Abstract Background Psychosocial stressors increase the risks for cardiovascular disease across diverse populations. However, neighborhood level resilience resources may protect against poor cardiovascular health (CVH). This study used data from three CVH cohorts to examine longitudinally the associations of a resilience resource, perceived neighborhood social cohesion (hereafter referred to as neighborhood social cohesion), with the American Heart Association’s Life’s Simple 7 (LS7), and whether psychosocial stressors modify observed relationships. Methods We examined neighborhood social cohesion...

Additional file 1: Figure S1. Proportions of different types of alteration in resection (A) and biopsy (B) specimens. CN_amp: copy number amplification; InDel: small insertion and deletion; LGR: large genomic rearrangement.

Additional file 1: Figure S1. Proportions of different types of alteration in resection (A) and biopsy (B) specimens. CN_amp: copy number amplification; InDel: small insertion and deletion; LGR: large genomic rearrangement.

Additional file 2: Figure S2. Comparative analyses of the sequencing depth (A) and allele frequency (B) between the matched alterations and specific alterations.

Additional file 3: Figure S3. The difference (A) and correlation (B) between resection specimen-based TMB and CNB-based TMB. RS: resection specimen; CNB: core needle biopsy; TMB: tumor mutation burden.

Additional file 1.

Additional file 1.

Abstract Background The aim of the study were to analyze the lumbar volumetric bone mineral density (BMD), fat distribution and changes of skeletal muscle with quantitative computed tomography (QCT) in postmenopausal women with type 2 diabetes mellitus (T2DM), and to evaluate the relationship between body composition and BMD. Methods One hundred seventy-seven postmenopausal women with T2DM and 136 postmenopausal women without diabetes were included in the study and were divided into two groups according to...

Supplemental material, sj-docx-1-ivi-10.1177_14738716221130338 for Compressing and interpreting word embeddings with latent space regularization and interactive semantics probing by Haoyu Li, Junpeng Wang, Yan Zheng, Liang Wang, Wei Zhang and Han-Wei Shen in Information Visualization

Supplemental material, sj-docx-1-ivi-10.1177_14738716221130338 for Compressing and interpreting word embeddings with latent space regularization and interactive semantics probing by Haoyu Li, Junpeng Wang, Yan Zheng, Liang Wang, Wei Zhang and Han-Wei Shen in Information Visualization

Supplemental material, sj-pdf-1-pss-10.1177_09567976221110243 for Radial Bias Alters Perceived Object Orientation by Melisa Menceloglu, Ken Nakayama and Joo-Hyun Song in Psychological Science

Supplemental material, sj-pdf-1-pss-10.1177_09567976221110243 for Radial Bias Alters Perceived Object Orientation by Melisa Menceloglu, Ken Nakayama and Joo-Hyun Song in Psychological Science

Additional file 4: Supplemental Figure 4. Epidermal and Neural Lineages Diverge at St 10.5 (A) Number of enriched Neural GO Terms in genes significantly higher in the neural lineage (blue) and epidermal lineage (red) at each developmental stage. (B) Heatmap of the top 20 DE genes by Log2FC with a minimum expression of 10TPM between the epidermal and neural lineages at stage 10.5 (C) KEGG enrichment analysis of genes differentially expressed between epidermal and neural...

Additional file 7: Supplemental Figure 7. Early not Exogenous BMP Causes Mesoderm Formation. (A) Number of differentially expressed genes between the epidermal and ventral mesoderm lineages at each developmental stage (padj ≤ 0.05). (B) Number of enriched Mesoderm GO Terms (solid line) and Epidermis GO Terms (dashed line) in genes significantly higher in the ventral mesoderm lineage (purple) and epidermal lineage (red) at each developmental stage. (C) Western Blot Analysis of lysates for epidermal (WT)...

Additional file 8: Supplemental Figure 8. Raw Images of Main Figure Western Blots. (A) Figure 4A Western blot analysis of lysates of developing epidermal (WT) and neural (20uM K02288) explants for pSmad1/5/8 and Smad1 with Actin loading control. Blot was cut prior to antibody hybridization to conserve antibody, unedited scan showing blot edges provided. (B) Figure 7A Western Blot analysis of lysates for developing epidermal (WT) and ventral mesoderm (BMP4/7 20ng/uL) explants for pSmad1/5/8 and...

Additional file 1: Supplemental file 1. Visualization of the Evolution of CIDO.

Additional file 1: Supplemental file 1. Visualization of the Evolution of CIDO.

Additional file 3: Supplemental Table 2. CIDO statistics including terms imported from major reference ontologies.

Additional file 3: Supplemental Table 2. CIDO statistics including terms imported from major reference ontologies.

Additional file 1. DSMB Charter, Version Dated September 14, 2021. Description: Charter outlining the roles, responsibilities, practices, and procedures of the EMO Trial Data and Safety Monitoring Board.

Additional file 3. CONSORT 2010 checklist

Additional file 5: Table S4. Top 20 hits of drugs identified by machine-learning aided drug repurposing pipeline in this study.

Additional file 1: Fig. S1: Scatter plots comparing BoostMEC predictions and measured efficiency values in test datasets. Table S1: LightGBM feature importance values for BoostMEC. Table S2: Full prediction interpretation table for GTCTGCCATCTCTGATGGATGTGATGGGCA. Table S3: Full prediction interpretation table for GGGGGGACTGTATCGACGCTGAATTGGGGG. Table S4: LightGBM hyperparameters.

Abstract Background In the CRISPR-Cas9 system, the efficiency of genetic modifications has been found to vary depending on the single guide RNA (sgRNA) used. A variety of sgRNA properties have been found to be predictive of CRISPR cleavage efficiency, including the position-specific sequence composition of sgRNAs, global sgRNA sequence properties, and thermodynamic features. While prevalent existing deep learning-based approaches provide competitive prediction accuracy, a more interpretable model is desirable to help understand how different features...

Additional file 3: This file contains a visual representation of BoostMEC’s second regression tree (tree_index = 1).