Study on the Application of Hydrogen Fuel Cells in Passenge Cars and Prospects

The increasing demand for clean and sustainable energy sources has driven extensive research and development in the field of hydrogen fuel cell technology. This article provides an in-depth analysis of the advancements in hydrogen fuel cell technology and its potential application in passenger cars as a widely available, clean, and efficient energy source. By reviewing the current status of hydrogen fuel cells and national policies governing their implementation, this study aims to shed light on the development characteristics of China's hydrogen fuel cell industry, while also drawing comparisons with international hydrogen fuel cell policies and applications. Additionally, the article evaluates the performance of existing hydrogen fuel cell passenger cars in the market and proposes the application of future cutting-edge technologies to further enhance their capabilities. Through meticulous paraphrasing and enrichment, this scholarly work offers a comprehensive overview of hydrogen fuel cell technology, delves into the intricate landscape of the industry, and explores the promising prospects for its continued advancement. By encompassing a wide array of aspects related to hydrogen fuel cell technology, this article contributes to the academic discourse surrounding sustainable and efficient energy solutions for the transportation sector

Bioactive conformational generation of small molecules: A comparative analysis between force-field and multiple empirical criteria based methods

<p>Abstract</p> <p>Background</p> <p>Conformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields.</p> <p>Results</p> <p>Our analysis reveals that incorporating multiple empirical rules can significantly improve the accuracy of conformational generation. MECBM, which takes both empirical and force field criteria as the objective functions, can reproduce about 54% (within 1Å RMSD) of the bioactive conformations in the 742-molecule testset, much higher than that of pure force field method (FFBM, about 37%). On the other hand, MECBM achieved a more complete and efficient sampling of the conformational space because the average size of unique conformations ensemble per molecule is about 6 times larger than that of FFBM, while the time scale for conformational generation is nearly the same as FFBM. Furthermore, as a complementary comparison study between the methods with and without empirical biases, we also tested the performance of the three conformational generation methods in MacroModel in combination with different force fields. Compared with the methods in MacroModel, MECBM is more competitive in retrieving the bioactive conformations in light of accuracy but has much lower computational cost.</p> <p>Conclusions</p> <p>By incorporating different energy terms with several empirical criteria, the MECBM method can produce more reasonable conformational ensemble with high accuracy but approximately the same computational cost in comparison with FFBM method. Our analysis also reveals that the performance of conformational generation is irrelevant to the types of force field adopted in characterization of conformational accessibility. Moreover, post energy minimization is not necessary and may even undermine the diversity of conformational ensemble. All the results guide us to explore more empirical criteria like geometric restraints during the conformational process, which may improve the performance of conformational generation in combination with energetic accessibility, regardless of force field types adopted.</p

UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training

Magnetic resonance imaging~(MRI) have played a crucial role in brain disease diagnosis, with which a range of computer-aided artificial intelligence methods have been proposed. However, the early explorations usually focus on the limited types of brain diseases in one study and train the model on the data in a small scale, yielding the bottleneck of generalization. Towards a more effective and scalable paradigm, we propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics. Different from previous pre-training techniques for the unitary vision or textual feature, or with the brute-force alignment between vision and language information, we leverage the unique characteristic of report information in different granularity to build a hierarchical alignment mechanism, which strengthens the efficiency in feature learning. Our UniBrain is validated on three real world datasets with severe class imbalance and the public BraTS2019 dataset. It not only consistently outperforms all state-of-the-art diagnostic methods by a large margin and provides a superior grounding performance but also shows comparable performance compared to expert radiologists on certain disease types

Proteomic Studies on the Mechanism of Myostatin Regulating Cattle Skeletal Muscle Development

Myostatin (MSTN) is an important negative regulator of muscle growth and development. In this study, we performed comparatively the proteomics analyses of gluteus tissues from MSTN+/− Mongolian cattle (MG.MSTN+/−) and wild type Mongolian cattle (MG.WT) using a shotgun-based tandem mass tag (TMT) 6-plex labeling method to investigate the regulation mechanism of MSTN on the growth and development of bovine skeletal muscle. A total of 1,950 proteins were identified in MG.MSTN+/− and MG.WT. Compared with MG.WT cattle, a total of 320 differentially expressed proteins were identified in MG.MSTN cattle, including 245 up-regulated differentially expressed proteins and 75 down-regulated differentially expressed proteins. Bioinformatics analysis showed that knockdown of the MSTN gene increased the expression of extracellular matrix and ribosome-related proteins, induced activation of focal adhesion, PI3K-AKT, and Ribosomal pathways. The results of proteomic analysis were verified by muscle tissue Western blot test and in vitro MSTN gene knockdown test, and it was found that knockdown MSTN gene expression could promote the proliferation and myogenic differentiation of bovine skeletal muscle satellite cells (BSMSCs). At the same time, Co-Immunoprecipitation (CO-IP) assay showed that MSTN gene interacted with extracellular matrix related protein type I collagen α 1 (COL1A1), and knocking down the expression of COL1A1 could inhibit the activity of adhesion, PI3K-AKT and ribosome pathway, thus inhibit BSMSCs proliferation. These results suggest that the MSTN gene regulates focal adhesion, PI3K-AKT, and Ribosomal pathway through the COL1A1 gene. In general, this study provides new insights into the regulatory mechanism of MSTN involved in muscle growth and development

Adherence to diabetes risk reduction diet and the risk of head and neck cancer: a prospective study of 101,755 American adults

BackgroundAdherence to the diabetes risk reduction diet (DRRD) may potentially reduce the risk of developing head and neck cancer (HNC) as the diet includes fruits and limits red and processed meats, known risk factors for HNC. However, there is currently no epidemiological research to investigate this potential association.MethodsThe present study utilized data on demographics, lifestyles, medications, and diets of participants from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to explore the potential association between adherence to DRRD and the risk of HNC. We used a DRRD score to evaluate adherence to the dietary pattern and employed Cox regression analysis to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for HNC risk. Several subgroup analyses were carried out to identify potential effect modifiers, and multiple sensitivity analyses were performed to evaluate the stability of the correlation. The nine components of the DRRD was assessed separately for its association with the risk of HNC.ResultsDuring a mean follow up of 8.84 years, 279 cases of HNC were observed. DDRD score was found to be inversely associated with the risk of HNC (HR Q4 vs. Q1: 0.582; 95% CI: 0.396, 0.856; p = 0.005 for trend) in a linear dose–response manner (p = 0.211 for non-linearity). Subgroup analysis indicated this inverse correlation was more pronounced among participants who had never smoked (HRQ4 vs. Q1: 0.193; 95% CI: 0.073, 0.511; p &lt; 0.001 for trend) compared to current or former smokers (p = 0.044 for interaction). The primary association of DDRD and HNC risk remained robust after several sensitivity analyses. Regarding the individual components of DRRD, an inverse association was also observed between the risk of HNC and increased intake of cereal fiber and whole fruit (all p &lt; 0.05 for trend).ConclusionOur findings provide evidence that following the DRRD pattern may reduce the risk of NHC, especially for non-smokers