Genetic Variation Is the Major Determinant of Individual Differences in Leukocyte Endothelial Adhesion

Objective: To determine the genetic contribution to leukocyte endothelial adhesion.Methods: Leukocyte endothelial adhesion was assessed through a novel cell-based assay using human lymphoblastoid cell lines. A high-throughput screening method was developed to evaluate the inter-individual variability in leukocyte endothelial adhesion using lymphoblastoid cell lines derived from different donors. To assess heritability, ninety-two lymphoblastoid cell lines derived from twenty-three monozygotic twin pairs and twenty-three sibling pairs were compared. These lymphoblastoid cell lines were plated with the endothelial cell line EA.hy926 and labeled with Calcein AM dye. Fluorescence was assessed to determine endothelial cell adhesion to each lymphoblastoid cell line. Intra-pair similarity was determined for monozygotic twins and siblings using Pearson pairwise correlation coefficients.Results: A leukocyte endothelial adhesion assay for lymphoblastoid cell lines was developed and optimized (CV = 8.68, Z′-factor = 0.67, SNR = 18.41). A higher adhesion correlation was found between the twins than that between the siblings. Intra-pair similarity for leukocyte endothelial adhesion in monozygotic twins was 0.60 compared to 0.25 in the siblings. The extent to which these differences are attributable to underlying genetic factors was quantified and the heritability of leukocyte endothelial adhesion was calculated to be 69.66% (p-valueConclusions: There is a heritable component to leukocyte endothelial adhesion. Underlying genetic predisposition plays a significant role in inter-individual variability of leukocyte endothelial adhesion.</p

Gendered vulnerabilities to climate change: insights from the semi-arid regions of Africa and Asia

Emerging and on-going research indicates that vulnerabilities to impacts of climate change are gendered. Still, policy approaches aimed at strengthening local communities’ adaptive capacity largely fail to recognize the gendered nature of everyday realities and experiences. This paper interrogates some of the emerging evidence in selected semi-arid countries of Africa and Asia from a gender perspective, using water scarcity as an illustrative example. It emphasizes the importance of moving beyond the counting of numbers of men and women to unpacking relations of power, of inclusion and exclusion in decision-making, and challenging cultural beliefs that have denied equal opportunities and rights to differently positioned people, especially those at the bottom of economic and social hierarchies. Such an approach would make policy and practice more relevant to people’s differentiated needs and responses

Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma

SummaryWe describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Fracture resistance of endodontically treated permanent anterior teeth restored with three different esthetic post systems: An in vitro study

Background: Esthetic coronal reconstruction of fractured anterior teeth is often performed using intra radicular posts. Most of the commonly used commercially esthetic post systems do not exhibit similar physical properties as dentin resulting in failures. Aim: To evaluate and compare the fracture resistance and mode of failure of simulated traumatized permanent central incisors restored with three different post systems including biologic dentin posts. Materials and Methods: A total of 40 recently extracted human maxillary central incisors with similar dimensions were decoronated 2 mm above the cemento-enamel junction and endodontically treated. Ten specimens were randomly selected as the Group I - Control group (core built teeth without intraradicular posts). The remaining 30 teeth were equally divided and restored with zirconia (Group II, n = 10), fiber re-inforced composite (FRC) (Group III, n = 10) and biologic dentin posts (Group IV, n = 10) using resin bonded cement and their cores built-up. These samples were embedded in acrylic resin and then secured in a Universal Testing Machine and subjected to fracture resistance testing. The location of failure in the specimens was evaluated using a stereomicroscope. Results: Intergroup comparison revealed that the control group and zirconia post group (522 ± 110 N) demonstrated the least fracture resistance, while dentin post group (721 ± 127 N) the highest. There was no statistically significant difference between fiber post and dentin post groups. Fractures that were repairable were observed in fiber post and dentin post groups, whereas mostly unrestorable, catastrophic fractures were observed in the zirconia post group. Conclusion: Teeth restored with the biologic dentin post system demonstrated the highest fracture resistance and repairable fractures, closely followed by FRC post system. The least fracture resistance and most catastrophic fractures were demonstrated by the zirconia post system

Not Available

Not AvailableThis study was conducted to characterize new plant type (NPT) traits among 650 genetically diverse rice genotypes of tropical japonica and indica and to establish an initial core set for NPT traits. Analysis of variance revealed highly significant differences among the genotypes for all the traits assessed except flag length and width and leaf angles. Dendrogram categorized the genotypes into five distinct duration groups. Genotypes viz., Pumphamah, IRGC5097, IRGC37015, IRGC43741, IRGC50448, IRGC53089, IRGC39111, IRGC18021, Haorei Machang, IRGC44069, IRGC8269, Thangmoi, IRGC33130 and IRGC29772 were identified as possessing strong culm. Long panicles with a length of more than 35 cm were found in IRGC8269, IRGC9147, IRGC14694, IRGC19642, IRGC27435, IRGC39111, IRGC31051, IRGC26011and IRGC25892. Ideal leaf angle of NPT genotypes of 5°, 10° and 20° of flag leaf, 1st and 2nd leaveswas not found in any genotype but with a combination of 5°, 10° and 10° was observed in IRGC63102 and IRGC66644. NPT flag leaf length and width of 50 and 2 cm, respectively, was seen in ‘Kemenya Kepeu’ and ‘IRGC29772’. High grain number of more than 350 was observed in IRGC53089, IRGC31063 and Azhoghi. A total of 72 genotypes were found with a combination of one or more ideal plant type traits of which, hierarchical cluster analysis based on genetic distances selected 32 as NPT core set. This core set will serve as an ideal genetic resource for breeding programs aimed at NPT development.Not Availabl