Regulation of RKIP Function by Helicobacter pylori in Gastric Cancer

Helicobacter pylori (H. pylori) is a gram-negative, spiral-shaped bacterium that infects more than half of the world’s population and is a major cause of gastric adenocarcinoma. The mechanisms that link H. pylori infection to gastric carcinogenesis are not well understood. In the present study, we report that the Raf-kinase inhibitor protein (RKIP) has a role in the induction of apoptosis by H. pylori in gastric epithelial cells. Western blot and luciferase transcription reporter assays demonstrate that the pathogenicity island of H. pylori rapidly phosphorylates RKIP, which then localizes to the nucleus where it activates its own transcription and induces apoptosis. Forced overexpression of RKIP enhances apoptosis in H. pylori-infected cells, whereas RKIP RNA inhibition suppresses the induction of apoptosis by H. pylori infection. While inducing the phosphorylation of RKIP, H. pylori simultaneously targets non-phosphorylated RKIP for proteasome-mediated degradation. The increase in RKIP transcription and phosphorylation is abrogated by mutating RKIP serine 153 to valine, demonstrating that regulation of RKIP activity by H. pylori is dependent upon RKIP’s S153 residue. In addition, H. pylori infection increases the expression of Snail, a transcriptional repressor of RKIP. Our results suggest that H. pylori utilizes a tumor suppressor protein, RKIP, to promote apoptosis in gastric cancer cells

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys

Performance of the CMS muon trigger system in proton-proton collisions at √s = 13 TeV

The muon trigger system of the CMS experiment uses a combination of hardware and software to identify events containing a muon. During Run 2 (covering 2015-2018) the LHC achieved instantaneous luminosities as high as 2 × 10 cm s while delivering proton-proton collisions at √s = 13 TeV. The challenge for the trigger system of the CMS experiment is to reduce the registered event rate from about 40 MHz to about 1 kHz. Significant improvements important for the success of the CMS physics program have been made to the muon trigger system via improved muon reconstruction and identification algorithms since the end of Run 1 and throughout the Run 2 data-taking period. The new algorithms maintain the acceptance of the muon triggers at the same or even lower rate throughout the data-taking period despite the increasing number of additional proton-proton interactions in each LHC bunch crossing. In this paper, the algorithms used in 2015 and 2016 and their improvements throughout 2017 and 2018 are described. Measurements of the CMS muon trigger performance for this data-taking period are presented, including efficiencies, transverse momentum resolution, trigger rates, and the purity of the selected muon sample. This paper focuses on the single- and double-muon triggers with the lowest sustainable transverse momentum thresholds used by CMS. The efficiency is measured in a transverse momentum range from 8 to several hundred GeV

Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach

In the rapidly advancing domain of vehicular traffic management and autonomous driving, accurate lane change predictions are paramount for ensuring safety and optimizing traffic flow. This study introduces a comprehensive two-stage prediction model that harnesses the capabilities of long short-term memory (LSTM) for anticipating vehicular lane changes. Initially, we employed a variety of models, such as regression methods, SVMs, and a multilayer perceptron, to categorize lane change behaviors. The dataset was then segmented based on vehicle trajectories and lane change patterns. In the subsequent phase, we utilized the superior classification outcomes from LinearSVC to curate our training data. We developed two dedicated LSTM networks tailored to specific datasets: the lane-keeping LSTM (LK-LSTM) and the lane-changing LSTM (LC-LSTM). By integrating insights from both models, we achieved a comprehensive prediction of vehicular lane changes. Our results indicate that the unified prediction model markedly enhances prediction precision. Accurate lane change predictions offer valuable contributions to advanced driver-assistance systems (ADAS), with the potential to minimize traffic mishaps and enhance traffic fluidity. As we transition to a more autonomous automotive era, refining these predictions becomes essential in seamlessly merging human and automated driving experiences

MIA-Former: Efficient and Robust Vision Transformers via Multi-Grained Input-Adaptation

Vision transformers have recently demonstrated great success in various computer vision tasks, motivating a tremendously increased interest in their deployment into many real-world IoT applications. However, powerful ViTs are often too computationally expensive to be fitted onto real-world resource-constrained platforms, due to (1) their quadratically increased complexity with the number of input tokens and (2) their overparameterized self-attention heads and model depth. In parallel, different images are of varied complexity and their different regions can contain various levels of visual information, e.g., a sky background is not as informative as a foreground object in object classification tasks, indicating that treating those regions equally in terms of model complexity is unnecessary while such opportunities for trimming down ViTs' complexity have not been fully exploited. To this end, we propose a Multi-grained Input-Adaptive Vision Transformer framework dubbed MIA-Former that can input-adaptively adjust the structure of ViTs at three coarse-to-fine-grained granularities (i.e., model depth and the number of model heads/tokens). In particular, our MIA-Former adopts a low-cost network trained with a hybrid supervised and reinforcement learning method to skip the unnecessary layers, heads, and tokens in an input adaptive manner, reducing the overall computational cost. Furthermore, an interesting side effect of our MIA-Former is that its resulting ViTs are naturally equipped with improved robustness against adversarial attacks over their static counterparts, because MIA-Former's multi-grained dynamic control improves the model diversity similar to the effect of ensemble and thus increases the difficulty of adversarial attacks against all its sub-models. Extensive experiments and ablation studies validate that the proposed MIA-Former framework can (1) effectively allocate adaptive computation budgets to the difficulty of input images, achieving state-of-the-art (SOTA) accuracy-efficiency trade-offs, e.g., up to 16.5\% computation savings with the same or even a higher accuracy compared with the SOTA dynamic transformer models, and (2) boost ViTs' robustness accuracy under various adversarial attacks over their vanilla counterparts by 2.4\% and 3.0\%, respectively. Our code is available at https://github.com/RICE-EIC/MIA-Former

Enhanced Degradation of Antibiotic by Peroxydisulfate Catalysis with CuO@CNT: Simultaneous ¹O₂ Oxidation and Electron-Transfer Regime

The nonradical process in the peroxydisulfate (PDS) oxidation system is a promising method for antibiotic removal in water. In this study, CuO@CNT was successfully synthesized by a facile approach to catalyze PDS. The removal efficiency of the antibiotic sulfamethoxazole (SMX) was 90.6% in 50 min, and the stoichiometric efficiency (ΔSMX/ΔPDS) was 0.402. The very different degradation efficiency of common organic contaminants revealed the selective oxidation of the surveyed system. The process of 1O2 oxidation and the electron-transfer regime was exhibited by chemical quenching tests, electron paramagnetic resonance (EPR) determination, a UV–vis spectrophotometer, X-ray photoelectron spectroscopy (XPS) detection, and cyclic voltammetry (CV) measurements. Sustainable catalysis was promoted by the circulation between the surface electron-rich centers of Cu(II) and Cu(III). Dissolved oxygen (DO) and a metastable Cu(III) intermediate contributed to the generation of 1O2. Still, a portion of SMX was removed by the mildly activated PDS. Moreover, the influence factors (pH, dosage, water matrix) were examined, and suppressions were acceptable by common anions and real water. Distinguished from the radical process, unique intermediate products were ascertained via the theoretical calculation and liquid chromatography–mass spectrometry (LC-MS) detection. Furthermore, CuO@CNT showed a satisfactory activation ability in the cycling experiments. Overall, this study developed CNT to be a supporter of CuO, unveiled the mechanism of catalysis, and evaluated the application potential of the nonradical process

Spherical Polydopamine-Modified Carbon-Felt Cathode with an Active Indole Structure for Efficient Hydrogen Peroxide Electroproduction

As one of the most promising methods for H2O2 production, H2O2 electroproduction has received increasingly more attention. In this study, a spherical particle polydopamine (pDA) modified carbon felt (noted as ht-pDA/ACF) for H2O2 production was fabricated. At a constant potential of 2.0 V and pH of 1.0, the H2O2 production of the ht-pDA/ACF cathode reached 220 mg/L after 6 h of electrolyzing, compared to the 30 mg/L H2O2 production of raw carbon felt. Firstly, the spherical pDA exposes more active sites that are favorable to the 2e− ORR compared to pDA film. Secondly, the ring cleavage and re-cyclization of indole structure in the pDA during electrolyzing could form the radicals that act as the intermediate to the H2O2 formation. This research exhibits a low-cost method to modify carbon materials for effective H2O2 electroproduction. The ht-pDA/ACF cathode is promising for green H2O2 production and wastewater treatment

Comparative genomic analysis of adenoviruses and exploration of a genome-based demarcation method

 All supplementary files of our study (Comparative genomic analysis of adenoviruses and exploration of a genome-based demarcation method).</p

Motility and tumor infiltration are key aspects of invariant natural killer T cell anti-tumor function

Abstract Dysfunction of invariant natural killer T (iNKT) cells contributes to immune resistance of tumors. Most mechanistic studies focus on their static functional status before or after activation, not considering motility as an important characteristic for antigen scanning and thus anti-tumor capability. Here we show via intravital imaging, that impaired motility of iNKT cells and their exclusion from tumors both contribute to the diminished anti-tumor iNKT cell response. Mechanistically, CD1d, expressed on macrophages, interferes with tumor infiltration of iNKT cells and iNKT-DC interactions but does not influence their intratumoral motility. VCAM1, expressed by cancer cells, restricts iNKT cell motility and inhibits their antigen scanning and activation by DCs via reducing CDC42 expression. Blocking VCAM1-CD49d signaling improves motility and activation of intratumoral iNKT cells, and consequently augments their anti-tumor function. Interference with macrophage-iNKT cell interactions further enhances the anti-tumor capability of iNKT cells. Thus, our findings provide a direction to enhance the efficacy of iNKT cell-based immunotherapy via motility regulation

Motility and tumor infiltration are key aspects of invariant natural killer T cell anti-tumor function

The source data for "Motility and tumor infiltration are key aspects of invariant natural killer T cell anti-tumor function"