Intestinal anti-inflammatory effects of goat whey on DNBS-induced colitis in mice

This study evaluated the intestinal anti-inflammatory effects of goat whey in a mouse model of colitis induced by 2,4-dinitrobenzenesulfonic acid that resembles human IBD. At a concentration of 4 g/kg/day, the goat whey improved the symptoms of intestinal inflammation, namely by decreasing the disease activity index, colonic weight/length, and leukocyte infiltration. Moreover, goat whey inhibited NF-kappa B p65 and p38 MAPK signaling pathways and consequently down-regulated the gene expression of various proinflammatory markers such as IL-1 beta, IL-6, IL-17, TNF-alpha, iNOS, MMP-9, ICAM-1. Also, goat whey increased the expression of proteins such as mucins, occludin proteins and cytokine signalling suppressors. The immunomodulatory properties of goat whey were also evaluated in vitro using the murine macrophage cell line Raw 264 and CMT-93 cells derived from mouse rectum carcinomas. The results revealed the ability of goat whey to inhibit the production of NO and reduce IL-6 production in LPS-stimulated cells. In conclusion, goat whey exhibited antiinflammatory effects in the DNBS model of intestinal inflammation, and these observations were confirmed by its immunomodulatory properties in vitro. Together, our results indicate that goat whey could have applications for the treatment of IBD.info:eu-repo/semantics/publishedVersio

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

ATLAS

% ATLAS \\ \\ ATLAS is a general-purpose experiment for recording proton-proton collisions at LHC. The ATLAS collaboration consists of 144 participating institutions (June 1998) with more than 1750~physicists and engineers (700 from non-Member States). The detector design has been optimized to cover the largest possible range of LHC physics: searches for Higgs bosons and alternative schemes for the spontaneous symmetry-breaking mechanism; searches for supersymmetric particles, new gauge bosons, leptoquarks, and quark and lepton compositeness indicating extensions to the Standard Model and new physics beyond it; studies of the origin of CP violation via high-precision measurements of CP-violating B-decays; high-precision measurements of the third quark family such as the top-quark mass and decay properties, rare decays of B-hadrons, spectroscopy of rare B-hadrons, and $B ^0 _{s}$-mixing. \\ \\The ATLAS dectector, shown in the Figure includes an inner tracking detector inside a 2~T~solenoid providing an axial field, electromagnetic and hadronic calorimeters outside the solenoid and in the forward regions, and barrel and end-cap air-core-toroid muon spectrometers. The precision measurements for photons, electrons, muons and hadrons, and identification of photons, electrons, muons, $\tau$-leptons and b-quark jets are performed over $| \eta |$ < 2.5. The complete hadronic energy measurement extends over $| \eta |$ < 4.7. \\ \\The inner tracking detector consists of straw drift tubes interleaved with transition radiators for robust pattern recognition and electron identification, and several layers of semiconductor strip and pixel detectors providing high-precision space points. \\ \\The e.m. calorimeter is a lead-Liquid Argon sampling calorimeter with an integrated preshower detector and a presampler layer immediately behind the cryostat wall for energy recovery. The end-cap hadronic calorimeters also use Liquid Argon technology, with copper absorber plates. The end-cap cryostats house the e.m., hadronic and forward calorimeters (tungsten-Liquid Argon sampling). The barrel hadronic calorimeter is an iron-scintillating tile sampling calorimeter with longitudinal tile geometry. \\ \\Air-core toroids are used for the muon spectrometer. Eight superconducting coils with warm voussoirs are used in the barrel region complemented with superconducting end-cap toroids in the forward regions. The toroids will be instrumented with Monitored Drift Tubes (Cathode Strip Chambers at large rapidity where there are high radiation levels). The muon trigger and second coordinate measurement for muon tracks are provide