Domain Of Factor Research Articles

Z [ − 5 ] : Halfway to Unique Factorization

It is well known that factorization is not unique in Z [ − 5 ] . We give a short, self-contained proof that Z [ − 5 ] is “halfway” toward being a unique factorization domain: For every nonzero, nonunit α ∈ Z [ − 5 ] , any two factorizations of α into irreducibles involve the same number of factors.

Measurement properties of the Regular Physical Exercise Adherence Scale (REPEAS) in individuals with chronic pain

ObjectiveTo examine the measurement properties of the Regular Physical Exercise Adherence Scale (REPEAS) in Brazilians with chronic pain.MethodsCross-sectional and longitudinal design (washout period for reliability). The study was conducted in two Brazilian states, Maranhão and São Paulo, and included Brazilian adults, irregular exercisers, former exercisers or non-exercise practitioners, aged 18 to 59 years and with chronic pain. The instruments used in this study were: the REPEAS, the Numerical Pain Rating Scale (NPRS), the Baecke Habitual Physical Activity Questionnaire (BHPAQ), the Pain Self-Efficacy Questionnaire (PSEQ), and the Roland-Morris Disability Questionnaire for general pain (RMDQ-g). The evaluation focused on structural validity, construct validity, reliability (with standard error of measurement and minimum detectable change), internal consistency, and floor and ceiling effects.ResultsThe two-dimensional structure was tested through confirmatory factor analysis, which resulted in adequate fit indeces: chi-square values/degrees of freedom = 1.541, Tucker-Lewis Index = 0.966, comparative fit index = 0.974, root mean square error of approximation = 0.074, and standardized root mean square residual = 0.068. Additionally, satisfactory factor loadings (> 0.40) were obtained. Test-retest reliability and internal consistency were adequate for the environmental factors domain (intraclass correlation coefficient [ICC] = 0.79, Cronbach’s alpha = 0.88) and the personal factors domain (ICC = 0.97, Cronbach’s alpha = 0.93). In hypothesis testing for construct validity, we observed a significant correlation with magnitude below 0.30 of the environmental factors domain of the REPEAS with RMDQ-g, PSEQ and sport domain of the BHPAQ. For the personal factors domain, we observed a significant correlation with a magnitude of 0.30 to 0.50 with RMDQ-g, PSEQ, and sport domain of the BHPAQ, and below 0.30 with leisure domain of the BHPAQ. No floor or ceiling effects were found for the REPEAS domains.ConclusionThe REPEAS is a valid instrument with a two-dimensional internal structure consisting of 12 items. It has a reliable construct and is suitable for use in the clinical and epidemiological context for adults with chronic pain in Brazil.

Configuration entropy and potential energy landscape in thermodynamics and dynamics of supercooled liquids

In thermodynamics of supercooled liquids, sub-molecular units, referred to as “beads,” are used. It has been reported that all thermodynamic functions as well as the parameters in the empirical distribution functions of the potential energy landscape approach appear to be explicit functions of the number of beads in molecules. This finding opens the possibility of measuring the number of beads from each of these functions and estimating the configuration and vibration components in their formation. An enthalpy factor has been introduced and found that within the temperature domain of the invariable enthalpy factor, the molecules partition to a constant number of beads. A correlation has been observed between temperature dependence of the potential barriers, restricting cooperative rearrangement of beads and heat capacity of liquids. Relations connecting the landscape approach with the number of beads in the molecules have been estimated. The molecular equation for configuration entropy obtained can provide guidance for the development of new materials with a desirable configuration entropy. A method for predicting thermodynamic and statistical quantities of supercooled liquids from kinetics is also suggested.

A novel transcription factor Sdr1 involving sulfur depletion response in fission yeast.

In the fission yeast Schizosaccharomyces pombe, the response to sulfur depletion has been less studied compared to the response to nitrogen depletion. Our study reveals that the fission yeast gene, SPCC417.09c, plays a significant role in the sulfur depletion response. This gene encodes a protein with a Zn2Cys6 fungal-type DNA-binding domain and a transcription factor domain, and we have named it sdr1+ (sulfur depletion response 1). Interestingly, while sulfur depletion typically induces autophagy akin to nitrogen depletion, we found that autophagy was not induced under sulfur depletion in the absence of sdr1+. This suggests that sdr1+ is necessary for the induction of autophagy under conditions of sulfur depletion. Although sdr1+ is not essential for the growth of fission yeast, its overexpression, driven by the nmt1 promoter, inhibits growth. This implies that Sdr1 may possess cell growth-inhibitory capabilities. In addition, our analysis of Δsdr1 cells revealed that sdr1+ also plays a role in regulating the expression of genes associated with the phosphate depletion response. In conclusion, our study introduces Sdr1 as a novel transcription factor that contributes to an appropriate cellular nutrient starvation response. It does so by inhibiting inappropriate cell growth and inducing autophagy in response to sulfur depletion.

Lengths of factorizations of integer-valued polynomials on Krull domains with prime elements

Let D be a Krull domain admitting a prime element with finite residue field and let K be its quotient field. We show that for all positive integers k and 1<n1≤⋯≤nk\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1 < n_1 \\le \\cdots \\le n_k$$\\end{document}, there exists an integer-valued polynomial on D, that is, an element of Int(D)={f∈K[X]∣f(D)⊆D}\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\ extrm{Int}\\,}}(D) = \\{ f \\in K[X] \\mid f(D) \\subseteq D \\}$$\\end{document}, which has precisely k essentially different factorizations into irreducible elements of Int(D)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\ extrm{Int}\\,}}(D)$$\\end{document} whose lengths are exactly n1,…,nk\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n_1, \\ldots , n_k$$\\end{document}. Using this, we characterize lengths of factorizations when D is a unique factorization domain and therefore also in case D is a discrete valuation domain. This solves an open problem proposed by Cahen, Fontana, Frisch, and Glaz.

Fear and perceived risk of cyber fraud victimization among Chinese University students

Cyber fraud has surfaced as a serious social problem in China, resulting in billions of Chinese yuan worth of financial losses in recent years. The high prevalence and large quantity financial losses from cyber fraud have sparked widespread public concerns about online safety. Based on survey data from over 1000 university students in China, this study explores the prevalence of fear and perceived risk of cyber fraud and its correlates among university students. The results showed that although only 10% of the respondents believed they would likely experience cyber fraud victimization in the next 12 months, approximately 50% reported feeling fearful of cyber fraud in the past 3 months. The logistic regression results show that both fear and perceived risk are influenced by different domains of risk factors, with higher self-control and deviant online behaviors explaining greater odds of fear. In contrast, the perceived risk of crime is linked to vicarious victimization experiences, perceived crime seriousness, and satisfaction with the police. Implications for future research and policy are discussed.

Psychometric properties of a questionnaire on medical students’ satisfaction with a community health program

Changes in the learning modes during the recent COVID-19 pandemic has provided a need to construct a feedback questionnaire to measure medical students’ satisfaction with community-based health programs. A total of 551 forms which has a 5-point Likert scale were submitted by the medical students upon concluding the public health program in 2018-2021 (which includes face-to-face and virtual learning). Reliability coefficients were estimated for each factor (domain) with values &amp;gt;0.70. Exploratory factor analysis (EFA) was used to assess the validity of the instrument, using principal components analysis with varimax rotation. EFA identified two factors; the first included 6 items (67.031%) of the total variance, the second explained 4 items (10.114%) and together explained 77.15%. root mean squared error of approximation (RMSEA): 0.074; normed fit index (NFI): 0.975; comparative fit index (CFI): 0.981; incremental fit index (IFI): 0.981; goodness of fit index (GFI): 0.956; Tucker Lewis index (TLI): 0.972. The overall students’ satisfaction had a Cronbach’s alpha test of 0.945 with a 95% confidence interval (0.938-0.952), and a reliability using Guttman Split-Half for overall was 0.894. Students’ satisfaction questionnaire with the public health program has a good reliability and validity in contrasting educational situations and it is worthwhile to include as part of the quality improvement.

Why do Service Designers Struggle to Address Power Dynamics?

To support transformation in service systems, scholars argue for greater attention to power dynamics in service design (SD). While current literature stresses the need for individual service designers to be reflexive about power, it neglects the context within which these designers predominantly work. We argue that such an individual focus overlooks key contextual factors that hinder service designers from addressing power dynamics. This narrowed view of the challenge unintentionally positions service designers as scapegoats, while their inability to address power dynamics effectively persists. In response, we draw on ecological theories in psychology, which offer insights into how individuals’ behaviors are interconnected with factors in their context. We introduce a framework for understanding these interconnections, detailing how we used it to identify and analyze domains of contextual factors that inhibit service designers to address power dynamics in practice. By proposing a systemic framework, identifying related contextual factors, and evaluating applicability with service designers, this study lays the groundwork for structural shifts to address power dynamics in service design more effectively.

MEF2C Directly Interacts with Pre-miRNAs and Distinct RNPs to Post-Transcriptionally Regulate miR-23a-miR-27a-miR-24-2 microRNA Cluster Member Expression.

Transcriptional regulation constitutes a key step in gene expression regulation. Myocyte enhancer factor 2C (MEF2C) is a transcription factor of the MADS box family involved in the early development of several cell types, including muscle cells. Over the last decade, a novel layer of complexity modulating gene regulation has emerged as non-coding RNAs have been identified, impacting both transcriptional and post-transcriptional regulation. microRNAs represent the most studied and abundantly expressed subtype of small non-coding RNAs, and their functional roles have been widely documented. On the other hand, our knowledge of the transcriptional and post-transcriptional regulatory mechanisms that drive microRNA expression is still incipient. We recently demonstrated that MEF2C is able to transactivate the long, but not short, regulatory element upstream of the miR-23a-miR-27a-miR-24-2 transcriptional start site. However, MEF2C over-expression and silencing, respectively, displayed distinct effects on each of the miR-23a-miR-27a-miR-24-2 mature cluster members without affecting pri-miRNA expression levels, thus supporting additional MEF2C-driven regulatory mechanisms. Within this study, we demonstrated a complex post-transcriptional regulatory mechanism directed by MEF2C in the regulation of miR-23a-miR-27a-miR-24-2 cluster members, distinctly involving different domains of the MEF2C transcription factor and the physical interaction with pre-miRNAs and Ksrp, HnRNPa3 and Ddx17 transcripts.

Data-driven identification of distinct pain drawing patterns and their association with clinical and psychological factors: a study of 21,123 patients with spinal pain.

The variability in pain drawing styles and analysis methods has raised concerns about the reliability of pain drawings as a screening tool for nonpain symptoms. In this study, a data-driven approach to pain drawing analysis has been used to enhance the reliability. The aim was to identify distinct clusters of pain patterns by using latent class analysis (LCA) on 46 predefined anatomical areas of a freehand digital pain drawing. Clusters were described in the clinical domains of activity limitation, pain intensity, and psychological factors. A total of 21,123 individuals were included from 2 subgroups by primary pain complaint (low back pain (LBP) [n = 15,465]) or midback/neck pain (MBPNP) [n = 5658]). Five clusters were identified for the LBP subgroup: LBP and radiating pain (19.9%), radiating pain (25.8%), local LBP (24.8%), LBP and whole leg pain (18.7%), and widespread pain (10.8%). Four clusters were identified for the MBPNP subgroup: MBPNP bilateral posterior (19.9%), MBPNP unilateral posterior + anterior (23.6%), MBPNP unilateral posterior (45.4%), and widespread pain (11.1%). The clusters derived by LCA corresponded to common, specific, and recognizable clinical presentations. Statistically significant differences were found between these clusters in every self-reported health domain. Similarly, for both LBP and MBPNP, pain drawings involving more extensive pain areas were associated with higher activity limitation, more intense pain, and more psychological distress. This study presents a versatile data-driven approach for analyzing pain drawings to assist in managing spinal pain.

Development and initial validation of a disease-specific instrument to measure health-related quality of life in hypersensitivity pneumonitis.

Disease-specific health-related quality of life (HRQOL) instruments enable us to capture domains that are most relevant to specific patient populations and are useful when a more individualised approach to patient assessment is desired. In this study, we assessed the validity and reliability of the first instrument specifically developed to measure HRQOL in hypersensitivity pneumonitis (HP). A 39-item HP-HRQOL instrument and several anchors were collected from a cohort of patients with HP. Exploratory factor analysis and item reduction were utilised to construct a shortened version of the instrument. Several validity and reliability analyses were conducted on this version of the HP-HRQOL. 59 patients with HP completed the study. The revised HP-HRQOL instrument comprises 15 items composing two factors (domains): 1) impacts on daily life; and 2) mental wellbeing. Internal consistency reliability was strong for Factor 1 (Cronbach's α=0.94, 95% CI 0.92-0.96) and Factor 2 (Cronbach's α=0.89, 95% CI 0.85-0.94). Test-retest reliability was strong (ICC 0.94, 95% CI 0.89-0.97). The HP-HRQOL strongly correlated with other validated patient-reported outcome measures and moderately correlated with % predicted forced vital capacity. The HP-HRQOL distinguished between those with different severities of HP as determined by lung function and supplemental oxygen use. The HP-HRQOL, the first patient-reported outcome instrument specific to adults with HP, possesses strong validity and reliability characteristics for measuring disease-specific HRQOL and distinguishes among patients with different severities of disease.

Intermolecular Interactions between Cysteine and Aromatic Amino Acids with a Phenyl Moiety in the DNA-Binding Domain of Heat Shock Factor 1 Regulate Thermal Stress-Induced Trimerization.

In this study, we investigated the trimerization mechanism and structure of heat shock factor 1 (HSF1) using western blotting, tryptophan (Trp) fluorescence spectroscopy, and molecular modeling. First, we examined the DNA-binding domains of human (Homo sapiens), goldfish (Carassius auratus), and walleye pollock (Gadus chalcogrammus) HSF1s by mutating key residues (36 and 103) that are thought to directly affect trimer formation. Human, goldfish, and walleye pollock HSF1s contain cysteine at residue 36 but cysteine (C), tyrosine (Y), and phenylalanine (F), respectively, at residue 103. The optimal trimerization temperatures for the wild-type HSF1s of each species were found to be 42, 37, and 20 °C, respectively. Interestingly, a mutation experiment revealed that trimerization occurred at 42 °C when residue 103 was cysteine, at 37 °C when it was tyrosine, and at 20 °C when it was phenylalanine, regardless of the species. In addition, it was confirmed that when residue 103 of the three species was mutated to alanine, trimerization did not occur. This suggests that in addition to trimerization via disulfide bond formation between the cysteine residues in human HSF1, trimerization can also occur via the formation of a different type of bond between cysteine and aromatic ring residues such as tyrosine and phenylalanine. We also confirmed that at least one cysteine is required for the trimerization of HSF1s, regardless of its position (residue 36 or 103). Additionally, it was shown that the trimer formation temperature is related to growth and survival in fish.

A Conservative Mutant Version of the Mrr1 Transcription Factor Correlates with Reduced Sensitivity to Fludioxonil in Botrytis cinerea.

Fludioxonil is a highly effective phenylpyrrole fungicide for controlling Botrytis cinerea. Although the field efficacy of fludioxonil remains high, Botrytis cinerea isolates with reduced sensitivity have been reported globally. The molecular target of fludioxonil still remains unknown; however, a mechanism of reduced sensitivity to fludioxonil underlies the overexpression of the ATP binding cassette (ABC) transporter AtrB in a dependent pathway of the Mrr1 transcription factor. Fludioxonil is a key player in controlling B. cinerea infection in table grapes in Chile. However, some isolates with a reduced sensitivity to fludioxonil were detected. This study observed endogenous atrB overexpression in Chilean isolates with reduced sensitivity to fludioxonil (n = 22) compared to the sensitive isolates (n = 10). All isolates increased the expression of atrB in a growth medium supplemented with fludioxonil (0.05 μg/mL). However, sensitive isolates showed lower atrB expression than those with reduced fludioxonil sensitivity. Remarkably, a mutant version of the transcription factor Mrr1 carrying 21 amino acid modifications was identified in all isolates with reduced sensitivity to fludioxonil. These changes alter the protein's transcription factor domain and the C-terminal portion of the protein but not the Zn (2)-C6 fungal-type DNA-binding domain. These results suggest a direct relationship between the conserved and divergent mutant version of mrr1 and sensitivity to fludioxonil. This study provides a new target for developing molecular diagnostic strategies to monitor B. cinerea's sensitivity to fludioxonil in the field.

The regulatory network of the White Collar complex during early mushroom development in Schizophyllum commune

Blue light is an important signal for fungal development. In the mushroom-forming basidiomycete Schizophyllum commune, blue light is detected by the White Collar complex, which consists of WC-1 and WC-2. Most of our knowledge on this complex is derived from the ascomycete Neurospora crassa, where both WC-1 and WC-2 contain GATA zinc-finger transcription factor domains. In basidiomycetes, WC-1 is truncated and does not contain a transcription factor domain, but both WC-1 and WC-2 are still important for development. We show that dimerization of WC-1 and WC-2 happens independent of light in S. commune, but that induction by light is required for promoter binding by the White Collar complex. Furthermore, the White Collar complex is a promoter of transcription, but binding of the complex alone is not always sufficient to initiate transcription. For its function, the White Collar complex associates directly with the promoters of structural genes involved in mushroom development, like hydrophobins, but also promotes the expression of other transcription factors that play a role in mushroom development.

Rethink data-driven human behavior prediction: A Psychology-powered Explainable Neural Network

Human behavior prediction is a rapidly evolving field with numerous applications in various domains. The rapid development of machine learning facilitates research on data-driven behavior prediction. At the same time, in human factors domains, researchers still focus on modeling psychological decision-making processes from the psychological perspectives of view based on subjective data. However, it is difficult to obtain one’s psychological states in real-world scenarios, given that the collection of subjective data in real-world settings is difficult and intrusive. Thus, psychology theories can hardly be applied to behavior prediction directly. To bridge the gap between data-driven behavior prediction and psychological decision-making models, we proposed a novel Psychology-powered Explainable Neural network (PEN). In PEN, for the first time, the psychological factors of individuals (i.e., human-side features such as attitudes to technologies) are modeled and recovered explicitly based on one’s historical behaviors in certain scenarios. The multi-task optimization ensures each component in PEN to be optimized with proper gradient. Assessment results over three evaluation protocols demonstrate that, based on the historical behaviors of an individual only, the PEN can outperform the best existing behavior prediction models by 1.87% and 4.68% on two datasets when a 1:9 training–testing data partition was adopted. The framework in PEN has provided a new solution for explainable human behavior prediction when psychological information is not directly available.

Development of a brief bedside tool to screen women sexual assault survivors for risk of persistent posttraumatic stress six months after sexual assault

This study aims to develop and validate a brief bedside tool to screen women survivors presenting for emergency care following sexual assault for risk of persistent elevated posttraumatic stress symptoms (PTSS) six months after assault. Participants were 547 cisgender women sexual assault survivors who presented to one of 13 sexual assault nurse examiner (SANE) programs for medical care within 72 h of a sexual assault and completed surveys one week and six months after the assault. Data on 222 potential predictors from the SANE visit and the week one survey spanning seven broadly-defined risk factor domains were candidates for inclusion in the screening tool. Elevated PTSS six months after assault were defined as PCL-5 > 38. LASSO logistic regression was applied to 20 randomly selected bootstrapped samples to evaluate variable importance. Logistic regression models comprised of the top 10, 20, and 30 candidate predictors were tested in 10 cross-validation samples drawn from 80% of the sample. The resulting instrument was validated in the remaining 20% of the sample. AUC of the finalized eight-item prediction tool was 0.77 and the Brier Score was 0.19. A raw score of 41 on the screener corresponds to a 70% risk of elevated PTSS at 6 months. Similar performance was observed for elevated PTSS at one year. This brief, eight-item risk stratification tool consists of easy-to-collect information and, if validated, may be useful for clinical trial enrichment and/or patient screening.

Expectant parents with intellectual disabilities in child protection: Risk factors for child safety.

Newborns of parents with intellectual disabilities face higher risks in their environment for child unsafety, despite parents' good intentions. To help parents prevent unsafe circumstances, a good understanding of the risk factors faced by these parents is needed. This casefile study examined (1) which risk factors were present for expectant parents with intellectual disabilities in child protection, (2) which domains of risk factors, and (3) whether a cumulation of risk factors was related to child safety. Expectant parents with intellectual disabilities in child protection before the child was born often experienced a cumulation of risk factors. Child, family, and care factors best predicted child safety. A cumulation of risk factors over multiple life domains increased the risks for child unsafety. To reduce the risks of unsafe parenting conditions for newborns, preventive interventions for expectant parents with intellectual disabilities should address their needs from an ecological perspective.

Imbalanced media portrayal: a content analysis of precision medicine in US news of 2015–2021

IntroductionEngaging diverse participants is vital to precision medicine (PM) but has been limited by low knowledge and concerns about various issues related to PM research. News media is an important channel of information that can shape public understanding and perception of PM. However, how PM is represented in news media has not been sufficiently understood.MethodsThe study used quantitative content analysis to evaluate the portrayal of PM in US print news media between 2015 and 2021 (N = 198). Three domains of factors related to PM portrayed in news articles were coded: (1) characteristics of PM, (2) target diseases of PM and their related characteristics, and (3) non-scientific news frames.ResultsThere was considerable news coverage of the treatment benefits of PM, especially for cancer. Potential risks or concerns, non-cancer diseases, and non-treatment issues that could be important to diverse populations were less covered. News articles frequently cited scientists, patients, and government officials with different focuses on PM.DiscussionThe study highlighted the need for accurate and complete information about PM in news media for diverse participants. News media should actively explore social, ethical, and legal issues to support the engagement of diverse populations.

Methods for Minimizing the Savage Function with Various Constraints

Introduction. When making decisions under uncertainty, Savage's criterion is sometimes used, or the criterion of minimizing regrets [1]. Usually, in the literature, this decision situation is described in matrix language. In other words, both the number of decision alternatives and the number of states of nature are finite. Of particular interest are situations where the admissible domain of decision variants is a convex set, and the regrets with respect to each state of nature are expressed by convex functions. In this paper, we propose numerical methods for minimizing Savage's regret function, constructed based on the subgradient projection method with automatic step size adjustment [2, 3]. The convergence of these methods is demonstrated. Goal. In the article, the Savage function is defined as a function that expresses the maximum regret value, assumed to be a convex function with respect to the decision factors. This function measures the effectiveness of each decision relative to the set of states of nature. It is important to note that computing the values of these functions is complex because of the need to know the optimal solution for each state of nature. This difficulty is successfully overcome in the process of solving the problem of minimizing functions on convex sets, thanks to parallel solutions of m "internal" algorithms based on the number of states of nature, and one external algorithm, aimed at minimizing the Savage function. Each of the m+1 algorithms represent modifications of the subgradient projection method with a programmable way of adjusting the step size. Depending on the complexity of the constraints and the required precision, three theorems have been proven, confirming the convergence of the investigated methods. Results obtained. Constructive numerical algorithms have been developed for determining optimal decision alternatives under uncertainty, when the number of states of nature is finite, the admissible domain of control factors is convex and compact, and the Savage regret function serves as a decision criterion. The convergence of the corresponding algorithms to the set of optimal solutions has been proven, without knowing the exact values of the Savage function. Instead, estimates obtained from parallel runs of algorithms were used, aimed at determining optimal solutions for each state of nature. Conclusions. Uncertainty poses significant difficulties in designing and making decisions. Any decision made under uncertainty represents a certain risk or a certain regret. In cases where the number of states of nature is finite, the decision domain is convex, the target function with respect to each state of nature is convex, and the Savage regret function is adopted as the decision criterion, the decision-making problem can be successfully solved using numerical algorithms based on the generalized gradient method. The implementation of the algorithm is relatively simple, and the fields of application can be very diverse. Keywords: uncertainty, decisions, Savage function, optimization.

Identification of Autoantigen Markers for SARS CoV-2 Infection with Machine Learning-based Feature Selection: An Insight into COVID Symptoms

Background and Introduction:: Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infection has been shown to trigger autoimmunity, and the phenomenon leads to several chronic human diseases such as Type-1 diabetes, Crohn’s disease, vasculitis, Guillian-Barrė syndrome, etc. The mechanism underlying SARS CoV-2-induced autoimmune response is unknown and is an active area of interest for the researchers. Objective:: The primary objective of this study is to identify the autoantigen markers for the classification of SARS CoV-2 (COVID-19 positive and negative samples) that trigger an immune response leading to autoimmunity using a machine learning approach that provides information to obtain a more accurate diagnosis for COVID-induced diseases. Materials and Methods:: Our study reports the transcriptomic profile of the COVID patient's whole blood samples collected from 0 to 35th day of acute infection as described in the GSE215865 dataset. The binary classification algorithm from the sci-kit learn python library, namely logistic regression and random forest with 10-fold cross-validation, was applied to the processed data, followed by a selection of the 20 best gene features with recursive feature elimination from a set of 10,719 gene features to obtain the classification accuracy of 87%. Results:: The fidgetin, microtubule severing factor (FIGN), SH3 and cysteine-rich domain (STAC), Cadherin-6 (CDH6), docking protein 6 (DOK6), nuclear RNA export factor 3 (NXF3) and maternally expressed 3 (MEG3) are the autoantigens markers identified for classification of COVID-positive and negative samples. Conclusion:: The identified autoantigen markers from transcriptomic datasets using machine learning techniques provide a deeper understanding of COVID-induced diseases and may play an important role as potential diagnostic and drug targets for COVID-19.