Recognize fish as food in policy discourse and development funding

The international development community is off-track from meeting targets for alleviating global malnutrition. Meanwhile, there is growing consensus across scientific disciplines that fish plays a crucial role in food and nutrition security. However, this ‘fish as food’ perspective has yet to translate into policy and development funding priorities. We argue that the traditional framing of fish as a natural resource emphasizes economic development and biodiversity conservation objectives, whereas situating fish within a food systems perspective can lead to innovative policies and investments that promote nutrition-sensitive and socially equitable capture fisheries and aquaculture. This paper highlights four pillars of research needs and policy directions toward this end. Ultimately, recognizing and working to enhance the role of fish in alleviating hunger and malnutrition can provide an additional long-term development incentive, beyond revenue generation and biodiversity conservation, for governments, international development organizations, and society more broadly to invest in the sustainability of capture fisheries and aquaculture

Phenomenological model for the Kbar N --> K Xi reaction

A phenomenological model for the Kbar N --> K Xi reaction is suggested. The model includes s and u channel exchanges by Lambda, Sigma, Sigma(1385), and Lambda(1520) and s channel exchanges by above-threshold hyperonic resonances. Explicit expression for the propagator for a particle with spin 7/2 is presented. High-mass and high-spin resonances play a significant role in the process. We deal with the whole set of existing experimental data on the cross sections and polarizations in the energy range from the threshold to 2.8 GeV in the center-of-mass system and reach a good agreement with experiments. Applications of the model to other elementary reactions of Xi production and to Xi hypernuclear spectroscopy are briefly discussed.Comment: Published version; minor change

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field

A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories. By performing molecular analyses of 2,579 TCGA gynecological (OV, UCEC, CESC, and UCS) and breast tumors, Berger et al. identify five prognostic subtypes using 16 key molecular features and propose a decision tree based on six clinically assessable features that classifies patients into the subtypes

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Dense genotyping of candidate gene loci identifies variants associated with high-density lipoprotein cholesterol.

BACKGROUND: Plasma levels of high-density lipoprotein cholesterol (HDL-C) are known to be heritable, but only a fraction of the heritability is explained. We used a high-density genotyping array containing single-nucleotide polymorphisms (SNPs) from HDL-C candidate genes selected on known biology of HDL-C metabolism, mouse genetic studies, and human genetic association studies. SNP selection was based on tagging SNPs and included low-frequency nonsynonymous SNPs. METHODS AND RESULTS: Association analysis in a cohort containing extremes of HDL-C (case-control, n=1733) provided a discovery phase, with replication in 3 additional populations for a total meta-analysis in 7857 individuals. We replicated the majority of loci identified through genome-wide association studies and present on the array (including ABCA1, APOA1/C3/A4/A5, APOB, APOE/C1/C2, CETP, CTCF-PRMT8, FADS1/2/3, GALNT2, LCAT, LILRA3, LIPC, LIPG, LPL, LRP4, SCARB1, TRIB1, ZNF664) and provide evidence that suggests an association in several previously unreported candidate gene loci (including ABCG1, GPR109A/B/81, NFKB1, PON1/2/3/4). There was evidence for multiple, independent association signals in 5 loci, including association with low-frequency nonsynonymous variants. CONCLUSIONS: Genetic loci associated with HDL-C are likely to harbor multiple, independent causative variants, frequently with opposite effects on the HDL-C phenotype. Cohorts comprising subjects at the extremes of the HDL-C distribution may be efficiently used in a case-control discovery of quantitative traits