Genome-wide transcriptome analysis reveals that Bisphenol A activates immune responses in skeletal muscle.

Cumulative human exposure to the environmental toxin, bisphenol A (BPA), has raised important health concerns in recent decades. However, the direct genomic regulation of BPA in skeletal muscles and its clinical significance are poorly understood. Therefore, we conducted a genome-wide transcriptome analysis after daily oral administration of BPA at the lowest observed adverse-effect level (LOAEL, 50 mg/kg) in male mice for six weeks to explore the gene-expression regulations in skeletal muscle induced by BPA. The primary Gene Ontology terms linked to BPA-dependent, differentially expressed genes at LOAEL comprised adaptive-immune response, positive regulation of T cell activation, and immune system process. The gene-set enrichment analysis disclosed increased complement-associated genes [complement components 3 (C3) and 4B, complement factor D, complement receptor 2, and immunoglobulin lambda constant 2] in the group administered with BPA, with a false-discovery rate of <0.05. Subsequent validation analysis conducted in BPA-fed animal skeletal muscle tissue and in vitro experiments confirmed that BPA induced immune activation, as evidenced by increased levels of C3 and C4α proteins in mice, C2C12 myoblasts, and mouse skeletal muscle cells. In addition, BPA markedly upregulated the transcription of tumor necrosis factor-α (Tnfα) in C2C12 myoblasts and mouse skeletal muscle cells, which was substantially inhibited by 5z-7-oxozeanol and parthenolide, providing further evidence of BPA-induced inflammation in muscle cells. Our bioinformatics and subsequent animal and in vitro validations demonstrate that BPA can activate inflammation in skeletal muscle, which could be a risk factor underlying chronic muscle weakness and wastage.

Metabolic Regulation of Longevity and Immune Response in Caenorhabditis elegans by Ingestion of Lacticaseibacillus rhamnosus IDCC 3201 Using Multi-Omics Analysis.

Probiotics, specifically Lacticaseibacillus rhamnosus, have garnered attention for their potential health benefits. This study focuses on evaluating the probiotic properties of candidate probiotics L. rhamnosus IDCC 3201 (3201) using the Caenorhabditis elegans surrogate animal model, a well-established in vivo system for studying host-bacteria interactions. The adhesive ability to the host's gastrointestinal tract is a crucial criterion for selecting potential probiotic bacteria. Our findings demonstrated that 3201 exhibits significantly higher adhesive capabilities compared with Escherichia coli OP50 (OP50), a standard laboratory food source for C. elegans and is comparable with the widely recognized probiotic L. rhamnosus GG (LGG). In lifespan assay, 3201 significantly increased the longevity of C. elegans compared with OP50. In addition, preconditioning with 3201 enhanced C. elegans immune response against four different foodborne pathogenic bacteria. To uncover the molecular basis of these effects, transcriptome analysis elucidated that 3201 modulates specific gene expression related to the innate immune response in C. elegans. C-type lectin-related genes and lysozyme-related genes, crucial components of the immune system, showed significant upregulation after feeding 3201 compared with OP50. These results suggested that preconditioning with 3201 may enhance the immune response against pathogens. Metabolome analysis revealed increased levels of fumaric acid and succinic acid, metabolites of the citric acid cycle, in C. elegans fed with 3201 compared with OP50. Furthermore, there was an increase in the levels of lactic acid, a well-known antimicrobial compound. This rise in lactic acid levels may have contributed to the robust defense mechanisms against pathogens. In conclusion, this study demonstrated the probiotic properties of the candidate probiotic L. rhamnosus IDCC 3201 by using multi-omics analysis.

Dietary supplementation with Lacticaseibacillus rhamnosus IDCC3201 alleviates sarcopenia by modulating the gut microbiota and metabolites in dexamethasone-induced models.

Probiotics can exert direct or indirect influences on various aspects of health claims by altering the composition of the gut microbiome and producing bioactive metabolites. The aim of this study was to examine the effect of Lacticaseibacillus rhamnosus IDCC3201 on skeletal muscle atrophy in dexamethasone-induced C2C12 cells and a mouse animal model. Dexamethasone treatment significantly reduced C2C12 muscle cell viability, myotube diameter, and levels of muscle atrophic markers (Atrogin-1 and MuRF-1). These effects were alleviated by conditioned media (CM) and cell extract (EX) derived from L. rhamnosus IDCC3201. In addition, we assessed the in vivo therapeutic effect of L. rhamnosus IDCC3201 in a mouse model of dexamethasone (DEX)-induced muscle atrophy. Supplementation with IDCC3201 resulted in significant enhancements in body composition, particularly in lean mass, muscle strength, and myofibril size, in DEX-induced muscle atrophy mice. In comparison to the DEX-treatment group, the normal and DEX + L. rhamnosus IDCC3201 groups showed a higher transcriptional level of myosin heavy chain family genes (MHC1, MHC1b, MHC2A, 2bB, and 2X) and a reduction in atrophic muscle makers. These analyses revealed that L. rhamnosus IDCC3201 supplementation led to increased production of branched-chain amino acids (BCAAs) and improved the Allobaculum genus within the gut microbiota of muscle atrophy-induced groups. Taken together, our findings suggest that L. rhamnosus IDCC3201 represents a promising dietary supplement with the potential to alleviate sarcopenia by modulating the gut microbiome and metabolites.

Deciphering the mechanisms and interactions of the endocrine disruptor bisphenol A and its analogs with the androgen receptor.

Bisphenol A (BPA) and its various forms used as BPA alternatives in industries are recognized toxic compounds and antiandrogenic endocrine disruptors. These chemicals are widespread in the environment and frequently detected in biological samples. Concerns exist about their impact on hormones, disrupting natural biological processes in humans, together with their negative impacts on the environment and biotic life. This study aims to characterize the interaction between BPA analogs and the androgen receptor (AR) and the effect on the receptor's normal activity. To achieve this goal, molecular docking was conducted with BPA and its analogs and dihydrotestosterone (DHT) as a reference ligand. Four BPA analogs exhibited higher affinity (-10.2 to -8.7 kcal/mol) for AR compared to BPA (-8.6 kcal/mol), displaying distinct interaction patterns. Interestingly, DHT (-11.0 kcal/mol) shared a binding pattern with BPA. ADMET analysis of the top 10 compounds, followed by molecular dynamics simulations, revealed toxicity and dynamic behavior. Experimental studies demonstrated that only BPA disrupts DHT-induced AR dimerization, thereby affecting AR's function due to its binding nature. This similarity to DHT was observed during computational analysis. These findings emphasize the importance of targeted strategies to mitigate BPA toxicity, offering crucial insights for interventions in human health and environmental well-being.

Microbial-transcriptome integrative analysis of heat stress effects on amino acid metabolism and lipid peroxidation in poultry jejunum.

Despite the significant threat of heat stress to livestock animals, only a few studies have considered the potential relationship between broiler chickens and their microbiota. Therefore, this study examined microbial modifications, transcriptional changes and host-microbiome interactions using a predicted metabolome data-based approach to understand the impact of heat stress on poultry. After the analysis, the host functional enrichment analysis revealed that pathways related to lipid and protein metabolism were elevated under heat stress conditions. In contrast, pathways related to the cell cycle were suppressed under normal environmental temperatures. In line with the transcriptome analysis, the microbial analysis results indicate that taxonomic changes affect lipid degradation. Heat stress engendered statistically significant difference in the abundance of 11 microorganisms, including Bacteroides and Peptostreptococcacea. Together, integrative approach analysis suggests that microbiota-induced metabolites affect host fatty acid peroxidation metabolism, which is correlated with the gene families of Acyl-CoA dehydrogenase long chain (ACADL), Acyl-CoA Oxidase (ACOX) and Acetyl-CoA Acyltransferase (ACAA). This integrated approach provides novel insights into heat stress problems and identifies potential biomarkers associated with heat stress.

Continual learning framework for a multicenter study with an application to electrocardiogram.

Deep learning has been increasingly utilized in the medical field and achieved many goals. Since the size of data dominates the performance of deep learning, several medical institutions are conducting joint research to obtain as much data as possible. However, sharing data is usually prohibited owing to the risk of privacy invasion. Federated learning is a reasonable idea to train distributed multicenter data without direct access; however, a central server to merge and distribute models is needed, which is expensive and hardly approved due to various legal regulations. This paper proposes a continual learning framework for a multicenter study, which does not require a central server and can prevent catastrophic forgetting of previously trained knowledge. The proposed framework contains the continual learning method selection process, assuming that a single method is not omnipotent for all involved datasets in a real-world setting and that there could be a proper method to be selected for specific data. We utilized the fake data based on a generative adversarial network to evaluate methods prospectively, not ex post facto. We used four independent electrocardiogram datasets for a multicenter study and trained the arrhythmia detection model. Our proposed framework was evaluated against supervised and federated learning methods, as well as finetuning approaches that do not include any regulation to preserve previous knowledge. Even without a central server and access to the past data, our framework achieved stable performance (AUROC 0.897) across all involved datasets, achieving comparable performance to federated learning (AUROC 0.901).

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare.

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.

Deep learning-based long-term risk evaluation of incident type 2 diabetes using electrocardiogram in a non-diabetic population: a retrospective, multicentre study.

Background: Diabetes is a major public health concern. We aimed to evaluate the long-term risk of incident type 2 diabetes in a non-diabetic population using a deep learning model (DLM) detecting prevalent type 2 diabetes using electrocardiogram (ECG). Methods: In this retrospective study, participants who underwent health checkups at two tertiary hospitals in Seoul, South Korea, between Jan 1, 2001 and Dec 31, 2022 were included. Type 2 diabetes was defined as glucose ≥126 mg/dL or glycated haemoglobin (HbA1c) ≥ 6.5%. For survival analysis on incident type 2 diabetes, we introduced an additional variable, diabetic ECG, which is determined by the DLM trained on ECG and corresponding prevalent diabetes. It was assumed that non-diabetic individuals with diabetic ECG had a higher risk of incident type 2 diabetes than those with non-diabetic ECG. The one-dimensional ResNet-based model was adopted for the DLM, and the Guided Grad-CAM was used to localise important regions of ECG. We divided the non-diabetic group into the diabetic ECG group (false positive) and the non-diabetic ECG (true negative) group according to the DLM decision, and performed a Cox proportional hazard model, considering the occurrence of type 2 diabetes more than six months after the visit. Findings: 190,581 individuals were included in the study with a median follow-up period of 11.84 years. The areas under the receiver operating characteristic curve for prevalent type 2 diabetes detection were 0.816 (0.807-0.825) and 0.762 (0.754-0.770) for the internal and external validations, respectively. The model primarily focused on the QRS duration and, occasionally, P or T waves. The diabetic ECG group exhibited an increased risk of incident type 2 diabetes compared with the non-diabetic ECG group, with hazard ratios of 2.15 (1.82-2.53) and 1.92 (1.74-2.11) for internal and external validation, respectively. Interpretation: In the non-diabetic group, those whose ECG was classified as diabetes by the DLM were at a higher risk of incident type 2 diabetes than those whose ECG was not. Additional clinical research on the relationship between the phenotype of ECG and diabetes to support the results and further investigation with tracked data and various ECG recording systems are suggested for future works. Funding: National Research Foundation of Korea.

Structural insight into the mechanisms and interacting features of endocrine disruptor Bisphenol A and its analogs with human estrogen-related receptor gamma.

Bisphenol A (BPA) is a very important chemical from the commercial perspective. Many useful products are made from it, so its production is increasing day by day. It is widely known that Bisphenol A (BPA) and its analogs are present in the environment and that they enter our body through various routes on a daily basis as we use things made of this chemical in our daily lives. BPA has already been reported to be an endocrine disruptor. Studies have shown that BPA binds strongly to the human estrogen-related receptor gamma (ERRγ) and is an important target of it. This study seeks to understand how it interacts with ERRγ. Molecular docking of BPA and its analogs with ERRγ was performed, and estradiol was taken as a reference. Then, physico-chemical and toxicological analysis of BPA compounds was performed. Subsequently, the dynamic behavior of ERRγ and ERRγ-BPA compound complexes was studied by molecular dynamics simulations over 500 ns, and using this simulated data, their binding energies were again calculated using the MM-PBSA method. We observed that the binding affinity of BPA and its analogs was much higher than that of estradiol, and apart from being toxic, they can be easily absorbed in our body as their physicochemical properties are similar to those of oral medicines. Therefore, this study facilitates the understanding of the structure-activity relationship of ERRγ and BPA compounds and provides information about the key amino acid residues of ERRγ that interact with BPA compounds, which can be helpful to design competitive inhibitors so that we can interrupt the interaction of BPA with ERRγ. In addition, it provides information on BPA and its analogs and will also be helpful in developing new therapeutics.

A Unified Multi-Modality Fusion Framework for Deep Spatio-Temporal-Spectral Feature Learning in Resting-State fMRI Denoising.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used functional neuroimaging technique to investigate the functional brain networks. However, rs-fMRI data are often contaminated with noise and artifacts that adversely affect the results of rs-fMRI studies. Several machine/deep learning methods have achieved impressive performance to automatically regress the noise-related components decomposed from rs-fMRI data, which are expressed as the pairs of a spatial map and its associated time series. However, most of the previous methods individually analyze each modality of the noise-related components and simply aggregate the decision-level information (or knowledge) extracted from each modality to make a final decision. Moreover, these approaches consider only the limited modalities making it difficult to explore class-discriminative spectral information of noise-related components. To overcome these limitations, we propose a unified deep attentive spatio-spectral-temporal feature fusion framework. We first adopt a learnable wavelet transform module at the input-level of the framework to elaborately explore the spectral information in subsequent processes. We then construct a feature-level multi-modality fusion module to efficiently exchange the information from multi-modality inputs in the feature space. Finally, we design confidence-based voting strategies for decision-level fusion at the end of the framework to make a robust final decision. In our experiments, the proposed method achieved remarkable performance for noise-related component detection on various rs-fMRI datasets.

Protective Effects of Lacticaseibacillus rhamnosus IDCC3201 on Motor Functions and Anxiety Levels in a Chronic Stress Mouse Model.

Growing evidence indicates a crucial role of the gut microbiota in physiological functions. Gut-brain axis imbalance has also been associated with neuropsychiatric and neurodegenerative disorders. Studies have suggested that probiotics regulate the stress response and alleviate mood-related symptoms. In this study, we investigated the effects of the probiotic Lacticaseibacillus rhamnosus IDCC3201 (L3201) on the behavioral response and fecal metabolite content in an unpredictable chronic mild stress (UCMS) mouse model. Our study shows that chronic stress in mice for three weeks resulted in significant changes in behavior, including lower locomotor activity, higher levels of anxiety, and depressive-like symptoms, compared to the control group. Metabolomic analysis demonstrated that disrupted fecal metabolites associated with aminoacyl-tRNA biosynthesis and valine, leucine, and isoleucine biosynthesis by UCMS were restored with the administration of L3201. Oral administration of the L3201 ameliorated the observed changes and improved the behavioral alterations along with fecal metabolites, suggesting that probiotics play a neuroprotective role.

Room-Temperature Quantum Diodes with Dynamic Memory for Neural Logic Operations.

The pursuit of high-performance, next-generation nanoelectronics is fundamentally reliant on exploiting quantum phenomena such as tunneling at room temperature. However, quantum tunneling and memory dynamics are governed by two conflicting parameters: the presence or absence of defects. Therefore, the integration of both attributes within a single device presents substantial challenges. Nevertheless, successful integration has the potential to prompt crucial breakthroughs by emulating biobrain-like dynamics, in turn enabling sophisticated in-material neural logic operations. In this work, we demonstrate that a conformal nanolaminate HfO2/ZrO2 structure on silicon enables high-performing (>106 s) Fowler-Nordheim tunneling at room temperature. In addition, the device exhibits unipolar dynamic hysteresis loop opening (on/off ratio >102) with high endurance (>104 cycles) along with negative differential resistance, which is attributed to the collective ferroelectric and capacitive effects and is utilized to emulate synaptic functions. Further, proof-of-concept logic gates based on voltage-dependent plasticity and time-domain were developed using a single device, offering in-material neural-like data processing. These findings pave the way for the realization of high-performing and scalability tunneling devices in advanced nanoelectronics, which mark a promising route toward the development of next-generation, fundamental neural logic computing systems.

Blood transcriptomic differences in the immune response under stressful environmental according to stocking density in pigs.

The implementation of animal welfare in the pig industry is becoming a global trend, and welfare can be improved through livestock management. In modern and intensive farming systems, it has become important to find a reasonable compromise between stocking density and productivity. The simultaneous detection of behavioral and physiological parameters is helpful when considering welfare levels for stocking density. This study aimed to confirm the effect of stocking density through transcriptome linkage. A comparison of three groups according to stocking density (low density, eight pigs and 1.0 m2 per head; normal density, eight pigs and 0.8 m2 per head; and high density, eight pigs and 0.6 m2 per head) was performed, and their transcriptomic changes were observed using the RNA-Seq method. Differentially expressed genes were identified for each comparison group (low density vs. normal density, 95 upregulated genes and 112 downregulated genes; high density vs. normal density, 133 upregulated genes and 217 downregulated genes; and high density vs. low density, 245 upregulated genes and 237 downregulated genes). Biological mechanisms according to stocking density were identified through functional annotation. T-cell differentiation and immune disease pathway enriched in the high-density group caused immune imbalance through dysregulated T-cell signaling. Moreover, oxidative stress, together with DNA damage, can lead to high susceptibility to disease. Our study confirmed the biological mechanisms through immunological expression patterns according to stocking density. The study results are expected to provide comprehensive insight into systematic operation strategies considering stocking density and biomarkers for use in welfare evaluation.

Animal welfare in the pig industry is a worldwide trend, and appropriate livestock management can improve welfare. Balancing stocking density and productivity is crucial in intensive farming systems. This study used behavioral and physiological parameters to assess welfare levels related to stocking density. The study identified biological mechanisms and immunological expression patterns influenced by stocking density by analyzing the transcriptome. These results offer comprehensive insights into operational strategies considering stocking density and biomarkers.

Comparative transcriptome analysis between long- and short-term survival after pig-to-monkey cardiac xenotransplantation reveals differential heart failure development.

Cardiac xenotransplantation is the potential treatment for end-stage heart failure, but the allogenic organ supply needs to catch up to clinical demand. Therefore, genetically-modified porcine heart xenotransplantation could be a potential alternative. So far, pig-to-monkey heart xenografts have been studied using multi-transgenic pigs, indicating various survival periods. However, functional mechanisms based on survival period-related gene expression are unclear. This study aimed to identify the differential mechanisms between pig-to-monkey post-xenotransplantation long- and short-term survivals. Heterotopic abdominal transplantation was performed using a donor CD46-expressing GTKO pig and a recipient cynomolgus monkey. RNA-seq was performed using samples from POD60 XH from monkey and NH from age-matched pigs, D35 and D95. Gene-annotated DEGs for POD60 XH were compared with those for POD9 XH (Park et al. 2021). DEGs were identified by comparing gene expression levels in POD60 XH versus either D35 or D95 NH. 1,804 and 1,655 DEGs were identified in POD60 XH versus D35 NH and POD60 XH versus D95 NH, respectively. Overlapped 1,148 DEGs were annotated and compared with 1,348 DEGs for POD9 XH. Transcriptomic features for heart failure and inhibition of T cell activation were observed in both long (POD60)- and short (POD9)-term survived monkeys. Only short-term survived monkey showed heart remodeling and regeneration features, while long-term survived monkey indicated multi-organ failure by neural and hormonal signaling as well as suppression of B cell activation. Our results reveal differential heart failure development and survival at the transcriptome level and suggest candidate genes for specific signals to control adverse cardiac xenotransplantation effects.

Effect of dietary betaine supplementation on the liver transcriptome profile in broiler chickens under heat stress conditions.

OBJECTIVE: The objective of the present study was to investigate the effect of dietary betaine (BT) supplementation on the hepatic transcriptome profiles in broiler chickens raised under heat stress (HS) conditions. METHODS: A total of 180 (21-d-old) Ross 308 male broiler chicks were allotted to 1 of 3 treatment groups with 6 replicated cages in a completely randomized design. One group was kept under thermoneutral conditions at all times and was fed a basal diet (PC). Other 2 groups were exposed to a cyclic heat stress condition. One of the 2 groups under heat stress conditions was fed the basal diet as a negative control (NC), whereas the other group was fed the basal diet supplemented with 0.2% BT. All chickens were provided with diets and water ad libitum for 21 d. Following the experiment, the liver samples were collected for RNA sequencing analysis. RESULTS: Broiler chickens in NC and BT group had decreased (p<0.05) growth performance. In the transcriptome analysis, the number of differentially expressed genes were identified in the liver by HS conditions and dietary BT supplementation. In the comparison between NC and PC treatments, genes related to energy and nucleic acid metabolism, amino acid metabolism, and immune system were altered by HS, which support the reason why heat-stressed poultry had decreased growth performance. In the comparison between NC and BT treatments, genes related to lipid metabolism, carbohydrate metabolism, and immune system were differently expressed under HS conditions. CONCLUSION: HS negatively impacts various physiological processes, including DNA replication, metabolism of amino acids, lipids, and carbohydrates, and cell cycle progression in broiler chickens. Dietary BT supplementation, however, offers potential counteractive effects by modulating liver function, facilitating gluconeogenesis, and enhancing immune systems. These findings provide a basis for understanding molecular responses by HS and the possible benefits of dietary BT supplementation in broiler chickens exposed to HS.

Identification of histidine kinase inhibitors through screening of natural compounds to combat mastitis caused by Streptococcus agalactiae in dairy cattle.

BACKGROUND: Mastitis poses a major threat to dairy farms globally; it results in reduced milk production, increased treatment costs, untimely compromised genetic potential, animal deaths, and economic losses. Streptococcus agalactiae is a highly virulent bacteria that cause mastitis. The administration of antibiotics for the treatment of this infection is not advised due to concerns about the emergence of antibiotic resistance and potential adverse effects on human health. Thus, there is a critical need to identify new therapeutic approaches to combat mastitis. One promising target for the development of antibacterial therapies is the transmembrane histidine kinase of bacteria, which plays a key role in signal transduction pathways, secretion systems, virulence, and antibiotic resistance. RESULTS: In this study, we aimed to identify novel natural compounds that can inhibit transmembrane histidine kinase. To achieve this goal, we conducted a virtual screening of 224,205 natural compounds, selecting the top ten based on their lowest binding energy and favorable protein-ligand interactions. Furthermore, molecular docking of eight selected antibiotics and five histidine kinase inhibitors with transmembrane histidine kinase was performed to evaluate the binding energy with respect to top-screened natural compounds. We also analyzed the ADMET properties of these compounds to assess their drug-likeness. The top two compounds (ZINC000085569031 and ZINC000257435291) and top-screened antibiotics (Tetracycline) that demonstrated a strong binding affinity were subjected to molecular dynamics simulations (100 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method. CONCLUSION: Our results suggest that the selected natural compounds have the potential to serve as effective inhibitors of transmembrane histidine kinase and can be utilized for the development of novel antibacterial veterinary medicine for mastitis after further validation through clinical studies.

Reducing Bonding Temperature and Energy Consumption in Electronic Packaging Using Flash Electro-Thermal Carbon Fiber Heating Elements.

Semiconductor packaging based on an epoxy molding compound (EMC) currently has several disadvantages including warpage, limited processing area, and variability that all negatively affect cost and production yield. We propose a facile EMC molding process method using a flash electro-thermal carbon fiber heating (FE-CH) device based on carbon fiber-based papers to manufacture an EMC molded to a copper substrate (EMC/Cu bi-layer package) via Joule heating, and using this device, a modified cure cycle that combines the conventional cure cycle (CCC) with rapid cooling was performed using FE-CH to reduce the curvature of the EMC/Cu bi-layer package. Compared to the conventional hot press process, which uses 3.17 MW of power, the FE-CH process only uses 32.87 kW, resulting in a power consumption reduction of over 100 times when following the CCC. Furthermore, the FE-CH-cured EMC/Cu bi-layer package exhibits mechanical properties equivalent to those of a hot press-cured specimen, including the degree of cure, elastic modulus, curvature, bonding temperature, residual strain, and peel strength. The modified cure cycle using the FE-CH results in a 31% reduction in residual strain, a 32% reduction in curvature, and a 47% increase in peel strength compared to the CCC, indicating that this new process method is very promising for reducing a semiconductor package's price by reducing the process cost and warpage.

Modified Korean MIND Diet: A Nutritional Intervention for Improved Cognitive Function in Elderly Women through Mitochondrial Respiration, Inflammation Suppression, and Amino Acid Metabolism Regulation.

SCOPE: Mild cognitive impairment is associated with a high prevalence of dementia. The study examines the benefits of a modified Korean MIND (K-MIND) diet and explores biomarkers using multi-omics analysis. METHODS AND RESULTS: The K-MIND diet, tailored to the elderly Korean population, includes perilla oil, milk, or fermented milk, and avoids alcohol consumption. As a result, the K-MIND diet significantly improves subjects "orientation to place" in the Korean version of the Mini-Mental State Examination, 2nd edition test. According to multi-omics analysis, the K-MIND diet upregulates genes associated with mitochondrial respiration, including ubiquinone oxidoreductase, cytochrome C oxidase, and ATP synthase, and immune system processes, and downregulates genes related to nuclear factor kappa B activity and inflammatory responses. In addition, K-MIND affects the metabolic pathways of glycine, serine, threonine, tryptophan, and sphingolipids, which are closely linked to cognitive function through synthesis of neurotransmitters and structures of brain cell membranes. CONCLUSION: The findings imply that the K-MIND diet improves cognitive function by upregulating key genes involved in oxidative phosphorylation and downregulating pro-inflammatory cytokines.

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets.

Introduction: The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI. Methods: In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario. Results and discussion: The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.