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Whether the common presence of plant viruses at an early age influences the infant's immune system, either directly or through interaction with other members of the microbiota, remains to be investigated.The estimated 20-30% of women who develop perimenopausal depression (PMD) are at an increased risk of cardiovascular and all-cause mortality. The therapeutic benefits of estradiol (E2) and symptom-provoking effects of E2-withdrawal (E2-WD) suggest that a greater sensitivity to changes in E2 at the cellular level contribute to PMD. We developed an in vitro model of PMD with lymphoblastoid cell lines (LCLs) derived from participants of a prior E2-WD clinical study. LCLs from women with past PMD (n = 8) or control women (n = 9) were cultured in three experimental conditions at vehicle baseline, during E2 treatment, and following E2-WD. Transcriptome analysis revealed significant differences in transcript expression in PMD in all experimental conditions, and significant overlap in genes that were changed in PMD regardless of experimental condition. Of these, chemokine CXCL10, previously linked to cardiovascular disease, was upregulated in women with PMD, but most so after E2-WD (p  less then  1.55 × 10-5). CYP7B1, an enzyme intrinsic to DHEA metabolism, was upregulated in PMD across experimental conditions (F(1,45) = 19.93, p  less then  0.0001). These transcripts were further validated via qRT-PCR. Gene networks dysregulated in PMD included inflammatory response, early/late E2-response, and cholesterol homeostasis. Our results provide evidence that differential behavioral responsivity to E2-WD in PMD reflects intrinsic differences in cellular gene expression. Genes such as CXCL10, CYP7B1, and corresponding proinflammatory and steroid biosynthetic gene networks, may represent biomarkers and molecular targets for intervention in PMD. Finally, this in vitro model allows for future investigations into the mechanisms of genes and gene networks involved in the vulnerability to, and consequences of, PMD.This study was conducted to compare soil particle density (ρs), soil total porosity (TP), liquid limit (LL), plastic limit (PL), and plasticity index, and their relations with soil organic matter (SOM), of non-carbonate silty clay Fluvisols under different land uses. Three neighboring land uses were studied native deciduous forest, arable land, and meadow, managed in the same way for more than 100 years. Soil was collected from 27 soil profiles and from three depths (0-15, 15-30 and 30-45 cm). Land use caused statistically significant but different impacts on soil properties, particularly in the topsoil. The forest topsoil measured the lowest ρs and bulk density (ρb) but the highest SOM and soil water content at PL, compared to meadow and arable soil. Statistically significant linear relationship was observed with the SOM content and ρs (- 0.851**), ρb (- 0.567**), calculated TP (0.567**) and measured TP (- 0.280**). There was a nonlinear relationship between SOM and LL (0.704**) and PL (0.845**) at the topsoil. The findings suggested that SOM content strongly affected ρs, ρb, TP, LL and LP. This regional study showed that the conversion of forestland into agricultural land without appropriate measures to conserve SOM leads to the degradation of physical and rheological soil properties.The Collaborative Filtering (CF) algorithm based on trust has been the main method used to solve the cold start problem in Recommendation Systems (RSs) for the past few years. Nevertheless, the current trust-based CF algorithm ignores the implicit influence contained in the ratings and trust data. In this paper, we propose a new rating prediction model named the Rating-Trust-based Recommendation Model (RTRM) to explore the influence of internal factors among the users. The proposed user internal factors include the user reliability and popularity. The internal factors derived from the explicit behavior data (ratings and trust), which can help us understand the user better and model the user more accurately. In addition, we incorporate the proposed internal factors into the Singular Value Decomposition Plus Plus (SVD + +) model to perform the rating prediction task. see more Experimental studies on two common datasets show that utilizing ratings and trust data simultaneously to mine the factors that influence the relationships among different users can improve the accuracy of rating prediction and effectively relieve the cold start problem.Infarct size is a major prognostic factor in ST-segment elevation myocardial infarction (STEMI). It is often assessed using repeated blood sampling and the estimation of biomarker area under the concentration versus time curve (AUC) in translational research. We aimed at developing limited sampling strategies (LSS) to accurately estimate biomarker AUC using only a limited number of blood samples in STEMI patients. This retrospective study was carried out on pooled data from five clinical trials of STEMI patients (TIMI blood flow 0/1) studies where repeated blood samples were collected within 72 h after admission to assess creatine kinase (CK), cardiac troponin I (cTnI) and muscle-brain CK (CK-MB). Biomarker kinetics was assessed using previously described biomarker kinetic models. A number of LSS models including combinations of 1 to 3 samples were developed to identify sampling times leading to the best estimation of AUC. Patients were randomly assigned to either learning (2/3) or validation (1/3) subsets. Descriptive and predictive performances of LSS models were compared using learning and validation subsets, respectively. An external validation cohort was used to validate the model and its applicability to different cTnI assays, including high-sensitive (hs) cTnI. 132 patients had full CK and cTnI dataset, 49 patients had CK-MB. For each biomarker, 180 LSS models were tested. Best LSS models were obtained for the following sampling times T4-16 for CK, T8-T20 for cTnI and T8-T16 for CK-MB for 2-sample LSS; and T4-T16-T24 for CK, T4-T12-T20 for cTnI and T8-T16-T20 for CK-MB for 3-sample LSS. External validation was achieved on 103 anterior STEMI patients (TIMI flow 0/1), and the cTnI model applicability to recommended hs cTnI confirmed. Biomarker kinetics can be assessed with a limited number of samples using kinetic modelling. This opens the way for substantial simplification of future cardioprotection studies, more acceptable for the patients.