Vitamin D Status of People 3 to 79 Years of Age from the Canadian Health Measures Survey 2012-2019.

BACKGROUND: Vitamin D is recognized in bone health and the prevention of rickets and osteomalacia. OBJECTIVE: This study aimed to assess vitamin D status of people in Canada and to identify factors associated with vitamin D inadequacy and deficiency. METHODS: Serum 25-hydroxyvitamin D (25(OH)D) from the Canadian Health Measures Survey (cycles 3-6, n = 21,770, 3-79 y) were evaluated for geometric means and proportions <40 (inadequate) and <30 (risk of deficiency) nmol/L. Factors associated with inadequacy or deficiency were tested using logistic regression. RESULTS: Mean serum 25(OH)D was 57.9 (95% CI: 55.4, 60.5) nmol/L; the prevalence of inadequacy was 19.0% (95% CI: 15.7, 22.3) and risk of deficiency was 8.4% (95% CI: 6.5, 10.3). Prominent dietary factors associated with inadequacy in adults included: not consuming fish compared with ≥1/wk (adjusted ORadj: 1.60; 95% CI: 1.21, 2.11), none compared with ≥1/d for cow's milk (ORadj: 1.41; 95% CI: 1.02, 1.94) or margarine (ORadj: 1.42; 95% CI: 1.08, 1.88); or nonuser compared with user of vitamin D supplements (ORadj: 5.21; 95% CI: 3.88, 7.01). Notable demographic factors included: younger adults compared with 71 to 79 y (19-30 y ORadj: 2.33; 95% CI: 1.66, 3.29); BMI ≥30 compared with <25 kg/m2 (ORadj: 2.30; 95% CI: 1.79, 2.95); lower household income quartile 1 compared with 4 (ORadj: 1.46; 95% CI: 1.00, 2.15); and self-reported Black (ORadj: 8.06; 95% CI: 4.71, 13.81), East/Southeast Asian (ORadj: 3.83; 95% CI: 2.14, 6.85), Middle Eastern (ORadj: 4.57; 95% CI: 3.02, 6.92), and South Asian (ORadj: 4.63; 95% CI: 2.62, 8.19) race compared with White. Similar factors were observed in children and for deficiency. CONCLUSIONS: Most people in Canada have adequate vitamin D status; nonetheless, racialized groups have an elevated prevalence of inadequacy. Further research is required to evaluate if current strategies to improve vitamin D status, including increasing vitamin D in fortified foods and supplements, and dietary guidance to include a source of vitamin D every day help to reduce health inequality in Canada.

Time-related multivariate strategy for the comprehensive evaluation of microbial chemical data.

INTRODUCTION: In microbial metabolomics, the use of multivariate data analysis (MDVA) has not been comprehensively explored regarding the different techniques available and the information that each gives about the metabolome. To overcome these limitations, here we show the use of Fusarium oxysporum cultured in the presence of exogenous alkaloids as a model system to demonstrate a comprehensive strategy for metabolic profiling. MATHERIALS AND METHODS: F. oxysporum was harvested on different days of incubation after alkaloidal addition, and the chemical profiles were compared using LC-MS data and MDVA. We show significant innovation to evaluate the chemical production of microbes during their life cycle by utilizing the full capabilities of Partial Least Square (PLS) with microbial-specific modeling that considers incubation days, media culture availability, and growth rate in solid media. RESULTS AND DISCUSSCION: Results showed that the treatment of the Y-data and the use of both PLS regression and discrimination (PLSr and PLS-DA) inferred complemental chemical information. PLSr revealed the metabolites that are produced/consumed during fungal growth, whereas PLS-DA focused on metabolites that are only consumed/produced at a specific period. Both regression and classificatory analysis were equally important to identify compounds that are regulated and/or selectively produced as a response to the presence of the alkaloids. Lastly, we report the annotation of analogs from the piperidine alkaloids biotransformed by F. oxysporum as a defense response to the toxic plant metabolites. These molecules do not show the antimicrobial potential of their precursors in the fungal extracts and were rapidly produced and consumed within 4 days of microbial growth.

Decoding and targeting the molecular basis of MACC1-driven metastatic spread: Lessons from big data mining and clinical-experimental approaches.

Metastasis remains the key issue impacting cancer patient survival and failure or success of cancer therapies. Metastatic spread is a complex process including dissemination of single cells or collective cell migration, penetration of the blood or lymphatic vessels and seeding at a distant organ site. Hundreds of genes involved in metastasis have been identified in studies across numerous cancer types. Here, we analyzed how the metastasis-associated gene MACC1 cooperates with other genes in metastatic spread and how these coactions could be exploited by combination therapies: We performed (i) a MACC1 correlation analysis across 33 cancer types in the mRNA expression data of TCGA and (ii) a comprehensive literature search on reported MACC1 combinations and regulation mechanisms. The key genes MET, HGF and MMP7 reported together with MACC1 showed significant positive correlations with MACC1 in more than half of the cancer types included in the big data analysis. However, ten other genes also reported together with MACC1 in the literature showed significant positive correlations with MACC1 in only a minority of 5 to 15 cancer types. To uncover transcriptional regulation mechanisms that are activated simultaneously with MACC1, we isolated pan-cancer consensus lists of 1306 positively and 590 negatively MACC1-correlating genes from the TCGA data and analyzed each of these lists for sharing transcription factor binding motifs in the promotor region. In these lists, binding sites for the transcription factors TELF1, ETS2, ETV4, TEAD1, FOXO4, NFE2L1, ELK1, SP1 and NFE2L2 were significantly enriched, but none of them except SP1 was reported in combination with MACC1 in the literature. Thus, while some of the results of the big data analysis were in line with the reported experimental results, hypotheses on new genes involved in MACC1-driven metastasis formation could be generated and warrant experimental validation. Furthermore, the results of the big data analysis can help to prioritize cancer types for experimental studies and testing of combination therapies.

Longitudinal assessment on the impact of caries status of nearby surfaces on caries progression on the mesial surface of first molars.

BACKGROUND: The mesial surface of the first permanent molar is the most caries-susceptible proximal surface of the permanent dentition in children under the age of 12. AIM: The aim of this study was to determine the association between caries progression on the mesial surface of the first permanent molar (T6M) and caries on the distal surface of the primary second molar (t5D) and the occlusal surface of the first permanent molar (T6O). DESIGN: Children (between 5 and 13 years old; N = 565) that had participated in a 4-year longitudinal caries study that at baseline had at least one T6 fully erupted with a t5 in proximal contact, with no restoration or sealant on T6O and t5D, and adequate bitewing radiographs were included. Clinical data using the International Caries Detection and Assessment System (ICDAS) and radiographs were used to determine the caries status of T6M, T6O, and t5D. RESULTS: Baseline caries presence on t5D and T6O were highly significantly associated with follow-up caries presence on T6M (P < .001). The adjusted odds ratios corresponding to t5D and T6O were 3.94 (95% CI: [1.78, 8.71]) and 3.26 (95% CI: [1.46, 7.31]), respectively. CONCLUSION: These findings highlight the need for prevention and management of caries on T6O and t5D.

RAId: Knowledge-Integrated Proteomics Web Service with Accurate Statistical Significance Assignment.

Mass spectrometry-based proteomics starts with identifications of peptides and proteins, which provide the bases for forming the next-level hypotheses whose "validations" are often employed for forming even higher level hypotheses and so forth. Scientifically meaningful conclusions are thus attainable only if the number of falsely identified peptides/proteins is accurately controlled. For this reason, RAId continued to be developed in the past decade. RAId employs rigorous statistics for peptides/proteins identification, hence assigning accurate P-values/E-values that can be used confidently to control the number of falsely identified peptides and proteins. The RAId web service is a versatile tool built to identify peptides and proteins from tandem mass spectrometry data. Not only recognizing various spectra file formats, the web service also allows four peptide scoring functions and choice of three statistical methods for assigning P-values/E-values to identified peptides. Users may upload their own protein database or use one of the available knowledge integrated organismal databases that contain annotated information such as single amino acid polymorphisms, post-translational modifications, and their disease associations. The web service also provides a friendly interface to display, sort using different criteria, and download the identified peptides and proteins. RAId web service is freely available at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/raid.

A network-based approach for identifying suitable biomarkers for oral immunotherapy of food allergy.

BACKGROUND: Oral immunotherapy (OIT) is a promising therapeutic approach to treat food allergic patients. However, concerns with regards to safety and long-term efficacy of OIT remain. There is a need to identify biomarkers that predict, monitor and/or evaluate the effects of OIT. Here we present a method to select candidate biomarkers for efficacy and safety assessment of OIT using the computational approaches Bayesian networks (BN) and Topological Data Analysis (TDA). RESULTS: Data were used from fructo-oligosaccharide diet-supported OIT experiments performed in 3 independent cow's milk allergy (CMA) and 2 independent peanut allergy (PNA) experiments in mice. Bioinformatical approaches were used to understand the data structure. The BN predicted the efficacy of OIT in the CMA with 86% and indicated a clear effect of scFOS/lcFOS on allergy parameters. For the PNA model, this BN (trained on CMA data) predicted an efficacy of OIT with 76% accuracy and shows similar effects of the allergen, treatment and diet as compared to the CMA model. The TDA identified clusters of biomarkers closely linked to biologically relevant clinical symptoms and also unrelated and redundant parameters within the network. CONCLUSIONS: Here we provide a promising application of computational approaches to a) compare mechanistic features of two different food allergies during OIT b) determine the biological relevance of candidate biomarkers c) generate new hypotheses to explain why CMA has a different disease pattern than PNA and d) select relevant biomarkers for future studies.

A Mobile-Based Screening System for Data Analyses of Early Dementia Traits Detection.

Existing early detection methods that deal with the pre-diagnosis of dementia have been criticised as not being comprehensive as they do not measure certain cognitive functioning domains besides being inaccessible. A more realistic approach is to develop a comprehensive outcome that includes cognitive functioning of dementia, as this will offer a robust and unbiased outcome for an individual. In this research, a mobile screening application for dementia traits called DementiaTest is proposed, which adopts the gold standard assessment criteria of Diagnostic and Statistical Manual of Mental Disorders (DSM-V). DementiaTest is implemented and tested on the Android and IOS stores. More importantly, it collects data from cases and controls using an easy, interactive, and accessible platform. It provides patients and their family with quick pre-diagnostic reports using certain cognitive functioning indicators; these can be utilized by general practitioners (GPs) for referrals for further assessment in cases of positive outcomes. The data gathered using the new application can be analysed using Artificial Intelligence methods to evaluate the performance of the screening to pinpoint early signs of the dementia.

Preventing Youth Internalizing Symptoms Through the Familias Unidas Intervention: Examining Variation in Response.

Prevention programs that strengthen parenting and family functioning have been found to reduce poor behavioral outcomes in adolescents, including substance use, HIV risk, externalizing and internalizing problems. However, there is evidence that not all youth benefit similarly from these programs. Familias Unidas is a family-focused intervention designed to prevent substance use and sexual risk among Hispanic youth and has recently demonstrated unanticipated reductions in internalizing symptoms for some youth. This paper examines variation in intervention response for internalizing symptoms using individual-level data pooled across four distinct Familias Unidas trials: (1) 266 eighth grade students recruited from the general school population; (2) 160 ninth grade students from the general school population; (3) 213 adolescents with conduct, aggression, and/or attention problems; and (4) 242 adolescents with a delinquency history. Causal inference growth mixture modeling suggests a three-class model. The two largest classes represent youth with low (60 %) and medium (27 %) internalizing symptoms at baseline, and both intervention and control participants show reductions in internalizing symptoms. The third class (13 %) represents youth with high levels of baseline internalizing symptoms who remain at steady levels of internalizing symptoms when exposed to the intervention, but who experience an increase in symptoms under the control condition. Female gender, low baseline levels of parent-adolescent communication, and older age were associated with membership in the high-risk class. These synthesis analyses involving a large sample of youth with varying initial risk levels represent a further step toward strengthening our knowledge of preventive intervention response and improving preventive interventions.

A Dyadic Examination of Marital Quality at the Empty-Nest Phase.

This study aims to achieve a better understanding of the later stages of the family life cycle by exploring couples' marital quality during the empty-nest years of their relationships, using the actor-partner interdependence mediation model. The empty nest is the family stage that begins with the departure of children from home. In the hypothesized model of marital quality, husbands' and wives' satisfaction at having raised successful children serve as predictor variables, whereas their levels of perceived stress act as mediators. A sample of 344 participants, forming 172 heterosexual couples, participated in the study. The results of path analyses indicated that the model provided an acceptable fit for the data. Closer examination suggested that wives' and husbands' perceived stress fully mediate the relationships between husbands' tendency to view their children as successful adults and both spouses' marital quality. The article discusses interdependence and gender differences between spouses concerning their empty-nest experience.

The association of trajectories of protein intake and age-specific protein intakes from 2 to 22 years with BMI in early adulthood.

No study has analysed how protein intake from early childhood to young adulthood relate to adult BMI in a single cohort. To estimate the association of protein intake at 2, 11, 15, 19 and 22 years with age- and sex-standardised BMI at 22 years (early adulthood), we used linear regression models with dietary and anthropometric data from a Filipino birth cohort (1985-2005, n 2586). We used latent growth curve analysis to identify trajectories of protein intake relative to age-specific recommended daily allowance (intake in g/kg body weight) from 2 to 22 years, then related trajectory membership to early adulthood BMI using linear regression models. Lean mass and fat mass were secondary outcomes. Regression models included socioeconomic, dietary and anthropometric confounders from early life and adulthood. Protein intake relative to needs at age 2 years was positively associated with BMI and lean mass at age 22 years, but intakes at ages 11, 15 and 22 years were inversely associated with early adulthood BMI. Individuals were classified into four mutually exclusive trajectories: (i) normal consumers (referent trajectory, 58 % of cohort), (ii) high protein consumers in infancy (20 %), (iii) usually high consumers (18 %) and (iv) always high consumers (5 %). Compared with the normal consumers, 'usually high' consumption was inversely associated with BMI, lean mass and fat mass at age 22 years whereas 'always high' consumption was inversely associated with male lean mass in males. Proximal protein intakes were more important contributors to early adult BMI relative to early-childhood protein intake; protein intake history was differentially associated with adulthood body size.

Understanding workers' exposure: Systematic review and data-analysis of emission potential for NOAA.

Exposure assessment for nano-objects, and their aggregates and agglomerates (NOAA), has evolved from explorative research toward more comprehensive exposure assessment, providing data to further develop currently used conservative control banding (CB) tools for risk assessment. This study aims to provide an overview of current knowledge on emission potential of NOAA across the occupational life cycle stages by a systematic review and subsequently use the results in a data analysis. Relevant parameters that influence emission were collected from peer-reviewed literature with a focus on the four source domains (SD) in the source-receptor conceptual framework for NOAA. To make the reviewed exposure data comparable, we applied an approach to normalize for workplace circumstances and measurement location, resulting in comparable "surrogate" emission levels. Finally, descriptive statistics were performed. During the synthesis of nanoparticles (SD1), mechanical reduction and gas phase synthesis resulted in the highest emission compared to wet chemistry and chemical vapor condensation. For the handling and transfer of bulk manufactured nanomaterial powders (SD2) the emission could be differentiated for five activity classes: (1) harvesting; (2) dumping; (3); mixing; (4) cleaning of a reactor; and (5) transferring. Additionally, SD2 was subdivided by the handled amount with cleaning further subdivided by energy level. Harvesting and dumping resulted in the highest emissions. Regarding processes with liquids (SD3b), it was possible to distinguish emissions for spraying (propellant gas, (high) pressure and pump), sonication and brushing/rolling. The highest emissions observed in SD3b were for propellant gas spraying and pressure spraying. The highest emissions for the handling of nano-articles (SD4) were found to nano-sized particles (including NOAA) for grinding. This study provides a valuable overview of emission assessments performed in the workplace during the occupational handling of NOAA. Analyses were made per source domain to derive emission levels which can be used for models to quantitatively predict the exposure.

Combination of CE-MS and advanced chemometric methods for high-throughput metabolic profiling.

In this work, an untargeted approach based on capillary electrophoresis-mass spectrometry (CE-MS) in combination with multivariate data analyses is proposed as a high-throughput general methodology for metabolomic studies. First, total ion electropherograms (TIEs) were considered for exploratory and classification purposes by means of principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Then, multivariate curve resolution alternating least squares (MCR-ALS) was applied to the multiple full scan MS data sets. This strategy permitted the resolution of a large number of metabolites being characterized by their electrophoretic peaks and their corresponding mass spectra. The proposed approach allowed solving additional electrophoretic issues, such as background noise contributions, low signal-to-noise ratios, asymmetric peaks and migration time shifts. The usefulness of the proposed methodology is demonstrated in a comparative study of the metabolic profiles from baker's yeast (Saccharomyces cerevisiae) samples cultured at two temperatures, 30°C and 37°C. A total number of 80 metabolites were relevant to yeast samples differentiation at the two temperatures and almost 50 of them were tentatively identified based on their accurate experimental molecular mass. The results show that changes in amino acid, nucleotide and lipid metabolic pathways participated in the acclimatization of yeast cells to grow at 37°C.

Automated longitudinal intra-subject analysis (ALISA) for diffusion MRI tractography.

Fiber tractography (FT), which aims to reconstruct the three-dimensional trajectories of white matter (WM) fibers non-invasively, is one of the most popular approaches for analyzing diffusion tensor imaging (DTI) data given its high inter- and intra-rater reliability and scan-rescan reproducibility. The major disadvantage of manual FT segmentations, unfortunately, is that placing regions-of-interest for tract selection can be very labor-intensive and time-consuming. Although there are several methods that can identify specific WM fiber bundles in an automated way, manual FT segmentations across multiple subjects performed by a trained rater with neuroanatomical expertise are generally assumed to be more accurate. However, for longitudinal DTI analyses it may still be beneficial to automate the FT segmentation across multiple time points, but then for each individual subject separately. Both the inter-subject and intra-subject automation in this situation are intended for subjects without gross pathology. In this work, we propose such an automated longitudinal intra-subject analysis (dubbed ALISA) approach, and assessed whether ALISA could preserve the same level of reliability as obtained with manual FT segmentations. In addition, we compared ALISA with an automated inter-subject analysis. Based on DTI data sets from (i) ten healthy subjects that were scanned five times (six-month intervals, aged 7.6-8.6years at the first scan) and (ii) one control subject that was scanned ten times (weekly intervals, 12.2years at the first scan), we demonstrate that the increased efficiency provided by ALISA does not compromise the high degrees of precision and accuracy that can be achieved with manual FT segmentations. Further automation for inter-subject analyses, however, did not provide similarly accurate FT segmentations.

Geographical variation in incidence of knee arthroscopy for patients with osteoarthritis: a population-based analysis of Victorian hospital separations data.

BACKGROUND/AIM: To evaluate the frequency and geographical variation in knee arthroscopy for adults (>25 years) with a concomitant diagnosis of osteoarthritis. METHODS: This was a retrospective cohort study of hospital separations involving an elective knee arthroscopy in public and private hospitals in Victoria, Australia. Participants included patients receiving knee arthroscopies with a diagnosis code indicating osteoarthritis (OA) from 1 July 2008 to 30 June 2009. Records were excluded if the patient was under 25 years or their arthroscopy involved a ligament reconstruction. Crude rates per 100 000 population and negative binomial regression offset by total knee arthroscopy volume were used to analyse differences by region. RESULTS: There were 9620 arthroscopic procedures meeting the inclusion criteria. There were 5500 (57.2%) admissions where the principal diagnosis was knee OA (gonarthrosis) and 3510 (36.5%) where the principal diagnosis indicated a mechanical derangement and there was a primary or associated diagnosis of OA. When we examined the incidence rate ratios (IRR) by region, after adjustment for relevant factors and accounting for the total knee arthroscopy volume within each region, we identified significant variation in knee arthroscopy rates for patients with OA. The region with the highest adjusted IRR was Barwon South Western (IRR: 1.26, 95% confidence interval (CI): 1.16-1.36) and the region with lowest adjusted incidence rate ratio was the Gippsland region (IRR: 0.89, 95% CI: 0.80-0.98). CONCLUSIONS: We identified considerable geographical variation in arthroscopies for people with OA across Victoria. Further investigation is needed to understand whether this variation is a reflection of differences in OA prevalence, clinical decision-making or access.

Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients.

This paper presents a comprehensive workflow for integrating revolving events into the transitive sequential pattern mining (tSPM+) algorithm and Machine Learning for Health Outcomes (MLHO) framework, emphasizing best practices and pitfalls in its application. We emphasize feature engineering and visualization techniques, demonstrating their efficacy in capturing temporal relationships. Applied to an EGFR lung cancer cohort, our approach showcases reliable temporal insights even in a small dataset. This work highlights the importance of temporal nuances in healthcare data analysis, paving the way for improved disease understanding and patient care.

OCRFinder: a noise-tolerance machine learning method for accurately estimating open chromatin regions.

Open chromatin regions are the genomic regions associated with basic cellular physiological activities, while chromatin accessibility is reported to affect gene expressions and functions. A basic computational problem is to efficiently estimate open chromatin regions, which could facilitate both genomic and epigenetic studies. Currently, ATAC-seq and cfDNA-seq (plasma cell-free DNA sequencing) are two popular strategies to detect OCRs. As cfDNA-seq can obtain more biomarkers in one round of sequencing, it is considered more effective and convenient. However, in processing cfDNA-seq data, due to the dynamically variable chromatin accessibility, it is quite difficult to obtain the training data with pure OCRs or non-OCRs, and leads to a noise problem for either feature-based approaches or learning-based approaches. In this paper, we propose a learning-based OCR estimation approach with a noise-tolerance design. The proposed approach, named OCRFinder, incorporates the ideas of ensemble learning framework and semi-supervised strategy to avoid potential overfitting of noisy labels, which are the false positives on OCRs and non-OCRs. Compared to different noise control strategies and state-of-the-art approaches, OCRFinder achieved higher accuracies and sensitivities in the experiments. In addition, OCRFinder also has an excellent performance in ATAC-seq or DNase-seq comparison experiments.

Identification of molecular candidates which regulate calcium-dependent CD8+ T-cell cytotoxicity.

Cytotoxic CD8+ T lymphocytes (CTL) eliminate infected cells or transformed tumor cells by releasing perforin-containing cytotoxic granules at the immunological synapse. The secretion of such granules depends on Ca2+-influx through store operated Ca2+ channels, formed by STIM (stromal interaction molecule)-activated Orai proteins. Whereas molecular mechanisms of the secretion machinery are well understood, much less is known about the molecular machinery that regulates the efficiency of Ca2+-dependent target cell killing. CTL killing efficiency is of high interest considering the number of studies on CD8+ T lymphocytes modified for clinical use. Here, we isolated total RNA from primary human cells: natural killer (NK) cells, non-stimulated CD8+ T-cells, and from Staphylococcus aureus enterotoxin A (SEA) stimulated CD8+ T-cells (SEA-CTL) and conducted whole genome expression profiling by microarray experiments. Based on differential expression analysis of the transcriptome data and analysis of master regulator genes, we identified 31 candidates which potentially regulate Ca2+-homeostasis in CTL. To investigate a putative function of these candidates in CTL cytotoxicity, we transfected either SEA-stimulated CTL (SEA-CTL) or antigen specific CD8+ T-cell clones (CTL-MART-1) with siRNAs specific against the identified candidates and analyzed the killing capacity using a real-time killing assay. In addition, we complemented the analysis by studying the effect of inhibitory substances acting on the candidate proteins if available. Finally, to unmask their involvement in Ca2+ dependent cytotoxicity, candidates were also analyzed under Ca2+-limiting conditions. Overall, we identified four hits, CCR5 (C-C chemokine receptor type five), KCNN4 (potassium calcium-activated channel subfamily N), RCAN3 (regulator of calcineurin) and BCL (B-cell lymphoma) 2 which clearly affect the efficiency of Ca2+ dependent cytotoxicity in CTL-MART-1 cells, CCR5, BCL2, and KCNN4 in a positive manner, and RCAN3 in a negative way.

The ATTAC guiding principles to openly and collaboratively share wildlife ecotoxicology data.

The inability to quantitatively integrate scattered data regarding potential threats posed by the increasing total amount and diversity of chemical substances in our environment limits our ability to understand whether existing regulations and management actions sufficiently protect wildlife. Systematic literature reviews and meta-analyses are great scientific tools to build upon the current push for accessibility under the Open Science and FAIR movements. Despite the potential of such integrative analyses, the emergence of innovative findings in wildlife ecology and ecotoxicology is still too rare relative to the potential that is hidden within the entirety of the available scattered data. To promote the reuse of wildlife ecotoxicology data, we propose the ATTAC workflow which comprises five key steps (Access, Transparency, Transferability, Add-ons, and Conservation sensitivity) along the chain of collecting, homogenizing, and integrating data for subsequent meta-analyses. The ATTAC workflow brings together guidelines supporting both the data prime movers and re-users. As such, the ATTAC workflow could promote an open and collaborative wildlife ecotoxicology able to reach a major objective in this applied field, namely, providing strong scientific support for regulations and management actions to protect and preserve wildlife species.

Defining the characteristics of interferon-alpha-stimulated human genes: insight from expression data and machine learning.

BACKGROUND: A virus-infected cell triggers a signalling cascade, resulting in the secretion of interferons (IFNs), which in turn induces the upregulation of the IFN-stimulated genes (ISGs) that play a role in antipathogen host defence. Here, we conducted analyses on large-scale data relating to evolutionary gene expression, sequence composition, and network properties to elucidate factors associated with the stimulation of human genes in response to IFN-α. RESULTS: We find that ISGs are less evolutionary conserved than genes that are not significantly stimulated in IFN experiments (non-ISGs). ISGs show obvious depletion of GC content in the coding region. This influences the representation of some compositions following the translation process. IFN-repressed human genes (IRGs), downregulated genes in IFN experiments, can have similar properties to the ISGs. Additionally, we design a machine learning framework integrating the support vector machine and novel feature selection algorithm that achieves an area under the receiver operating characteristic curve (AUC) of 0.7455 for ISG prediction. Its application in other IFN systems suggests the similarity between the ISGs triggered by type I and III IFNs. CONCLUSIONS: ISGs have some unique properties that make them different from the non-ISGs. The representation of some properties has a strong correlation with gene expression following IFN-α stimulation, which can be used as a predictive feature in machine learning. Our model predicts several genes as putative ISGs that so far have shown no significant differential expression when stimulated with IFN-α in the cell/tissue types in the available databases. A web server implementing our method is accessible at http://isgpre.cvr.gla.ac.uk/. The docker image at https://hub.docker.com/r/hchai01/isgpre can be downloaded to reproduce the prediction.

Diabetic retinopathy as a predictor of cardiovascular morbidity and mortality in subjects with type 2 diabetes.

This study aimed to evaluate the predictive value of diabetic retinopathy (DR) and its stages with the incidence of major cardiovascular events and all-cause mortality in type 2 diabetes mellitus (T2DM) persons in our large primary healthcare database from Catalonia (Spain). A retrospective cohort study with pseudo-anonymized routinely collected health data from SIDIAP was conducted from 2008 to 2016. We calculated incidence rates of major cardiovascular events [coronary heart disease (CHD), stroke, or both-macrovascular events] and all-cause mortality for subjects with and without DR and for different stages of DR. The proportional hazards regression analysis was done to assess the probability of occurrence between DR and the study events. About 22,402 T2DM subjects with DR were identified in the database and 196,983 subjects without DR. During the follow-up period among the subjects with DR, we observed the highest incidence of all-cause mortally. In the second place were the macrovascular events among the subjects with DR. In the multivariable analysis, fully adjusted for DR, sex, age, body mass index (BMI), tobacco, duration of T2DM, an antiplatelet or antihypertensive drug, and HbA1c, we observed that subjects with any stage of DR had higher risks for all of the study events, except for stroke. We observed the highest probability of all-cause death events (adjusted hazard ratios, AHRs: 1.34, 95% CI: 1.28; 1.41). In conclusion, our results show that DR is related to CHD, macrovascular events, and all-cause mortality among persons with T2DM.