Biomarkers of inflammation and breast cancer risk: A case-control study nested in the EPIC-Varese cohort

Abstract Breast cancer (BC) is the leading cause of cancer death in women. Adipokines, and other inflammation molecules linked to adiposity, are suspected to be involved in breast carcinogenesis, however prospective findings are inconclusive. In a prospective nested case-control study within the EPIC-Varese cohort, we used conditional logistic regression to estimate rate ratios (RRs) for BC, with 95% confidence intervals (CI), in relation to plasma levels of C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), interleukin-6, leptin, and adiponectin, controlling for BC risk factors. After a median 14.9 years, 351 BC cases were identified and matched to 351 controls. No marker was significantly associated with BC risk overall. Significant interactions between menopausal status and CRP, leptin, and adiponectin were found. Among postmenopausal women, high CRP was significantly associated with increased BC risk, and high adiponectin with significantly reduced risk. Among premenopausal women, high TNF-α was associated with significantly increased risk, and high leptin with reduced risk; interleukin-6 was associated with increased risk only in a continuous model. These findings constitute further evidence that inflammation plays a role in breast cancer. Interventions to lower CRP, TNF-α, and interleukin-6 and increase adiponectin levels may contribute to preventing BC

Catalytic fast pyrolysis of biomass : catalyst characterization reveals the feed-dependent deactivation of a technical ZSM-5-based catalyst

Catalyst deactivation due to coking is a major challenge in the catalytic fast pyrolysis (CFP) of biomass. Here, a multitechnique investigation of a technical Al2O3-bound ZSM-5-based extrudate catalyst, used for the CFP of pine wood and cellulose (at a reactor temperature of 500 °C), provided insight into the effects of extrusion, the catalytic pyrolysis process, and catalyst regeneration on the catalyst structure. As a result of a reduction in acidity and surface area due to the coking catalyst, the activity dropped drastically with increasing time-on-stream (TOS), as evidenced by a decrease in aromatics yield. Strikingly, confocal fluorescence microscopy at the single-particle level revealed that vapor components derived from whole biomass or just the cellulose component coke differently. While pine-wood-derived species mainly blocked the external area of the catalyst particle, larger carbon deposits were formed inside the catalyst’s micropores with cellulose-derived species. Pyridine FT-IR and solid-state NMR spectroscopy demonstrated irreversible changes after regeneration, likely due to partial dealumination. Taken together with <30 g kg–1 aromatics yield on a feed basis, the results show a mismatch between biomass pyrolysis vapors and the technical catalyst used due to a complex interplay of mass transfer limitations and CFP chemistry

Dietary intake of animal and plant proteins and risk of all cause and cause-specific mortality: The Epic-Italy cohort

BACKGROUND: To examine the associations of animal and plant protein intake with all-cause, cardiovascular and cancer mortality risk in middle-aged Italian men and women with substantially lower animal protein intake than North Americans. METHODS AND RESULTS: Food consumption was assessed by validated Epic semiquantitative FFQs. Multivariable Cox models stratified by center, age, and sex, and adjusted for confounders, estimated associations of animal and plant protein consumption with mortality for all causes, cardiovascular disease, and cancer. After a median follow-up of 15.2 years, 2,449 deaths were identified in 45,009 participants. No significant association between intake of total, animal or plant protein and mortality was found in the fully adjusted models. Substitution of plant protein for animal protein was inversely associated with cardiovascular mortality (HR, 0.47; 95% CI, 0.24–0.92) only in people with at least 1 unhealthy lifestyle risk factor and poor adherence to a Mediterranean diet. Participants in the highest quintile group of animal protein intake had higher glucose, total and LDL cholesterol levels than those in the lowest quintile. In contrast, higher plant protein intake was negatively associated with fasting insulin and cholesterol, despite higher BMI, physical inactivity and starch consumption. CONCLUSIONS: Replacing plant protein for animal protein was associated with lower cardiovascular mortality among individuals with unhealthy lifestyle risk factors. High animal but not plant protein intake is associated with impaired fasting glucose and hypercholesterolemia, despite lower calorie and carbohydrate intake, suggesting that protein source plays crucial roles in modulating cardiometabolic health independently of body weight

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Immunopathological signatures in multisystem inflammatory syndrome in children and pediatric COVID-19

: Pediatric Coronavirus Disease 2019 (pCOVID-19) is rarely severe; however, a minority of children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) might develop multisystem inflammatory syndrome in children (MIS-C), with substantial morbidity. In this longitudinal multi-institutional study, we applied multi-omics (analysis of soluble biomarkers, proteomics, single-cell gene expression and immune repertoire analysis) to profile children with COVID-19 (n = 110) and MIS-C (n = 76), along with pediatric healthy controls (pHCs; n = 76). pCOVID-19 was characterized by robust type I interferon (IFN) responses, whereas prominent type II IFN-dependent and NF-κB-dependent signatures, matrisome activation and increased levels of circulating spike protein were detected in MIS-C, with no correlation with SARS-CoV-2 PCR status around the time of admission. Transient expansion of TRBV11-2 T cell clonotypes in MIS-C was associated with signatures of inflammation and T cell activation. The association of MIS-C with the combination of HLA A*02, B*35 and C*04 alleles suggests genetic susceptibility. MIS-C B cells showed higher mutation load than pCOVID-19 and pHC. These results identify distinct immunopathological signatures in pCOVID-19 and MIS-C that might help better define the pathophysiology of these disorders and guide therapy

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Overexpression of the Cytokine BAFF and Autoimmunity Risk

$\textbf{BACKGROUND}$: Genomewide association studies of autoimmune diseases have mapped hundreds of susceptibility regions in the genome. However, only for a few association signals has the causal gene been identified, and for even fewer have the causal variant and underlying mechanism been defined. Coincident associations of DNA variants affecting both the risk of autoimmune disease and quantitative immune variables provide an informative route to explore disease mechanisms and drug-targetable pathways. $\textbf{METHODS}$: Using case-control samples from Sardinia, Italy, we performed a genomewide association study in multiple sclerosis followed by TNFSF13B locus-specific association testing in systemic lupus erythematosus (SLE). Extensive phenotyping of quantitative immune variables, sequence-based fine mapping, cross-population and cross-phenotype analyses, and gene-expression studies were used to identify the causal variant and elucidate its mechanism of action. Signatures of positive selection were also investigated. $\textbf{RESULTS}$: A variant in TNFSF13B, encoding the cytokine and drug target B-cell activating factor (BAFF), was associated with multiple sclerosis as well as SLE. The disease-risk allele was also associated with up-regulated humoral immunity through increased levels of soluble BAFF, B lymphocytes, and immunoglobulins. The causal variant was identified: an insertion-deletion variant, GCTGT→A (in which A is the risk allele), yielded a shorter transcript that escaped microRNA inhibition and increased production of soluble BAFF, which in turn up-regulated humoral immunity. Population genetic signatures indicated that this autoimmunity variant has been evolutionarily advantageous, most likely by augmenting resistance to malaria. $\textbf{CONCLUSIONS}$: A TNFSF13B variant was associated with multiple sclerosis and SLE, and its effects were clarified at the population, cellular, and molecular levels. (Funded by the Italian Foundation for Multiple Sclerosis and others.).Supported by grants (2011/R/13 and 2015/R/09, to Dr. Cucca) from the Italian Foundation for Multiple Sclerosis; contracts (N01-AG-1-2109 and HHSN271201100005C, to Dr. Cucca) from the Intramural Research Program of the National Institute on Aging, National Institutes of Health (NIH); a grant (FaReBio2011 “Farmaci e Reti Biotecnologiche di Qualità,” to Dr. Cucca) from the Italian Ministry of Economy and Finance; a grant (633964, to Dr. Cucca) from the Horizon 2020 Research and Innovation Program of the European Union; a grant (U1301.2015/AI.1157.BE Prat. 2015-1651, to Dr. Cucca) from Fondazione di Sardegna; grants (“Centro per la ricerca di nuovi farmaci per malattie rare, trascurate e della povertà” and “Progetto collezione di composti chimici ed attività di screening,” to Dr. Cucca) from Ministero dell’Istruzione, dell’Università e della Ricerca; grants (HG005581, HG005552, HG006513, and HG007022, to Dr. Abecasis) from the National Human Genome Research Institute; a grant (9-2011-253, to Dr. Todd) from JDRF; a grant (091157, to Dr. Todd) from the Wellcome Trust; a grant (to Dr. Todd) from the National Institute for Health Research (NIHR); and the NIHR Cambridge Biomedical Research Centre. Dr. Idda was a recipient of a Master and Back fellowship from the Autonomous Region of Sardinia