expHRD: an individualized, transcriptome-based prediction model for homologous recombination deficiency assessment in cancer.

BACKGROUND: Homologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy and poly ADP-ribose polymerase (PARP) inhibitors. One of the conventional approaches to HRD prognostication has generally centered on identifying deleterious mutations within the BRCA1/2 genes, along with quantifying the genomic scars, such as Genomic Instability Score (GIS) estimation with scarHRD. However, the scarHRD method has limitations in scenarios involving tumors bereft of corresponding germline data. Although several RNA-seq-based HRD prediction algorithms have been developed, they mainly support cohort-wise classification, thereby yielding HRD status without furnishing an analogous quantitative metric akin to scarHRD. This study introduces the expHRD method, which operates as a novel transcriptome-based framework tailored to n-of-1-style HRD scoring. RESULTS: The prediction model has been established using the elastic net regression method in the Cancer Genome Atlas (TCGA) pan-cancer training set. The bootstrap technique derived the HRD geneset for applying the expHRD calculation. The expHRD demonstrated a notable correlation with scarHRD and superior performance in predicting HRD-high samples. We also performed intra- and extra-cohort evaluations for clinical feasibility in the TCGA-OV and the Genomic Data Commons (GDC) ovarian cancer cohort, respectively. The innovative web service designed for ease of use is poised to extend the realms of HRD prediction across diverse malignancies, with ovarian cancer standing as an emblematic example. CONCLUSIONS: Our novel approach leverages the transcriptome data, enabling the prediction of HRD status with remarkable precision. This innovative method addresses the challenges associated with limited available data, opening new avenues for utilizing transcriptomics to inform clinical decisions.

Face recognition ability can be predicted by microstructural properties of white matter: a study of diffusion tensor imaging (DTI).

There is a great individual difference in people's face recognition ability (FRA). This study aimed to reveal the neural mechanism underlying such individual differences. Elastic-net regression models were constructed to predict FRA based on the white matter (WM) microstructural properties. We found that FRA can be accurately predicted by the WM microstructural properties. For the right inferior longitudinal fasciculus (ILF) and bilateral arcuate fasciculus (AF), FRA was correlated negatively to fractional anisotropy (FA), but positively to radial diffusivity (RD). In contrast, for the corpus callosum forceps minor (CFM), FRA was correlated positively to FA, but negatively to RD. Such various patterns of the WM microstructural properties suggested a positive correlation between FRA and fiber diameter for the right ILF and bilateral AF, but a negative correlation between FRA and diameter of the CFM. These findings reflected that FRA was correlated positively to connectivities of the right ILF and bilateral AF, but negatively to those of the CFM. These findings not only confirmed the significant role of the right ILF in face recognition, but also revealed the involvement of the bilateral AF and CFM in face recognition, particularly implying the important role of hemisphere lateralization modulated by transcallosal connectivity in face recognition.

Associations between exposure to pesticides mixture and semen quality among the non-occupationally exposed males: Four statistical models.

Most epidemiological studies on the associations between pesticides exposure and semen quality have been based on a single pesticide, with inconsistent major results. In contrast, there was limited human evidence on the potential effect of pesticides mixture on semen quality. Our study aimed to investigate the relationship of pesticide profiles with semen quality parameters among 299 non-occupationally exposed males aged 25-50 without any clinical abnormalities. Serum concentrations of 21 pesticides were quantified by gas chromatography-tandem mass spectrometry (GC-MS/MS). Semen quality parameters were abstracted from medical records. Generalized linear regression models (GLMs) and three mixture approaches, including weighted quantile sum regression (WQS), elastic net regression (ENR) and Bayesian kernel machine regression (BKMR), were applied to explore the single and mixed effects of pesticide exposure on semen quality. In GLMs, as the serum levels of Bendiocarb, ß-BHC, Clomazone, Dicrotophos, Dimethenamid, Paclobutrazole, Pentachloroaniline and Pyrimethanil increased, the straight-line velocity (VSL), linearity (LIN) and straightness (STR) decreased. This negative association also occurred between the concentration of ß-BHC, Pentachloroaniline, Pyrimethanil and progressive motility, total motility. In the WQS models, pesticides mixture was negatively associated with total motility and several sperm motility parameters (ß: -3.07∼-1.02 per decile, FDR-P<0.05). After screening the important pesticides derived from the mixture by ENR model, the BKMR models showed that the decreased qualities for VSL, LIN, and STR were also observed when pesticide mixtures were at ≥ 70th percentiles. Clomazone, Dimethenamid, and Pyrimethanil (Posterior inclusion probability, PIP: 0.2850-0.8900) were identified as relatively important contributors. The study provides evidence that exposure to single or mixed pesticide was associated with impaired semen quality.

A machine learning-derived neuroanatomical pattern predicts delayed reward discounting in the Human Connectome Project Young Adult sample.

Delayed reward discounting (DRD) is defined as the extent to which person favors smaller rewards that are immediately available over larger rewards available in the future. Higher levels of DRD have been identified in individuals with a wide range of clinical disorders. Although there have been studies adopting larger samples and using only gray matter volume to characterize the neuroanatomical correlates of DRD, it is still unclear whether previously identified relationships are generalizable (out-of-sample) and how cortical thickness and cortical surface area contribute to DRD. In this study, using the Human Connectome Project Young Adult dataset (N = 1038), a machine learning cross-validated elastic net regression approach was used to characterize the neuroanatomical pattern of structural magnetic resonance imaging variables associated with DRD. The results revealed a multi-region neuroanatomical pattern predicted DRD and this was robust in a held-out test set (morphometry-only R2 = 3.34%, morphometry + demographics R2  = 6.96%). The neuroanatomical pattern included regions implicated in the default mode network, executive control network, and salience network. The relationship of these regions with DRD was further supported by univariate linear mixed effects modeling results, in which many of the regions identified as part of this pattern showed significant univariate associations with DRD. Taken together, these findings provide evidence that a machine learning-derived neuroanatomical pattern encompassing various theoretically relevant brain networks produces robustly predicts DRD in a large sample of healthy young adults.

A hybrid of regularization method and generalized path analysis: modeling single-vehicle run-off-road crashes in a cross-sectional study.

BACKGROUND: Determining risk factors of single-vehicle run-off-road (SV-ROR) crashes, as a significant number of all the single-vehicle crashes and all the fatalities, may provide infrastructure for quicker and more effective safety measures to explore the influencing and moderating variables in SV-ROR. Therefore, this paper emphasizes utilizing a hybrid of regularization method and generalized path analysis for studying SV-ROR crashes to identify variables influencing their happening and severity. METHODS: This cross-sectional study investigated 724 highway SV-ROR crashes from 2015 to 2016. To drive the key variables influencing SV-ROR crashes Ridge, Least Absolute Shrinkage and Selection Operator (Lasso), and Elastic net regularization methods were implemented. The goodness of fit of utilized methods in a testing sample was assessed using the deviance and deviance ratio. A hybrid of Lasso regression (LR) and generalized path analysis (gPath) was used to detect the cause and mediators of SV-ROR crashes. RESULTS: Findings indicated that the final modified model fitted the data accurately with [Formula: see text]= 16.09, P < .001, [Formula: see text]/ degrees of freedom = 5.36 > 5, CFI = .94 > .9, TLI = .71 < .9, RMSEA = 1.00 > .08 (90% CI = (.06 to .15)). Also, the presence of passenger (odds ratio (OR) = 2.31, 95% CI = (1.73 to 3.06)), collision type (OR = 1.21, 95% CI = (1.07 to 1.37)), driver misconduct (OR = 1.54, 95% CI = (1.32 to 1.79)) and vehicle age (OR = 2.08, 95% CI = (1.77 to 2.46)) were significant cause of fatality outcome. The proposed causal model identified collision type and driver misconduct as mediators. CONCLUSIONS: The proposed HLR-gPath model can be considered a useful theoretical structure to describe how the presence of passenger, collision type, driver misconduct, and vehicle age can both predict and mediate fatality among SV-ROR crashes. While notable progress has been made in implementing road safety measures, it is essential to emphasize that operative preventative measures still remain the most effective approach for reducing the burden of crashes, considering the critical components identified in this study.

A longitudinal investigation of COVID-19 pandemic experiences and mental health among university students.

OBJECTIVES: Previous studies have established a link between the COVID-19 pandemic and poor mental health. They further suggest that young adults may be especially vulnerable to worsened mental health during the pandemic, but few studies have investigated which specific aspects of the COVID-19 experience affect psychological well-being over time. To better understand concrete predictors of poor mental health outcomes in this population, we identified several pandemic-related experiences and evaluated their effects on mental health symptoms (depression, anxiety, stress, alcohol, and substance use) in a sample of U.S. college students (N = 176). METHODS: Both mental health symptoms and pandemic-related experiences were evaluated at the start of quarantine (March/April 2020, Time 1) and the end of the Spring 2020 semester (May 2020, Time 2). Given the limited literature on specific predictors of mental health during a pandemic, we used elastic net regression, a novel analytic method that helps with variable selection when theoretical background is limited, to narrow our field of possible predictors. RESULTS: While mental health symptoms were elevated at both timepoints, there were no clinically significant changes from Time 1 to Time 2 and few differences between sociodemographic groups. Both disruption due to the pandemic (ß = .25, p = .021) and limited confidence in the federal government's response (ß = -.14, p = .038) were significant predictors of depression symptoms at the end of the semester, even when controlling for baseline depression. Further, predictions that the pandemic would continue to impact daily life further into the future were linked with pandemic stress response symptoms (ß = .15, p = .032) at Time 2, beyond the effects of baseline symptoms. Alcohol (ß = -.22, p = .024) and substance use (ß = -.26, p = .01) were associated with reduced adherence to COVID-19 guidelines. CONCLUSIONS: Our findings indicate that specific aspects of the pandemic experience may be influencing internalizing symptoms and alcohol/substance use in college students, pointing to potential avenues for targeted support and intervention. PRACTITIONER POINTS: A range of factors may influence university student mental health during the COVID-19 pandemic. Students who expect the pandemic will continue to impact daily life further into the future maybe more likely to report stress symptoms. Disruption due to the pandemic and limited confidence in the federal government's response may be associated with depression symptoms. Alcohol and substance use are associated with lower COVID-19 guideline adherence in university students.

Can health service equity alleviate the health expenditure poverty of Chinese patients? Evidence from the CFPS and China health statistics yearbook.

OBJECTIVES: To comprehend the relationship between various indicators of health service equity and patients' health expenditure poverty in different regions of China, identify areas where equity in health service is lacking and provide ideas for improving patients' health expenditure poverty. METHOD: Data from China Family Panel Studies (CFPS) in 2018 and the HFGT index formula were used to calculate the health expenditure poverty index of each province. Moreover, Global Moran's I and Local Moran's I test are applied to measure whether there is spatial aggregation of health expenditure poverty. Finally, an elastic net regression model is established to analyze the impact of health service equity on health expenditure poverty, with the breadth of health expenditure poverty as the dependent variable and health service equity as the independent variable. RESULTS: In the developed eastern provinces of China, the breadth of health expenditure poverty is relatively low. There is a significant positive spatial agglomeration. "Primary medical and health institutions per 1,000 population", "rural doctors and health workers per 1,000 population", "beds in primary medical institutions per 1,000 population", "proportion of government health expenditure" and "number of times to participate in medical insurance (be aided) per 1,000 population" have a positive impact on health expenditure poverty. "Number of health examinations per capita" and "total health expenditure per capita" have a negative impact on health expenditure poverty. Both effects passed the significance test. CONCLUSION: To enhance the fairness of health resource allocation in China and to alleviate health expenditure poverty, China should rationally plan the allocation of health resources at the grassroots level, strengthen the implementation of hierarchical diagnosis and treatment and encourage the investment in business medical insurance industry. Meanwhile, it is necessary to increase the intensity of medical assistance and enrich financing methods. All medical expenses of the poorest should be covered by the government.

A rapid method to quantify casein in fluid milk by front-face fluorescence spectroscopy combined with chemometrics.

Casein in fluid milk determines cheese yield and affects cheese quality. Traditional methods of measuring casein in milk involve lengthy sample preparations with labor-intensive nitrogen-based protein quantifications. The objective of this study was to quantify casein in fluid milk with different casein-to-crude-protein ratios using front-face fluorescence spectroscopy (FFFS) and chemometrics. We constructed calibration samples by mixing microfiltration and ultrafiltration retentate and permeate in different ratios to obtain different casein concentrations and casein-to-crude-protein ratios. We developed partial least squares regression and elastic net regression models for casein prediction in fluid milk using FFFS tryptophan emission spectra and reference casein contents. We used a set of 20 validation samples (including raw, skim, and ultrafiltered milk) to optimize and validate model performance. We externally tested another independent set of 20 test samples (including raw, skim, and ultrafiltered milk) by root mean square error of prediction (RMSEP), residual prediction deviation (RPD), and relative prediction error (RPE). The RMSEP for casein content quantification in raw, skim, and ultrafiltered milk ranged from 0.12 to 0.13%, and the RPD ranged from 3.2 to 3.4. The externally validated error of prediction was comparable to the existing rapid method and showed practical model performance for quality-control purposes. This FFFS-based method can be implemented as a routine quality-control tool in the dairy industry, providing rapid quantification of casein content in fluid milk intended for cheese manufacturing.

Applying elastic-net regression to identify the best models predicting changes in civic purpose during the emerging adulthood.

INTRODUCTION: Changes in civic purpose during the emerging adulthood has been a significant research topic since it is closely associated with active civic engagement later in human lives. While standard regression methods have been used in previous studies to predict civic purpose development, they have limitations that may not always lead to best prediction models. We aimed to address these limitations by utilizing elastic-net multinomial logistic regression, which favors models with the least number of necessary predictors, in exploration of predictors for civic purpose development in a data-driven manner. METHODS: We analyzed data from the longitudinal Civic Purpose Project while focusing on the model that best predicted civic purpose from Wave 1 (12th grade before high school graduation) to Wave 2 (two years after Wave 1). The reanalyzed data included responses from 476 participants (60.29% females, 39.08% males) who were recruited from Californian high schools in the United States and completed the survey at both Waves. The elastic-net regression was performed 5000 times for predicting three dependent variables, Wave 2 political purpose, community service purpose, and expressive activity purpose, with Wave 1 predictors. We identified which predictors were selected as the constituents of the best regression models during the elastic-net regression process. RESULTS: Results showed that civic purpose, moral and political identity, and external supports (e.g., parental and peer involvement, school civic opportunities, etc.) in Wave 1 significantly predicted civic purpose in Wave 2. Several predictors were excluded from the regression models during the elastic-net regression process. CONCLUSION: We found that the elastic-net regression was able to present the more regularized model for prediction. Implications for promoting civic purpose are discussed as well as utilizing the elastic-net regression method.

Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models.

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils.

[Analysis of spectrum-activity relationship among antioxidant parts of Lycii Fructus using three different chemometrics methods].

The chemical components of Lycii Fructus were analyzed by liquid chromatography( LC) and mass spectrometry( MS for the establishment of spectrum-activity relationship,on the basis of which its antioxidant active ingredients were determined. In this experiment,Lycii Fructus was extracted with different solvents and then separated into 80 samples by macroporous adsorption resin and reversed-phase chromatography,respectively. The antioxidant components were enriched into 11 samples and their scavenging abilities against DPPH free radical and ferric ion reducing antioxidant power( FRAP) were significantly stronger than those before the treatment( P<0. 05). The spectrum-activity relationship regarding the antioxidant activity in vitro of Lycii Fructus was established by Pearson correlation analysis,orthogonal partial least squares( OPLS) and elastic net regression. Six chromatographic peaks greatly contributing to the antioxidant activity in vitro of Lycii Fructus were identified as rutin( P6),quercetin( P35),scopoletin( P14),N-cis-feruloyl-4-O-ß-D-glucopyranosyl-tyramine or N-( 4-O-ß-D-glucopyranosyl-trans-feruloyl)-tyramine( P8), ferulic acid( P13) and1,3,5-dihydroxy-2-isoprenyl-3-xanthone( P23). The active components associated with free radical scavenging were rutin and quercetin both belonging to flavonoids. The reduction of Fe3+was based on phenylpropanoids such as ferulic acid,scopoletin,xanthone and phenolic amides. These results indicated that the antioxidant activity of Lycii Fructus was ascribed to the synergistic action of different products through different ways. Besides,the data analysis model should be chosen carefully for the establishment of spectrum-activity relationship,thus ensuring the reliability of results.

Survival analysis using a 5-step stratified testing and amalgamation routine (5-STAR) in randomized clinical trials.

Randomized clinical trials are often designed to assess whether a test treatment prolongs survival relative to a control treatment. Increased patient heterogeneity, while desirable for generalizability of results, can weaken the ability of common statistical approaches to detect treatment differences, potentially hampering the regulatory approval of safe and efficacious therapies. A novel solution to this problem is proposed. A list of baseline covariates that have the potential to be prognostic for survival under either treatment is pre-specified in the analysis plan. At the analysis stage, using all observed survival times but blinded to patient-level treatment assignment, "noise" covariates are removed with elastic net Cox regression. The shortened covariate list is used by a conditional inference tree algorithm to segment the heterogeneous trial population into subpopulations of prognostically homogeneous patients (risk strata). After patient-level treatment unblinding, a treatment comparison is done within each formed risk stratum and stratum-level results are combined for overall statistical inference. The impressive power-boosting performance of our proposed 5-step stratified testing and amalgamation routine (5-STAR), relative to that of the logrank test and other common approaches that do not leverage inherently structured patient heterogeneity, is illustrated using a hypothetical and two real datasets along with simulation results. Furthermore, the importance of reporting stratum-level comparative treatment effects (time ratios from accelerated failure time model fits in conjunction with model averaging and, as needed, hazard ratios from Cox proportional hazard model fits) is highlighted as a potential enabler of personalized medicine. An R package is available at https://github.com/rmarceauwest/fiveSTAR.

Improving the monitoring of suicide incidence by estimating the probability of news reporting.

A timely estimate of suicide incidence is important for surveillance and monitoring but always difficult if not possible. The delay in reporting suicide cases between the time of occurrence of the deaths and them being registered is unavoidable. There is at least one year if not more of the delay time in the latest WHO website reporting the suicide statistics of most countries. Based on the daily newspaper reporting on suicide incidence, this study proposes a method to estimate the unknown incidence in a timely manner. It is shown that demographic characteristics such as age, suicide methods, and the districts of the deceased were significantly associated with the probability of the newspapers reporting the suicides. By incorporating this information on the daily suicide news reports into estimating the probability of the newspapers reporting the suicides, the daily number of suicide cases can be estimated. The proposed method is applied to estimate the number of suicides in Hong Kong where there is the Coroner's Court to investigate into suicide deaths, but it takes at least six months to deliver a verdict. The present method can generate timely and accurate estimations on the daily count of suicide deaths with only a one day lag. In a threefold nested cross-validation, the proposed approach has achieved an average RMSE of 1.38, MAE of 1.10, and R2 of 0.24. It can also serve as a surveillance system in providing estimations of temporal clusters of suicides with certain characteristics timelessly and accurately.

Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference.

The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method.

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma.

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method "miRlastic", which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic.

An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19.

This study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROCICU [95%CI]: 0.858 [0.803,0.899]; AUROCdeath [95%CI]: 0.906 [0.850,0.948]); and good calibrations (Spiegelhalter-ZICU: - 0.821 (p-value: 0.412); Spiegelhalter-Zdeath: 0.173) in the test set. We developed and validated prediction models to help clinicians identify high risk patients for death and ICU admission after COVID-19 infection.

A genomic predictor for age at sexual maturity for mammalian species.

Age at sexual maturity is a key life history trait that can be used to predict population growth rates and develop life history models. In many wild animal species, the age at sexual maturity is not accurately quantified. This results in a reduced ability to accurately model demography of wild populations. Recent studies have indicated the potential for CpG density within gene promoters to be predictive of other life history traits, specifically maximum lifespan. Here, we have developed a machine learning model using gene promoter CpG density to predict the mean age at sexual maturity in mammalian species. In total, 91 genomes were used to identify 101 unique gene promoters predictive of age at sexual maturity across males and females. We found these gene promoters to be most predictive of age at sexual maturity in females (R 2 = 0.881) compared to males (R 2 = 0.758). The median absolute error rate was also found to be lower in females (0.427 years) compared to males (0.785 years). This model provides a novel method for species-level age at sexual maturity prediction without the need for long-term monitoring. This study also highlights a potential epigenetic mechanism for the onset of sexual maturity, indicating the possibility of using epigenetic biomarkers for this important life history trait.

Depression and fatigue six months post-COVID-19 disease are associated with overlapping symptom constellations: A prospective, multi-center, population-based cohort study.

BACKGROUND: Depression and fatigue are commonly observed sequelae following viral diseases such as COVID-19. Identifying symptom constellations that differentially classify post-COVID depression and fatigue may be helpful to individualize treatment strategies. Here, we investigated whether self-reported post-COVID depression and post-COVID fatigue are associated with the same or different symptom constellations. METHODS: To address this question, we used data from COVIDOM, a population-based cohort study conducted as part of the NAPKON-POP platform. Data were collected in three different German regions (Kiel, Berlin, Würzburg). We analyzed data from >2000 individuals at least six months past a PCR-confirmed COVID-19 disease, using elastic net regression and cluster analysis. The regression model was developed in the Kiel data set, and externally validated using data sets from Berlin and Würzburg. RESULTS: Our results revealed that post-COVID depression and fatigue are associated with overlapping symptom constellations consisting of difficulties with daily activities, perceived health-related quality of life, chronic exhaustion, unrestful sleep, and impaired concentration. Confirming the overlap in symptom constellations, a follow-up cluster analysis could categorize individuals as scoring high or low on depression and fatigue but could not differentiate between both dimensions. LIMITATIONS: The data presented are cross-sectional, consisting primarily of self-reported questionnaire or medical records rather than biometric data. CONCLUSIONS: In summary, our results suggest a strong link between post-COVID depression and fatigue, highlighting the need for integrative treatment approaches.

The interaction effect of metals exposure and dietary habit on cognitive function in Chinese older adult cohort.

BACKGROUND: As the important factors in cognitive function, dietary habits and metal exposures are interactive with each other. However, fewer studies have investigated the interaction effect of them on cognitive dysfunction in older adults. METHODS: 2,445 registered citizens aged 60-85 years from 51 community health centers in Luohu District, Shenzhen, were recruited in this study based on the Chinese older adult cohort. All subjects underwent physical examination and Mini-cognitive assessment scale. A semi quantitative food frequency questionnaire was used to obtain their food intake frequency, and 21 metal concentrations in their urine were measured. RESULTS: Elastic-net regression model, a machine learning technique, identified six variables that were significantly associated with cognitive dysfunction in older adults. These variables included education level, gender, urinary concentration of arsenic (As) and cadmium (Cd), and the frequency of monthly intake of egg and bean products. After adjusting for multiple factors, As and Cd concentrations were positively associated with increased risk of mild cognitive impairment (MCI) in the older people, with OR values of 1.19 (95% CI: 1.05-1.42) and 1.32 (95% CI: 1.01-1.74), respectively. In addition, older adults with high frequency of egg intake (≥30 times/month) and bean products intake (≥8 times/month) had a reduced risk of MCI than those with low protein egg intake (<30 times/month) and low bean products intake (<8 times/month), respectively. Furthermore, additive interaction were observed between the As exposure and egg products intake, as well as bean products. Cd exposure also showed additive interactions with egg and bean products intake. CONCLUSIONS: The consumption of eggs and bean products, as well as the levels of exposure to the heavy metals Cd and As, have been shown to have a substantial influence on cognitive impairment in the elderly population.

Spatial Relationships in the Tumor Microenvironment Demonstrate Association with Pathologic Response to Neoadjuvant Chemoimmunotherapy in Muscle-invasive Bladder Cancer.

BACKGROUND: Platinum-based neoadjuvant chemotherapy (NAC) is standard for patients with muscle-invasive bladder cancer (MIBC). Pathologic response (complete: ypT0N0 and partial: