Models of Intragroup Conflict in Management: A Literature Review

The study of intragroup dynamics in management studies views conflict as a contingency process that can benefit or harm a group based of characteristics of the group and context. We review five models of intragroup conflict in management studies. These models include diversity-conflict and behavioral negotiation models that focus primarily on conflict within a group of people; social exchange and transaction cost economics models that focus primarily on conflict within a group of firms; and social dilemma models that focus on conflict in collectives of people, organizations, communities, and generations. The review is constituted by summarizing the insights of each model, foundational papers to each model; the most recent uses and developments of the models in the last decade; the complementarity of these models; and the future research directions

The prevalence of mild cognitive impairment in Gulf War veterans: a follow-up study

IntroductionGulf War Illness (GWI), also called Chronic Multisymptom Illness (CMI), is a multi-faceted condition that plagues an estimated 250,000 Gulf War (GW) veterans. Symptoms of GWI/CMI include fatigue, pain, and cognitive dysfunction. We previously reported that 12% of a convenience sample of middle aged (median age 52 years) GW veterans met criteria for mild cognitive impairment (MCI), a clinical syndrome most prevalent in older adults (e.g., ≥70 years). The current study sought to replicate and extend this finding.MethodsWe used the actuarial neuropsychological criteria and the Montreal Cognitive Assessment (MoCA) to assess the cognitive status of 952 GW veterans. We also examined regional brain volumes in a subset of GW veterans (n = 368) who had three Tesla magnetic resonance images (MRIs).ResultsWe replicated our previous finding of a greater than 10% rate of MCI in four additional cohorts of GW veterans. In the combined sample of 952 GW veterans (median age 51 years at time of cognitive testing), 17% met criteria for MCI. Veterans classified as MCI were more likely to have CMI, history of depression, and prolonged (≥31 days) deployment-related exposures to smoke from oil well fires and chemical nerve agents compared to veterans with unimpaired and intermediate cognitive status. We also replicated our previous finding of hippocampal atrophy in veterans with MCI, and found significant group differences in lateral ventricle volumes.DiscussionBecause MCI increases the risk for late-life dementia and impacts quality of life, it may be prudent to counsel GW veterans with cognitive dysfunction, CMI, history of depression, and high levels of exposures to deployment-related toxicants to adopt lifestyle habits that have been associated with lowering dementia risk. With the Food and Drug Administration’s recent approval of and the VA’s decision to cover the cost for anti-amyloid β (Aβ) therapies, a logical next step for this research is to determine if GW veterans with MCI have elevated Aβ in their brains

Neuroimaging markers for studying Gulf-War illness: single-subject level analytical method based on machine learning

Gulf War illness (GWI) refers to the multitude of chronic health symptoms, spanning from fatigue, musculoskeletal pain, and neurological complaints to respiratory, gastrointestinal, and dermatologic symptoms experienced by about 250,000 GW veterans who served in the 1991 Gulf War (GW). Longitudinal studies showed that the severity of these symptoms often remain unchanged even years after the GW, and these veterans with GWI continue to have poorer general health and increased chronic medical conditions than their non-deployed counterparts. For better management and treatment of this condition, there is an urgent need for developing objective biomarkers that can help with simple and accurate diagnosis of GWI. In this study, we applied multiple neuroimaging techniques, including T1-weighted magnetic resonance imaging (T1W-MRI), diffusion tensor imaging (DTI), and novel neurite density imaging (NDI) to perform both a group-level statistical comparison and a single-subject level machine learning (ML) analysis to identify diagnostic imaging features of GWI. Our results supported NDI as the most sensitive in defining GWI characteristics. In particular, our classifier trained with white matter NDI features achieved an accuracy of 90% and F-score of 0.941 for classifying GWI cases from controls after the cross-validation. These results are consistent with our previous study which suggests that NDI measures are sensitive to the microstructural and macrostructural changes in the brain of veterans with GWI, which can be valuable for designing better diagnosis method and treatment efficacy studies.W81XWH-17-1-0440 - a department of Defense CDMRP new investigator awardPublished versio

Single-Cell Dna Methylome and 3D Multi-Omic Atlas of the Adult Mouse Brain

Cytosine DNA methylation is essential in brain development and is implicated in various neurological disorders. Understanding DNA methylation diversity across the entire brain in a spatial context is fundamental for a complete molecular atlas of brain cell types and their gene regulatory landscapes. Here we used single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq)1 technologies to generate 301,626 methylomes and 176,003 chromatin conformation–methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell taxonomy with 4,673 cell groups and 274 cross-modality-annotated subclasses. We identified 2.6 million differentially methylated regions across the genome that represent potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide spatial transcriptomics data validated the association of spatial epigenetic diversity with transcription and improved the anatomical mapping of our epigenetic datasets. Furthermore, chromatin conformation diversities occurred in important neuronal genes and were highly associated with DNA methylation and transcription changes. Brain-wide cell-type comparisons enabled the construction of regulatory networks that incorporate transcription factors, regulatory elements and their potential downstream gene targets. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a whole-brain SMART-seq2 dataset. Our study establishes a brain-wide, single-cell DNA methylome and 3D multi-omic atlas and provides a valuable resource for comprehending the cellular–spatial and regulatory genome diversity of the mouse brain

Association of genetic variants in complement factor H and factor H-related genes with systemic lupus erythematosus susceptibility

Systemic lupus erythematosus (SLE), a complex polygenic autoimmune disease, is associated with increased complement activation. Variants of genes encoding complement regulator factor H (CFH) and five CFH-related proteins (CFHR1-CFHR5) within the chromosome 1q32 locus linked to SLE, have been associated with multiple human diseases and may contribute to dysregulated complement activation predisposing to SLE. We assessed 60 SNPs covering the CFH-CFHRs region for association with SLE in 15,864 case-control subjects derived from four ethnic groups. Significant allelic associations with SLE were detected in European Americans (EA) and African Americans (AA), which could be attributed to an intronic CFH SNP (rs6677604, in intron 11, Pmeta = 6.6×10-8, OR = 1.18) and an intergenic SNP between CFHR1 and CFHR4 (rs16840639, Pmeta = 2.9×10-7, OR = 1.17) rather than to previously identified disease-associated CFH exonic SNPs, including I62V, Y402H, A474A, and D936E. In addition, allelic association of rs6677604 with SLE was subsequently confirmed in Asians (AS). Haplotype analysis revealed that the underlying causal variant, tagged by rs6677604 and rs16840639, was localized to a ~146 kb block extending from intron 9 of CFH to downstream of CFHR1. Within this block, the deletion of CFHR3 and CFHR1 (CFHR3-1Δ), a likely causal variant measured using multiplex ligation-dependent probe amplification, was tagged by rs6677604 in EA and AS and rs16840639 in AA, respectively. Deduced from genotypic associations of tag SNPs in EA, AA, and AS, homozygous deletion of CFHR3-1Δ (Pmeta = 3.2×10-7, OR = 1.47) conferred a higher risk of SLE than heterozygous deletion (Pmeta = 3.5×10-4, OR = 1.14). These results suggested that the CFHR3-1Δ deletion within the SLE-associated block, but not the previously described exonic SNPs of CFH, might contribute to the development of SLE in EA, AA, and AS, providing new insights into the role of complement regulators in the pathogenesis of SLE

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype

Anticitrullinated protein antibody (ACPA) in rheumatoid arthritis: influence of an interaction between HLA-DRB1 shared epitope and a deletion polymorphism in glutathione s-transferase in a cross-sectional study

Abstract Introduction A deletion polymorphism in glutathione S-transferase Mu-1 (GSTM1-null) has previously been implicated to play a role in rheumatoid arthritis (RA) risk and progression, although no prior investigations have examined its associations with anticitrullinated protein antibody (ACPA) positivity. The purpose of this study was to examine the associations of GSTM1-null with ACPA positivity in RA and to assess for evidence of interaction between GSTM1 and HLA-DRB1 shared epitope (SE). Methods Associations of GSTM1-null with ACPA positivity were examined separately in two RA cohorts, the Veterans Affairs Rheumatoid Arthritis (VARA) registry (n = 703) and the Study of New-Onset RA (SONORA; n = 610). Interactions were examined by calculating an attributable proportion (AP) due to interaction. Results A majority of patients in the VARA registry (76%) and SONORA (69%) were positive for ACPA with a similar frequency of GSTM1-null (53% and 52%, respectively) and HLA-DRB1 SE positivity (76% and 71%, respectively). The parameter of patients who had ever smoked was more common in the VARA registry (80%) than in SONORA (65%). GSTM1-null was significantly associated with ACPA positivity in the VARA registry (odds ratio (OR), 1.45; 95% confidence interval (CI), 1.02 to 2.05), but not in SONORA (OR, 1.00; 95% CI, 0.71 to 1.42). There were significant additive interactions between GSTM1 and HLA-DRB1 SE in the VARA registry (AP, 0.49; 95% CI, 0.21 to 0.77; P < 0.001) in ACPA positivity, an interaction replicated in SONORA (AP, 0.38; 95% CI, 0.00 to 0.76; P = 0.050). Conclusions This study is the first to show that the GSTM1-null genotype, a common genetic variant, exerts significant additive interaction with HLA-DRB1 SE on the risk of ACPA positivity in RA. Since GSTM1 has known antioxidant functions, these data suggest that oxidative stress may be important in the development of RA-specific autoimmunity in genetically susceptible individuals

Multi -ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10−8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.We thank the Director of Health Malaysia for supporting the work described in the South Asian (SAS) population: the Malaysian Epidemiological Investigation of Rheumatoid Arthritis (MyEIRA) study. The MyEIRA study was funded by grants from Ministry of Health Malaysia (NMRR-08-820-1975) and the Swedish National Research Council (DNR-348-2009-6468). The GENRA study and the CARDERA genetics cohort genotyping were funded by Versus Arthritis (grant reference 19739 to I.C.S.). The Nurses’ Health Study (NHS cohort) is funded by the National Institutes of Health (NIH) (R01 AR049880, UM1 CA186107, R01 CA49449, U01 CA176726 and R01 CA67262). The Swedish EIRA study was supported by the Swedish Research Council (to L.K., L.P. and L.A.). S.S. was in part supported by the Mochida Memorial Foundation for Medical and Pharmaceutical Research, Kanae Foundation for the Promotion of Medical Science, Astellas Foundation for Research on Metabolic Disorders, JCR Grant for Promoting Basic Rheumatology, and Manabe Scholarship Grant for Allergic and Rheumatic Diseases. I.C.S. is funded by the National Institute for Health and Care Research (NIHR) Advanced Research Fellowship (grant reference NIHR300826). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. K.A.S. is supported by the Sherman Family Chair in Genomic Medicine and by a Canadian Institutes for Health Research Foundation Grant (FDN 148457) and grants from the Ontario Research Fund (RE-09-090) and Canadian Foundation for Innovation (33374). S.-C.B. is supported by the Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2021R1A6A1A03038899). R.P.K. and J.C.E. are funded by NIH (UL1 TR003096). C.M.L. is partly funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. T. Arayssi was partially supported by the National Priorities Research Program (grant 4-344-3-105 from the Qatar National Research Fund, a member of Qatar Foundation). M. Kerick and J.M. are funded by Rheumatology Cooperative Research Thematic Network program RD16/0012/0013 from the Instituto de Salud Carlos III (Spanish Ministry of Science and Innovation). Y.O. is funded by JSPS KAKENHI (19H01021 and 20K21834), AMED (JP21km0405211, JP21ek0109413, JP21ek0410075, JP21gm4010006 and JP21km0405217), JST Moonshot R&D (JPMJMS2021 and JPMJMS2024), Takeda Science Foundation, and the Bioinformatics Initiative of Osaka University Graduate School of Medicine. Y. Kochi is funded by grants from Nanken-Kyoten, TMDU and Medical Research Center Initiative for High Depth Omics. S.R. is supported by UH2AR067677, U01HG009379, R01AR063759 and U01HG012009

Mesostructured Block Copolymer Nanoparticles: Versatile Templates for Hybrid Inorganic/Organic Nanostructures

We present a versatile strategy to prepare a range of nanostructured poly(styrene)-block-poly(2-vinyl pyridine) copolymer particles with tunable interior morphology and controlled size by a simple solvent exchange procedure. A key feature of this strategy is the use of functional block copolymers incorporating reactive pyridyl moieties which allow the absorption of metal salts and other inorganic precursors to be directed. Upon reduction of the metal salts, well-defined hybrid metal nanoparticle arrays could be prepared, whereas the use of oxide precursors followed by calcination permits the synthesis of silica and titania particles. In both cases, ordered morphologies templated by the original block copolymer domains were obtained