Highly selective PtCo bimetallic nanoparticles on silica for continuous production of hydrogen from aqueous phase reforming of xylose

Hydrogen (H2) is a promising energy vector for mitigating greenhouse gas emissions. Lignocellulosic biomass waste has been introduced as one of the abundant and carbon-neutral H2 sources. Among those, xylose with its short carbon chain has emerged attractive, where H2 can be catalytically released in an aqueous reactor. In this study, a composite catalyst system consisting of silica (SiO2)-supported platinum (Pt)-cobalt (Co) bimetallic nanoparticles was developed for aqueous phase reforming of xylose conducted at 225 °C and 29.3 bar. The PtCo/SiO2 catalyst showed a significantly higher H2 production rate and selectivity than that of Pt/SiO2, whereas Co/SiO2 shows no activity in H2 production. The highest selectivity for useful liquid byproducts was obtained with PtCo/SiO2. Moreover, CO2 emissions throughout the reaction were reduced compared to those of monometallic Pt/SiO2. The PtCo bimetallic nanocatalyst offers an inexpensive, sustainable, and durable solution with high chemical selectivity for scalable reforming of hard-to-ferment pentose sugars

Dehydrogenation of homocyclic liquid organic hydrogen carriers (LOHCs) over Pt supported on an ordered pore structure of 3-D cubic mesoporous KIT-6 silica

Pt supported on ordered mesoporous silica (KIT-6) catalyst was examined for the dehydrogenation of homocyclic liquid organic hydrogen carriers (LOHCs, 1: MCH, 2: hydrogenated biphenyl-based eutectic mixture (H-BPDM)) conditions. The longer pore-residence time of the MCH molecules in the 3D bicontinuous pore structure of the Pt/KIT-6 catalyst strongly affected the catalytic activity because a higher MCH concentration was achieved in the vicinity of the Pt active sites. Pt/KIT-6 catalyst exhibited a higher surface area, pore volume, and Pt dispersion with narrower particle size distribution (average Pt particle size: ~1.3 nm). Therefore, higher LOHC conversion with faster hydrogen production occurred, with a higher hydrogen selectivity over Pt/KIT-6 compared with Pt/SiO2 and Pt/Al2O3. Long-term experiment results indicated that the Pt/KIT-6 catalytic activity was stable over the reaction time than that of the other catalysts. No significant structural collapse occurred in KIT-6 during the dehydrogenation. Carbon coking was observed for all three samples

The Distinct Metabolic Phenotype of Lung Squamous Cell Carcinoma Defines Selective Vulnerability to Glycolytic Inhibition

Adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) are the two predominant subtypes of non-small cell lung cancer (NSCLC) and are distinct in their histological, molecular and clinical presentation. However, metabolic signatures specific to individual NSCLC subtypes remain unknown. Here, we perform an integrative analysis of human NSCLC tumour samples, patient-derived xenografts, murine model of NSCLC, NSCLC cell lines and The Cancer Genome Atlas (TCGA) and reveal a markedly elevated expression of the GLUT1 glucose transporter in lung SqCC, which augments glucose uptake and glycolytic flux. We show that a critical reliance on glycolysis renders lung SqCC vulnerable to glycolytic inhibition, while lung ADC exhibits significant glucose independence. Clinically, elevated GLUT1-mediated glycolysis in lung SqCC strongly correlates with high 18F-FDG uptake and poor prognosis. This previously undescribed metabolic heterogeneity of NSCLC subtypes implicates significant potential for the development of diagnostic, prognostic and targeted therapeutic strategies for lung SqCC, a cancer for which existing therapeutic options are clinically insufficient

Emotion-Aware Speaker Identification With Transfer Learning

Speech is a natural communication method used by humans. Speaker identification (SI) technology based on human speech has been used as an entry point for many human–computer-interaction applications. The performance of SI models can degrade when dealing with expressive speech uttered in emotional situations because emotion databases do not have sufficient data on expressive speech to train SI models for various emotional states. Generally, SI models are trained using relatively more samples of “neutral” speech than samples of other emotion classes. In this study, we propose an emotion-aware SI (em-SI) method that uses an emotion-embedding vector generated from a pre-trained speech emotion recognition (SER) model along with the acoustic features of speech data. We assess the performance of this method using individual English and Korean corpora and confirm that the proposed method provides an improved performance on multilingual corpora. The evaluation results show that the SI accuracy of em-SI on the Korean Emotion Multimodal Database (KEMDy19) improved by 3.2%, and the average speaker verification (SV) performance in terms of the equal error rate (EER) was improved by 1.3% compared to that of the baseline SI model. The visualization of the embedding vector of em-SI shows that em-SI maps speech data to an embedding space where both SI and emotional information are simultaneously represented. Through the experiments conducted in this study, we confirmed that the em-SI model, which learns by integrating emotion and speaker embedding information, improved the performance of SI for expressive speech

Metabolic Disorders in Menopause

Menopause is an aging process and an important time equivalent to one-third of a woman’s lifetime. Menopause significantly increases the risk of cardiometabolic diseases, such as obesity, type 2 diabetes, cardiovascular diseases, non-alcoholic liver disease (NAFLD)/metabolic associated fatty liver disease (MFFLD), and metabolic syndrome (MetS). Women experience a variety of symptoms in the perimenopausal period, and these symptoms are distressing for most women. Many factors worsen a woman’s menopausal experience, and controlling these factors may be a strategy to improve postmenopausal women’s health. This review aimed to confirm the association between menopause and metabolic diseases (especially MetS), including pathophysiology, definition, prevalence, diagnosis, management, and prevention

Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization

Effects of Regular Taekwondo Intervention on Health-Related Physical Fitness, Cardiovascular Disease Risk Factors and Epicardial Adipose Tissue in Elderly Women with Hypertension

Regular exercise has been proven to prevent hypertension and to help in the management of hypertension. There is a lack of studies examining changes in these issues as a result of Taekwondo training intervention. The aim of the current trial is to identify the effects of a regular Taekwondo (TKD) training program on health-related physical fitness (HRPF), cardiovascular disease (CVD) risk factors, inflammatory factors, and epicardial adipose tissue (EAT) in elderly women with hypertension. To accomplish this, 20 participants, who were older women with hypertension, were divided into a TKD group (n = 10) and a control group (n = 10). The TKD program was conducted in program for 90 min, three times a week, for 12 weeks. Outcomes, including body composition, blood pressure (BP), HRPF, cardiovascular risk factor and EAT, were measured before and after the Taekwondo program. The 12-week TKD program improved body composition, BP, HRPF, CVD risk factor, and EAT in elderly women with hypertension relative to controls. Meanwhile, EAT and interukin-1β (r = 0.530, p < 0.05), monocyte chemotactic protein-1 (r = 0.524, p < 0.05), triglyceride (r = 0.493, p < 0.05) and sedentary behavior (r = 0.459, p < 0.05) presented a positive correlation, while EAT and lean body mass (r = −0.453, p < 0.05) showed a negative correlation. The 12-week regular TKD training intervention was found to be effective in reducing the thickness of EAT measured by multi-detector computed tomography and can also enhance health-related physical fitness and risk factors of CVD in older individuals with hypertension

Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning

In this paper, we perform a systematic study about the on-body sensor positioning and data acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that consists of eight body-worn Inertial Measurement Units (IMU) sensors and an Android mobile device for activity data collection. We develop a Long Short-Term Memory (LSTM) network framework to support training of a deep learning model on human activity data, which is acquired in both real-world and controlled environments. From the experiment results, we identify that activity data with sampling rate as low as 10 Hz from four sensors at both sides of wrists, right ankle, and waist is sufficient in recognizing Activities of Daily Living (ADLs) including eating and driving activity. We adopt a two-level ensemble model to combine class-probabilities of multiple sensor modalities, and demonstrate that a classifier-level sensor fusion technique can improve the classification performance. By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect the characteristic of individual activities

Effect of weight loss before in vitro fertilization in women with obesity or overweight and infertility: a systematic review and meta-analysis

Abstract The effect of weight loss before in vitro fertilization (IVF) procedures on pregnancy outcomes in women with overweight or obesity and infertility remains controversial. In this systematic review and meta-analysis, we investigated whether weight loss before IVF in these women affected the IVF results and reproductive outcomes. PubMed, Embase, and the Cochrane Library databases were searched from the inception dates until December 2022, using combinations of relevant keywords. Only six randomized controlled trials, including 1627 women with obesity or overweight, were analyzed. The weight change in the intensive care group, compared to the control group who underwent IVF without weight loss was – 4.62 kg (mean difference; 95% confidence interval [CI] − 8.10, − 1.14). Weight loss before IVF did not significantly increase the live birth rate in women with obesity or overweight and infertility (odds ratio, 1.38; 95% CI 0.88, 2.10). The clinical pregnancy, miscarriage, ongoing pregnancy, and ectopic pregnancy rates did not differ between the weight loss and control groups before IVF. This meta-analysis demonstrated that even significant weight loss before IVF in women with obesity or overweight and infertility did not improve the live birth, clinical pregnancy, ongoing pregnancy, or ectopic pregnancy rates. PROSPERO Registration Number: CRD42023455800

Hybrid seawater desalination-carbon capture using modified seawater battery system

The water and carbon cycles are central to the Earth's ecosystem, enabling the sustainable development of human societies. To mitigate the global issues of water shortages and climate change, we report a new electrochemical system that fulfills two functions-seawater desalination and carbon dioxide air-capture-during the charge and discharge processes. The seawater desalination-carbon capture system utilizes a seawater battery platform, consisting of three major compartments (desalination, sodium-collection, and carbon-capture), which are separated by sodium superionic conducting ceramic membranes. It is found that the concentrations of sodium ions and chloride ions in fresh seawater (total dissolved solids approximate to 34,000 ppm) are significantly decreased by the charging of the seawater desalination-carbon capture system, resulting in brackish water (total dissolved solids approximate to 7000 ppm). The discharge process induces the air-capture of ambient carbon dioxide gases through carbonation reactions, which is demonstrated by the carbon dioxide gas removal in this compartment. The hybrid system suggests a new electrochemical approach for both desalination and carbon capture