Grape seed extract prevents skeletal muscle wasting in interleukin 10 knockout mice

BACKGROUND: Muscle wasting is frequently a result of cancers, AIDS, chronic diseases and aging, which often links to muscle inflammation. Although grape seed extract (GSE) has been widely used as a human dietary supplement for health promotion and disease prevention primarily due to its anti-oxidative and anti-inflammative effects, it is unknown whether GSE affects muscle wasting. The objective is to test the effects of GSE supplementation on inflammation and muscle wasting in interleukin (IL)-10 knockout mice, a recently developed model for human frailty. METHODS: Male IL-10 knockout (IL10KO) C57BL/6 mice at 6 weeks of age were assigned to either 0% or 0.1% GSE (in drinking water) groups (n = 10) for 12 weeks, when skeletal muscle was sampled for analyses. Wild-type C57BL/6 male mice were used as controls. RESULTS: Tibialis anterior muscle weight and fiber size of IL10KO mice were much lower than wild-type mice. IL10KO enhanced nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling and inflammasome formation when compared to wild-type mice. Phosphorylation of anabolic signaling was inhibited, whereas muscle specific ubiquitin ligase, AMP-activated protein kinase (AMPK) and apoptotic signaling were up-regulated in IL10KO mice. GSE supplementation effectively rectified these adverse changes in IL10KO muscle, which provide an explanation for the enhanced muscle mass, reduced protein degradation and apoptosis in GSE supplemented mice compared to IL10KO mice without supplementation. CONCLUSION: GSE supplementation effectively prevents muscle wasting in IL10KO mice, showing that GSE can be used as an auxiliary treatment for muscle loss associated with chronic inflammation and frailty

ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation

Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations. Existing research has shown that it is beneficial for recommendation performance to combine (user- and item-) ID embeddings with multimodal salient features, indicating the value of IDs. However, there is a lack of a thorough analysis of the ID embeddings in terms of feature semantics in the literature. In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures. Then, we propose a novel recommendation model by incorporating ID embeddings to enhance the semantic features of both content and structures. Specifically, we put forward a hierarchical attention mechanism to incorporate ID embeddings in modality fusing, coupled with contrastive learning, to enhance content representations. Meanwhile, we propose a lightweight graph convolutional network for each modality to amalgamate neighborhood and ID embeddings for improving structural representations. Finally, the content and structure representations are combined to form the ultimate item embedding for recommendation. Extensive experiments on three real-world datasets (Baby, Sports, and Clothing) demonstrate the superiority of our method over state-of-the-art multimodal recommendation methods and the effectiveness of fine-grained ID embeddings

Resveratrol enhances brown adipocyte formation and function by activating AMP-activated protein kinase (AMPK) α1 in mice fed high-fat diet

© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Scope: Enhancing the formation and function of brown adipose tissue (BAT) increases thermogenesis and hence reduces obesity. Thus, we investigate the effects of resveratrol (Resv) on brown adipocyte formation and function in mouse interscapular BAT (iBAT). Methods and results: CD1 mice and stromal vascular cells (SVCs) isolated from iBAT were treated with Resv. Expression of brown adipogenic and thermogenic markers, and involvement of AMP-activated protein kinase (AMPK)α1 were assessed. In vivo, Resv-enhanced expression of brown adipogenic markers, PR domain-containing 16 (PRDM16) and thermogenic genes, uncoupling protein 1 (UCP1) and cytochrome C in iBAT, along with smaller lipid droplets, elevated AMPKα activity and increased oxygen consumption. Meanwhile, Resv promoted expression of PRDM16, UCP1, PGC1α, cytochrome C and pyruvate dehydrogenase (PDH) in differentiated iBAT SVCs, suggesting that Resv enhanced brown adipocyte formation and function in vitro. In addition, Resv stimulated AMPKα and oxygen consumption in differentiated iBAT SVCs. However, the promotional effects of Resv were diminished by AMPK inhibition or AMPKα1 knockout, implying the involvement of AMPKα1 in this process. Conclusion: Resv enhanced brown adipocyte formation and thermogenic function in mouse iBAT by promoting the expression of brown adipogenic markers via activating AMPKα1, which contributed to the anti-obesity effects of Resv

Efficient estimation of nonparametric genetic risk function with censored data

With an increasing number of causal genes discovered for complex human disorders, it is crucial to assess the genetic risk of disease onset for individuals who are carriers of these causal mutations and compare the distribution of age-at-onset with that in non-carriers. In many genetic epidemiological studies aiming at estimating causal gene effect on disease, the age-at-onset of disease is subject to censoring. In addition, some individuals’ mutation carrier or non-carrier status can be unknown due to the high cost of in-person ascertainment to collect DNA samples or death in older individuals. Instead, the probability of these individuals’ mutation status can be obtained from various sources. When mutation status is missing, the available data take the form of censored mixture data. Recently, various methods have been proposed for risk estimation from such data, but none is efficient for estimating a nonparametric distribution. We propose a fully efficient sieve maximum likelihood estimation method, in which we estimate the logarithm of the hazard ratio between genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation for the reference baseline hazard function. Our estimator can be calculated via an expectation-maximization algorithm which is much faster than existing methods. We show that our estimator is consistent and semiparametrically efficient and establish its asymptotic distribution. Simulation studies demonstrate superior performance of the proposed method, which is applied to the estimation of the distribution of the age-at-onset of Parkinson's disease for carriers of mutations in the leucine-rich repeat kinase 2 gene

A Miniature Fiber Optic Refractive Index Sensor Built in a MEMS-Based Microchannel

A small, highly sensitive, and electromagnetic interference (EMI)-immune refractive index (RI) sensor based on the Fabry-Perot (FP) interferometer is presented. The sensor’s FP cavity was fabricated by aligning two metal-deposited, single-mode optical fiber endfaces inside a microchannel on a silicon chip. The mirrors on the fiber endfaces were made of thermal-deposited metal films, which provided the high finesse necessary to produce a highly sensitive sensor. Microelectromechanical systems (MEMS) fabrication techniques, specifically photolithography and deep dry etching, were used to precisely control the profile and depth of the microchannel on the silicon chip with an accuracy of 2 μm. The RI change within the FP cavity was determined by demodulating the transmission spectrum phase shift. The sensitivity and finesse of the transmission spectrum were controlled by adjusting the cavity length and the thickness of the deposited metal. Our experimental results showed that the sensor’s sensitivity was 665.90 nm/RIU (RI Unit), and the limit of detection was 6 × 10−6 RIU. Using MEMS fabrication techniques to fabricate these sensors could make high yield mass production a real possibility. Multiple sensors could be integrated on a single small silicon chip to simultaneously measure RI, temperature, and biomolecule targets

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

How to Promote University Students to Innovative Use Renewable Energy? An Inquiry-Based Learning Course Model

This paper aims to explore a student-oriented curriculum model on Renewable Energy Sources (RES), since RES is an important solution to the energy problem, and training talents with relevant skills and qualities has become a key part of our overall energy strategy. Based on Taylor Principle and PDCA Cycle Theory (Plan, Do, Check, Act), this paper proposed a “Student-centered Inquiry” RES course model together with three reference templates for the design, teaching, and evaluation processes of the course. This dissertation conducted a case study on 27 students from China University of Petroleum (East China), and the primary purpose of the study was to find out how changes in the curriculum could affect the learning effects of both high achievement students and low achievement students. By adopting the paired T-test and independent T-test, the results indicated that: (a) There was a relationship between the new curriculum model and the students’ academic performance, (b) the improvement in low-achievement students was more significant than in high-achievement students, and (c) the new curriculum model has positive effects on students in terms of knowledge transfer, methodology, reductionism, and consciousness formation. In view of the limitations of current RES related education in universities, these results can be used as templates to improve the quality of RES education

The Status Quo and Ways of STEAM Education Promoting China’s Future Social Sustainable Development

In the process of future sustainable development, human society faces problems such as severe population load, economic transition, and a lack of educational measures. One of the root causes of these problems is the shortage of innovative talents. Therefore, how to cultivate learners with multidisciplinary integration and innovation ability is a key point that should be paid attention to when promoting the concept of quality education and coping with the future sustainable development process. This paper uses the questionnaire survey and literature analysis methods to analyze the development of many educational institutions with the Science, Technology, Engineering, Art, and Mathematics (STEAM) concept as the core in China since 2017. It is found that the existing STEAM educational institutions generally have the following problems: a lack of team composition, difficulty in independent research and development, single course content, and an insufficient validation of course effectiveness. In order to more effectively promote the sustainable development of China’s future course, STEAM education institutions should focus on strengthening the following development strategies: gradient team building, continuous independent research and development, cutting-edge projects, mechanisms of curriculum transformation, and multi-angle course effectiveness verification

SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation

Emotion Recognition in Conversation~(ERC) across modalities is of vital importance for a variety of applications, including intelligent healthcare, artificial intelligence for conversation, and opinion mining over chat history. The crux of ERC is to model both cross-modality and cross-time interactions throughout the conversation. Previous methods have made progress in learning the time series information of conversation while lacking the ability to trace down the different emotional states of each speaker in a conversation. In this paper, we propose a recurrent structure called Speaker Information Enhanced Long-Short Term Memory (SI-LSTM) for the ERC task, where the emotional states of the distinct speaker can be tracked in a sequential way to enhance the learning of the emotion in conversation. Further, to improve the learning of multimodal features in ERC, we utilize a cross-modal attention component to fuse the features between different modalities and model the interaction of the important information from different modalities. Experimental results on two benchmark datasets demonstrate the superiority of the proposed SI-LSTM against the state-of-the-art baseline methods in the ERC task on multimodal data