Analyzing Herd Behavior in Global Stock Markets: An Intercontinental Comparison

Herd behavior is an important economic phenomenon, especially in the context of the recent financial crises. In this paper, herd behavior in global stock markets is investigated with a focus on intercontinental comparison. Since most existing herd behavior indices do not provide a comparative method, we propose a new herd behavior index and demonstrate its desirable properties through simple theoretical models. As for empirical analysis, we use global stock market data from Morgan Stanley Capital International to study herd behavior especially during periods of financial crises in detail

Super-sparse principal component analyses for high-throughput genomic data

<p>Abstract</p> <p>Background</p> <p>Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are typically nonzero. These nonzero values also reflect poor estimation of the true vector loadings; for example, for gene expression data, biologically we expect only a portion of the genes to be expressed in any tissue, and an even smaller fraction to be involved in a particular process. Sparse PCA methods have recently been introduced for reducing the number of nonzero coefficients, but these existing methods are not satisfactory for high-dimensional data applications because they still give too many nonzero coefficients.</p> <p>Results</p> <p>Here we propose a new PCA method that uses two innovations to produce an extremely sparse loading vector: (i) a random-effect model on the loadings that leads to an unbounded penalty at the origin and (ii) shrinkage of the singular values obtained from the singular value decomposition of the data matrix. We develop a stable computing algorithm by modifying nonlinear iterative partial least square (NIPALS) algorithm, and illustrate the method with an analysis of the NCI cancer dataset that contains 21,225 genes.</p> <p>Conclusions</p> <p>The new method has better performance than several existing methods, particularly in the estimation of the loading vectors.</p

(LC)2^2: LiDAR-Camera Loop Constraints For Cross-Modal Place Recognition

Localization has been a challenging task for autonomous navigation. A loop detection algorithm must overcome environmental changes for the place recognition and re-localization of robots. Therefore, deep learning has been extensively studied for the consistent transformation of measurements into localization descriptors. Street view images are easily accessible; however, images are vulnerable to appearance changes. LiDAR can robustly provide precise structural information. However, constructing a point cloud database is expensive, and point clouds exist only in limited places. Different from previous works that train networks to produce shared embedding directly between the 2D image and 3D point cloud, we transform both data into 2.5D depth images for matching. In this work, we propose a novel cross-matching method, called (LC)$^2$, for achieving LiDAR localization without a prior point cloud map. To this end, LiDAR measurements are expressed in the form of range images before matching them to reduce the modality discrepancy. Subsequently, the network is trained to extract localization descriptors from disparity and range images. Next, the best matches are employed as a loop factor in a pose graph. Using public datasets that include multiple sessions in significantly different lighting conditions, we demonstrated that LiDAR-based navigation systems could be optimized from image databases and vice versa.Comment: 8 pages, 11 figures, Accepted to IEEE Robotics and Automation Letters (RA-L

Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

<p>Abstract</p> <p>Background</p> <p>Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.</p> <p>Results</p> <p>We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.</p> <p>Conclusions</p> <p>The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.</p

BALLI: Bartlett-adjusted likelihood-based linear model approach for identifying differentially expressed genes with RNA-seq data

Background Transcriptomic profiles can improve our understanding of the phenotypic molecular basis of biological research, and many statistical methods have been proposed to identify differentially expressed genes (DEGs) under two or more conditions with RNA-seq data. However, statistical analyses with RNA-seq data are often limited by small sample sizes, and global variance estimates of RNA expression levels have been utilized as prior distributions for gene-specific variance estimates, making it difficult to generalize the methods to more complicated settings. We herein proposed a Bartlett-Adjusted Likelihood-based LInear mixed model approach (BALLI) to analyze more complicated RNA-seq data. The proposed method estimates the technical and biological variances with a linear mixed-effects model, with and without adjusting small sample bias using Bartlketts corrections. Results We conducted extensive simulations to compare the performance of BALLI with those of existing approaches (edgeR, DESeq2, and voom). Results from the simulation studies showed that BALLI correctly controlled the type-1 error rates at various nominal significance levels and produced better statistical power and precision estimates than those of other competing methods in various scenarios. Furthermore, BALLI was robust to variation of library size. It was also successfully applied to Holstein milk yield data, illustrating its practical value. Conclusions; BALLI is statistically more efficient and valid than existing methods, and we conclude that it is useful for identifying DEGs in RNA-seq analysis.This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI16C2037) and the National Research Foundation of Korea (2017M3A9F3046543). The funding body was not involved in the study design, data collection, analysis and interpretation, and writing the manuscript

Evaluation of a technology-enhanced, integrated community health and wellness program for seniors (HWePS): protocol of a non-randomized comparison trial

Background Healthy aging for all in the community is a shared public health agenda for countries with aging populations, but there is a lack of empirical evidence on community-wide preventive models that promote the health of older people residing in socially-disadvantaged communities. The Health and Wellness Program for Seniors (HWePS) is a technology-enhanced, multi-level, integrated health equity intervention model. This study evaluates the effect of the HWePS on the health and well-being of older adults residing in urban, low-income communities.  Methods/design HWePS is a prospective, non-randomized comparison trial conducted in an intervention and a control neighborhood (dong) in Seoul, South Korea, over 12 months. Older people who reside in the small areas and meet the inclusion/exclusion criteria are eligible to participate. The multi-level, multi-faceted HWePS intervention is a preventive community care model for older residents guided by the expanded chronic care model, the comprehensive health literacy intervention model, and the Systems for Person-centered Elder Care model along with health equity frameworks. HWePS consists of four components: a health literacy intervention based on individual and community needs assessments, personalized (self-)care management featuring nurse coaching and peer support, a healthy-living and healthy-aging community initiative, and information and communication technology (ICT) systems. The primary outcomes are self-reported health and health-related quality of life. Outcome assessors and data analysts are blinded to group assignment. Process evaluation will be also conducted. Discussion As a multi-level health equity project, HWePS has adopted a novel study design that simultaneously targets individual- and community-level factors known to contribute to health inequality in later life in the community. The study will provide insights into the effectiveness and implementation process of an integrated, multi-level, preventive community care model, which in turn can help improve the health outcomes of older residents and reduce disparities in underserved urban communities. Trial registration ISRCTN29103760. Registered 2 September 2021, https://www.isrctn.com/ISRCTN29103760This work was based on the Project to Empower Communities to Reduce Health Disparities, supported by the Korea Disease Control and Prevention Agency and the Seoul Metropolitan Government; the project was executed in Jungnang-gu (district) in Seoul. The funding sources had no role in the study design; data collection and management; writing the manuscript; or the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the ofcial views of the funding sources

Associations of water, sanitation, and hygiene with typhoid fever in case–control studies: a systematic review and meta-analysis

This work was supported, in whole or in part, by Gavi, the Vaccine Alliance, Bowdoin College, and the Bill & Melinda Gates Foundation, via the Vaccine Impact Modelling Consortium (Grant Number OPP1157270 / INV-009125). The funders were not involved in the study design, data analysis, data interpretation, and writing of the manuscript. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of their affiliated organisationsBackground Water, sanitation, and hygiene (WASH) play a pivotal role in controlling typhoid fever, as it is primarily transmitted through oral-fecal pathways. Given our constrained resources, staying current with the most recent research is crucial. This ensures we remain informed about practical insights regarding effective typhoid fever control strategies across various WASH components. We conducted a systematic review and meta-analysis of case-control studies to estimate the associations of water, sanitation, and hygiene exposures with typhoid fever. Methods We updated the previous review conducted by Brockett et al. We included new findings published between June 2018 and October 2022 in Web of Science, Embase, and PubMed. We used the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool for risk of bias (ROB) assessment. We classified WASH exposures according to the classification provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene (JMP) update in 2015. We conducted the meta-analyses by only including studies that did not have a critical ROB in both Bayesian and frequentist random-effects models. Results We identified 8 new studies and analyzed 27 studies in total. Our analyses showed that while the general insights on the protective (or harmful) impact of improved (or unimproved) WASH remain the same, the pooled estimates of OR differed. Pooled estimates of limited hygiene (OR = 2.26, 95% CrI: 1.38 to 3.64), untreated water (OR = 1.96, 95% CrI: 1.28 to 3.27) and surface water (OR = 2.14, 95% CrI: 1.03 to 4.06) showed 3% increase, 18% decrease, and 16% increase, respectively, from the existing estimates. On the other hand, improved WASH reduced the odds of typhoid fever with pooled estimates for improved water source (OR = 0.54, 95% CrI: 0.31 to 1.08), basic hygiene (OR = 0.6, 95% CrI: 0.38 to 0.97) and treated water (OR = 0.54, 95% CrI: 0.36 to 0.8) showing 26% decrease, 15% increase, and 8% decrease, respectively, from the existing estimates. Conclusions The updated pooled estimates of ORs for the association of WASH with typhoid fever showed clear changes from the existing estimates. Our study affirms that relatively low-cost WASH strategies such as basic hygiene or water treatment can be an effective tool to provide protection against typhoid fever in addition to other resource-intensive ways to improve WASH

Affordable method of video recording for ecologists and citizen-science participants

Abstract Observations and video documentation of interactions between animals living in dens, cavities, and other enclosed spaces are difficult, but they play an important role in field biology, ecology, and conservation. For example, bird parents visiting nests and feeding their nestlings may provide crucial information for testing of ecological hypotheses and may easily attract attention of participants of citizen-science ecological and conservation projects. Because of the nest concealment of cavity-nesting birds, their behaviors in the nest can only be studied by using video surveillance. Professional wildlife surveillance systems are extremely expensive. Here, we describe an inexpensive video setup that can be constructed with relatively little effort and is more affordable than any previously described system. We anticipate that the relatively low cost of about 250 USD for a battery-operated system is an important feature for citizen-science type of projects and for applications in heavily populated areas where the potential for theft and vandalism may be high. Based on our experiences, we provide methodological advice on practical aspects of using this system in the field for ecological research on birds. We highlight the low cost, easiness of construction, and potential availability to a large number of observers taking part in wildlife monitoring projects, and we offer technical help to participants of such research projects