Solenoid-free current drive via ECRH in EXL-50 spherical torus plasmas

As a new spherical tokamak (ST) designed to simplify engineering requirements of a possible future fusion power source, the EXL-50 experiment features a low aspect ratio (A) vacuum vessel (VV), encircling a central post assembly containing the toroidal field coil conductors without a central solenoid. Multiple electron cyclotron resonance heating (ECRH) resonances are located within the VV to improve current drive effectiveness. Copious energetic electrons are produced and measured with hard X-ray detectors, carry the bulk of the plasma current ranging from 50kA to 150kA, which is maintained for more than 1s duration. It is observed that over one Ampere current can be maintained per Watt of ECRH power issued from the 28-GHz gyrotrons. The plasma current reaches Ip>80kA for high density (>5e18me-2) discharge with 150kW ECHR heating. An analysis was carried out combining reconstructed multi-fluid equilibrium, guiding-center orbits of energetic electrons, and resonant heating mechanisms. It is verified that in EXL-50 a broadly distributed current of energetic electrons creates smaller closed magnetic-flux surfaces of low aspect ratio that in turn confine the thermal plasma electrons and ions and participate in maintaining the equilibrium force-balance

Nlrp2, a Maternal Effect Gene Required for Early Embryonic Development in the Mouse

Maternal effect genes encode proteins that are produced during oogenesis and play an essential role during early embryogenesis. Genetic ablation of such genes in oocytes can result in female subfertility or infertility. Here we report a newly identified maternal effect gene, Nlrp2, which plays a role in early embryogenesis in the mouse. Nlrp2 mRNAs and their proteins (∼118 KDa) are expressed in oocytes and granulosa cells during folliculogenesis. The transcripts show a striking decline in early preimplantation embryos before zygotic genome activation, but the proteins remain present through to the blastocyst stage. Immunogold electron microscopy revealed that the NLRP2 protein is located in the cytoplasm, nucleus and close to nuclear pores in the oocytes, as well as in the surrounding granulosa cells. Using RNA interference, we knocked down Nlrp2 transcription specifically in mouse germinal vesicle oocytes. The knockdown oocytes could progress through the metaphase of meiosis I and emit the first polar body. However, the development of parthenogenetic embryos derived from Nlrp2 knockdown oocytes mainly blocked at the 2-cell stage. The maternal depletion of Nlrp2 in zygotes led to early embryonic arrest. In addition, overexpression of Nlrp2 in zygotes appears to lead to normal development, but increases blastomere apoptosis in blastocysts. These results provide the first evidence that Nlrp2 is a member of the mammalian maternal effect genes and required for early embryonic development in the mouse

Cross-modal change detection flood extraction based on convolutional neural network

Flood events are often accompanied by rainy weather, which limits the applicability of optical satellite images, whereas synthetic aperture radar (SAR) is less sensitive to weather and sunlight conditions. Although remarkable progress has been made in flood detection using heterogeneous multispectral and SAR images, there is a lack of publicly available large-scale datasets and more efforts are required for exploiting deep neural networks in heterogeneous flood detection. This study constructed a pre-disaster Sentinel-2 and post-disaster Sentinel-1 heterogeneous flood mapping dataset named CAU-Flood containing 18 study plots with careful image preprocessing and human annotation. A new deep convolutional neural network (CNN), named cross-modal change detection network (CMCDNet), was also proposed for flood detection using multispectral and SAR images. The proposed network employs a encoder-decoder structure and performs feature fusion at multiple stages using gating and self-attention modules. Furthermore, the network overcomes the feature misalignment issue during decoding by embedding a feature alignment module in the upsampling operation. The proposed CMCDNet outperformed SOTA methods in terms of flood detection accuracy and achieved an intersection over union (IoU) of 89.84%. The codes and datasets are available at: https://github.com/CAU-HE/CMCDNet

The complete mitochondrial genome of Tenebroides mauritanicus Linnaeus, 1758 (Coleoptera: Trogossitidae)

Tenebroides mauritanicus Linnaeus, 1758 (Coleoptera: Trogossitidae) is a storage pest that feeds mainly on soybean and corn. In this study, we sequenced the entire mitochondrial genome of Tenebroides mauritanicus (GenBank accession number: OM161967). The total length of the mitochondrial genome is 15,696 bp, GC content is 29.65%, and the contents of each base is 38.37% A, 18.35% C, 11.30% G and 31.98% T, respectively. The genome encodes 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs) and 2 ribosomal RNA genes (rRNAs). Phylogenetic analysis showed that Tenebroides mauritanicus is clustered with Byturus ochraceus. This study provides a piece of valuable genomic information for the population genetics, phylogeny, and molecular taxonomy of Tenebroides mauritanicus

Progress on Noble-Metal-Free Organic–Inorganic Hybrids for Electrochemical Water Oxidation

Emerging as a new class of advanced functional materials with hierarchical architectures and redox characters, organic–inorganic hybrid materials (OIHs) have been well developed and widely applied in various energy conversion reactions recently. In this review, we focus on the applications and structure–performance relationship of OIHs for electrochemical water oxidation. The general principles of water oxidation will be presented first, followed by the progresses on the applications of OIHs that are classified as metal organic frameworks (MOFs) and their derivates, covalent organic framework (COF)-based hybrids and other OIHs. The roles of organic counterparts on catalytic active centers will be fully discussed and highlighted with typical examples. Finally, the challenges and perspectives assessing this promising hybrid material as an electrocatalyst will be provided

An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

With the development of emerging intelligent traffic signal (I-SIG) system, congestion-involved security issues are drawing attentions of researchers and developers on the vulnerability introduced by connected vehicle technology, which empowers vehicles to communicate with the surrounding environment such as road-side infrastructure and traffic control units. A congestion attack to the controlled optimization of phases algorithm (COP) of I-SIG is recently revealed. Unfortunately, such analysis still lacks a timely visualized prediction on later congestion when launching an initial attack. In this paper, we argue that traffic image feature-based learning has available knowledge to reflect the relation between attack and caused congestion and propose a novel analysis framework based on cycle generative adversarial network (CycleGAN). Based on phase order, we first extract four-direction road images of one intersection and perform phase-based composition for generating new sample image of training. We then design a weighted L1 regularization loss that considers both last-vehicle attack and first-vehicle attack, to improve the training of CycleGAN with two generators and two discriminators. Experiments on simulated traffic flow data from VISSIM platform show the effectiveness of our approach

Zinc Prevents the Development of Diabetic Cardiomyopathy in db/db Mice

Diabetic cardiomyopathy (DCM) is highly prevalent in type 2 diabetes (T2DM) patients. Zinc is an important essential trace metal, whose deficiency is associated with various chronic ailments, including vascular diseases. We assessed T2DM B6.BKS(D)-Leprdb/J (db/db) mice fed for six months on a normal diet containing three zinc levels (deficient, adequate, and supplemented), to explore the role of zinc in DCM development and progression. Cardiac function, reflected by ejection fraction, was significantly decreased, along with increased left ventricle mass and heart weight to tibial length ratio, in db/db mice. As a molecular cardiac hypertrophy marker, atrial natriuretic peptide levels were also significantly increased. Cardiac dysfunction and hypertrophy were accompanied by significantly increased fibrotic (elevated collagen accumulation as well as transforming growth factor β and connective tissue growth factor levels) and inflammatory (enhanced expression of tumor necrosis factor alpha, interleukin-1β, caspase recruitment domain family member 9, and B-cell lymphoma/leukemia 10, and activated p38 mitogen-activated protein kinase) responses in the heart. All these diabetic effects were exacerbated by zinc deficiency, and not affected by zinc supplementation, respectively. Mechanistically, oxidative stress and damage, mirrored by the accumulation of 3-nitrotyrosine and 4-hydroxy-2-nonenal, was significantly increased along with significantly decreased expression of Nrf2 and its downstream antioxidants (NQO-1 and catalase). This was also exacerbated by zinc deficiency in the db/db mouse heart. These results suggested that zinc deficiency promotes the development and progression of DCM in T2DM db/db mice. The exacerbated effects by zinc deficiency on the heart of db/db mice may be related to further suppression of Nrf2 expression and function

Sentiment analysis of video danmakus based on MIBE-RoBERTa-FF-BiLSTM

Abstract Danmakus are user-generated comments that overlay on videos, enabling real-time interactions between viewers and video content. The emotional orientation of danmakus can reflect the attitudes and opinions of viewers on video segments, which can help video platforms optimize video content recommendation and evaluate users’ abnormal emotion levels. Aiming at the problems of low transferability of traditional sentiment analysis methods in the danmaku domain, low accuracy of danmaku text segmentation, poor consistency of sentiment annotation, and insufficient semantic feature extraction, this paper proposes a video danmaku sentiment analysis method based on MIBE-RoBERTa-FF-BiLSTM. This paper constructs a “Bilibili Must-Watch List and Top Video Danmaku Sentiment Dataset” by ourselves, covering 10,000 positive and negative sentiment danmaku texts of 18 themes. A new word recognition algorithm based on mutual information (MI) and branch entropy (BE) is used to discover 2610 irregular network popular new words from trigrams to heptagrams in the dataset, forming a domain lexicon. The Maslow’s hierarchy of needs theory is applied to guide the consistent sentiment annotation. The domain lexicon is integrated into the feature fusion layer of the RoBERTa-FF-BiLSTM model to fully learn the semantic features of word information, character information, and context information of danmaku texts and perform sentiment classification. Comparative experiments on the dataset show that the model proposed in this paper has the best comprehensive performance among the mainstream models for video danmaku text sentiment classification, with an F1 value of 94.06%, and its accuracy and robustness are also better than other models. The limitations of this paper are that the construction of the domain lexicon still requires manual participation and review, the semantic information of danmaku video content and the positive case preference are ignored