Gene-Based Association Tests Using New Polygenic Risk Scores and Incorporating Gene Expression Data

Recently, gene-based association studies have shown that integrating genome-wide association studies (GWAS) with expression quantitative trait locus (eQTL) data can boost statistical power and that the genetic liability of traits can be captured by polygenic risk scores (PRSs). In this paper, we propose a new gene-based statistical method that leverages gene-expression measure-ments and new PRSs to identify genes that are associated with phenotypes of interest. We used a generalized linear model to associate phenotypes with gene expression and PRSs and used a score-test statistic to test the association between phenotypes and genes. Our simulation studies show that the newly developed method has correct type I error rates and can boost statistical power compared with other methods that use either gene expression or PRS in association tests. A real data analysis Figurebased on UK Biobank data for asthma shows that the proposed method is applicable to GWAS

Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search

Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. In recent years, TBPS has made remarkable progress and state-of-the-art methods achieve superior performance by learning local fine-grained correspondence between images and texts. However, most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities, which is unreliable due to the lack of contextual information or the potential introduction of noise. Moreover, existing methods seldom consider the information inequality problem between modalities caused by image-specific information. To address these limitations, we propose an efficient joint Multi-level Alignment Network (MANet) for TBPS, which can learn aligned image/text feature representations between modalities at multiple levels, and realize fast and effective person search. Specifically, we first design an image-specific information suppression module, which suppresses image background and environmental factors by relation-guided localization and channel attention filtration respectively. This module effectively alleviates the information inequality problem and realizes the alignment of information volume between images and texts. Secondly, we propose an implicit local alignment module to adaptively aggregate all pixel/word features of image/text to a set of modality-shared semantic topic centers and implicitly learn the local fine-grained correspondence between modalities without additional supervision and cross-modal interactions. And a global alignment is introduced as a supplement to the local perspective. The cooperation of global and local alignment modules enables better semantic alignment between modalities. Extensive experiments on multiple databases demonstrate the effectiveness and superiority of our MANet

Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification

Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational resources and only consider the issue of missing information caused by occlusion. In this paper, we propose a simple yet effective framework, termed Erasing, Transforming, and Noising Defense Network (ETNDNet), which treats occlusion as a noise disturbance and solves occluded person re-ID from the perspective of adversarial defense. In the proposed ETNDNet, we introduce three strategies: Firstly, we randomly erase the feature map to create an adversarial representation with incomplete information, enabling adversarial learning of identity loss to protect the re-ID system from the disturbance of missing information. Secondly, we introduce random transformations to simulate the position misalignment caused by occlusion, training the extractor and classifier adversarially to learn robust representations immune to misaligned information. Thirdly, we perturb the feature map with random values to address noisy information introduced by obstacles and non-target pedestrians, and employ adversarial gaming in the re-ID system to enhance its resistance to occlusion noise. Without bells and whistles, ETNDNet has three key highlights: (i) it does not require any external modules with parameters, (ii) it effectively handles various issues caused by occlusion from obstacles and non-target pedestrians, and (iii) it designs the first GAN-based adversarial defense paradigm for occluded person re-ID. Extensive experiments on five public datasets fully demonstrate the effectiveness, superiority, and practicality of the proposed ETNDNet. The code will be released at \url{https://github.com/nengdong96/ETNDNet}

Integrating External Controls by Regression Calibration for Genome-Wide Association Study

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study

Control for population stratification in genetic association studies based on GWAS summary statistics

Over the past years, genome-wide association studies (GWAS) have generated a wealth of new information. Summary data from many GWAS are now publicly available, promoting the development of many statistical methods for association studies based on GWAS summary statistics, which avoids the increasing challenges associated with individual-level genotype and phenotype data sharing. However, for population-based association studies such as GWAS, it has been long recognized that population stratification can seriously confound association results. For large GWAS, it is very likely that there exist population stratification and cryptic relatedness, which will result in inflated Type I error in association testing. Although many methods have been developed to control for population stratification, only two of these approaches can be used to control population stratification without individual-level data: one is based on genomic control (GC) and the other one is based on linkage disequilibrium score regression (LDSC). However, the performance of these two approaches is currently unknown. In this study, we use extensive simulation studies including populations with subpopulations, spatially structured populations, and populations with cryptic relatedness to compare the performance of these two approaches to control for population stratification using only GWAS summary statistics without individual-level data. Data sets from the genetic analysis workshop 19 and UK Biobank are also used to evaluate these two approaches. We demonstrate that the intercept of LDSC can be used as a more accurate correction factor than GC. The results from this study will provide very useful information for researchers using GWAS summary statistics while trying to control for population stratification

Inhibitory Effects and Mechanism of Action of Elsinochrome A on <i>Candida albicans</i> and Its Biofilm

Biofilm-associated Candida albicans infections, the leading cause of invasive candidiasis, can cause high mortality rates in immunocompromised patients. Photodynamic antimicrobial chemotherapy (PACT) is a promising approach for controlling infections caused by biofilm-associated C. albicans. This study shows the effect of Elsinochrome A (EA) against different stages of C. albicans biofilms in vitro by XTT reduction assay and crystal violet staining. The mechanism of action of EA on C. albicans biofilm was analyzed with flow cytometry, confocal laser microscopy, and the Real-Time Quantitative Reverse Transcription PCR (qRT-PCR). EA-mediated PACT significantly reduced the viability of C. albicans, with an inhibition rate on biofilm of 89.38% under a concentration of 32 μg/mL EA. We found that EA could not only inhibit the adhesion of C. albicans in the early stage of biofilm formation, but that it also had good effects on pre-formed mature biofilms with a clearance rate of 35.16%. It was observed that EA-mediated PACT promotes the production of a large amount of reactive oxygen species (ROS) in C. albicans and down-regulates the intracellular expression of oxidative-stress-related genes, which further disrupted the permeability of cell membranes, leading to mitochondrial and nuclear damage. These results indicate that EA has good photodynamic antagonizing activity against the C. albicans biofilm, and potential clinical value

Characterization of the complete chloroplast genome of Lindera aggregate

In this study, we sequenced the complete chloroplast genome of Lindera aggregate (Sims) Kosterm., an important Chinese herbal medicine. The complete chloroplast genome with a size of 152,714 bp in length, contained two inverted repeats (IRa and IRb) regions of 20,090 bp each, which were separated by a large single copy (LSC, 93,743 bp) regions and a small single copy (SSC, 18,791 bp) regions, the overall GC content was 42.84%. The chloroplast genome contained 122 genes, 77 protein-coding, 37 tRNA, and 8 rRNA genes. The phylogenetic tree showed that Lindera aggregate (Sims) Kosterm. has a close relationship with Lindera chuni

Identification and functional characterization of sex pheromone receptors in the common cutworm (<em>Spodoptera litura</em>)

International audienceMale moths can finely discriminate the sex pheromone emitted by conspecific females from similar compounds. Pheromone receptors, expressed on the dendritic membrane of sensory neurons housed in the long trichoid sensilla of antennae, are thought to be associated with the pheromone reception. In this study, we identified and functionally characterized 4 pheromone receptors from the antennae of Spodoptera litura (Lepidoptera: Noctuidae). A tissue distribution analysis showed that the expression of the 4 SlituPRs was restricted to antennae. In addition, SlituOR6 and SlituOR13 were specifically expressed in male antennae whereas SlituOR11 and SlituOR16 were male-biased. Functional investigation by heterologous expression in Xenopus oocytes revealed that SlituOR6 was specifically tuned to the second major pheromone component, Z9,E12-14:OAc, SlituOR13 was equally tuned to Z9,E12-14:OAc and Z9-14:OAc, with a small response to the major pheromone component Z9,E11-14:OAc, SlituOR16 significantly responded to the behavioral antagonist Z9-14:OH, whereas SlituOR11 did not show response to any of the pheromone compounds tested in this study. Our results provide molecular data to better understand the mechanisms of sex pheromone detection in the moth S. litura and bring clues to investigate the evolution of the sexual communication channel in closely related species through comparison with previously reported pheromone receptors in other Spodoptera species

Hypocrellin A-based photodynamic action induces apoptosis in A549 cells through ROS-mediated mitochondrial signaling pathway

Over recent decades, many studies have reported that hypocrellin A (HA) can eliminate cancer cells with proper irradiation in several cancer cell lines. However, the precise molecular mechanism underlying its anticancer effect has not been fully defined. HA-mediated cytotoxicity and apoptosis in human lung adenocarcinoma A549 cells were evaluated after photodynamic therapy (PDT). A temporal quantitative proteomics approach by isobaric tag for relative and absolute quantitation (iTRAQ) 2D liquid chromatography with tandem mass spectrometric (LC–MS/MS) was introduced to help clarify molecular cytotoxic mechanisms and identify candidate targets of HA-induced apoptotic cell death. Specific caspase inhibitors were used to further elucidate the molecular pathway underlying apoptosis in PDT-treated A549 cells. Finally, down-stream apoptosis-related protein was evaluated. Apoptosis induced by HA was associated with cell shrinkage, externalization of cell membrane phosphatidylserine, DNA fragmentation, and mitochondrial disruption, which were preceded by increased intracellular reactive oxygen species (ROS) generations. Further studies showed that PDT treatment with 0.08 µmol/L HA resulted in mitochondrial disruption, pronounced release of cytochrome c, and activation of caspase-3, -9, and -7. Together, HA may be a possible therapeutic agent directed toward mitochondria and a promising photodynamic anticancer candidate for further evaluation. KEY WORDS: Hypocrellin A, Photodynamic therapy, Reactive oxygen species, Proteomic, LC–MS/MS, iTRA