NUMERICAL DISTRIBUTION SIMULATION OF TYPHOONS’ WAVE ENERGY IN THE TAIWAN STRAIT AND ITS ADJACENT WATERS

As new energy technologies boom in recent years, marine renewable energy, especially wave power is one potential trend. However, few relevant studies focus on extreme sea conditions. In this paper, a numerical model of typhoon waves in the Taiwan Strait is established based on the third-generation ocean wave model SWAN and then calculated by the wave energy empirical equation. Typhoon No. 200808 Fung-wong, strong typhoon No. 200815 Jangmi and strong typhoon No. 201808 Maria are used for verification and analysis. Finally, the results show that most concentrated wave energy values are more than 300 kW/m for typhoon and more than 900 kW/m for strong typhoons, over 60 times and 180 times the annual average (5 kW/m) in the Chinese sea area, respectively. In terms of other locations, corresponding values are more than 50 kW/m and over 100 kW/m. Therefore, typhoons’ wave energy is certainly a huge asset if fully utilized

Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201

Systematic Analysis of Impact of Sampling Regions and Storage Methods on Fecal Gut Microbiome and Metabolome Profiles.

The contribution of human gastrointestinal (GI) microbiota and metabolites to host health has recently become much clearer. However, many confounding factors can influence the accuracy of gut microbiome and metabolome studies, resulting in inconsistencies in published results. In this study, we systematically investigated the effects of fecal sampling regions and storage and retrieval conditions on gut microbiome and metabolite profiles from three healthy children. Our analysis indicated that compared to homogenized and snap-frozen samples (standard control [SC]), different sampling regions did not affect microbial community alpha diversity, while a total of 22 of 176 identified metabolites varied significantly across different sampling regions. In contrast, storage conditions significantly influenced the microbiome and metabolome. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles. Sample storage in RNALater showed a significant level of variation in both microbiome and metabolome profiles, independent of the storage or retrieval conditions. The effect of RNALater on the metabolome was stronger than the effect on the microbiome, and individual variability between study participants outweighed the effect of RNALater on the microbiome. We conclude that homogenizing stool samples was critical for metabolomic analysis but not necessary for microbiome analysis. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles and is recommended for short-term fecal sample storage. In addition, our study indicates that the use of RNALater as a storage medium of stool samples for microbial and metabolomic analyses is not recommended.IMPORTANCE The gastrointestinal microbiome and metabolome can provide a new angle to understand the development of health and disease. Stool samples are most frequently used for large-scale cohort studies. Standardized procedures for stool sample handling and storage can be a determining factor for performing microbiome or metabolome studies. In this study, we focused on the effects of stool sampling regions and stool sample storage conditions on variations in the gut microbiome composition and metabolome profile

Lipid mediators in innate immunity against tuberculosis: opposing roles of PGE2 and LXA4 in the induction of macrophage death

Virulent Mycobacterium tuberculosis (Mtb) induces a maladaptive cytolytic death modality, necrosis, which is advantageous for the pathogen. We report that necrosis of macrophages infected with the virulent Mtb strains H37Rv and Erdmann depends on predominant LXA4 production that is part of the antiinflammatory and inflammation-resolving action induced by Mtb. Infection of macrophages with the avirulent H37Ra triggers production of high levels of the prostanoid PGE2, which promotes protection against mitochondrial inner membrane perturbation and necrosis. In contrast to H37Ra infection, PGE2 production is significantly reduced in H37Rv-infected macrophages. PGE2 acts by engaging the PGE2 receptor EP2, which induces cyclic AMP production and protein kinase A activation. To verify a role for PGE2 in control of bacterial growth, we show that infection of prostaglandin E synthase (PGES)−/− macrophages in vitro with H37Rv resulted in significantly higher bacterial burden compared with wild-type macrophages. More importantly, PGES−/− mice harbor significantly higher Mtb lung burden 5 wk after low-dose aerosol infection with virulent Mtb. These in vitro and in vivo data indicate that PGE2 plays a critical role in inhibition of Mtb replication

Analysis of Metabolic Alterations Related to Pathogenic Process of Diabetic Encephalopathy Rats

Diabetic encephalopathy (DE) is a diabetic complication characterized by alterations in cognitive function and nervous system structure. The pathogenic transition from hyperglycemia to DE is a long-term process accompanied by multiple metabolic disorders. Exploring time-dependent metabolic changes in hippocampus will facilitate our understanding of the pathogenesis of DE. In the present study, we first performed behavioral and histopathological experiments to confirm the appearance of DE in rats with streptozotocin-induced diabetes. We then utilized nuclear magnetic resonance-based metabonomics to analyze metabolic disorders in the hippocampus at different stages of DE. After 1 week, we observed no cognitive or structural impairments in diabetic rats, although some metabolic changes were observed in local hippocampal extracts. At 5 weeks, while cognitive function was still normal, we then examined initial levels of neuronal apoptosis. The characteristic metabolic changes of this stage included elevated levels of energy metabolites (i.e., ATP, ADP, AMP, and creatine phosphate/creatine). At 9 weeks, significant cognitive decline and histopathological brain damage were observed, in conjunction with reduced levels of some amino acids. Thus, this stage was classified as the DE period. Our findings indicated that the pathogenesis of DE is associated with time-dependent alterations in metabolic features in hippocampal regions, such as glycolysis, osmoregulation, energy metabolism, choline metabolism, branched-chain amino acid metabolism, and the glutamate–glutamine cycle. Furthermore, we observed alterations in levels of lactate and its receptor in hippocampal cells, which may be involved in the pathogenesis of DE

Characteristic Metabolic Alterations Identified in Primary Neurons Under High Glucose Exposure

Cognitive dysfunction is a central nervous system (CNS) complication of diabetes mellitus (DM) that is characterized by impaired memory and cognitive ability. An in-depth understanding of metabolic alterations in the brain associated with DM will facilitate our understanding of the pathogenesis of cognitive dysfunction. The present study used an in vitro culture of primary neurons in a high-glucose (HG) environment to investigate characteristic alterations in neuron metabolism using nuclear magnetic resonance (NMR)-based metabonomics. High performance liquid chromatography (HPLC) was also used to measure changes in the adenosine phosphate levels in the hippocampal regions of streptozotocin (STZ)-induced diabetic rats. Our results revealed significant elevations in phosphocholine and ATP production in neurons and decreased formate, nicotinamide adenine dinucleotide (NAD+), tyrosine, methionine, acetate and phenylalanine levels after HG treatment. However, the significant changes in lactate, glutamate, taurine and myo-inositol levels in astrocytes we defined previously in astrocytes, were not found in neurons, suggested cell-specific metabolic alterations. We also confirmed an astrocyte-neuron lactate shuttle between different compartments in the brain under HG conditions, which was accompanied by abnormal acetate transport. These alterations reveal specific information on the metabolite levels and transport processes related to neurons under diabetic conditions. Our findings contribute to the understanding of the metabolic alterations and underlying pathogenesis of cognitive decline in diabetic patients

The Serum microRNA Profile of Intrahepatic Cholestasis of Pregnancy: Identification of Novel Noninvasive Biomarkers

Background/Aims: Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific disease that significantly increases the risk of fetal complications. Here, we measured serum miRNA levels in ICP patients to identify candidate biomarkers for ICP. Methods: We used the Agilent miRNA array followed by reverse transcription-polymerase chain reaction assays to identify and validate the serum miRNA profiles of 40 pregnant women with ICP and 40 healthy pregnant controls. We used bioinformatics to identify metabolic processes related to differentially expressed miRNAs. Results: The expression levels of three miRNAs (miR-371a- 5p, miR-6865-5p, and miR-1182) were significantly increased in ICP patients compared to controls; the areas under the receiver operating characteristic (ROC) curves (AUCs) were 0.771, 0.811, and 0.798, respectively. Multiple logistic regression analysis showed that a combination of the levels of the three miRNAs afforded a greater AUC (0.845), thus more reliably diagnosing ICP. The levels of all three miRNAs were positively associated with that of total bile acids. Furthermore, bioinformatics analysis indicated that the three miRNAs principally affected lipid phosphorylation, apoptosis, and the MAPK signaling pathway. Conclusion: This preliminary work improves our understanding of serum miRNA changes in pregnant women with ICP. The three miRNAs may serve as novel noninvasive biomarkers of ICP