Measured Regional Division Optimization for Acoustic Tomography Velocity Field Reconstruction in a Circular Area.

The acoustic tomography (AT) velocity field reconstruction technique has become a research hotspot in recent years due to its noninvasive nature, high accuracy, and real-time measurement advantages. However, most of the existing studies are limited to the reconstruction of the velocity field in a rectangular area, and there are very few studies on a circular area, mainly because the layout of acoustic transducers, selection of acoustic paths, and division of measured regions are more difficult in a circular area than in a rectangular area. Therefore, based on AT and using the reconstruction algorithm of the Markov function and singular value decomposition (MK-SVD), this paper proposes a measured regional division optimization algorithm for velocity field reconstruction in a circular area. First, an acoustic path distribution based on the multipath effect is designed to solve the problem of the limited emission angle of the acoustic transducer. On this basis, this paper proposes an adaptive optimization algorithm for measurement area division based on multiple sub-objectives. The steps are as follows: first, two optimization objectives, the condition number of coefficient matrix and the uniformity of acoustic path distribution, were designed. Then, the weights of each sub-objective are calculated using the coefficient of variation (CV). Finally, the measured regional division is optimized based on particle swarm optimization (PSO). The reconstruction effect of the algorithm and the anti-interference ability are verified through the reconstruction experiments of the model velocity field and the simulated velocity field.

DeePMD-kit v2: A software package for deep potential models.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

A randomized, double-blinded, placebo-controlled clinical trial of duloxetine hydrochloride enteric-coated tablets in the treatment of refractory chronic cough.

INTRODUCTION: Refractory cough, a chronic cough with an unclear diagnosis or poor treatment response. The symptoms are often stubborn and persistent, causing serious complications and lowering the patient's quality of life. Cough hypersensitivity syndrome (CHS) is proposed as a potential cause, and reducing sensory nerve hyperresponsiveness is suggested as an effective treatment. However, current drugs have low efficacy and benefit rates and numerous side effects. This trail proposes using duloxetine, a selective 5-HT and norepinephrine reuptake inhibitor, as a potential treatment for refractory cough, which has shown promise in treating pain and depression. Duloxetine may inhibit pain conduction and oxidative stress in peripheral nerves by inhibiting the activity of TRPV1 channels, which play an important role in the peripheral afferent pathway of refractory cough. Meanwhile, the antidepressant effects of duloxetine may also play a role in the treatment of refractory cough. METHODS AND ANALYSIS: This is a single-center, prospective, randomized, double-blind, and controlled trial. A total of 98 individuals will be randomized in a 1:1 ratio to duloxetine group and placebo control group (starting with 20 mg QD, increasing 20 mg daily until 20 mg TID). After a screening period, the second stage runs from baseline to the 42nd (last) day of treatment, with follow-up visits on the 3rd, 7th, 14th, 21st, 28th, 35th, 42nd and 49th days. The main end-stage observation indicators include objective cough frequency, cough visual analog scale (VAS), cough symptom score, Leicester Cough Questionnaire (LCQ), and cough evaluation test (CET); the secondary end-stage observation indicators include capsaicin cough sensitivity, Patient Health Questionnaire-9 (PHQ-9), Major Depression Inventory (MDI), the Generalized Anxiety Disorder-7 scale (GAD-7), Life Events Scale (LES-32), induced sputum supernatant. The safety measures will be AEs/SAEs, vital signs, liver and kidney function, fecal occult blood test. DISCUSSION: This study is the first randomized, double-blind, and controlled clinical trial investigating the use of duloxetine in the treatment of refractory coughs. The study aims to provide a high-quality basis for evaluating the efficacy and safety of duloxetine for this condition. TRIAL REGISTRATION: Our study was registered in the Chinese Clinical Trials Register ( www.chictr.org.cn/ ) (ChiCTR2000037429) in 28/08/2020.

Patient characteristics and procedural variables are associated with length of stay and hospital cost among unilateral primary total hip arthroplasty patients: a single-center retrospective cohort study.

BACKGROUND: Total hip arthroplasty (THA) is a successful treatment for many hip diseases. Length of stay (LOS) and hospital cost are crucial parameters to quantify the medical efficacy and quality of unilateral primary THA patients. Clinical variables associated with LOS and hospital costs haven't been investigated thoroughly. METHODS: The present study retrospectively explored the contributors of LOS and hospital costs among a total of 452 unilateral primary THA patients from January 2019 to January 2020. All patients received conventional in-house rehabilitation services within our institute prior to discharge. Outcome parameters included LOS and hospital cost while clinical variables included patient characteristics and procedural variables. Multivariable linear regression analysis was performed to assess the association between outcome parameters and clinical variables by controlling confounding factors. Moreover, we analyzed patients in two groups according to their diagnosis with femur neck fracture (FNF) (confine THA) or non-FNF (elective THA) separately. RESULTS: Among all 452 eligible participants (266 females and 186 males; age 57.05 ± 15.99 year-old), 145 (32.08%) patients diagnosed with FNF and 307 (67.92%) diagnosed with non-FNF were analyzed separately. Multivariable linear regression analysis revealed that clinical variables including surgery duration, transfusion, and comorbidity (stroke) among the elective THA patients while the approach and comorbidities (stoke, diabetes mellitus, coronary heart disease) among the confine THA patients were associated with a prolonged LOS (P < 0.05). Variables including the American Society of Anesthesiologists classification (ASA), duration, blood loss, and transfusion among the elective THA while the approach, duration, blood loss, transfusion, catheter, and comorbidities (stoke and coronary heart disease) among the confine THA were associated with higher hospital cost (P < 0.05). The results revealed that variables were associated with LOS and hospital cost at different degrees among both elective and confine THA. CONCLUSIONS: Specific clinical variables of the patient characteristics and procedural variables are associated the LOS and hospital cost, which may be different between the elective and confine THA patients. The findings may indicate that evaluation and identification of detailed perioperative factors are beneficial in managing perioperative preparation, adjusting patients' anticipation, decreasing LOS, and reducing hospital cost.

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials.

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications.

An Osteoporosis Risk SNP at 1p36.12 Acts as an Allele-Specific Enhancer to Modulate LINC00339 Expression via Long-Range Loop Formation.

Genome-wide association studies (GWASs) have reproducibly associated variants within intergenic regions of 1p36.12 locus with osteoporosis, but the functional roles underlying these noncoding variants are unknown. Through an integrative functional genomic and epigenomic analyses, we prioritized rs6426749 as a potential causal SNP for osteoporosis at 1p36.12. Dual-luciferase assay and CRISPR/Cas9 experiments demonstrate that rs6426749 acts as a distal allele-specific enhancer regulating expression of a lncRNA (LINC00339) (∼360 kb) via long-range chromatin loop formation and that this loop is mediated by CTCF occupied near rs6426749 and LINC00339 promoter region. Specifically, rs6426749-G allele can bind transcription factor TFAP2A, which efficiently elevates the enhancer activity and increases LINC00339 expression. Downregulation of LINC00339 significantly increases the expression of CDC42 in osteoblast cells, which is a pivotal regulator involved in bone metabolism. Our study provides mechanistic insight into how a noncoding SNP affects osteoporosis by long-range interaction, a finding that could indicate promising therapeutic targets for osteoporosis.

Integrating regulatory features data for prediction of functional disease-associated SNPs.

Genome-wide association studies (GWASs) are an effective strategy to identify susceptibility loci for human complex diseases. However, missing heritability is still a big problem. Most GWASs single-nucleotide polymorphisms (SNPs) are located in noncoding regions, which has been considered to be the unexplored territory of the genome. Recently, data from the Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomics projects have shown that many GWASs SNPs in the noncoding regions fall within regulatory elements. In this study, we developed a pipeline named functional disease-associated SNPs prediction (FDSP), to identify novel susceptibility loci for complex diseases based on the interpretation of the functional features for known disease-associated variants with machine learning. We applied our pipeline to predict novel susceptibility SNPs for type 2 diabetes (T2D) and hypertension. The predicted SNPs could explain heritability beyond that explained by GWAS-associated SNPs. Functional annotation by expression quantitative trait loci analyses showed that the target genes of the predicted SNPs were significantly enriched in T2D or hypertension-related pathways in multiple tissues. Our results suggest that combining GWASs and regulatory features data could identify additional functional susceptibility SNPs for complex diseases. We hope FDSP could help to identify novel susceptibility loci for complex diseases and solve the missing heritability problem.

Modeling circRNA expression pattern with integrated sequence and epigenetic features demonstrates the potential involvement of H3K79me2 in circRNA expression.

MOTIVATION: CircRNAs are an abundant class of non-coding RNAs with widespread, cell-/tissue-specific patterns. Previous work suggested that epigenetic features might be related to circRNA expression. However, the contribution of epigenetic changes to circRNA expression has not been investigated systematically. Here, we built a machine learning framework named CIRCScan, to predict circRNA expression in various cell lines based on the sequence and epigenetic features. RESULTS: The predicted accuracy of the expression status models was high with area under the curve of receiver operating characteristic (ROC) values of 0.89-0.92 and the false-positive rates of 0.17-0.25. Predicted expressed circRNAs were further validated by RNA-seq data. The performance of expression-level prediction models was also good with normalized root-mean-square errors of 0.28-0.30 and Pearson's correlation coefficient r over 0.4 in all cell lines, along with Spearman's correlation coefficient ρ of 0.33-0.46. Noteworthy, H3K79me2 was highly ranked in modeling both circRNA expression status and levels across different cells. Further analysis in additional nine cell lines demonstrated a significant enrichment of H3K79me2 in circRNA flanking intron regions, supporting the potential involvement of H3K79me2 in circRNA expression regulation. AVAILABILITY AND IMPLEMENTATION: The CIRCScan assembler is freely available online for academic use at https://github.com/johnlcd/CIRCScan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sex-specific SNP-SNP interaction analyses within topologically associated domains reveals ANGPT1 as a novel tumor suppressor gene for lung cancer.

Genetic interaction has been recognized to be an important cause of the missing heritability. The topologically associating domain (TAD) is a self-interacting genomic region, and the DNA sequences within a TAD physically interact with each other more frequently. Sex differences influence cancer susceptibility at the genetic level. Here, we performed both regular and sex-specific genetic interaction analyses within TAD to identify susceptibility genes for lung cancer in 5204 lung cancer patients and 7389 controls. We found that one SNP pair, rs4262299-rs1654701, was associated with lung cancer in women after multiple testing corrections (combined P = 8.52 × 10-9 ). Single-SNP analyses did not detect significant association signals for these two SNPs. Both identified SNPs are located in the intron region of ANGPT1. We further found that 5% of nonsmall cell lung cancer patients have an alteration in ANGPT1, indicated the potential role of ANGPT1 in the neoplastic progression in lung cancer. The expression of ANGPT1 was significantly down-regulated in patients in lung squamous cell carcinoma and lung adenocarcinoma. We checked the interaction effect on the ANGPT1 expression and lung cancer and found that the minor allele "G" of rs1654701 increased ANGPT1 gene expression and decreased lung cancer risk with the increased dosage of "A" of rs4262299, which consistent with the tumor suppressor function of ANGPT1. Survival analyses found that the high expression of ANGPT1 was individually associated with a higher survival probability in lung cancer patients. In summary, our results suggest that ANGPT1 may be a novel tumor suppressor gene for lung cancer.

Ground State Energy Functional with Hartree-Fock Efficiency and Chemical Accuracy.

We introduce the deep post Hartree-Fock (DeePHF) method, a machine learning-based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predicts the energy difference between results of highly accurate models such as the coupled cluster method and low accuracy models such as the Hartree-Fock (HF) method, using the ground-state electronic orbitals as the input. It preserves all the symmetries of the original high accuracy model. The added computational cost is less than that of the reference HF or DFT and scales linearly with respect to system size. We examine the performance of DeePHF on organic molecular systems using publicly available data sets and obtain the state-of-art performance, particularly on large data sets.

Detecting epistasis within chromatin regulatory circuitry reveals CAND2 as a novel susceptibility gene for obesity.

BACKGROUND: Genome-wide association studies have identified many susceptibility loci for obesity. However, missing heritability problem is still challenging and ignorance of genetic interactions is believed to be an important cause. Current methods for detecting interactions usually do not consider regulatory elements in non-coding regions. Interaction analyses within chromatin regulatory circuitry may identify new susceptibility loci. METHODS: We developed a pipeline named interaction analyses within chromatin regulatory circuitry (IACRC), to identify genetic interactions impacting body mass index (BMI). Potential interacting SNP pairs were obtained based on Hi-C datasets, PreSTIGE (Predicting Specific Tissue Interactions of Genes and Enhancers) algorithm, and super enhancer regions. SNP × SNP analyses were next performed in three GWAS datasets, including 2286 unrelated Caucasians from Kansas City, 3062 healthy Caucasians from the Gene Environment Association Studies initiative, and 3164 Hispanic subjects from the Women's Health Initiative. RESULTS: A total of 16,643,227 SNP × SNP analyses were performed. Meta-analyses showed that two SNP pairs, rs6808450-rs9813534 (combined P = 2.39 × 10-9) and rs6808450-rs3773306 (combined P = 2.89 × 10-9) were associated with BMI after multiple testing corrections. Single-SNP analyses did not detect significant association signals for these three SNPs. In obesity relevant cells, rs6808450 is located in intergenic enhancers, while rs9813534 and rs3773306 are located in the region of strong transcription regions of CAND2 and RPL32, respectively. The expression of CAND2 was significantly downregulated after the differentiation of human Simpson-Golabi-Behmel syndrome (SGBS) preadipocyte cells (P = 0.0241). Functional validation in the International Mouse Phenotyping Consortium database showed that CAND2 was associated with increased lean body mass and decreased total body fat amount. CONCLUSIONS: Detecting epistasis within chromatin regulatory circuitry identified CAND2 as a novel obesity susceptibility gene. We hope IACRC could facilitate the interaction analyses for complex diseases and offer new insights into solving the missing heritability problem.

Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis.

Despite genome-wide association studies (GWASs) have identified many susceptibility genes for osteoporosis, it still leaves a large part of missing heritability to be discovered. Integrating regulatory information and GWASs could offer new insights into the biological link between the susceptibility SNPs and osteoporosis. We generated five machine learning classifiers with osteoporosis-associated variants and regulatory features data. We gained the optimal classifier and predicted genome-wide SNPs to discover susceptibility regulatory variants. We further utilized Genetic Factors for Osteoporosis Consortium (GEFOS) and three in-house GWASs samples to validate the associations for predicted positive SNPs. The random forest classifier performed best among all machine learning methods with the F1 score of 0.8871. Using the optimized model, we predicted 37,584 candidate SNPs for osteoporosis. According to the meta-analysis results, a list of regulatory variants was significantly associated with osteoporosis after multiple testing corrections and contributed to the expression of known osteoporosis-associated protein-coding genes. In summary, combining GWASs and regulatory elements through machine learning could provide additional information for understanding the mechanism of osteoporosis. The regulatory variants we predicted will provide novel targets for etiology research and treatment of osteoporosis.

Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review

BACKGROUND: ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored. METHODS: A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included. RESULTS: ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students. CONCLUSION: ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.

Dual-Band All-Optical Logic Gates by Coherent Absorption in an Amorphous Silicon Graphene Metasurface.

The dual-band polarization-independent all-optical logic gate by coherent absorption effect in an amorphous silicon (a-Si) graphene metasurface is investigated theoretically and numerically. Taking the substrate effect into consideration, the coherent perfect absorption condition of the a-Si graphene metasurface is derived on the basis of the Cartesian multipole method. The coherent nearly perfect absorption of the a-Si graphene metasurface is realized by the interference of multipole moments and the interband transition of monolayer graphene, achieving peak values of 91% and 92% at 894.5 nm and 991.5 nm, respectively. The polarization independence of the coherent absorption is revealed due to the center symmetry of the structure of the a-Si graphene metasurface. The dual-band polarization-independent all-optical XOR and OR logic gates are implemented at 894.5 nm and 991.5 nm by the a-Si graphene metasurface based on the coherent nearly perfect absorption, which has the opportunity to be utilized in all-optical computing, all-optical data processing, and future all-optical networks.

The underlying mechanisms of DNA methylation in high salt memory in hypertensive vascular disease.

This study demonstrates the effect and DNA methylation-related mechanisms of a high-salt diet and salt memory-induced hypertension and vasculopathy. Thirty Sprague Dawley rats were randomly divided into a control (CON) group (n = 6) and a modeling group (n = 24). A 12% NaCl solution (1 mL/100 g) was intragastrically administered for 60 consecutive days for modeling. An increase in blood pressure up to 140 mmHg was considered successful modeling. Twelve of fifteen successfully modeled rats were randomly selected and divided into a High Salt Diet (HSD) group and a High Salt Memory (HSM) group (n = 6). Rats in HSD group were intragastrically administered a 12% NaCl solution, while rats in HSM group were administered a 3% NaCl solution twice a day for 30 days. At the end of the intervention, blood pressure and the serum levels of ET-1, NO, TNF-α and IL-1ß were measured. RRBS-heavy sulfite sequencing technology was selected for DNA methylation analysis. The systolic blood pressure of rats in the HSD group and HSM group was significantly higher than that in the CON group. Compared with those in the CON group, the serum levels of ET-1 in the HSM group and the serum levels of NO in the HSD group and HSM group were significantly increased. The methylation level of the CON group was lower than that of the HSD group and the HSM group, and there was no significant difference between the HSD group and the HSM group. The methylation level of Myoz3 was downregulated in the HSD group and HSM group. The methylation level of Fgd3 were upregulated in HSD group and downregulated in the HSM group. The methylation levels of AC095693.1, Adamts3, PDGFA and PDGFRα were downregulated in the HSD group and upregulated in the HSM group. According to the GO database, the differentially methylated genes were significantly enriched in the coordination of cell function, genetic development, and RNA transcription. There were three main metabolic pathways that were enriched in the differentially expressed genes between the groups: the PI3K-Akt signaling pathway, MAPK signaling pathway, and Hippo signaling pathway. Excessive salt intake may cause hypertension and vascular damage, and this damage may continue after the reduction of salt intake. Therefore, salt memory phenomenon exists, and this memory effect may be correlated with the levels of DNA methylation.

Chloroplast genome data of five Amygdalus species: Clarifying genome structure and phylogenetic relationships.

Amygdalus species have considerable ecological and economic value, however, the phylogenetic relationships among Amygdalus remain controversy. In this study, we sequenced and assembled the chloroplast (cp) genomes of five Amygdalus species: Prunus communis, P. mongolica, P. pedunculata, P. triloba, and P. mira. We then conducted comparative genomic analyses and constructed their phylogenetic relationships. The genome length ranged from 157,870 to 158,451 bp, and 131 genes were annotated (86 protein-coding genes, 37 tRNAs, and 8 rRNAs). Additionally, 49-57 simple sequence repeats were detected, with most in the large single-copy region and with AT base preferences. Comparative genomic analyses revealed high similarities in structure, order, and gene content. However, we identified four highly divergent sequences: trnR-UCU-atpA, nbdhC-trnV-UAC, ycf4-cemA, and rpl32-trnL-UAG. The phylogenomic relationship analysis suggested that the Amygdalus species were grouped together, in which P. pedunculata, P. triloba, and Prunus tangutica were categorized into a branch, P. mongolica and Prunus davidiana were clustered a branch. This study provides an improved understanding of the genetic relationships among the Amygdalus and provides a basis for the development and utilization of Amygdalus resources.

Oxygen tension regulating hydrogels for vascularization and osteogenesis via sequential activation of HIF-1α and ERK1/2 signaling pathways in bone regeneration.

Angiogenesis plays a crucial role in bone regeneration. Hypoxia is a driving force of angiogenesis at the initial stage of tissue repair. The hypoxic microenvironment could activate the hypoxia-inducible factor (HIF)-1α signaling pathway in cells, thereby enhancing the proliferation, migration and pro-angiogenic functions of stem cells. However, long-term chronic hypoxia could inhibit osteogenic differentiation and even lead to apoptosis. Therefore, shutdown of the HIF-1α signaling pathway and providing oxygen at later stage probably facilitate osteogenic differentiation and bone regeneration. Herein, an oxygen tension regulating hydrogel that sequentially activate and deactivate the HIF-1α signaling pathway were prepared in this study. Its effect and mechanism on stem cell differentiation were investigated both in vitro and in vivo. We proposed a gelatin-based hydrogel capable of sequentially delivering a hypoxic inducer (copper ions) and oxygen generator (calcium peroxide). The copper ions released from the hydrogels significantly enhanced cell viability and VEGF secretion of BMSCs via upregulating HIF-1α expression and facilitating its translocation into the nucleus. Additionally, calcium peroxide promoted alkaline phosphatase activity, osteopontin secretion, and calcium deposition through the activation of ERK1/2. Both Cu2+ and calcium peroxide demonstrated osteogenic promotion individually, while their synergistic effect within the hydrogels led to a superior osteogenic effect by potentially activating the HIF-1α and ERK1/2 signaling pathways.

Will R&D make investors more tolerant? Analysis based on the performance forecast of Chinese listed companies.

In the era of innovation dividends, whether investors, as the main participants in the capital market, can tolerate enterprise innovation activities is the key to whether the capital market can help enterprises innovate. This paper takes the listed companies of Shanghai and Shenzhen A-shares in China that disclosed quantitative performance forecasts from 2016 to 2021 as samples, obtains the market reaction of performance forecasts through the event study method and uses them as proxy variables of investors' short-term performance expectations, and uses multiple regression analysis to explore the impact of corporate R&D on investors' short-term performance expectations. The results are as follows: (1) corporate R&D investment significantly reduces investors' short-term performance expectations (that is, investors have a significant tolerance effect on enterprises with higher R&D investment); (2) the increase in the shareholding ratio of institutional investors weakens the tolerance effect; and (3) with the implementation of China's innovation-driven strategy, the tolerance effect of its capital market on enterprise R&D gradually increases, especially for high-tech companies, but has a low tolerance effect on state-owned companies' R&D risk. The results show that investors in China's capital market are not completely rational in their response to corporate R&D, and investors are willing to bear more short-term performance losses for high R&D investment, which is consistent with prospect theory.

Hybrid Auxiliary Field Quantum Monte Carlo for Molecular Systems.

We propose a quantum Monte Carlo approach to solve the many-body Schrödinger equation for the electronic ground state. The method combines optimization from variational Monte Carlo and propagation from auxiliary field quantum Monte Carlo in a way that significantly alleviates the sign problem. In application to molecular systems, we obtain highly accurate results for configurations dominated by either dynamic or static electronic correlation.