Cross-Corpus Speech Emotion Recognition Based on Multi-Task Learning and Subdomain Adaptation

To solve the problem of feature distribution discrepancy in cross-corpus speech emotion recognition tasks, this paper proposed an emotion recognition model based on multi-task learning and subdomain adaptation, which alleviates the impact on emotion recognition. Existing methods have shortcomings in speech feature representation and cross-corpus feature distribution alignment. The proposed model uses a deep denoising auto-encoder as a shared feature extraction network for multi-task learning, and the fully connected layer and softmax layer are added before each recognition task as task-specific layers. Subsequently, the subdomain adaptation algorithm of emotion and gender features is added to the shared network to obtain the shared emotion features and gender features of the source domain and target domain, respectively. Multi-task learning effectively enhances the representation ability of features, a subdomain adaptive algorithm promotes the migrating ability of features and effectively alleviates the impact of feature distribution differences in emotional features. The average results of six cross-corpus speech emotion recognition experiments show that, compared with other models, the weighted average recall rate is increased by 1.89~10.07%, the experimental results verify the validity of the proposed model

Reduced Fitness and Elevated Oxidative Stress in the Marine Copepod <i>Tigriopus japonicus</i> Exposed to the Toxic Dinoflagellate <i>Karenia mikimotoi</i>

Blooms of the toxic dinoflagellate Karenia mikimotoi cause devastation to marine life, including declines of fitness and population recruitment. However, little is known about the effects of them on benthic copepods. Here, we assessed the acute and chronic effects of K. mikimotoi on the marine benthic copepod Tigriopus japonicus. Results showed that adult females maintained high survival (>85%) throughout 14-d incubation, but time-dependent reduction of survival was detected in the highest K. mikimotoi concentration, and nauplii and copepodites were more vulnerable compared to adults. Ingestion of K. mikimotoi depressed the grazing of copepods but significantly induced the generation of reactive oxygen species (ROS), total antioxidant capacity, activities of antioxidant enzymes (superoxide dismutase, catalase, and glutathione peroxidase), and acetylcholinesterase. Under sublethal concentrations for two generations, K. mikimotoi reduced the fitness of copepods by prolonging development time and decreasing successful development rate, egg production, and the number of clutches. Our findings suggest that the bloom of K. mikimotoi may threaten copepod population recruitment, and its adverse effects are associated with oxidative stress

Cyclic Behavior and Stress–Strain Model of Nano-SiO₂-Modified Recycled Aggregate Concrete

Recycled aggregate concrete (RAC) possesses different mechanical properties than ordinary concrete because of inherent faults in recycled aggregates (RAs), such as the old interfacial transition zone (ITZ). However, the application of nano-SiO2 presents an effective methodology to enhance the quality of RA. In this study, nano-SiO2-modified recycled aggregate (SRA) was used to replace natural aggregate (NA), and the stress–strain relationships and cyclic behavior of nano-SiO2-modified recycled aggregate concrete (SRAC) with different SRA replacement rates were investigated. After evaluating the skeleton curve of SRAC specimens, the existing constitutive models were compared. Additionally, the study also proposed a stress–strain model designed to predict the mechanical behavior of concrete in relation to the SRA replacement rate. The results show that compared with RAC, the axial compressive strength of SRAC specimens showed increases of 40.27%, 29.21%, 26.55%, 16.37%, and 8.41% at specific SRA replacement rates of 0%, 30%, 50%, 70%, and 100%, respectively. Moreover, the study found that the Guo model’s calculated results can accurately predict the skeleton curves of SRAC specimens

Negative Binomial Mixed Models for Analyzing Longitudinal Microbiome Data

The metagenomics sequencing data provide valuable resources for investigating the associations between the microbiome and host environmental/clinical factors and the dynamic changes of microbial abundance over time. The distinct properties of microbiome measurements include varied total sequence reads across samples, over-dispersion and zero-inflation. Additionally, microbiome studies usually collect samples longitudinally, which introduces time-dependent and correlation structures among the samples and thus further complicates the analysis and interpretation of microbiome count data. In this article, we propose negative binomial mixed models (NBMMs) for longitudinal microbiome studies. The proposed NBMMs can efficiently handle over-dispersion and varying total reads, and can account for the dynamic trend and correlation among longitudinal samples. We develop an efficient and stable algorithm to fit the NBMMs. We evaluate and demonstrate the NBMMs method via extensive simulation studies and application to a longitudinal microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of flexible framework for modeling correlation structures and detecting dynamic effects. We have developed an R package NBZIMM to implement the proposed method, which is freely available from the public GitHub repository http://github.com//nyiuab//NBZIMM and provides a useful tool for analyzing longitudinal microbiome data

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression

Abstract Background Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients’ response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. Methods It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. Results We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. Conclusions The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies

Interface-induced transverse resistivity anomaly in AgNbO3/SrRuO3 heterostructures

The transverse resistivity anomaly with a hump feature, associated with topological magnetic textures, is of paramount importance for the applications of next-generation chiral spintronic devices. However, the origin of the hump feature still remains debated due to the complicated mechanism, not merely assigned to the intrinsic topological Hall effect (THE). In this work, we observe the apparent transverse resistivity hump characteristic superimposed on the Hall signals in AgNbO3/SrRuO3 (ANO/SRO) heterostructures. The intrinsic THE is ruled out by minor-loop and current density measurements. Combining the microscopic characterization and the two-channel anomalous Hall effect fitting, the hump feature is unambiguously attributed to the synergetic contribution from the SRO layer and the interfacial intermixing thin layer of ANO and SRO

Four-dimensional hydrogel dressing adaptable to the urethral microenvironment for scarless urethral reconstruction

Abstract The harsh urethral microenvironment (UME) after trauma severely hinders the current hydrogel-based urethral repair. In fact, four-dimensional (4D) consideration to mimic time-dependent physiological processes is essential for scarless urethral reconstruction, which requires balancing extracellular matrix (ECM) deposition and remodeling at different healing stages. In this study, we develop a UME-adaptable 4D hydrogel dressing to sequentially provide an early-vascularized microenvironment and later-antifibrogenic microenvironment for scarless urethral reconstruction. With the combination of dynamic boronic ester crosslinking and covalent photopolymerization, the resultant gelatin methacryloyl phenylboronic acid/cis-diol-crosslinked (GMPD) hydrogels exhibit mussel-mimetic viscoelasticity, satisfactory adhesion, and acid-reinforced stability, which can adapt to harsh UME. In addition, a temporally on-demand regulatory (TOR) technical platform is introduced into GMPD hydrogels to create a time-dependent 4D microenvironment. As a result, physiological urethral recovery is successfully mimicked by means of an early-vascularized microenvironment to promote wound healing by activating the vascular endothelial growth factor (VEGF) signaling pathway, as well as a later-antifibrogenic microenvironment to prevent hypertrophic scar formation by timing transforming growth factor-β (TGFβ) signaling pathway inhibition. Both in vitro molecular mechanisms of the physiological healing process and in vivo scarless urethral reconstruction in a rabbit model are effectively verified, providing a promising alternative for urethral injury treatment

Presentation_1_Negative Binomial Mixed Models for Analyzing Longitudinal Microbiome Data.PDF

<p>The metagenomics sequencing data provide valuable resources for investigating the associations between the microbiome and host environmental/clinical factors and the dynamic changes of microbial abundance over time. The distinct properties of microbiome measurements include varied total sequence reads across samples, over-dispersion and zero-inflation. Additionally, microbiome studies usually collect samples longitudinally, which introduces time-dependent and correlation structures among the samples and thus further complicates the analysis and interpretation of microbiome count data. In this article, we propose negative binomial mixed models (NBMMs) for longitudinal microbiome studies. The proposed NBMMs can efficiently handle over-dispersion and varying total reads, and can account for the dynamic trend and correlation among longitudinal samples. We develop an efficient and stable algorithm to fit the NBMMs. We evaluate and demonstrate the NBMMs method via extensive simulation studies and application to a longitudinal microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of flexible framework for modeling correlation structures and detecting dynamic effects. We have developed an R package NBZIMM to implement the proposed method, which is freely available from the public GitHub repository http://github.com//nyiuab//NBZIMM and provides a useful tool for analyzing longitudinal microbiome data.</p

Additional file 2: of Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression

Figure S1. Heatmap with Top 50 Significantly Probes with Drug response (Three Levels) in Mulligan et al. [10]. (DOCX 77 kb