The Impacts of Firm Characteristics and Corporate Governance on Corporate Cash Holdings: An Empirical Investigation of US Companies

This dissertation examines the impact of firm effects and corporate governance characteristics to cash holdings based the sample of US public traded firms from the period of 2004 to 2015. The Pooled OLS model, fixed effect model and SGMM model applied in this study. The final results is mostly consistent with previous study, which shows that the firm with larger size, higher amount of non-cash liquid asset and has poison pill strategy tends to holds lower amount of excess cash. Moreover, the leverage, research and development, cash flow to asset and board size are positively related to corporate cash holdings. The impacts of determinant on cash holding also examined across different industries, and the result indicate that firm size and growth opportunities have significant impacts on corporate cash holdings in most industries. Finally, the robustness check is applied to ensure the empirical result is validity

Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning

Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face a number of challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and capability of utilizing very limited or no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a novel multi-modal medical foundation model that explores masked contrastive learning to achieve granular alignment and zero-shot learning for a variety of medical imaging tasks. MaCo incorporates a correlation weighting mechanism to adjust the correlation between masked image patches and their corresponding reports, thereby enhancing the representation learning capabilities. We evaluate MaCo on six well-known open-source X-ray datasets, and the experimental results show it outperforms seven state-of-the-art approaches for classification, segmentation, and zero-shot phase grounding, demonstrating its great potential to promote a wide range of medical image analysis tasks

LiSum: Open Source Software License Summarization with Multi-Task Learning

Open source software (OSS) licenses regulate the conditions under which users can reuse, modify, and distribute the software legally. However, there exist various OSS licenses in the community, written in a formal language, which are typically long and complicated to understand. In this paper, we conducted a 661-participants online survey to investigate the perspectives and practices of developers towards OSS licenses. The user study revealed an indeed need for an automated tool to facilitate license understanding. Motivated by the user study and the fast growth of licenses in the community, we propose the first study towards automated license summarization. Specifically, we released the first high quality text summarization dataset and designed two tasks, i.e., license text summarization (LTS), aiming at generating a relatively short summary for an arbitrary license, and license term classification (LTC), focusing on the attitude inference towards a predefined set of key license terms (e.g., Distribute). Aiming at the two tasks, we present LiSum, a multi-task learning method to help developers overcome the obstacles of understanding OSS licenses. Comprehensive experiments demonstrated that the proposed jointly training objective boosted the performance on both tasks, surpassing state-of-the-art baselines with gains of at least 5 points w.r.t. F1 scores of four summarization metrics and achieving 95.13% micro average F1 score for classification simultaneously. We released all the datasets, the replication package, and the questionnaires for the community

Outsourced Privacy-Preserving kNN Classifier Model Based on Multi-Key Homomorphic Encryption

Outsourcing the k-Nearest Neighbor (kNN) classifier to the cloud is useful, yet it will lead to serious privacy leakage due to sensitive outsourced data and models. In this paper, we design, implement and evaluate a new system employing an outsourced privacy-preserving kNN Classifier Model based on Multi-Key Homomorphic Encryption (kNNCM-MKHE). We firstly propose a security protocol based on Multi-key Brakerski-Gentry-Vaikuntanathan (BGV) for collaborative evaluation of the kNN classifier provided by multiple model owners. Analyze the operations of kNN and extract basic operations, such as addition, multiplication, and comparison. It supports the computation of encrypted data with different public keys. At the same time, we further design a new scheme that outsources evaluation works to a third-party evaluator who should not have access to the models and data. In the evaluation process, each model owner encrypts the model and uploads the encrypted models to the evaluator. After receiving encrypted the kNN classifier and the user’s inputs, the evaluator calculated the aggregated results. The evaluator will perform a secure computing protocol to aggregate the number of each class label. Then, it sends the class labels with their associated counts to the user. Each model owner and user encrypt the result together. No information will be disclosed to the evaluator. The experimental results show that our new system can securely allow multiple model owners to delegate the evaluation of kNN classifier

Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques

The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications. While previous studies have commonly focused on feature learning at a single learning scale, investigation on integrating multi-scale information is lacking, which may hinder the potential for mutual reinforcement among these features. This paper aims to bridge this gap by proposing a method that effectively exploits multi-scale information to enhance the performance of medical foundation models. The proposed method simultaneously exploits features at the local, instance, modality and global aspects, facilitating comprehensive representation learning within the models. We evaluate the effectiveness of the proposed method on six open-source datasets across different clinical tasks, demonstrating its ability to enhance the performance of medical foundation models

High Genetic Diversity of HIV-1 and Active Transmission Clusters among Male-to-Male Sexual Contacts (MMSCs) in Zhuhai, China

Monitoring genetic diversity and recent HIV infections (RHIs) is critical for understanding HIV epidemiology. Here, we report HIV-1 genetic diversity and RHIs in blood samples from 190 HIV-positive MMSCs in Zhuhai, China. MMSCs with newly reported HIV were enrolled from January 2020 to June 2022. A nested PCR was performed to amplify the HIV polymerase gene fragments at HXB2 positions 2604–3606. We constructed genetic transmission network at both 0.5% and 1.5% distance thresholds using the Tamura-Nei93 model. RHIs were identified using a recent infection testing algorithm (RITA) combining limiting antigen avidity enzyme immunoassay (LAg-EIA) assay with clinical data. The results revealed that 19.5% (37/190) were RHIs and 48.4% (92/190) were CRF07_BC. Two clusters were identified at a 0.5% distance threshold. Among them, one was infected with CRF07_BC for the long term, and the other was infected with CRF55_01B recently. We identified a total of 15 clusters at a 1.5% distance threshold. Among them, nine were infected with CRF07_BC subtype, and RHIs were found in 38.8% (19/49) distributed in eight genetic clusters. We identified a large active transmission cluster (n = 10) infected with a genetic variant, CRF79_0107. The multivariable logistic regression model showed that clusters were more likely to be RHIs (adjusted OR: 3.64, 95% CI: 1.51~9.01). The RHI algorithm can help to identify recent or ongoing transmission clusters where the prevention tools are mostly needed. Prompt public health measures are needed to contain the further spread of active transmission clusters

OsteoporosAtlas: a human osteoporosis-related gene database

Background Osteoporosis is a common, complex disease of bone with a strong heritable component, characterized by low bone mineral density, microarchitectural deterioration of bone tissue and an increased risk of fracture. Due to limited drug selection for osteoporosis and increasing morbidity, mortality of osteoporotic fractures, osteoporosis has become a major health burden in aging societies. Current researches for identifying specific loci or genes involved in osteoporosis contribute to a greater understanding of the pathogenesis of osteoporosis and the development of better diagnosis, prevention and treatment strategies. However, little is known about how most causal genes work and interact to influence osteoporosis. Therefore, it is greatly significant to collect and analyze the studies involved in osteoporosis-related genes. Unfortunately, the information about all these osteoporosis-related genes is scattered in a large amount of extensive literature. Currently, there is no specialized database for easily accessing relevant information about osteoporosis-related genes and miRNAs. Methods We extracted data from literature abstracts in PubMed by text-mining and manual curation. Moreover, a local MySQL database containing all the data was developed with PHP on a Windows server. Results OsteoporosAtlas (http://biokb.ncpsb.org/osteoporosis/), the first specialized database for easily accessing relevant information such as osteoporosis-related genes and miRNAs, was constructed and served for researchers. OsteoporosAtlas enables users to retrieve, browse and download osteoporosis-related genes and miRNAs. Gene ontology and pathway analyses were integrated into OsteoporosAtlas. It currently includes 617 human encoding genes, 131 human non-coding miRNAs, and 128 functional roles. We think that OsteoporosAtlas will be an important bioinformatics resource to facilitate a better understanding of the pathogenesis of osteoporosis and developing better diagnosis, prevention and treatment strategies

The role of inflammatory biomarkers in the development and progression of pre-eclampsia: a systematic review and meta-analysis

BackgroundPre-eclampsia (PE) is a pregnancy complication associated with maternal and fetal morbidity and mortality. Among the potential pathogenesis discussed, inflammation is considered an essential initiator of PE. Previous studies have compared the levels of various inflammatory biomarkers that indicate the existence of PE; however, the relative levels of pro-inflammatory and anti-inflammatory biomarkers and their dynamic changes during PE progression remain unclear. This knowledge is essential to explain the occurrence and progression of the disease.ObjectiveWe aimed to identify the relationship between inflammatory status and PE using inflammatory biomarkers as indicators. We also discussed the underlying mechanism by which inflammatory imbalance contributes to PE by comparing the relative levels of pro-inflammatory and anti-inflammatory biomarkers. Furthermore, we identified additional risk factors for PE.MethodsWe reviewed PubMed, Embase, and the Cochrane Library for articles published until 15th September 2022. Original articles that investigated inflammatory biomarkers in PE and normal pregnancy were included. We selected healthy pregnant women as controls. The inflammatory biomarkers in the case and control groups were expressed as standardized mean differences and 95% confidence intervals using a random-effects model. Study quality was assessed using the Newcastle-Ottawa Scale. Publication bias was assessed using Egger’s test.ResultsThirteen articles that investigated 2,549 participants were included in this meta-analysis. Patients with PE had significantly higher levels of C-reactive protein (CRP), interleukin (IL)-4, IL-6, IL-8, IL-10, and tumor necrosis factor (TNF) than the controls. CRP and pro-inflammatory cytokine levels were higher than those of anti-inflammatory cytokines. Patients with gestational age > 34 weeks had significantly higher IL-6 and TNF levels. Patients with higher systolic blood pressure had significantly higher IL-8, IL-10, and CRP levels.ConclusionInflammatory imbalance is an independent risk factor for PE development. Impairment of the anti-inflammatory system is a crucial initiating factor for PE development. Failed autoregulation, manifested as prolonged exposure to pro-inflammatory cytokines, leads to PE progression. Higher levels of inflammatory biomarkers suggest more severe symptoms, and pregnant women after 34 weeks of gestation are more susceptible to PE

Comparison between HIV self-testing and facility-based HIV testing approach on HIV early detection among men who have sex with men: A cross-sectional study

Background To assess whether HIV self-testing (HIVST) has a better performance in identifying HIV-infected cases than the facility-based HIV testing (HIVFBT) approach. Methods A cross-sectional study was conducted among men who have sex with men (MSM) by using an online questionnaire (including information on sociodemographic, sexual biography, and HIV testing history) and blood samples (for limiting antigen avidity enzyme immunoassay, gene subtype testing, and taking confirmed HIV test). MSM who were firstly identified as HIV positive through HIVST and HIVFBT were compared. Chi-square or Fisher’s exact test was used to explore any association between both groups and their subgroups. Results In total, 124 MSM HIV cases were identified from 2017 to 2021 in Zhuhai, China, including 60 identified through HIVST and 64 through HIVFBT. Participants in the HIVST group were younger (≤30 years, 76.7% vs. 46.9%), were better educated (>high school, 61.7% vs. 39.1%), and had higher viral load (≥1,000 copies/ml, 71.7% vs. 50.0%) than MSM cases identified through HIVFBT. The proportion of early HIV infection in the HIVST group was higher than in the HIVFBT group, identified using four recent infection testing algorithms (RITAs) (RITA 1, 46.7% vs. 25.0%; RITA 2, 43.3% vs. 20.3%; RITA 3, 30.0% vs. 14.1%; RITA 4, 26.7% vs. 10.9%; all p < 0.05). Conclusions The study showed that HIVST has better HIV early detection among MSM and that recent HIV infection cases mainly occur in younger and better-educated MSM. Compared with HIVFBT, HIVST is more accessible to the most at-risk population on time and tends to identify the case early. Further implementation studies are needed to fill the knowledge gap on this medical service model among MSM and other target populations