Recent Progress of TiO 2

TiO2-based materials have been widely studied in the field of photocatalysis, sensors, and solar cells. Besides that, TiO2-based materials are of great interest for energy storage and conversion devices, in particular rechargeable lithium ion batteries (LIBs). TiO2 has significant advantage due to its low volume change (<4%) during Li ion insertion/desertions process, short paths for fast lithium ion diffusion, and large exposed surface offering more lithium insertion channels. However, the relatively low theoretical capacity and electrical conductivity of TiO2 greatly hampered its practical application. Various strategies have been developed to solve these problems, such as designing different nanostructured TiO2 to improve electronic conductivity, coating or combining TiO2 with carbonaceous materials, incorporating metal oxides to enhance its capacity, and doping with cationic or anionic dopants to form more open channels and active sites for Li ion transport. This review is devoted to the recent progress in enhancing the LIBs performance of TiO2 with various synthetic strategies and architectures control. Based on the lithium storage mechanism, we will also bring forward the existing challenges for future exploitation and development of TiO2-based anodes in energy storage, which would guide the development for rationally and efficiently designing more efficient TiO2-based LIBs anodes

Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology

Field studies are essential to reliably quantify ecological responses to global change because they are exposed to realistic climate manipulations. Yet such studies are limited in replicates, resulting in less power and, therefore, potentially unreliable effect estimates. Furthermore, while manipulative field experiments are assumed to be more powerful than non-manipulative observations, it has rarely been scrutinized using extensive data. Here, using 3847 field experiments that were designed to estimate the effect of environmental stressors on ecosystems, we systematically quantified their statistical power and magnitude (Type M) and sign (Type S) errors. Our investigations focused upon the reliability of field experiments to assess the effect of stressors on both ecosystem's response magnitude and variability. When controlling for publication bias, single experiments were underpowered to detect response magnitude (median power: 18%–38% depending on effect sizes). Single experiments also had much lower power to detect response variability (6%–12% depending on effect sizes) than response magnitude. Such underpowered studies could exaggerate estimates of response magnitude by 2–3 times (Type M errors) and variability by 4–10 times. Type S errors were comparatively rare. These observations indicate that low power, coupled with publication bias, inflates the estimates of anthropogenic impacts. Importantly, we found that meta-analyses largely mitigated the issues of low power and exaggerated effect size estimates. Rather surprisingly, manipulative experiments and non-manipulative observations had very similar results in terms of their power, Type M and S errors. Therefore, the previous assumption about the superiority of manipulative experiments in terms of power is overstated. These results call for highly powered field studies to reliably inform theory building and policymaking, via more collaboration and team science, and large-scale ecosystem facilities. Future studies also require transparent reporting and open science practices to approach reproducible and reliable empirical work and evidence synthesis

Transcriptional Regulation of opaR, qrr2–4 and aphA by the Master Quorum-Sensing Regulator OpaR in Vibrio parahaemolyticus

Background: Vibrio parahaemolyticus is a leading cause of infectious diarrhea and enterogastritis via the fecal-oral route. V. harveyi is a pathogen of fishes and invertebrates, and has been used as a model for quorum sensing (QS) studies. LuxR is the master QS regulator (MQSR) of V. harveyi, and LuxR-dependent expression of its own gene, qrr2–4 and aphA have been established in V. harveyi. Molecular regulation of target genes by the V. parahaemolyticus MQSR OpaR is still poorly understood. Methodology/Principal Findings: The bioinformatics analysis indicated that V. parahaemolyticus OpaR, V. harveyi LuxR, V. vulnificu SmcR, and V. alginolyticus ValR were extremely conserved, and that these four MQSRs appeared to recognize the same conserved cis-acting signals, which was represented by the consensus constructs manifesting as a position frequency matrix and as a 20 bp box, within their target promoters. The MQSR box-like sequences were found within the upstream DNA regions of opaR, qrr2–4 and aphA in V. parahaemolyticus, and the direct transcriptional regulation of these target genes by OpaR were further confirmed by multiple biochemical experiments including primer extension assay, gel mobility shift assay, and DNase I footprinting analysis. Translation and transcription starts, core promoter elements for sigma factor recognition, Shine-Dalgarno sequences for ribosome recognition, and OpaR-binding sites were determined for the five target genes of OpaR, which gave a structural map of the OpaR-dependent promoters. Further computational promote

MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations across different slices. This presents a significant hurdle to medical image segmentation, as conventional segmentation methods may either overlook the minority class or overly emphasize the majority class, ultimately leading to a decrease in the overall generalization ability of the segmentation results. To overcome this, we propose a novel approach based on multi-step reinforcement learning, which integrates prior knowledge of medical images and pixel-wise segmentation difficulty into the reward function. Our method treats each pixel as an individual agent, utilizing diverse actions to evaluate its relevance for segmentation. To validate the effectiveness of our approach, we conduct experiments on four imbalanced medical datasets, and the results show that our approach surpasses other state-of-the-art methods in highly imbalanced scenarios. These findings hold substantial implications for clinical diagnosis and research

Qualifying Chinese Medical Licensing Examination with Knowledge Enhanced Generative Pre-training Model

Generative Pre-Training (GPT) models like ChatGPT have demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. Although ChatGPT has been integrated into the overall workflow to boost efficiency in many domains, the lack of flexibility in the finetuning process hinders its applications in areas that demand extensive domain expertise and semantic knowledge, such as healthcare. In this paper, we evaluate ChatGPT on the China National Medical Licensing Examination (CNMLE) and propose a novel approach to improve ChatGPT from two perspectives: integrating medical domain knowledge and enabling few-shot learning. By using a simple but effective retrieval method, medical background knowledge is extracted as semantic instructions to guide the inference of ChatGPT. Similarly, relevant medical questions are identified and fed as demonstrations to ChatGPT. Experimental results show that directly applying ChatGPT fails to qualify the CNMLE at a score of 51 (i.e., only 51\% of questions are answered correctly). While our knowledge-enhanced model achieves a high score of 70 on CNMLE-2022 which not only passes the qualification but also surpasses the average score of humans (61). This research demonstrates the potential of knowledge-enhanced ChatGPT to serve as versatile medical assistants, capable of analyzing real-world medical problems in a more accessible, user-friendly, and adaptable manner

Cross-talk between cuproptosis and ferroptosis regulators defines the tumor microenvironment for the prediction of prognosis and therapies in lung adenocarcinoma

Cuproptosis, a newly identified form of programmed cell death, plays vital roles in tumorigenesis. However, the interconnectivity of cuproptosis and ferroptosis is poorly understood. In our study, we explored genomic alterations in 1162 lung adenocarcinoma (LUAD) samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) cohort to comprehensively evaluate the cuproptosis regulators. We systematically performed a pancancer genomic analysis by depicting the molecular correlations between the cuproptosis and ferroptosis regulators in 33 cancer types, indicating cross-talk between cuproptosis and ferroptosis regulators at the multiomic level. We successfully identified three distinct clusters based on cuproptosis and ferroptosis regulators, termed CuFeclusters, as well as the three distinct cuproptosis/ferroptosis gene subsets. The tumor microenvironment cell-infiltrating characteristics of three CuFeclusters were highly consistent with the three immune phenotypes of tumors. Furthermore, a CuFescore was constructed and validated to predict the cuproptosis/ferroptosis pathways in individuals and the response to chemotherapeutic drugs and immunotherapy. The CuFescore was significantly associated with the expression of miRNA and the regulation of post-transcription. Thus, our research established an applied scoring scheme, based on the regulators of cuproptosis/ferroptosis to identify LUAD patients who are candidates for immunotherapy and to predict patient sensitivity to chemotherapeutic drugs