MANDATING DISCLOSURE OF R&D BENEFITS AND COSTS TO EXTRACT MANAGERS' PRIVATE INFORMATION: OBSTACLES AND PRACTICAL CONSIDERATIONS

This study suggests that mandating managers to disclose information about the net benefit of R&D outside financial statements is worth to be considered as one potential approach to improve the market's valuation of R&D and to improve managers' R&D-related decision making process. A transition of the R&D reporting practice from cost focused to net benefit focused is viewed necessary. A stream of two mandatory reporting systems is established for the transition to take place more smoothly. It is expected that information asymmetry can be reduced after information about R&D net benefit becomes publicly available. This study contributes to the literature in three ways. First, this is the first study which seriously considers the direct disclosure of net benefit of R&D as a way to improve the R&D reporting practice. Second, this study proposes a stream of reporting systems in the transition. The current R&D reporting practice can be transited gradually toward the desirable R&D reporting practice following the stream. Finally, this study points out that both market participants and firms will be potentially benefited in the transition. Not only the negative impact of information asymmetry will be reduced but also some potential subsidiary benefits will be provided by the transition

Using Image Recognition to Process Unbalanced Data in Genetic Diseases From Biobanks

[[abstract]]With precision medicine as the goal, the human biobank of each country should be analyzed to determine the complete research results related to genetic diseases. In addition, with the increase in medical imaging data, automatic image processing with image recognition has been widely studied and applied in biomedicine. However, case–control data imbalance often occurs in human biobanks, which is usually solved by the statistical method SAIGE. Due to the huge amount of genetic data in human biobanks, the direct use of the SAIGE method often faces the problem of insufficient computer memory to support calculations and excessive calculation time. The other method is to use sampling to adjust the data to balance the case–control ratio, which is called Synthetic Minority Oversampling Technique (SMOTE). Our study employed the Manhattan plot and genetic disease information from the Taiwan Biobank to adjust the imbalance in the case–control ratio by SMOTE, called “TW-SMOTE.” We further used a deep learning image recognition system to identify the TW-SMOTE. We found that TW-SMOTE can achieve the same results as that of SAIGE and the UK Biobank (UKB). The processing of the technical data can be equivalent to the use of data plots with a relatively large UKB sample size and achieve the same effect as that of SAIGE in addressing data imbalance.[[notice]]補正完

Modeling expression quantitative trait loci in data combining ethnic populations

<p>Abstract</p> <p>Background</p> <p>Combining data from different ethnic populations in a study can increase efficacy of methods designed to identify expression quantitative trait loci (eQTL) compared to analyzing each population independently. In such studies, however, the genetic diversity of minor allele frequencies among populations has rarely been taken into account. Due to the fact that allele frequency diversity and population-level expression differences are present in populations, a consensus regarding the optimal statistical approach for analysis of eQTL in data combining different populations remains inconclusive.</p> <p>Results</p> <p>In this report, we explored the applicability of a constrained two-way model to identify eQTL for combined ethnic data that might contain genetic diversity among ethnic populations. In addition, gene expression differences resulted from ethnic allele frequency diversity between populations were directly estimated and analyzed by the constrained two-way model. Through simulation, we investigated effects of genetic diversity on eQTL identification by examining gene expression data pooled from normal quantile transformation of each population. Using the constrained two-way model to reanalyze data from Caucasians and Asian individuals available from HapMap, a large number of eQTL were identified with similar genetic effects on the gene expression levels in these two populations. Furthermore, 19 single nucleotide polymorphisms with inter-population differences with respect to both genotype frequency and gene expression levels directed by genotypes were identified and reflected a clear distinction between Caucasians and Asian individuals.</p> <p>Conclusions</p> <p>This study illustrates the influence of minor allele frequencies on common eQTL identification using either separate or combined population data. Our findings are important for future eQTL studies in which different datasets are combined to increase the power of eQTL identification.</p

PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion

Semantic Scene Completion (SSC) aims to jointly generate space occupancies and semantic labels for complex 3D scenes. Most existing SSC models focus on volumetric representations, which are memory-inefficient for large outdoor spaces. Point clouds provide a lightweight alternative but existing benchmarks lack outdoor point cloud scenes with semantic labels. To address this, we introduce PointSSC, the first cooperative vehicle-infrastructure point cloud benchmark for semantic scene completion. These scenes exhibit long-range perception and minimal occlusion. We develop an automated annotation pipeline leveraging Segment Anything to efficiently assign semantics. To benchmark progress, we propose a LiDAR-based model with a Spatial-Aware Transformer for global and local feature extraction and a Completion and Segmentation Cooperative Module for joint completion and segmentation. PointSSC provides a challenging testbed to drive advances in semantic point cloud completion for real-world navigation.Comment: 8 pages, 5 figures, submitted to ICRA202

Determining Population Stratification and Subgroup effects in Association Studies of Rare Genetic Variants for Nicotine Dependence

[[abstract]]Background Rare variants (minor allele frequency < 1% or 5 %) can help researchers to deal with the confounding issue of ‘missing heritability’ and have a proven role in dissecting the etiology for human diseases and complex traits. Methods We extended the combined multivariate and collapsing (CMC) and weighted sum statistic (WSS) methods and accounted for the effects of population stratification and subgroup effects using stratified analyses by the principal component analysis, named here as ‘str-CMC’ and ‘str-WSS’. To evaluate the validity of the extended methods, we analyzed the Genetic Architecture of Smoking and Smoking Cessation database, which includes African Americans and European Americans genotyped on Illumina Human Omni2.5, and we compared the results with those obtained with the sequence kernel association test (SKAT) and its modification, SKAT-O that included population stratification and subgroup effect as covariates. We utilized the Cochran–Mantel–Haenszel test to check for possible differences in single nucleotide polymorphism allele frequency between subgroups within a gene. We aimed to detect rare variants and considered population stratification and subgroup effects in the genomic region containing 39 acetylcholine receptor-related genes. Results The Cochran–Mantel–Haenszel test as applied to GABRG2 (P = 0.001) was significant. However, GABRG2 was detected both by str-CMC (P= 8.04E-06) and str-WSS (P= 0.046) in African Americans but not by SKAT or SKAT-O. Conclusions Our results imply that if associated rare variants are only specific to a subgroup, a stratified analysis might be a better approach than a combined analysis.[[notice]]補正完

Malaria in Sokoto, North Western Nigeria

Malaria remains a major cause of mortality among children under the age of five years; it is endemic throughout Nigeria with seasonal variation in different geographic zones of the country. Malaria prevalence studies had been undertaken in many parts of Nigeria but there is probably no dataavailable from the far North Western region. This research study was undertaken to determine the prevalence, monthly distribution of malaria in Sokoto, North Western Nigeria in order to generate baseline information. A total of 1,297 blood samples were collected by simple random sampling, from patients attending the two health centres over the twelve calendar months. Thick and thin blood films were Giemsa stained and observed for the presence of malaria parasites. A total of 354 (27.29%) werepositive for malaria parasites with the highest prevalence rate being recorded in the month of August with 72 (59.5%) positive cases and the month of March having the least infection rate of 9 (9.18%). Theinfection rate according to gender showed that males had the higher infection rate of 192 (n = 635) or 30.24% than the females who had a total 162 infection (n = 662) or 24.47%. The age group 0 - 5 years hadthe highest infection rate of 123 (43.77%) while the age group 36 - 40 years had the least infection rate of 10 (9.8%). The study has revealed the presence of malaria transmission throughout the year in Sokoto, North Western Nigeria and the infection rate can be considered as moderately high