemics on consumer risk perception associated with online food purchases. A heterogeneity analysis accounted for regional, city, and food type effects. The estimation results are shown in The results indicate that consumers in non-affected cities in the epidemic-affected province, compared to non-affected provinces, have significantly higher risk perceptions of coronavirus on online-purchased food . The reshttp://www.gov.cn/xinwen/2022-06/28/content_5698168.htm (accessed on 29 September 2022).The results support the idea that central, provincial, and local governments should focus on prevention and control around the affected cities, especially in the neighboring cities within the province. Such efforts involve information sharing and strengthening public health monitoring, especially in high-risk occupations including catering and courier delivery. The Joint Prevention and Control Mechanism Comprehensive Team of the State Council formulated and issued the ninth edition of the Prevention and Control Plan for Novel Coronavirus Pneumonia, proposing to strengthen regional joint prevention and control on the basis of targeted prevention and control. See The five categories of food purchased online include: online-ordered meals, fresh agri-products, vegetables and fruits, frozen food, and shelf-stable food. Among them, fresh agri-products include fresh aquatic products, meat, poultry, eggs, and dairy products. Frozen food includes frozen aquatic products, seafood, meat and poultry, and quick-frozen flour food . The marginal effects estimation results show  that theConsumers have the highest perceived risk of fresh agri-products carrying COVID due to the effects of the regional epidemic. The coronavirus can infect animals, and animal COVID-19 cases have been reported . ConsumeFrozen food was the online food category that most consumers felt may contain SARS-COV-2 . China COnline-ordered meals are the third category of food that consumers are worried may contain viruses. During the epidemic, online catering services supplemented the food distribution system, raising concerns about a higher risk of infection due to contact with high-risk groups such as meal delivery couriers.Vegetables, fruits, and shelf-stable foods, commonly purchased online, are a daily source of nutrition, and the possibility of carrying a virus has caused less concern among consumers. Indeed, the case of transmission through those products has not been reported, supporting the absence of a perceived threat from those products to consumer safety.This study investigated the influence of province and city affected by the COVID-19 epidemics on risk perceptions that online-purchased food is a source of possible infection using survey data collected between December 2021 and March 2022. Online purchases offer uninterrupted access to food. Reducing perceptions of virus transmission using this distribution channel to limit the public’s anxiety while ensuring food access is therefore important. The results showed that the regional epidemic significantly increased consumer perceptions of the risk of food packaging carrying the virus that can infect the delivery recipient. In addition, the regional epidemic increased the perception that online food purchases are riskier than buying food offline. The use of social media is also linked to an increased perception of the risk of virus transmission through online food purchases. The robustness of the results was verified using additional testing.The results of heterogeneity analysis showed that, compared with the provinces not affected by the epidemic, the province epidemic had the greatest influence on purchase risk perception of online food safety in the epidemic-affected cities, followed by the effect of non-affected cities. Since proximity to the area affected by the epidemic is a key factor related to the level of risk perception, efforts to inform the public about precautions taken in food distribution are important, and the dissemination of information pertaining to the safe handling of online-ordered food through social media is warranted. Online food ordering seems less likely to expose the recipient to the virus than shopping in conventional food retail outlets because of the reduced number of people a recipient meets.In the current study, the effect of the epidemic had a particularly great impact on the risk perception of the safety of online aquatic products and online-ordered meals, suggesting that to assure the public of the safety of those products, special efforts regarding the handling of such foods (such as cooking methods that destroy the viability of any virus) have to be conveyed by restaurants offering meal delivery. The efforts of the meal preparer need to be supported by information from the government agencies responsible for monitoring the enforcement of food safety regulations, including the monitoring of regulations covering couriers delivering online-ordered meals. The diligence of such efforts is essential to building trust in product safety.The government has an opportunity to select special measures to further improve the online food supply safety system under the real conditions of epidemics, especially regional outbreaks. Unified and standardized prevention and control method development should allow for input from consumers regarding their views of food safety during the recent pandemic. Once developed, the prevention and control methods have to be communicated to the public and require the training of managers in charge of future outbreaks. It is essential to assure a degree of flexibility in prevention and control management that discriminates between the affected cities and adjacent cities, especially those not affected, in the same province and in neighboring provinces.Social media can inform consumers of steps to take when handling deliveries to protect against the disease. An understanding of how to handle various types of food empowers the consumer and helps reduce the perceived risk of contracting the disease. Improving the transparency of the methods used in handling ordered food in the preparation of the shipment, tracking the shipment, and tracing the original suppliers will enhance the safety of food under normal operating conditions as well. Educating the public will assure that food safety is a priority. Logistics systems have to be upgraded, the speed of delivery must be increased, and safe distribution must be planned in advance to allow continuity of deliveries, according to the latest epidemic information. Sharing such information with the public will minimize information asymmetry in the online food supply. Strengthening the prevention of misinformation on online social media platforms by releasing information through official channels needs to be considered. Such efforts would empower consumers to understand the food safety risks associated with the epidemic and help temper the negative effects of an epidemic.In terms of regional prevention and control strategies, it is stressed that all regions should have both uniform prevention and control systems that are distinct and specific to the conditions in the regions in which they are implemented. Uniformity of prevention and control implies that all regions can have access to records allowing for the tracing of food shipments and monitoring of the health of personnel handling food supplied to distributors and outgoing shipments. This allows public health managers to directly and quickly connect with others or share information when necessary, such as in the event of a regional outbreak. Regional prevention and control management emphasizes the differences in local levels of public emergency, geographical conditions, and local customs. Regions are encouraged to create unique and effective prevention and control management methods specific to their conditions. In terms of regional prevention and control priorities, joint prevention and control should be strengthened in urban border areas.To minimize and possibly eliminate the risk of disease transmission by the packaging of online food deliveries of either raw food or ordered meals, it seems warranted to review the existing handling procedures and possibly formulate new working procedures. Emphasizing food traceability and regulation enforcement, for example, HACCP standards, can enhance food processing safety.The role of social media has proven important in shaping consumer perceptions during the pandemic. Not surprisingly, restrictions prevented many essential aspects of daily living, including food shopping, and consumers came to rely upon available sources of information and communication enabled by social media. Social media postings frequently list erroneous or unverified information, adding to the anxiety caused by the fear of becoming infected with an unfamiliar virus. Operators of the social media platforms need to develop and apply methods and procedures for screening the posted information, especially as it pertains to food safety, distribution, and handling. Government regulation may foster digital innovation to improve the accuracy of postings with a view to managing future outbreaks more effectively. Users of social media need to be educated on how to verify information and accurately assess its credibility.As the world gradually enters the post-pandemic era and countries relax public control programs or declare the end of the pandemic, one should bear in mind the impact of the pandemic on the safety of online-purchased food. The experience of the pandemic is invaluable in keeping online food safe and offers opportunities to undertake the challenge of minimizing the transmission of disease through packaging in online food purchasing.The study was limited in the methods of data collection that could be employed. Under the restrictions imposed in the COVID-19-affected areas, the online completion of a questionnaire was the only viable method. Face-to-face interviews were not possible during that time. Consequently, those without broadband Internet or cell phone service, among them older consumers, might have been excluded from the study. Therefore, the survey sample may not fully reflect the population of consumers, although those without access to the Internet could not use the online food purchase opportunities. A future study that applies the conventional face-to-face interview method may establish if and to what extent the current sample missed potential respondents.The focus of this study was on a single area affected by the COVID-19 pandemic. A future study needs to involve other regions in China or even other countries. Additionally, a larger regional sample could verify the statistical results obtained in this study and provide further insights. A future project may also provide perspectives on online food shopping by probing for risk perceptions after the COVID-19 pandemic, showing to what extent food choices and shopping habits may have shifted and how such changes need to be considered in managing future outbreaks.


Understanding the optical transmission property of human hair, especially in the infrared regime, is vital in physical, clinical, and biomedical research. However, the majority of infrared spectroscopy on human hair is performed in the reflection mode, which only probes the absorptance of the surface layer.The direct transmission spectrum of individual hair without horizontal cut offers a rapid and non-destructive test of the hair cortex but is less investigated experimentally due to the small size and strong absorption of the hair.In this work, we conduct a transmission infrared micro-spectroscopic study on individual human hair with the help of Fourier-transform infrared microscope experimentally. Its high spatial resolution of infrared micro-spectroscopy further allows the comparison among different regions of hair. The geometry effect of the internal hair structure is also quantified using the finite-element simulation, which supports the experimental results.By utilizing direct measurements of the transmission spectrum using a Fourier-transform infrared microscope, the human hair is found to display prominent band filtering behavior. In a case study of adult-onset Still’s disease, the corresponding infrared transmission exhibits systematic variations of spectral weight as the disease evolves.Our work implies that the variation of spectral weight may relate to the disordered microscopic structure variation of the hair cortex during an inflammatory attack. Our work reveals the potential of hair infrared transmission spectrum in tracing the variation of hair cortex retrospectively. As its spectral range covers the energy of vibrational modes of chemical bonds, infrared spectroscopy is widely recognized as a powerful approach for the identification of chemical components and functional materials in physical, chemical, and biomedical research.––––Hair is fiber-like, with a typical diameter of about 50 to –However, most infrared spectroscopic study of hair has been performed in the attenuated total reflection (ATR) mode, which unfortunately probes the absorptance of the surface layer only. Detecting internal parts in hair in ATR-FTIR requires sophisticated microtomy, which may introduce external contamination to the infrared spectrum measurement. As In this work, we investigated the infrared transmission properties of human hair using FTIR microscopy. Prominent band filtering behaviors were observed in the spectra. The transmission spectra were well reproduced from the respective absorption of the hair cuticle, cortex, and medulla. The high spatial resolution of infrared micro-spectroscopy further offered the ability to compare the infrared spectrum at different regions of hair. In a case study of adult-onset Still’s disease (AOSD), the corresponding infrared transmission displayed a systematic variation of spectral weight. Although the patient population in this study was not enough for solid conclusion, it reflected a possible link between spectrum and disease-induced hair structure change. We further quantified the geometry effects of the hair’s internal structure by means of the finite-element analysis method, and the results suggested that the variation of spectral weight probably correlated with the structure variation of the hair cortex. These findings pointed to the potential of the infrared micro-spectroscopy as a promising tool for non-destructive test for individual human hair.22.1Hair samples used in this study were collected directly from the scalp of the host body and had not been stained, permed, or experienced other types of severe physical damage during the growth. The internal short hair samples were selected to avoid possible damage during the host’s daily life. The samples were cleaned with ethanol to remove possible residue on the surface before the infrared transmission experiments.2.2The hair samples were characterized by FTIR (FTIR Bruker Vertex 80V) with infrared microscope (Bruker Hyperion 2000). The schematic of experimental setup is presented as 2.3–To simulate the transmission spectrum of human hair, the finite-element analysis was conducted using the COMSOL Multiphysics software. During the simulation, we simplified the structure to a coaxial cylinder with three uniform layers. From the inside out, the three layers are the medulla, cortex, and cuticle, respectively. In the infrared transmission model, we ignored the light scattering from each hair structure and set the monochromatic light beam perpendicular to the longitudinal axis. With these assumptions, we simulated the infrared transmission process in the two-dimensional cross-section using the Lambert–Beer law. For solid substances, Lambert–Beer law describes the relationship between the absorbed light intensity at a certain wavelength and the thickness of the substance. We write the Lambert–Beer law in differential form as The diameter of human hair is typically 50 to 33.1–In principle, the absorption and transmission spectra are equivalent and interconvertible for a given objective. However, for regions with strong absorption, the transmission signal is challenging to be precisely probed experimentally and is usually overwhelmed in the background. It causes the characteristic peaks in the low-transmission bands of the transmission spectrum to be absent, whereas the absorption peaks in the absorption spectrum become prominent. Likewise, the characteristic peak signals in absorption are quite difficult to probe precisely in regions with strong transmission. Thus, the ATR and transmission modes of FTIR are sensitive to strong absorption (weak transmission) and strong transmission (weak absorption) regions, respectively. It clearly indicates that the transmission FTIR of human hair is capable of serving as an important complement to the extensive research on the infrared absorption by ATR-FTIR. For example, the peak intensity of typical black-body radiation spectral at 310 K  is around 3.2The implementation of transverse transmission configuration further provides a rapid and non-destructive approach to investigate the spatial variation of infrared spectrum in hair by FTIR microscope. As illustrated in the inset of ––,In To further elaborate on the possible link between the change in hair infrared transmission spectra and AOSD, data from four hair samples of the same P-AOSD were collected and then plotted as a function of time in It must be acknowledged that the rarity of AOSD gives rise to a rare patient population and therefore the difficulty of replicating the experiment on a large scale. Thus this study could not reach a solid conclusion but reflects the potential of hair infrared transmission spectrum in tracing variation retrospectively.3.3To better understand the observed spectral variation, we quantify the impact of the geometric size of hair structure and theoretically model the infrared transmission using the finite-element analysis method. As shown in me value . When dCof 19.8% , and thetogether .–The variation of –As presented in 4We propose that the transmission-mode microscopic infrared spectroscopy could serve as a complementary tool for testing individual human hair. According to the spatial-resolved infrared spectra, the hair from an AOSD patient shows substantial variation in infrared transmission as the disease evolves or recrudesces. Combining the experimental results and the finite-element analysis, we systematical analyze the geometric size effect of the hair cuticle, cortex, and medulla and find the infrared transmission is mainly dominated by the cortex layer. Our work not only generates fresh insight into a better understanding of human hair’s infrared transmission spectrum but also presents a possibility of tracing the pathological change during the hair growth retrospectively.


The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines.Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the “Berlin Definition”. This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R Respiratory diseases endanger the ability of the respiratory system to supply the body with oxygen and to eliminate carbon dioxide sufficiently, potentially causing life-threatening consequences. These conditions are caused on one hand primarily by damaging the pulmonary tissue through, for instance, infection, toxic effects of inhaled gases or fluids or trauma. On the other hand, the lung can be affected indirectly as a side-effect of diseases of other organs . Early daO2) to Fraction of Inspired Oxygen (FiO2) (P/F ratio) of less than 300 mmHg in combination with bilateral opacities in pulmonary imaging and absence of hypervolemia and heart failure [ultima ratio [ARDS is further characterised by its heterogeneity and the difficulty with respect to diagnosing it, leading clinicians and researchers to establish the “Berlin Definition” by which ARDS onset is defined as a ratio of Partial Pressure of Arterial Oxygen , Hardman et al. launched an in silico tool for modeling pulmonary disease progression and determining the potential effectiveness of treatment methods . This moThe development of models such as the NPS was simplified with the advent of Electronic Health Records (EHRs). Making large amounts of clinical data easily accessible has enabled a lot of research in healthcare, and has helped highlight pathological patterns and uncover treatment methods, but has also sparked discussions about patient privacy and data security ,18,19. Ahttps://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels (accessed on 3 February 2023)) supercomputing cluster and booster to perform pre-training of the ResNet-152 DL network. Their goal was to highlight the speed-up achieved using the HPC resources and to eventually perform large-scale transfer learning using the publicly available COVIDx (https://www.kaggle.com/datasets/andyczhao/covidx-cxr2 (accessed on 3 February 2023)) dataset to develop a tool for rapid COVID-19 detection from Chest X-rays (CXRs) [It follows that several teams have already employed available HPC resources in the storage and analysis of Medical Big Data or in training Machine Learning (ML) and Deep Learning (DL) models. Kesselheim et al. applied the Jülich Wizard for European Leadership Science (JUWELS) (s (CXRs) . The ress (CXRs) ,26. Boths (CXRs) . Their wMedical data collection, cleaning, analysis and visualisation.Data augmentation through statistical analysis of the available clinical data.Parallel simulation of patient states using a ported NPS.Parallel hyperparameter optimisation of the developed DL model using Ray Tune .Final training of the DL-based surrogate model and validation of the results with the original simulation.This paper describes the process by which an ML and data science platform that takes advantage of Modular Supercomputing Architecture (MSA) available from the Jülich Supercomputing Centre (JSC) is used to build a surrogate model of the NPS with the intention of implementing it for streamlined ARDS-diagnosis support ,29,30. IAs Gherman et al. highlighted, several researchers have already developed ML surrogate models from complex mechanistic models . These sThis work is conducted as part of the use case Algorithmic Surveillance of Intensive Care Unit patients with ARDS (ASIC) which is part of the Smart Medical Information Technology for Healthcare (SMITH) project under the guidance of the German Federal Ministry of Education and Research (BMBF) ,34. FurtThe experimental process leading towards completion of the research objective described in the Introduction is represented in https://www.deep-projects.eu/ (accessed on 3 February 2023)) was set up to highlight the benefits of using heterogeneous architectures in HPC to pave the way towards exascale computing by introducing boosters alongside traditional supercomputing clusters [The Dynamic Exascale Entry Platform (DEEP) series of projects ) script accompanied by peripheral functions written either in MATLAB or in C-script and converted at initial startup into the MATLAB executable (.mex) format. Version 1.4 of the simulator was made available for this research as part of the SMITH project. Further updates to the NPS have already been implemented which improve its performance [The NPS is made available as part of the SMITH project as a central MATLAB ) software plugin is used in order to export the simulation as a C-script and package it for implementation on the HPC cluster.As mentioned above, the NPS is built in MATLAB and thus is implemented on a local machine running MATLAB version R2019a within Windows 10 version 22H2. Additionally, the MATLAB Coder (https://www.Python.org/ (accessed on 3 February 2023)) programming language with additional packages installed through the built-in pip function or loaded from the list of pre-installed modules available on the HPC cluster. The packages include Numerical Python (NumPy) (https://numpy.org/ (accessed on 3 February 2023)) and Pandas (https://pandas.pydata.org/ (accessed on 3 February 2023)) for data structure manipulation, MatPlotLib (https://matplotlib.org/ (accessed on 3 February 2023)) for data visualisation, Keras (https://keras.io/ (accessed on 3 February 2023)) (running from within TensorFlow (https://www.tensorflow.org/ (accessed on 3 February 2023))) and Scikit-Learn (https://scikit-learn.org/ (accessed on 3 February 2023)) for performing the ML tasks and mpi4py to bind to the Message Passing Interface (MPI) and handle the parallelisation aspect of some of the data-manipulation tasks [https://slurm.schedmd.com/ (accessed on 3 February 2023)) in order to distribute the submitted training and tuning jobs onto the available computing resources. The submission of jobs is done using shell scripts that define the environments to load and the resources to recruit for each specific job.The remainder of the programming done for this project uses the Python , pH and Bicarbonate).Additionally, the original model outputs an array containing several parameters recorded over every time step of the simulation, which made exporting values difficult. Therefore, the output parameters are reduced to only include the final values of markers for a pulmonary impairment, which can be consistent with an ARDS onset  database as part of the research done by Sharafutdinov et al. also within the scope of the SMITH project ,43,44. DAs these data are fed into the reduced simulation, the aforementioned markers of ARDS onset of these patients are generated. The end result of this data-manipulation step is a collection of 1,000,000 initial states of patients made up of 19 input parameters and 4 associated expected outputs. The output parameters were chosen based on a sensitivity analysis done by Sharafutdinov et al. in previously published research  and are In order to select the model architecture that offers the greatest potential training performance, several different approaches are tested. However, the choice was limited by two major factors: first, the architecture does not need to be adapted for timeseries data since the inputs chosen are snapshots of patients’ states, as described above; therefore, Recurrent Neural Networks (RNNs) are excluded. Second, no advanced neural network architectures, such as residual layers or transformers, are to be used in order to maintain a reduced model complexity. Accordingly, the models tested out in this step were made up of stacked fully connected layers, convolutional layers or a combination of both.aO2, PaCO2, pH and HCO3) with the outputs generated by the original simulation. Further improvements of this model are done through hyperparameter optimisation as described in the next section.Several models of both architectures were tested, with varying depths and types of layers, including regularisation, dropout and normalisation layers and with different layer sizes, dropout rates, regularisation factors, learning rates, batch sizes and loss functions. This was done in order to uncover the hyperparameters that have a significant effect on the training process. Each of these architectures was trained for 50 epochs. After this initial testing phase, a provisional best performing model structure is decided on based on a statistical comparison of the four output parameters listed in https://www.ray.io/ (accessed on 3 February 2023)) framework is employed to perform hyperparameter tuning [Hyperparameters are the variables that affect the way in which a model is built or its training process and can be altered either through a process of trial and error or automatically using optimisation algorithms ,46. In or tuning ,47. ThisThe schedulers used by Ray Tune in the optimisation process described in this manuscript are HyperBand, Asynchronous HyperBand, Population-Based Training (PBT) and the default First-In, First-Out (FIFO) ,49,50. T2 scores are closest to 1 for all output parameters. These scores quantify the deviation of the model results from the outputs generated by the original simulation.In this experiment, the tuned parameters are the learning rate, the batch size, the dropout rate, the loss function and the presence of an intermediate fully connected layer before the output layer in the network architecture. The choice of tuning these specific hyperparameters stemmed from the initial testing carried out in the model design and training phase described in The data generated as per Different types of neural network architectures with varying depths were tested with the available data. The best performing were based on fully connected layers and on 1D convolutional layers. Further experiments with different depths of the two architectures were performed. From these experiments, it was clear that models built with stacked fully connected layers underperformed compared to the approaches using convolutional layers. Additionally, extending the training duration did not lead to improvement in the results and in some cases led to overfitting.Applying CNN-based models to the task at hand resulted in more consistent performance even with shallow architectures. Additionally, some of the Convolutional Neural Network (CNN) models did overfit, but in general the models using this architecture reached lower Mean Absolute Error (MAE) in fewer training steps than the fully connected models. The evolution of the MAE during training and validation for several models of both network architectures are presented in For both of these architectures, several iterations of testing were done during which the depths of the networks were varied, as well as the widths of their layers and the addition of dropout and pooling layers into the network design. This process was done in combination with updating the learning rate, batch size and regularisation rates in order to find a rough estimate of the range of hyperparameters as well as the combination of layers that produced a promising model. Based on the results of these experiments, the parameters to be tuned during the hyperparameter tuning process were selected and the ranges over which the tuning would occur were estimated. Furthermore, the chosen network architecture would be based on CNNs with the possibility of adapting the architecture during the tuning process. Additionally, this network would have four one-dimensional convolution layers, with kernel size of 64 for the first layer and 128 for the remaining layers. The output from the final convolution layer is flattened before being fed either to an intermediate fully connected layer or directly to the output layer.Four different schedulers were used in the hyperparameter-tuning step of this experiment in order to provide performance comparisons of the different applications. Running on 16 nodes of DEEP-ESB cluster, the FIFO scheduler provides a benchmark as it distributes the 64 available tuning jobs. In this approach, new trials cannot be started until the prescribed maximum number of training epochs of previously scheduled tasks is completed. Completing all the trials required a total of 78 min. The HyperBand scheduler performed early stopping on many trials that were underperforming, which allowed the tuning process to complete within a shorter duration (46 min). Asynchronous HyperBand performed in a similar manner, taking 64 min to complete all the trials. The early stopping is evident in the learning curves of these two scheduling algorithms b,c. FinaThe best performing model parameters from each scheduler are presented in 2 score graphs for the output parameters, where it can be seen that prediction performance for PaCO2, bicarbonate concentration and pH is reduced compared to the networks from the HyperBand and PBT trials. Additionally, the R2 score graphs show that PaO2 prediction accuracy is consistently lower than the remaining output parameters, although still above 0.90.The results from each parameter combination listed in Converting the NPS to C helped highlight the speed-up that can be achieved through the use of HPC resources. Running multiple simulations simultaneously as well as the increased efficiency and reduced overhead of C code reduced the code execution times and made it possible to generate more data with which to train the proposed ML models. In the end, the average duration of simulations was less than half the average duration of simulations in MATLAB. Additionally, processing and storing the output data was a computation- and communication-intensive process which was greatly simplified through the availability of online storage on the pre-established HPC-enabled platform for medical ML and data science .https://keras.io/getting_started/faq/ (accessed 10 February 2023)).The results of the DL model training step of this research highlighted the inherent differences between a fully connected ) architecture and a convolution-based approach. While ANNs are more likely to give value to every input parameter, CNNs are more adapted to uncover connections between the inputs and infer meaning from them, which might explain why these networks consistently performed better. The results in https://docs.ray.io/en/latest/tune/api_docs/schedulers.html (accessed 10 February 2023)). In the case of PBT, resource efficiency is secondary to uncovering more effective approaches through parameter perturbations. Although this approach could be beneficial for applications where minor changes of parameters might greatly alter the outcome of the experiment, the computational overhead necessary for PBT to complete the trials was judged too great for the research purposes described in this manuscript.Similarly, when considering resource efficiency, HyperBand and its successor Asynchronous HyperBand make the best use of the available resources to distribute the available tasks. Besides the reduction in computation time, these two approaches minimise stragglers, that is the number of allocated resources that are not effectively being used for computational tasks. Furthermore, the recommendations from the Ray framework highlight Asynchronous HyperBand as a more capable and efficient scheduler than the original HyperBand , although the prediction of PaO2 was consistently lower. This could be due to possible physiological patterns that were not effectively represented within the data, although future tests with larger data sizes might shed more light on the issue.The models trained on the best parameters generated from the tuning process highlight the need to take advantage of early stopping during training. Such an approach might produce better predictive performance from the models trained on the parameters selected by FIFO and Asynchronous HyperBand where overfitting was a clear issue. The model trained on the parameters selected by HyperBand took the longest time to train due to the lower batch size but still had a performance similar to that of the PBT model in terms of RThese results highlight the fact that the surrogate model manages to accurately emulate the performance of the NPS within a statistically acceptable range. Although the performance of the models developed through this approach has not been compared with existing diagnostic support models, the surrogate model benefits greatly from the accuracy that is inherent to the original mechanistic simulation. On the other hand, in replacing the NPS with the DL-based surrogate model, the computational overhead due to nested calculation and equilibration loops is reduced. Additionally, following the experimental procedure described herein, further surrogate models can easily be developed from the NPS with the intent of diagnosing other conditions.modus operandi of model development for clinical applications where explainable AI methods are recommended. It follows that developing explainable AI models for clinical diagnosis is one of the research focus points within the developed ML and data science platform described in this manuscript.The results described in this research further showcase the benefits of building specialised surrogate models from existing complex medical mechanistic models, a process that is well established in many scientific fields as described by Gherman et al. . ThroughThis article described the process by which a pre-established machine learning and data science platform was used to facilitate the conversion of a MATLAB-based virtual patient model. The process took advantage of available HPC infrastructure to parallelise the original model in order to generate synthetic data that was later used to train ML-based surrogate models. The performance of the models was improved through hyperparameter tuning, which also took advantage of parallelisation. The resulting model performance closely mimics the performance of the original model, though with a massive improvement in the speed with which the results are generated. Additionally, the work shed light on the resource use as a means by which to improve efficiency; algorithmic finetuning of the models using parallel computing can efficiently uncover parameter combinations that would otherwise require a long process of trial and error. The work on model conversion is far from complete but offers a glimpse into the clinical applications of virtual patient simulators as real-time diagnostic support tools for clinicians and ICU personnel, especially in situations where early warning can greatly improve outcomes for patients.


The accuracy of image segmentation is critical for quantitative analysis. We report a lightweight network FRUNet based on the U-Net, which combines the advantages of Fourier channel attention (FCA Block) and Residual unit to improve the accuracy. FCA Block automatically assigns the weight of the learned frequency information to the spatial domain, paying more attention to the precise high-frequency information of diverse biomedical images. While FCA is widely used in image super-resolution with residual network backbones, its role in semantic segmentation is less explored. Here we study the combination of FCA and U-Net, the skip connection of which can fuse the encoder information with the decoder. Extensive experimental results of FRUNet on three public datasets show that the method outperforms other advanced medical image segmentation methods in terms of using fewer network parameters and improved accuracy. It excels in pathological Section segmentation of nuclei and glands. I.Medical image processing becomes essential in computer-aided diagnosis(CAD) Conventional methods, based on morphological operations to segment cells or intracellular compartments, lack sufficient accuracy in segmentation results and are inefficient in generalizing the new datasets. Deep-learning-based segmentation has been rapidly developed with improved accuracy and can be adapted to a variety of pathological slice cell maps. Al-Kofahi et al. use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation, forming hierarchical features from low-level to high-level to perform cell segmentation A.Methods based on convolutional neural networks (CNNs) have overcome many shortcomings of traditional algorithms in the field of medical image segmentation. Compared with traditional methods, deep learning has higher accuracy and robustness, even under dynamic backgrounds, resolution, or light source conditions. Deep learning has good flexibility, as CNN models and frameworks can train multiple types of datasets and are more versatile. Deep learning is end-to-end learning, and the network can automatically find the most descriptive and obvious features without relying on the judgment of engineers and long-term debugging and error handling Long et al. As the network deepens, vanishing/exploding gradients and network degradation problems are prone to occur. ResNet The attention mechanism is to imitate the process of paying attention to the Region of Interest. We can adaptively make the network focus on more effective features by exploiting the interdependencies between channel or spatial domain features. This enables the network to extract richer information in limited steps. At the same time, it improves the interpretability of the network and has the potential to increase clinician acceptance and trust in predictions given by AI algorithms Frequency domain conversion is very common in traditional image processing because the high and low frequency of the image represents the contour and detail information of the image, which is very important for image processing. Yu et al. proposed importing spatial frequency domain instead of RGB image into CNN to extract features. This method can significantly reduce the amount of data transmission and improve the accuracy of the model to a certain extent B.Inspired by these successful approaches above, we draw on the encoder-decoder structure and use the Residual Unit as the basic structure. The encoder downsamples three times, reducing the size of the feature map by half each time, and the decoder symmetrically upsamples three times symmetrically, enlarging the feature map by half each time. When down-sampling, the encoder will obtain a high-level semantic feature map as the depth deepens. Up-sampling combines the low-level semantic features of the encoder with the corresponding layer to enrich the overall features while restoring the resolution. Every Residual Unit is composed of two layers of FCA Block and the BN layer that follows it. FCA Block gives the frequency domain information after Fourier transforms as the weight of the channel to the spatial domain through the attention mechanism so that the spatial domain can obtain richer information to improve the segmentation performance. We have evaluated our method on three publicly available datasets. Our experiments show that our model outperforms most of the baseline models and achieves comparable performances with SOTA nucleus and gland segmentation, and the network significantly reduces the number of parameters and computational costs while ensuring segmentation performance.In summary, the contributions of the paper include:The combination of FCA and U-Net – We propose a novel FRUNet architecture that leverages the advantages of FCA blocks and residual blocks based on an excellent U-Net network. These ingenious structures enable the network to quickly extract useful and effective features, accelerate network convergence, and improve training efficiency while improving segmentation accuracy.Systematic evaluation – The effectiveness of FRUNet has been evaluated using three public datasets: Kaggle 2018 Data Science Bowl, GlaS, and MoNuSeg. These three datasets are mainly used for nucleus and gland segmentation, and each of them has its characteristics to verify the network’s robustness in handling diverse nuclei, its ability to segment irregular and dense nuclei, and its capability to handle small sample datasets.Network lightweight — The FRUNet parameters is several times smaller than that of the current segmentation networks, which results in near SOTA performance with far fewer computational cost. Because the network has a strong feature extraction ability and fast convergence, it can obtain sufficient accuracy with a small number of layers and channels.II.A.The overall design is illustrated in The squeeze-and-excitation (SE) block improves model accuracy by weighting different channels to emphasize the important features. To make the network pay attention to the high-density information of the features, we referenced the SE block and proposed a Fourier channel attention block. The extraction of frequency domain information and the channel attention mechanism greatly improve the richness of information Therefore, we designed the FCA block to focus on the high-frequency information of the edge. The FCA Block structure first passes the input feature map through two layers of The block diagram of FRUNet is shown in Input channels are determined as 3 channels or 1 channel depending on whether the input image is an RGB or grayscale image. The B.The segmentation of cells and gland is performed on the pixel level, and the samples are usually unbalanced. Therefore, two different loss functions are chosen in this work. The first is the cross-entropy loss function, which is commonly used to describe the difference between two probability distributions. However, when the number of foreground pixels is much smaller than the number of background pixels in the segmentation task, the model with only cross-entropy loss will be heavily biased towards the background, resulting in poor results. We further combine the Dice coefficient to calculate the similarity between two images, which is more suitable for unbalanced samples. The final loss function is described in Eq. The following metrics are used for the quantitative evaluation of our method with other recent methods, which are commonly used metrics in medical image segmentation.C.We have used three medical image segmentation datasets to validate the performance of our proposed FRUNet network: Kaggle 2018 Data Science Bowl, GlaS, and MoNuSeg, which are publicly available from their official websites. The 2018 Data Science Bowl contained a large number of segmental nuclear images of different cell types obtained under a variety of conditions and varied in cell type, magnification, and imaging modality D.Our experiment uses Keras neural network API and runs on TensorFlow III.In this section, we discuss the results of experiments conducted by our proposed network FRUNet. All 5-fold cross validation results are also in the corresponding results table.A.Kaggle 2018 Data Science Bowl dataset contains 670 images with image sizes GlaS contains 165 images with image size MoNuSeg contains 51 images with a size of B.From the result table, we can see that the number of network parameters of FRUNet is less than all networks except MedT. We conduct a series of comparative experiments on the dataset Kaggle 2018 Data Science Bowl. Based on FRUNet, change the number of channels, and the number of layers of the network to determine the optimal network structure. As shown in C.We conduct extensive ablation experiments to verify the effect of each block of the network in the proposed method. In this experiment, the dataset we have used is GlaS. We have analyzed the effect of the Fourier transforms, the channel attention mechanism, and the different loss functions on the whole network separately. IV.https://zenodo.org/record/6919253#.Y2utT3ZByUk.In order to help clinicians make an accurate diagnosis, it is necessary to segment some key objects in medical images and extract features from the segmented regions. This paper segmented three different tissues or cells from different patients and hospitals and verified the good segmentation performance and robustness of the proposed network, which helps doctors understand the pathological structure more clearly. We propose a lightweight network FRUNet for cell and gland segmentation that takes advantage of Fourier domain channel attention and encoder-decoder architecture. Such an ingenious structure allows the network to extract more useful features with a much smaller model size, resulting in high segmentation accuracy. FRUNet is based on the U-Net, FCA Block, and Residual block. FCA Block utilizes frequency-domain channel attention to give weights to the network’s spatial-domain features, making the network more sensitive to important features. Residual networks allow deeper neural networks that can address each encoder degradation problem, improve channel interdependencies, and save computational resources. The encoder-decoder structure of U-Net, combined with skip-connection, can make the high-level feature map integrate more low-level features, which is conducive to the accurate classification of pixels. FRUNet performs significantly on three datasets of cell and gland segmentation. We did experiments to compare our network with the current classical networks with better segmentation effects such as U-Net, U-Net++, and ResUNet, as shown in The ablation experiment demonstrates that the frequency domain information after the Fourier transforms and channel attention mechanism is added to greatly increase the segmentation accuracy. Dice coefficient loss can get better results, but combined with Binary cross-entropy loss, the best results of the experiment are obtained.Our preprocessing of images is resizing, which may lose image information to a certain extent. The datasets we have chosen are the segmentation of nuclei and glands, and their segmentation results can outperform the performance of the SOTA network, but for other datasets, we can only achieve comparable performances with SOTA and cannot surpass the best.


Bulimia Nervosa (BN) is a highly prevalent eating disorder related to multiple risk factors. In this regard, familial variables can play a critical role in the development and maintenance of BN.The existing studies frequently explored mothers and maternal factors, while fathers and paternal variables have been less extensively investigated in this field. Therefore, we aimed to systematically review the studies on the role of paternal factors in BN.This systematic review process was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. As a result of the literature search on PubMed, Web of Science TM, and APA PsycINFO, 419 candidate papers were determined and evaluated based on the eligibility criteria. The quality assessment of the final 59 studies was conducted using the JBI Critical Appraisal Tools.Then, we thematically arranged and narratively reported the qualitative and quantitative research findings. Paternal attitudes , family dynamics , and father-specific features  were found as three main groups that could be directly or indirectly associated with the development and maintenance of BN symptoms. The eligible qualitative studies also indicated that fathers could positively influence the recovery process of their daughters with BN .The contradictory outcomes were discussed for further research and clinical implications.None Declared


Pleobot—a unique krill-inspired robotic swimming appendage constituting the first platform to study metachronal propulsion comprehensively. We combine a multi-link 3D printed mechanism with active and passive actuation of the joints to generate natural kinematics. Using force and fluid flow measurements in parallel with biological data, we show the link between the flow around the appendage and thrust. Further, we provide the first account of a leading-edge suction effect contributing to lift during the power stroke. The repeatability and modularity of the Pleobot enable the independent manipulation of particular motions and traits to test hypotheses central to understanding the relationship between form and function. Lastly, we outline future directions for the Pleobot, including adapting morphological features. We foresee a broad appeal to a wide array of scientific disciplines, from fundamental studies in ecology, biology, and engineering, to developing new bio-inspired platforms for studying oceans across the solar system.Metachronal propulsion is widespread in aquatic swarming organisms to achieve performance and maneuverability at intermediate Reynolds numbers. Studying only live organisms limits our understanding of the mechanisms driving these abilities. Thus, we present the design, manufacture, and validation of the  Independent manipulation of a particular trait or behavior of interest enables testing hypotheses central to exploring the underlying mechanisms driving maneuverability and performance. While several test platforms have shed light on important aspects of biological underwater propulsion5, they have also produced practical engineering solutions for unmanned underwater vehicles (UUV). Previous robotic solutions for engineering problems, including tunas6, scallop7, and dolphins8 demonstrate improved propulsive efficiency and optimal operation in a range of environments. However, because of experimental or technical challenges, most advances have been for relatively high Reynolds numbers (Re). Consequently, despite the tremendous diversity of species inhabiting the moderate realm, we still need a comprehensive understanding of aquatic propulsion and its derived engineering applications at low-to-intermediate Re.Nature-inspired robotics have become valuable tools in biology and ecology as they allow better-controlled comparative studies than traditional organismal methods9. Metachrony enables large-scale behaviors in krill, particularly the ability to perform diel vertical migrations (DVM) from the sea surface to oxygen minimum zones located up to 1 km at depth11. Efficient swimming is achieved by leveraging drag during properly tuned power-and-recovery strokes12. During the power stroke, the swimming appendages (pleopods) move opposite to the swimming direction while maximizing their surface area. This generates drag in each pleopod, producing thrust to propel the organism. Conversely, during the recovery stroke, the profile area decreases, thus reducing the drag on the appendages and creating a net thrust force sufficient to overcome the drag on the body. Previous works showed drag-based metachronal swimming is more effective than lift-based propulsion at intermediate Re for accelerating, braking, and turning because it generates significant thrust over short periods15.Metachronal swimming is a ubiquitous swimming mode among many of the most abundant invertebrate species inhabiting the transitional flow regime (Re < 1000), such as copepods and krill. This propulsive gait is characterized by the sequential beating of several closely spaced swimming appendages, phase-shifted in time, in a tail-to-head traveling wave17. Murphy et al. characterized the swimming kinematics of live krill and linked the success of the propulsion system to appendage morphology, stroke kinematics, and the resulting hydrodynamic effects16. Tip vortices forming on the pleopods of krill17 and mantis shrimp18 were shown to be central in the production of thrust20. Kim and Gharib extended these results using idealized pleopod shapes with simplified kinematics. They demonstrated that the area over the surface of the appendage enclosed by the tip vortex, specifically, modulates thrust production19. However, challenges in characterizing the near-field flow and measuring the forces generated during swimming have hindered efforts to establish the relationship between fluid dynamics and force distribution  in intermediate Re metachronal swimmers.Fluid flow experiments on live krill have established the foundations of our understanding of the effects of fluid-structure interactions on the far-field flow25. For instance, Ford et al. showed that at low Re, the individual jets produced by adjacent pleopods do not interact due to viscous dissipation, but at Re around 800, they form a near-steady jet22. The outcome of this difference is an increase in vertical and horizontal momentum that enables krill to generate thrust and lift forces required for locomotion and hovering. Phase lag also emerges as an important factor contributing to metachronal swimming efficiency, as it yields near-maximal efficiency and thrust, and achieves higher average body velocity when compared to synchronous motions26. Likewise, appendage spacing is a critical morphological factor linked to stroke kinematics. Previous works demonstrated that greater swimming performance is achieved when the ratio of appendage spacing to appendage length is less than one27. This was hypothesized to be the direct consequence of inter-pleopod fluid flow interactions enhancing the vortex strength and circulation during the power stroke18.Simplified robotic models and numerical simulations have complemented these efforts, shedding light on the role of varying Re, phase lag, and appendage spacing on the hydrodynamicsWhile the insights gathered through simplified systems are invaluable, we still need a unifying theory of drag-based metachronal swimming to explain the relationship between appendage kinematics, fluid flows, and force production. For instance, even the production of lift needed for hovering still needs to be explored. Adding complexity to better match the natural biological and hydrodynamic phenomena is necessary to elucidate the effects of morphological and kinematic characteristics on the near- and far-field flow. We should then adopt an integrative framework to comprehensively study the link between locomotor kinematics, form, and function.Pleobot to study the near-field flow of a metachronal pleopod for the first time and provide evidence for a previously unreported leading-edge suction effect that contributes to lift during the power stroke. The repeatability and modularity of the Pleobot enable various configurations and motion programs to investigate metachrony across Re, taxa, and morphological attributes. As such, we anticipate the Pleobot will broadly appeal to a wide array of scientific disciplines, such as ecology, biology, and engineering.Motivated by a robotics-inspired biology approach to address this problem, we present the first fully articulated, multi-link, metachronal robotic appendage reproducing the pleopod kinematics of krill. Our novel design achieves active control of both the proximal and distal appendages and the passive out-of-plane actuation of the two rami of the distal appendages through the interaction between the pleopod and the surrounding fluid. In addition, we implemented the main morphological features of krill pleopods through the assembly of 3D-printed modules. Using force and fluid flow measurements, we quantify the effects of particular flow features on thrust and lift production and disentangle their role during the power and recovery phases of a stroke. We use biological data to validate our model as a suitable test platform. In this study, we leverage the Pleobot are characterized by three main angles: (1) Pleobot as seen in Euphausia superba . The cycle-averaged E. superba .Our unique study shows that Pleobot. At the beginning of the power stroke, the endopodite contributes to thrust and lift through the formation of tip and bound vortices. Similarly, the exopodite also contributes to the generation of both forces. The creation of lift during steady forward swimming is necessary for krill because they are negatively buoyant and must create lift to maintain their position in the water column17. Here, the lift coefficient is positive during the first half of the power stroke and negative for the remainder of the cycle, which may be attributed to the rigidity of the robot. While shrimp induce spanwise bending of their distal appendages during the recovery stroke to reduce drag, the robot remains rigid throughout the cycle33. This highlights the importance of structural parameters and their inclusion in next versions of the Pleobot.In our study, the analysis of force and PIV data allows us to evaluate the contribution of specific flow features to the generation of lift and thrust in As the stroke progresses, the exopodite becomes more vertically oriented, and its contribution to lift decreases while its contribution to thrust increases Fig. A,B. ThisPleobot, not only as a platform to engineer solutions for ocean exploration in the intermediate Reynolds number regime but also as a framework to understand nature. Force measurements on a biologically-inspired appendage highlight the importance of understanding the vortex dynamics around the pleopod and reveal that both lift and thrust are generated simultaneously.Notably, we reveal the existence of a LEV along the exopodite during the power stroke. Force measurements show that lift is enhanced by the formation of this attached LEV on the exopodite during the power stroke. Furthermore, this vortex delays the loss in lift forces slightly to maintain positive lift throughout part of the power stroke. This lift-based mechanism likely supplements the effects of the drag-based mechanism stemming from the pleopod pushing on water. These results in the near field illustrate the importance of the Pleobot provides promising results and incentives for increasing the complexity of our robotic model and elucidating additional mechanisms contributing to efficiency in krill and other metachronal swimmers. Pleobot represents a tethered version of our robotic platform. Next versions will include morphological features in an untethered construct. Markedly, the presence of hair-like structures (setae) around the endopodite and exopodite, and the flexibility of the propulsors have the most potential for altering the flow field around the pleopods. Setae are defining features in metachronal organisms in the low-to-intermediate Re that increase the surface area in contact with water to enhance thrust35. Flexibility has been shown to be an important characteristic of propulsors in smoothing out thrust peaks to generate nearly constant thrust during the power stroke19. Pleobot provides a platform to evaluate the importance of these additional morphological features. Moreover, its modular design can be easily replicated and grouped in several units to investigate mechanisms such as constructive pleopod interactions . Its modular design allows for the quick prototyping of different appendage shapes and sizes. Furthermore, controlling both its proximal and distal segments facilitates studying a wide range of systems, from small copepods to krill and larger species like mantis shrimp and lobsters. The Pleobot can thus be employed to facilitate the comprehensive characterization of metachronal, drag-based propulsion and help establish a unifying theory for this locomotor mechanism.Finally, while organismal investigations form the core of our knowledge about metachronal swimmingEuphausia superba, as reported by Murphy et al.16, we use values for Pleobot. The Pleobot is dynamically scaled based on the Reynolds number. This dimensionless parameter is defined using the velocity and the length scale of the appendages and is approximately 600 for live krill:L is the length of the pleopod, n, and the length L as To reproduce the kinematics of The locomotor system is implemented by a multi-link mechanism using a transmission gearbox actuating both the proximal and distal segments . The exopodite and endopodite adduct during the recovery stroke.In forward-swimming krill, exopodite abduction occurs at the beginning of the power stroke and reaches a maximum of 77°Pleobot was set to match the mean quantified value of 37Appendage cupping forms the angle mentclass2pt{minim16. The beat frequency of the appendage is 37).Modular CAD designs were printed with polylactic acid (PLA) using a Prusa i3 MKS3+ 3D printer  for fast prototyping. Each pair of appendages is actuated by two servos , controlled by a microcontroller  programmed using Matlab  via two repeating sequence interpolated blocks, one for Pleobot were analyzed and compared to those reported for live krill16. One robotic appendage was tethered to a traverse beam and submerged in a glycerin-water mixture at room temperature  at 125 frames per second, a Nikon lens (Nikon AF-S VR Micro-NIKKOR 100 mm), and a continuous laser  with a cylindrical lens to create a laser sheet. The flow was seeded with 10 μm particles   model of the distal appendage by using a 6-axis force transducer  set up in a cubic tank of 30 cm by side  at 2000 fps and a resolution of 2048 Specimens (n = 4) were tethered to a vertical wire using cyanoacrylate applied to the dorsal surface of their carapace and hung in a filming vessel . Image pairs were analyzed with three passes of overlapping interrogation windows (75%) with decreasing size (96 Supplementary Figures.


To the Editor,I am pleased to present a Thematic Series “In vivo imaging dissecting inflammation and regeneration of living body” that delves into the intricacies of inflammation and regeneration through the lens of cutting-edge in vivo imaging techniques. The three papers compiled in this series offer a comprehensive exploration of inflammatory responses within various physiological contexts, shedding light on the dynamic interplay between cells in living organisms.The first paper, authored by Konishi Y and Terai K, provides a detailed examination of the role of inflammation in the development and progression of cancer . RecogniThe second paper, authored by Honda M et al. focuses on the critical role of intravital microscopy in unraveling the mysteries of immune responses within the gastrointestinal tract . The autThe third paper, a systematic review authored by Harada Y et al. addresses the localization and movement of regulatory T cells (Tregs) in the gastrointestinal tract . AcknowlIn conclusion, this Thematic Series offers a panoramic view of the in vivo imaging landscape, showcasing its pivotal role in dissecting inflammation and regeneration within living organisms. These papers contribute not only to the current body of knowledge but also pave the way for future advancements in our understanding of dynamic cellular interactions in health and disease.