Measurements and Performance Factor Comparisons of Magnetic Materials at High Frequency

The design of power magnetic components for operation at high frequency (HF, 3–30MHz) has been hindered by a lack of performance data and by the limited design theory in that frequency range. To address these deficiencies, we have measured and present core loss data for a variety of commercially available magnetic materials in the HF range. In addition, we extend the theory of performance factor for appropriate use in HF design. Since magnetic materials suitable for HF applications tend to have low permeability, we also consider the impact of low permeability on design. We conclude that, with appropriate material selection and design, increased frequencies can continue to yield improved power density well into the HF regime.MIT Energy Initiative (Lockheed Martin)Texas Instruments Incorporate

Cis interactions in the Irf8 locus regulate stage-dependent enhancer activation

Individual elements within a superenhancer can act in a cooperative or temporal manner, but the underlying mechanisms remain obscure. We recently identified a

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef

Affinity-restricted memory B cells dominate recall responses to heterologous flaviviruses

Memory B cells (MBCs) can respond to heterologous antigens either by molding new specificities through secondary germinal centers (GCs) or selecting pre-existing clones without further affinity maturation. To distinguish these mechanisms in flavivirus infections and immunizations, we studied recall responses to envelope protein domain III (DIII). Conditional deletion of activation induced cytidine deaminase (AID) between heterologous challenges of West Nile, Japanese encephalitis, Zika, and Dengue viruses did not affect recall responses. DIII-specific MBCs were contained mostly within the plasma cell-biased CD80(+) subset and few GCs arose following heterologous boosters, demonstrating that recall responses are confined by pre-existing clonal diversity. Measurement of monoclonal antibody binding affinity to DIII proteins, timed AID deletion, single cell RNA-sequencing, and lineage tracing experiments point to selection of relatively low affinity MBCs as a mechanism to promote diversity. Engineering immunogens to avoid this MBC diversity may facilitate flavivirus type-specific vaccines with minimized potential for infection enhancement

Clonal Hematopoiesis is Associated With Protection From Alzheimer\u27s Disease

Clonal hematopoiesis of indeterminate potential (CHIP) is a premalignant expansion of mutated hematopoietic stem cells. As CHIP-associated mutations are known to alter the development and function of myeloid cells, we hypothesized that CHIP may also be associated with the risk of Alzheimer\u27s disease (AD), a disease in which brain-resident myeloid cells are thought to have a major role. To perform association tests between CHIP and AD dementia, we analyzed blood DNA sequencing data from 1,362 individuals with AD and 4,368 individuals without AD. Individuals with CHIP had a lower risk of AD dementia (meta-analysis odds ratio (OR) = 0.64, P = 3.8 × 1

Stability and control of ad hoc dc microgrids

Ad hoc electrical networks are formed by connecting power sources and loads without pre-determining the network topology. These systems are well-suited to addressing the lack of electricity in rural areas because they can be assembled and modified by non-expert users without central oversight. There are two core aspects to ad hoc system design: (1) designing source and load units such that the microgrid formed from the arbitrary interconnection of many units is always stable and (2) developing control strategies to autonomously manage the microgrid (i.e., perform power dispatch and voltage regulation) in a decentralized manner and under large uncertainty. To address these challenges we apply a number of nonlinear control techniques-including Brayton-Moser potential theory and primal-dual dynamics-to obtain conditions under which an ad hoc dc microgrid will have a suitable and asymptotically stable equilibrium point. Further, we propose a new decentralized control scheme that coordinates many sources to achieve a specified power dispatch from each. A simulated comparison to previous research is included

Genome-wide CRISPR screens of T cell exhaustion identify chromatin remodeling factors that limit T cell persistence

T cell exhaustion limits antitumor immunity, but the molecular determinants of this process remain poorly understood. Using a chronic stimulation assay, we performed genome-wide CRISPR-Cas9 screens to systematically discover regulators of T cell exhaustion, which identified an enrichment of epigenetic factors. In vivo CRISPR screens in murine and human tumor models demonstrated that perturbation of the INO80 and BAF chromatin remodeling complexes improved T cell persistence in tumors. In vivo Perturb-seq revealed distinct transcriptional roles of each complex and that depletion of canonical BAF complex members, including Arid1a, resulted in the maintenance of an effector program and downregulation of exhaustion-related genes in tumor-infiltrating T cells. Finally, Arid1a depletion limited the acquisition of exhaustion-associated chromatin accessibility and led to improved antitumor immunity. In summary, we provide an atlas of the genetic regulators of T cell exhaustion and demonstrate that modulation of epigenetic state can improve T cell responses in cancer immunotherapy

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Abstract Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef

A human mutation in STAT3 promotes type 1 diabetes through a defect in CD8+ T cell tolerance.

Naturally occurring cases of monogenic type 1 diabetes (T1D) help establish direct mechanisms driving this complex autoimmune disease. A recently identified de novo germline gain-of-function (GOF) mutation in the transcriptional regulator STAT3 was found to cause neonatal T1D. We engineered a novel knock-in mouse incorporating this highly diabetogenic human STAT3 mutation (K392R) and found that these mice recapitulated the human autoimmune diabetes phenotype. Paired single-cell TCR and RNA sequencing revealed that STAT3-GOF drives proliferation and clonal expansion of effector CD8+ cells that resist terminal exhaustion. Single-cell ATAC-seq showed that these effector T cells are epigenetically distinct and have differential chromatin architecture induced by STAT3-GOF. Analysis of islet TCR clonotypes revealed a CD8+ cell reacting against known antigen IGRP, and STAT3-GOF in an IGRP-reactive TCR transgenic model demonstrated that STAT3-GOF intrinsic to CD8+ cells is sufficient to accelerate diabetes onset. Altogether, these findings reveal a diabetogenic CD8+ T cell response that is restrained in the presence of normal STAT3 activity and drives diabetes pathogenesis