Notch1 Inhibits Rosiglitazone-Induced Adipogenic Differentiation in Primary Thymic Stromal Cells

Adipocyte deposition is believed to be a primary characteristic of age-related thymic involution. Herein, we cultured primary thymic stromal cells (TSCs), used rosiglitazone, a potent peroxisome proliferator-activated receptor γ (PPARγ) agonist, to induce adipogenic differentiation, and investigated the differentially expressed genes during adipogenic differentiation by using RNA-sequencing analysis. Furthermore, the effects of Notch1 on rosiglitazone-induced adipogenic differentiation of TSCs as well as the underlying mechanisms were also investigated. As a result, we identified a total of 1737 differentially expressed genes, among which 965 genes were up-regulated and 772 genes were down-regulated in rosiglitazone-treated cells compared with control cells. Gene ontology (GO) enrichment analysis showed that the GO terms were enriched in metabolic process, intracellular, and protein binding. Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that a number of pathways, including ubiquitin mediated proteolysis, PPAR signaling pathway, and mammalian target of rapamycin (mTOR) signaling pathway were predominantly over-represented. Meanwhile, overexpression of Notch1 suppressed and inhibition of Notch1 promoted rosiglitazone-induced adipogenic differentiation in TSCs, and the pro-adipogenic effects of the Notch inhibitor DAPT were associated with the activation of autophagy. Taken together, our results suggest that Notch1 is a key regulator in thymic adipogenesis and may serve as a potential target to hinder thymic adiposity in age-related thymic involution

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road

The phenomenon of stop-and-go traffic and its environmental impact has become a crucial issue that needs to be tackled, in terms of the junctions between freeway and urban road networks, which consist of freeway off-ramps, downstream intersections, and the junction section. The development of Connected and Automated Vehicles (CAVs) has provided promising solutions to tackle the difficulties that arise along intersections and freeway off-ramps separately. However, several problems still exist that need to be handled in terms of junction structure, including vehicle merging trajectory optimization, vehicle crossing trajectory optimization, and heterogeneous decision-making. In this paper, a two-stage CAV trajectory optimization strategy is presented to improve fuel economy and to reduce delays through a joint framework. The first stage considers an approach to determine travel time considering the different topological structures of each subarea to ensure maximum capacity. In the second stage, Pontryagin’s Minimum Principle (PMP) is employed to construct Hamiltonian equations to smooth vehicle trajectory under the requirements of vehicle dynamics and safety. Targeted methods are devised to avoid driving backwards and to ensure an optimal vehicle gap, which make up for the shortcomings of the PMP theory. Finally, simulation experiments are designed to verify the effectiveness of the proposed strategy. The evaluation results show that our strategy could effectively militate travel delays and fuel consumption

Interface Synergistic Effect from Hierarchically Porous Cu(OH)₂@FCN MOF/CF Nanosheet Arrays Boosting Electrocatalytic Oxygen Evolution

The electrolysis of water is an efficient and environmentally friendly technology for large-scale hydrogen production. However, the oxygen evolution reaction (OER) involves a multi-electron–proton coupling transfer step that limits the efficiency of water splitting. Therefore, there is an urgent need to develop electrocatalysts with expected activity and stability to accelerate the kinetics of the oxygen evolution reaction. In this paper, hierarchically porous Cu(OH)2@(Fe, Co, Ni)MOF/CF nanosheet (denoted as Cu(OH)2@FCN MOF/CF) arrays were successfully prepared by the hydrothermally induced in situ growth of FCN MOF nanosheets using modified Cu(OH)2 nanowires as carriers; herein, the tuned active species of metal ligands in the FCN MOF composition structure are used as the main catalytic reaction size in the OER. The synergistic effect of a unique porous structure and the active metal-ligand species in the MOF render the catalyst a large electrochemically active surface area and more active species. Then, the active material is fully contacted with the electrolyte to expose more electrochemically active sites, thus greatly improving the electrocatalytic activity and durability of the OER. Specifically, the Cu(OH)2@FCN MOF/CF delivers a minimum overpotential of 290 mV and low Tafel slope of 96.15 mV·dec−1 at 10 mA·cm−2 as well as ultra-long cycling stability. The resulted OER performance is superior to most reported MOF-based electrocatalysts. This novel structural design not only provides a new strategy for the facile preparation of low-cost and high-efficiency OER electrocatalysts but also paves an avenue for the development of other MOF-based composite electrocatalysts with excellent electrocatalytic performances

Effective Optimization Strategy for Electron Beam Lithography of Molecular Glass Negative Photoresist

Abstract As the crucial dimension (CD) of logic circuits continues to shrink, the photoresist metrics, including resolution, line edge roughness, and sensitivity, are faced with significant challenges. Photoresists are indispensable in the integrated circuit manufacturing industry, and specifically in achieving smaller critical dimensions. In this study, the effects of two categories of photosensitive compounds on lithography performance are explored, through a series of sulfonium salt‐based photoacid generators (PAGs) with diverse reactivity and photodegradable nucleophiles (PDNs) with varying nucleophilicity. The detailed characterization and exposure experiments suggest that the reactive alterations of different PAGs are mostly associated with the amount of phenyl composed of cations in PAGs. The “PDN first, PAG second” strategy, which employs a combination of low reactivity PAG and high reactivity PDN and involves PDN decomposition first and PAG decomposition second in the electron beam lithography process, achieves high sensitivity (100–270 µC cm−2), high resolution (25 nm 1:1 line/space, L/S), and low line edge roughness (LER ≤ 3.3 nm) stripes. This approach outperforms conventional formulations and may provide a potentially effective and useful strategy to improve electron beam photoresists

Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials

With the characteristics of high-speed calculation and high-accuracy prediction, artificial intelligence (AI) which also known as machine intelligence, including deep learning, machine learning, etc., have shown great advantages in cross-field applications. In material science field, AI can be used to discover new materials and predict corresponding critical properties. At present, AI has been used in the exploitation of energy conversion materials and other energy-related materials. In this review, we summary the current achievements of AI applications in energy conversions, analyze the advantages and disadvantages of AI techniques in material researches and point out future development prospects