Deep learning techniques have demonstrated significant capabilities across numerous applications, with deep neural networks (DNNs) showing promising results. However, training these networks efficiently, especially when determining the most suitable nonlinear activation functions, remains a significant challenge. While the ReLU activation function has been widely adopted, other hand-designed functions have been proposed. One such approach is the trainable activation functions. This paper introduces a novel neural network design, the SWAG. In this structure, instead of evolving, activation functions consistently form a polynomial basis. Each hidden layer in this architecture comprises k sub-layers that use polynomial activation functions adjusted by a factorial coefficient, followed by a Concatenate layer and a layer employing a linear activation function. Leveraging the Stone-Weierstrass approximation theorem, we demonstrate that utilizing a diverse set of polynomial activation functions allows neural networks to retain universal approximation capabilities. The SWAG algorithm’s architecture is then presented, where data normalization is emphasized, and a new optimized version of SWAG is proposed, which reduces the computational challenge of managing higher degrees of input. This optimization harnesses the Taylor series method by utilizing lower-degree terms to compute higher-degree terms efficiently. This paper thus contributes an innovative neural network architecture that optimizes polynomial activation functions, promising more efficient and robust deep learning applications.

Effective monitoring of infectious disease incidence remains a major challenge to public health. Difficulties in estimating the trends in disease incidence arise mainly from the time delay between case diagnosis and the reporting of cases to public health databases. However, predictive models usually assume that public data sets faithfully reflect the state of disease transmission. In this paper, we study the effect of delayed case reporting by comparing data reported by the Johns Hopkins Coronavirus Resource Center (CRC) with that of the raw clinical data collected from the San Antonio Metro Health District (SAMHD), San Antonio, Texas. An insight on the subtle effect that such reporting errors potentially have on predictive modeling is presented. We use an exponential distribution model for the regression analysis of the reporting delay. The proposed model for correcting reporting delays was applied to our recently developed SEYAR (Susceptible, Exposed, Symptomatic, Asymptomatic, Recovered) dynamical model for COVID-19 transmission dynamics. Employing data from SAMHD, we demonstrate that the forecasting ability of the SEYAR model is substantially improved when the rectified reporting obtained from our proposed model is utilized. The methods and findings demonstrated in this work have ample applicability in the forecasting of infectious disease outbreaks. Our findings suggest that failure to consider reporting delays in surveillance data can significantly alter forecasts.

This study explores how a few high school-aged youth, employed as Youth Teachers at an after-school fabrication laboratory (FabLab), talk to one another as they rehearse part of a lesson in preparation to teach elementary school-aged youth to program robot Finches. Rehearsals, which usually consist of running through lines, walking through direct instruction, and transitioning between speakers in front of other Youth Teachers, are a regular practice at Learn 2 Teach, Teach 2 Learn as youth prepare to teach at an outreach site the next day. Using interaction analysis of one typical rehearsal, we illuminate linguistic shifts as youth negotiate identities, meanings, and leadership. These movements help us understand how youth teachers navigate the multiplicities of their identities as Black and Brown young people positioned as teachers while learning to program.

In this study, College STEM Literacy Workers act as co-teachers in 9th and 10th grade classrooms alongside mathematics teachers as they both learn to integrate a 30-hour integrated computer science/mathematics curriculum. As part of the curriculum for educator learning, we adapted the Algebra Project’s “Model of Excellence” for culturally relevant-sustaining pedagogies. We used the framework to explore how teachers understand College STEM Literacy Workers’ contributions in the model as well as College STEM Literacy Workers’ own experiences in the classroom. We found that while teachers and College STEM Literacy Workers did not individually instantiate the model of excellence, they were able to achieve its constituent components between them. We explore differences between teachers and College STEM Literacy Workers and the ways in which they worked together. We end with implications for a new model of excellence.

Plasmodium cynomolgi causes zoonotic malarial infections in Southeast Asia and this parasite species is important as a model for Plasmodium vivax and Plasmodium ovale . Each of these species produces hypnozoites in the liver, which can cause relapsing infections in the blood. Here we present methods and data generated from iterative longitudinal systems biology infection experiments designed and performed by the Malaria Host-Pathogen Interaction Center (MaHPIC) to delve deeper into the biology, pathogenesis, and immune responses of P. cynomolgi in the Macaca mulatta host. Infections were initiated by sporozoite inoculation. Blood and bone marrow samples were collected at defined timepoints for biological and computational experiments and integrative analyses revolving around primary illness, relapse illness, and subsequent disease and immune response patterns. Parasitological, clinical, haematological, immune response, and -omic datasets (transcriptomics, proteomics, metabolomics, and lipidomics) including metadata and computational results have been deposited in public repositories. The scope and depth of these datasets are unprecedented in studies of malaria, and they are projected to be a F.A.I.R., reliable data resource for decades.

Plasmodium knowlesi poses a health threat throughout Southeast Asian communities and currently causes most cases of malaria in Malaysia. This zoonotic parasite species has been studied in Macaca mulatta (rhesus monkeys) as a model for severe malarial infections, chronicity, and antigenic variation. The phenomenon of Plasmodium antigenic variation was first recognized during rhesus monkey infections. Plasmodium-encoded variant proteins were first discovered in this species and found to be expressed at the surface of infected erythrocytes, and then named the Schizont-Infected Cell Agglutination (SICA) antigens. SICA expression was shown to be spleen dependent, as SICA expression is lost after P. knowlesi is passaged in splenectomized rhesus. Here we present data from longitudinal P. knowlesi infections in rhesus with the most comprehensive analysis to date of clinical parameters and infected red blood cell sequestration in the vasculature of tissues from 22 organs. Based on the histopathological analysis of 22 tissue types from 11 rhesus monkeys, we show a comparative distribution of parasitized erythrocytes and the degree of margination of the infected erythrocytes with the endothelium. Interestingly, there was a significantly higher burden of parasites in the gastrointestinal tissues, and extensive margination of the parasites along the endothelium, which may help explain gastrointestinal symptoms frequently reported by patients with P. knowlesi malarial infections. Moreover, this margination was not observed in splenectomized rhesus that were infected with parasites not expressing the SICA proteins. This work provides data that directly supports the view that a subpopulation of P. knowlesi parasites cytoadheres and sequesters, likely via SICA variant antigens acting as ligands. This process is akin to the cytoadhesive function of the related variant antigen proteins, namely Erythrocyte Membrane Protein-1, expressed by Plasmodium falciparum.

Coronavirus disease 2019 (COVID-19) is a novel human respiratory disease caused by the SARS-CoV-2 virus. Asymptomatic carriers of the COVID-19 virus display no clinical symptoms but are known to be contagious. Recent evidence reveals that this subpopulation, as well as persons with mild disease, are a major contributor in the propagation of the disease. The rapid spread of COVID-19 forced governments around the world to establish and enforce generalized risk mitigation strategies, from lockdowns to guidelines for social distancing, in an effort to minimize community transmission. This created an unprecedented epidemiological situation not properly characterized by existing mathematical models of isolation and quarantine. In this manuscript, we present a mathematical model for community transmission of COVID-19 taking into account asymptomatic carriers and varying degrees of risk mitigation. The main results consist of an exact calculation of the effective reproduction number ![Graphic][1]</img>, and a modeling framework that enables the quantification of the effect of risk mitigation and asymptomatism on community transmission. A computation of ![Graphic][2]</img> is provided using mean parameters. The point estimate of the basic reproduction number is ![Graphic][3]</img>. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement N/A ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data used in this article is available from public sources. <https://mathresearch.utsa.edu/wp/?p=58> [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif [3]: /embed/inline-graphic-3.gif

Coronavirus disease 2019 (COVID-19) is a novel human respiratory disease caused by the SARS-CoV-2 virus. Asymptomatic carriers of the virus display no clinical symptoms but are known to be contagious. Recent evidence reveals that this sub-population, as well as persons with mild and undocumented disease, represent a major contributor in the propagation of this disease. However, this method of transmission frequently escapes detection by public health surveillance systems. Because of this, the currently accepted estimates of the basic reproduction number (ℛ0) of the virus are inaccurate. Further, it is unlikely that a pathogen can blanket the planet in three months with an ℛ0 in the vicinity of 3, as reported (1–6). In this manuscript, we present a mathematical model taking into account asymptomatic and other undocumented carriers. Our results indicate that an initial value of the effective reproduction number could range from 5.5 to 26.5. Results from the first three weeks of the model exhibit exponential growth, which is in agreement with average case data collected from thirteen countries with universal health care and robust communicable disease surveillance systems; the average rate of growth in the number of reported cases is 23.3% per day during this period.

Coronavirus disease 2019 (COVID-19) is a novel human respiratory disease caused by the SARS-CoV-2 virus. Asymptomatic carriers of the COVID-19 virus display no clinical symptoms but are known to be contagious. Recent evidence reveals that this sub-population, as well as persons with mild disease, are a major contributor in the propagation of the disease. The fast spread of COVID-19 forced governments around the world to establish and enforce generalized lockdowns, geared towards minimizing community transmission. This created an unprecedented epidemiological situation not properly characterized by existing mathematical models of isolation and quarantine. In this manuscript, we present a mathematical model for community transmission of COVID-19 taking into account asymptomatic carriers during the lockdown period. The main results consist of an exact calculation of the effective reproduction number , and a modeling framework that enables the quantification of generalized lockdowns on community transmission. A computation of is provided, assuming mean parameters. The first three weeks of the model exhibit exponential growth, which is in agreement with average case data collected from thirteen countries with universal health care and robust communicable disease surveillance systems. The point estimate of the basic reproduction number is .

Drug repurposing has the advantage of identifying potential treatments on a shortened timescale. In response to the pandemic spread of SARS-CoV-2, we took advantage of a high-content screen of 3,713 compounds at different stages of clinical development to identify FDA-approved compounds that reduce mucin-1 (MUC1) protein abundance. Elevated MUC1 levels predict the development of acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) and correlate with poor clinical outcomes. Our screen identifies fostamatinib (R788), an inhibitor of spleen tyrosine kinase (SYK) approved for the treatment of chronic immune thrombocytopenia, as a repurposing candidate for the treatment of ALI. In vivo, fostamatinib reduces MUC1 abundance in lung epithelial cells in a mouse model of ALI. In vitro, SYK inhibition by the active metabolite R406 promotes MUC1 removal from the cell surface. Our work suggests fostamatinib as a repurposing drug candidate for ALI.

Asymptomatic individuals in the context of malarial disease refers to subjects who carry a parasite load but do not show clinical symptoms. A correct understanding of the influence of asymptomatic individuals on transmission dynamics will provide a comprehensive description of the complex interplay between the definitive host (female \textit{Anopheles} mosquito), intermediate host (human) and agent (\textit{Plasmodium} parasite). The goal of this article is to conduct a rigorous mathematical analysis of a new compartmentalized malaria model accounting for asymptomatic human hosts for the purpose of calculating the basic reproductive number ($\mathcal{R}_0$), and determining the bifurcations that might occur at the onset of disease free equilibrium. A point of departure of this model from others appearing in literature is that the asymptomatic compartment is decomposed into two mutually disjoint sub-compartments by making use of the naturally acquired immunity (NAI) of the population under consideration. After deriving the model, a qualitative analysis is carried out to classify the stability of the equilibria of the system. Our results show that the dynamical system is locally asymptotically stable provided that $\mathcal{R}_0<1$. However this stability is not global, owning to the occurrence of a sub-critical bifurcation in which additional non-trivial sub-threshold equilibrium solutions appear in response to a specified parameter being perturbed. To ensure that the model does not undergo a backward bifurcation, we demand that an auxiliary parameter denoted $\Lambda<1$ in addition to the threshold constraint $\mathcal{R}_0<1$. The authors hope that this qualitative analysis will fill in the gaps of what is currently known about asymptomatic malaria and aid in designing strategies that assist the further development of malaria control and eradication efforts.

To address the challenge of multiscale dynamics of infectious diseases, the Mathematical Biosciences Institute organized a workshop at The Ohio State University to bring together scientists from a variety of disciplines to share expertise gained through looking at infectious diseases across different scales. The researchers at the workshop, held in April 2018, were specifically looking at three model systems: foot-and-mouth disease, vector-borne diseases and enteric diseases. Although every multiscale model must be necessarily derived from a multiscale system, not every multiscale system has to lead to multiscale models. These three model systems seem to have produced a variety of both multiscale and integrated single-scale mechanistic models that have developed their own strengths and particular challenges. Here, we present papers from some of the workshop participants to show the breadth of the field.

As part of the type I IFN signaling, the 2’-5’- oligoadenylate synthetase (OAS) proteins have been involved in the progression of several non-viral diseases. Notably, OAS has been correlated with immune-modulatory functions that promote chronic inflammatory conditions, autoimmune disorders, cancer, and infectious diseases. In spite of this, OAS enzymes have been ignored as drug targets, and to date, there are no reports of compounds that can inhibit their activity. In this study, we have used homology modeling and virtual high-throughput screening to identify potential inhibitors of the human proteins OAS1, OAS2, and OAS3. Altogether, we have found 37 molecules that could exert a competitive inhibition in the ATP binding sites of OAS proteins, independently of the activation state of the enzyme. This latter characteristic, which might be crucial for a versatile inhibitor, was observed in compounds interacting with the residues Asp75, Asp77, Gln229, and Tyr230 in OAS1, and their equivalents in OAS2 and OAS3. Although there was little correlation between specific chemical fragments and particular interactions, intermolecular contacts with OAS catalytic triad and other critical amino acids were mainly promoted by heterocycles with π electrons and hydrogen bond acceptors. In conclusion, this study provides a potential set of OAS inhibitors as well as valuable information for their design, development, and optimization.

Drug repurposing is the only method capable of delivering treatments on the shortened time-scale required for patients afflicted with lung disease arising from SARS-CoV-2 infection. Mucin-1 (MUC1), a membrane-bound molecule expressed on the apical surfaces of most mucosal epithelial cells, is a biochemical marker whose elevated levels predict the development of acute lung injury (ALI) and respiratory distress syndrome (ARDS), and correlate with poor clinical outcomes. In response to the pandemic spread of SARS-CoV-2, we took advantage of a high content screen of 3,713 compounds at different stages of clinical development to identify FDA-approved compounds that reduce MUC1 protein abundance. Our screen identified Fostamatinib (R788), an inhibitor of spleen tyrosine kinase (SYK) approved for the treatment of chronic immune thrombocytopenia, as a repurposing candidate for the treatment of ALI. In vivo , Fostamatinib reduced MUC1 abundance in lung epithelial cells in a mouse model of ALI. In vitro , SYK inhibition by Fostamatinib promoted MUC1 removal from the cell surface. Our work reveals Fostamatinib as a repurposing drug candidate for ALI and provides the rationale for rapidly standing up clinical trials to test Fostamatinib efficacy in patients with COVID-19 lung injury.

Projects in the life sciences continue to increase in complexity as they scale to answer deeper and more diverse questions. They employ technologies that generate increasingly large ‘omic’ datasets and research teams regularly include experts ranging from animal care technicians, veterinarians, human health clinicians, geneticists, immunologists, and biochemists to computer scientists, mathematical modelers, and data scientists, often located at different institutions. Providing the cyberinfrastructure support framework (IT, data management, communication, documentation, and aspects of project management related to these areas) for these projects requires a diverse set of technical tools and soft skills. These skills must be able to meet both the broad needs of data generators and consumers within the project and the needs of the larger scientific community. Here we describe recommendations for cyberinfrastructure support teams responsible for systems biology research programs. Recommendations are based on lessons learned while establishing and leading a complex, transdisciplinary, host-pathogen malaria systems biology consortium involving many institutions, a variety of disciplines, animal infectious disease models, and clinical studies. While some technical suggestions are included, the primary foci are situational and sociological challenges and tips for handling them.

Muscular dystrophy describes generalized progressive muscular weakness due to the wasting of muscle fibers. The progression of the disease is affected by known immunological and mechanical factors, and possibly other unknown mechanisms. This article introduces a new mathematical model, the FRiND model, to further elucidate these known immunological actions. We will perform stability and sensitivity analyses on this model. The models time course results will be verified by biological studies in the literature. This model could be the foundation for further understanding of immunological muscle repair.

Intracellular accumulation of misfolded proteins causes toxic proteinopathies, diseases without targeted therapies. Mucin 1 kidney disease (MKD) results from a frameshift mutation in the MUC1 gene (MUC1-fs). Here, we show that MKD is a toxic proteinopathy. Intracellular MUC1-fs accumulation activated the ATF6 unfolded protein response (UPR) branch. We identified BRD4780, a small molecule that clears MUC1-fs from patient cells, from kidneys of knockin mice and from patient kidney organoids. MUC1-fs is trapped in TMED9 cargo receptor-containing vesicles of the early secretory pathway. BRD4780 binds TMED9, releases MUC1-fs, and re-routes it for lysosomal degradation, an effect phenocopied by TMED9 deletion. Our findings reveal BRD4780 as a promising lead for the treatment of MKD and other toxic proteinopathies. Generally, we elucidate a novel mechanism for the entrapment of misfolded proteins by cargo receptors and a strategy for their release and anterograde trafficking to the lysosome.

Plasmodium relapses are attributed to the activation of dormant liver-stage parasites and are responsible for a significant number of recurring malaria blood-stage infections. While characteristic of human infections caused by P. vivax and P. ovale, their relative contribution to malaria disease burden and transmission remains poorly understood. This is largely because it is difficult to identify 'bona fide' relapse infections due to ongoing transmission in most endemic areas. Here, we use the P. cynomolgi-rhesus macaque model of relapsing malaria to demonstrate that clinical immunity can form after a single sporozoite-initiated blood-stage infection and prevent illness during relapses and homologous reinfections. By integrating data from whole blood RNA-sequencing, flow cytometry, P. cynomolgi-specific ELISAs, and opsonic phagocytosis assays, we demonstrate that this immunity is associated with a rapid recall response by memory B cells that expand and produce anti-parasite IgG1 that can mediate parasite clearance of relapsing parasites. The reduction in parasitemia during relapses was mirrored by a reduction in the total number of circulating gametocytes, but importantly, the cumulative proportion of gametocytes increased during relapses. Overall, this study reveals that P. cynomolgi relapse infections can be clinically silent in macaques due to rapid memory B cell responses that help to clear asexual-stage parasites but still carry gametocytes.

Muscular dystrophy (MD) describes generalized progressive muscular weakness due to the wasting of muscle fibers. The progression of the disease is affected by known immunological and mechanical factors, and possibly other unknown mechanisms. These dynamics have begun to be elucidated in the last two decades. This article reviews mathematical models of MD that characterize molecular and cellular components implicated in MD progression. A biological background for these processes is also presented. Molecular effectors that contribute to MD include mitochondrial bioenergetics and genetic factors; both drive cellular metabolism, communication and signaling. These molecular events leave cells vulnerable to mechanical stress which can activate an immunological cascade that weakens cells and surrounding tissues. This review article lays the foundation for a systems biology approach to study MD progression.