Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models

Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). Although conventional physics-based docking tools are widely utilized, their accuracy is compromised by limited conformational sampling and imprecise scoring functions. Recent advances have incorporated deep learning techniques to improve the accuracy of structure prediction. Nevertheless, the experimental validation of docking conformations remains costly, it raises concerns regarding the generalizability of these deep learning-based methods due to the limited training data. In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance. This process involved the generation of 100 million docking conformations, consuming roughly 1 million CPU core days. The proposed model, HelixDock, aims to acquire the physical knowledge encapsulated by the physics-based docking tools during the pre-training phase. HelixDock has been benchmarked against both physics-based and deep learning-based baselines, showing that it outperforms its closest competitor by over 40% for RMSD. HelixDock also exhibits enhanced performance on a dataset that poses a greater challenge, thereby highlighting its robustness. Moreover, our investigation reveals the scaling laws governing pre-trained structure prediction models, indicating a consistent enhancement in performance with increases in model parameters and pre-training data. This study illuminates the strategic advantage of leveraging a vast and varied repository of generated data to advance the frontiers of AI-driven drug discovery

Output Constraint Transfer For Kernelized Correlation Filter In Tracking

The kernelized correlation filter (KCF) is one of the state-of-the-art object trackers. However, it does not reasonably model the distribution of correlation response during tracking process, which might cause the drifting problem, especially when targets undergo significant appearance changes due to occlusion, camera shaking, and/or deformation. In this paper, we propose an output constraint transfer (OCT) method that by modeling the distribution of correlation response in a Bayesian optimization framework is able to mitigate the drifting problem. OCT builds upon the reasonable assumption that the correlation response to the target image follows a Gaussian distribution, which we exploit to select training samples and reduce model uncertainty. OCT is rooted in a new theory which transfers data distribution to a constraint of the optimized variable, leading to an efficient framework to calculate correlation filters. Extensive experiments on a commonly used tracking benchmark show that the proposed method significantly improves KCF, and achieves better performance than other state-of-the-art trackers. To encourage further developments, the source code is made available

Impact of coronavirus disease 2019 on lung cancer patients: A meta-analysis

Background: The coronavirus disease 2019 (COVID-19) pandemic poses a great challenge to the treatment of lung cancer patients. Materials and methods: The PubMed, Embase, and Web of Science databases were searched for studies published before March 15, 2022, and Stata 14.0 software was used to perform a meta-analysis with a random-effects model. The odds ratio (OR) along with the corresponding 95% confidence interval (CI) was reported. Results: Our meta-analysis included 80 articles with 318,352 patients involved. The proportion of lung cancer patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was 2.4% (95% CI: 0.02–0.03) prior to the Omicron variant outbreak. Among COVID-19 patients, those with lung cancer showed a higher mortality rate than those with other types of malignant solid tumors (OR = 1.82, 95% CI: 1.61–2.06) and non-cancer patients (OR = 4.67, 95% CI: 3.61–6.05); however, no significant difference was observed in the mortality rate between patients with lung cancer and those with hematologic malignancies (OR = 1.07, 95% CI: 0.85–1.33). SARS-CoV-2 infection significantly increased the mortality rate in lung cancer patients (OR = 8.94, 95% CI: 6.50–12.31). By contrast, the all-cause mortality rate in lung cancer patients (OR = 1.04, 95% CI: 0.69–1.57) and the proportion of patients diagnosed with advanced lung cancer (OR = 1.04, 95% CI: 0.85–1.27) did not significantly change before and after the pandemic. Conclusions: More attention should be paid on improving the health of lung cancer patients during the COVID-19 pandemic

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Data_Sheet_1_Acceptance of coronavirus disease 2019 (COVID-19) vaccines among healthcare workers: A meta-analysis.zip

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has posed increasing challenges to global health systems. Vaccination against COVID-19 can effectively prevent the public, particularly healthcare workers (HCWs), from being infected by this disease.ObjectivesWe aim to understand the factors influencing HCWs' acceptance of COVID-19 vaccines.MethodsWe searched PubMed, Embase and Web of Science to collect literature published before May 15, 2022, about HCWs' acceptance of COVID-19 vaccines. The Newcastle–Ottawa quality assessment scale was used to assess the risk of bias and the quality of the included studies. We utilized Stata 14.0 software for this meta-analysis with a random-effects model, and odds ratios (ORs) with 95% confidence intervals (CIs) were reported. This meta-analysis was conducted in alignment with the preferred reporting items for systematic review and meta-analysis (PRISMA) guideline.ResultsOur meta-analysis included 71 articles with 93,508 HCWs involved. The research showed that the acceptance of vaccines had significantly increased among HCWs compared to non-HCWs (OR = 1.91, 95% CI: 1.16–3.12). A willingness to undergo COVID-19 vaccination was observed in 66% (95% CI: 0.61–0.67) of HCWs. Among the HCWs involved, doctors showed a generally increased intention to be vaccinated compared with nurses (OR = 2.22, 95% CI: 1.71–2.89). Additionally, males were found to hold more positive attitudes toward vaccination than females (OR = 1.81, 95% CI: 1.55–2.12). When the effectiveness of COVID-19 vaccines was improved, the vaccination acceptance of HCWs was greatly increased accordingly (OR = 5.03, 95% CI: 2.77–9.11). The HCWs who were willing to vaccinate against seasonal influenza showed an increased acceptance of COVID-19 vaccines (OR = 3.52, 95% CI: 2.34–5.28). Our study also showed that HCWs who were willing to be vaccinated against COVID-19 experienced a reduced rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (OR = 0.78, 95% CI: 0.66–0.92).ConclusionsOur analysis revealed that the five factors of occupation, gender, vaccine effectiveness, seasonal influenza vaccines, and SARS-CoV-2 infection presumably affected the acceptance of COVID-19 vaccines among HCWs. It is essential to boost the confidence of HCWs in COVID-19 vaccines for the containment of the epidemic.</p

Prognostic value of neutrophil count to albumin ratio in patients with decompensated cirrhosis

Abstract Our study aimed to investigate the prognostic value of neutrophil count to albumin ratio (NAR) in predicting short-term mortality of patients with decompensated cirrhosis (DC). A total of 623 DC patients were recruited from a retrospective observational cohort study. They were admitted to our hospital from January 2014 to December 2015. NAR of each patient was calculated and analyzed for the association with 90-day liver transplantation-free (LT-free) outcome. The performance of NAR and the integrated model were tested by a receiver-operator curve (ROC) and C-index. The 90-day LT-free mortality of patients with DC was 10.6%. NAR was significantly higher in 90-day non-survivors than in survivors (The median: 1.73 vs 0.76, P < 0.001). A threshold of 1.40 of NAR differentiated patients with a high risk of death (27.45%) from those with a low risk (5.11%). By multivariate analysis, high NAR was independently associated with poor short-term prognosis (high group: 5.07 (2.78, 9.22)). NAR alone had an area under the ROC curve of 0.794 and C-index of 0.7789 (0.7287, 0.8291) in predicting 90-day mortality. The integrated MELD–NAR (iMELD) model had a higher area under the ROC (0.872) and C-index (0.8558 (0.8122, 0.8994)) than the original MELD in predicting 90-day mortality. NAR can be used as an independent predictor of poor outcomes for patients with DC during short-term follow-up

De novo transcriptome analysis and identification of genes associated with immunity, detoxification and energy metabolism from the fat body of the tephritid gall fly, Procecidochares utilis.

The fat body, a multifunctional organ analogous to the liver and fat tissue of vertebrates, plays an important role in insect life cycles. The fat body is involved in protein storage, energy metabolism, elimination of xenobiotics, and production of immunity regulator-like proteins. However, the molecular mechanism of the fat body's physiological functions in the tephritid stem gall-forming fly, Procecidochares utilis, are still unknown. In this study, we performed transcriptome analysis of the fat body of P. utilis using Illumina sequencing technology. In total, 3.71 G of clean reads were obtained and assembled into 30,559 unigenes, with an average length of 539 bp. Among those unigenes, 21,439 (70.16%) were annotated based on sequence similarity to proteins in NCBI's non-redundant protein sequence database (Nr). Sequences were also compared to NCBI's non-redundant nucleotide sequence database (Nt), a manually curated and reviewed protein sequence database (SwissProt), and KEGG and gene ontology annotations were applied to better understand the functions of these unigenes. A comparative analysis was performed to identify unigenes related to detoxification, immunity and energy metabolism. Many unigenes involved in detoxification were identified, including 50 unigenes of putative cytochrome P450s (P450s), 18 of glutathione S-transferases (GSTs), 35 of carboxylesterases (CarEs) and 26 of ATP-binding cassette (ABC) transporters. Many unigenes related to immunity were identified, including 17 putative serpin genes, five peptidoglycan recognition proteins (PGRPs) and four lysozyme genes. In addition, unigenes potentially involved in energy metabolism, including 18 lipase genes, five fatty acid synthase (FAS) genes and six elongases of very long chain fatty acid (ELOVL) genes, were identified. This transcriptome improves our genetic understanding of P. utilis and the identification of a numerous transcripts in the fat body of P. utilis offer a series of valuable molecular resources for future studies on the functions of these genes

The Multi-Modal Video Reasoning and Analyzing Competition

In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021. This competition is composed of four different tracks, namely, video question answering, skeleton-based action recognition, fisheye video-based action recognition, and person re-identification, which are based on two datasets: SUTD-TrafficQA and UAV-Human. We summarize the top-performing methods submitted by the participants in this competition and show their results achieved in the competition