Chronic sleep disruption alters gut microbiota, induces systemic and adipose tissue inflammation and insulin resistance in mice.

Chronic sleep fragmentation (SF) commonly occurs in human populations, and although it does not involve circadian shifts or sleep deprivation, it markedly alters feeding behaviors ultimately promoting obesity and insulin resistance. These symptoms are known to be related to the host gut microbiota. Mice were exposed to SF for 4 weeks and then allowed to recover for 2 weeks. Taxonomic profiles of fecal microbiota were obtained prospectively, and conventionalization experiments were performed in germ-free mice. Adipose tissue insulin sensitivity and inflammation, as well as circulating measures of inflammation, were assayed. Effect of fecal water on colonic epithelial permeability was also examined. Chronic SF-induced increased food intake and reversible gut microbiota changes characterized by the preferential growth of highly fermentative members of Lachnospiraceae and Ruminococcaceae and a decrease of Lactobacillaceae families. These lead to systemic and visceral white adipose tissue inflammation in addition to altered insulin sensitivity in mice, most likely via enhanced colonic epithelium barrier disruption. Conventionalization of germ-free mice with SF-derived microbiota confirmed these findings. Thus, SF-induced metabolic alterations may be mediated, in part, by concurrent changes in gut microbiota, thereby opening the way for gut microbiome-targeted therapeutics aimed at reducing the major end-organ morbidities of chronic SF

Exclusion limits on the WIMP-nucleon cross-section from the Cryogenic Dark Matter Search

The Cryogenic Dark Matter Search (CDMS) employs low-temperature Ge and Si detectors to search for Weakly Interacting Massive Particles (WIMPs) via their elastic-scattering interactions with nuclei while discriminating against interactions of background particles. For recoil energies above 10 keV, events due to background photons are rejected with >99.9% efficiency, and surface events are rejected with >95% efficiency. The estimate of the background due to neutrons is based primarily on the observation of multiple-scatter events that should all be neutrons. Data selection is determined primarily by examining calibration data and vetoed events. Resulting efficiencies should be accurate to about 10%. Results of CDMS data from 1998 and 1999 with a relaxed fiducial-volume cut (resulting in 15.8 kg-days exposure on Ge) are consistent with an earlier analysis with a more restrictive fiducial-volume cut. Twenty-three WIMP candidate events are observed, but these events are consistent with a background from neutrons in all ways tested. Resulting limits on the spin-independent WIMP-nucleon elastic-scattering cross-section exclude unexplored parameter space for WIMPs with masses between 10-70 GeV c^{-2}. These limits border, but do not exclude, parameter space allowed by supersymmetry models and accelerator constraints. Results are compatible with some regions reported as allowed at 3-sigma by the annual-modulation measurement of the DAMA collaboration. However, under the assumptions of standard WIMP interactions and a standard halo, the results are incompatible with the DAMA most likely value at >99.9% CL, and are incompatible with the model-independent annual-modulation signal of DAMA at 99.99% CL in the asymptotic limit.Comment: 40 pages, 49 figures (4 in color), submitted to Phys. Rev. D; v.2:clarified conclusions, added content and references based on referee's and readers' comments; v.3: clarified introductory sections, added figure based on referee's comment

New Results from the Cryogenic Dark Matter Search Experiment

Using improved Ge and Si detectors, better neutron shielding, and increased counting time, the Cryogenic Dark Matter Search (CDMS) experiment has obtained stricter limits on the cross section of weakly interacting massive particles (WIMPs) elastically scattering from nuclei. Increased discrimination against electromagnetic backgrounds and reduction of neutron flux confirm WIMP-candidate events previously detected by CDMS were consistent with neutrons and give limits on spin-independent WIMP interactions which are >2X lower than previous CDMS results for high WIMP mass, and which exclude new parameter space for WIMPs with mass between 8-20 GeV/c^2.Comment: 4 pages, 4 figure

Autoimmunity-Associated LYP-W620 Does Not Impair Thymic Negative Selection of Autoreactive T Cells.

A C1858T (R620W) variation in the PTPN22 gene encoding the tyrosine phosphatase LYP is a major risk factor for human autoimmunity. LYP is a known negative regulator of signaling through the T cell receptor (TCR), and murine Ptpn22 plays a role in thymic selection. However, the mechanism of action of the R620W variant in autoimmunity remains unclear. One model holds that LYP-W620 is a gain-of-function phosphatase that causes alterations in thymic negative selection and/or thymic output of regulatory T cells (Treg) through inhibition of thymic TCR signaling. To test this model, we generated mice in which the human LYP-W620 variant or its phosphatase-inactive mutant are expressed in developing thymocytes under control of the proximal Lck promoter. We found that LYP-W620 expression results in diminished thymocyte TCR signaling, thus modeling a "gain-of-function" of LYP at the signaling level. However, LYP-W620 transgenic mice display no alterations of thymic negative selection and no anomalies in thymic output of CD4(+)Foxp3(+) Treg were detected in these mice. Lck promoter-directed expression of the human transgene also causes no alteration in thymic repertoire or increase in disease severity in a model of rheumatoid arthritis, which depends on skewed thymic selection of CD4(+) T cells. Our data suggest that a gain-of-function of LYP is unlikely to increase risk of autoimmunity through alterations of thymic selection and that LYP likely acts in the periphery perhaps selectively in regulatory T cells or in another cell type to increase risk of autoimmunity

The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation

Type 2 innate lymphoid cells (ILC2s) both contribute to mucosal homeostasis and initiate pathologic inflammation in allergic asthma. However, the signals that direct ILC2s to promote homeostasis versus inflammation are unclear. To identify such molecular cues, we profiled mouse lung-resident ILCs using single-cell RNA sequencing at steady state and after in vivo stimulation with the alarmin cytokines IL-25 and IL-33. ILC2s were transcriptionally heterogeneous after activation, with subpopulations distinguished by expression of proliferative, homeostatic and effector genes. The neuropeptide receptor Nmur1 was preferentially expressed by ILC2s at steady state and after IL-25 stimulation. Neuromedin U (NMU), the ligand of NMUR1, activated ILC2s in vitro, and in vivo co-administration of NMU with IL-25 strongly amplified allergic inflammation. Loss of NMU-NMUR1 signalling reduced ILC2 frequency and effector function, and altered transcriptional programs following allergen challenge in vivo. Thus, NMUR1 signalling promotes inflammatory ILC2 responses, highlighting the importance of neuro-immune crosstalk in allergic inflammation at mucosal surfaces

New technologies for examining neuronal ensembles in drug addiction and fear

Correlational data suggest that learned associations are encoded within neuronal ensembles. However, it has been difficult to prove that neuronal ensembles mediate learned behaviours because traditional pharmacological and lesion methods, and even newer cell type-specific methods, affect both activated and non-activated neurons. Additionally, previous studies on synaptic and molecular alterations induced by learning did not distinguish between behaviourally activated and non-activated neurons. Here, we describe three new approaches—Daun02 inactivation, FACS sorting of activated neurons and c-fos-GFP transgenic rats — that have been used to selectively target and study activated neuronal ensembles in models of conditioned drug effects and relapse. We also describe two new tools — c-fos-tTA mice and inactivation of CREB-overexpressing neurons — that have been used to study the role of neuronal ensembles in conditioned fear

Black bone MRI with 3D reconstruction for the detection of skull fractures in children with suspected abusive head trauma.

PURPOSE: The purpose of this study was to determine the accuracy of "black bone" (BB) MRI for the detection of skull fractures in children with potential abusive head trauma. METHODS: A total of 34 pediatric patients were evaluated for potential abusive head trauma. All patients had both a non-contrast head CT (HCT) with multiplanar reformatted images and 3D volumetric reformatted images where available (gold standard) for fracture diagnosis and BB of the head with multiplanar reformatted images and 3D volumetric images. BB was performed using an ultrashort TE pointwise encoding time reduction with radial acquisition (PETRA) sequence at 1.5 T or 3 T. BB datasets were post-processed and 3D images created using Fovia's High Definition Volume Rendering® software. Two board-certified pediatric neuroradiologists independently reviewed the HCT and BB imaging, blinded to the findings from the other modality. RESULTS: Median patient age was 4 months (range 1.2-30 months). A total of 20 skull fractures in six patients (18% incidence of skull fractures) were detected on HCT. BB demonstrated 83% sensitivity (95%[CI] 36-99%), 100% specificity (95%[CI] 88-100%), 100% PPV (95%[CI] 46-100%), 97% NPV (95%[CI] 82-99%), and 97% accuracy (95%[CI] 85-99%) for diagnosis of a skull fracture. BB detected 95% (19/20) of the skull fractures detected by CT. CONCLUSION: A black bone MRI sequence may provide high sensitivity and specificity for detection of skull fractures in pediatric patients with abusive head trauma

SSL4EO-L: Datasets and Foundation Models for Landsat Imagery

The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fields. Despite the increasing popularity of deep learning and remote sensing, the majority of researchers still use decision trees and random forests for Landsat image analysis due to the prevalence of small labeled datasets and lack of foundation models. In this paper, we introduce SSL4EO-L, the first ever dataset designed for Self-Supervised Learning for Earth Observation for the Landsat family of satellites (including 3 sensors and 2 product levels) and the largest Landsat dataset in history (5M image patches). Additionally, we modernize and re-release the L7 Irish and L8 Biome cloud detection datasets, and introduce the first ML benchmark datasets for Landsats 4-5 TM and Landsat 7 ETM+ SR. Finally, we pre-train the first foundation models for Landsat imagery using SSL4EO-L and evaluate their performance on multiple semantic segmentation tasks. All datasets and model weights are available via the TorchGeo (https://github.com/microsoft/torchgeo) library, making reproducibility and experimentation easy, and enabling scientific advancements in the burgeoning field of remote sensing for a multitude of downstream applications