Motor imagery (MI) is a popular paradigm for controlling electroencephalogram (EEG) based Brain-Computer Interface (BCI) systems. Many methods have been developed to attempt to accurately classify ...MI-related EEG activity. Recently, the development of deep learning has begun to draw increasing attention in the BCI research community because it does not need to use sophisticated signal preprocessing and can automatically extract features. In this paper, we propose a deep learning model for use in MI-based BCI systems. Our model makes use of a convolutional neural network based on a multi-scale and channel-temporal attention module (CTAM), which called MSCTANN. The multi-scale module is able to extract a large number of features, while the attention module includes both a channel attention module and a temporal attention module, which together allow the model to focus attention on the most important features extracted from the data. The multi-scale module and the attention module are connected by a residual module, which avoids the degradation of the network. Our network model is built from these three core modules, which combine to improve the recognition ability of the network for EEG signals. Our experimental results on three datasets (BCI competition IV 2a, III IIIa and IV 1) show that our proposed method has better performance than other state-of-the-art methods, with accuracy rates of 80.6%, 83.56% and 79.84%. Our model has stable performance in decoding EEG signals and achieves efficient classification performance while using fewer network parameters than other comparable state-of-the-art methods.
In order to maximize the work efficiency of wireless mobile charger, a payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger is proposed. Based on the mesh ...structure and multi‐node charging technology, the recharging optimization for massive devices is modelled as a problem of payoff maximization. According to energy allocation, anchor point deployment and time allocation, we decompose it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimized in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy, respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by our innovative gain recall mechanism. The trade‐off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharging time in a fixed cycle, and mobile charger can always work in efficient recharging positions, whose effect is exploited utmost.
With the rapid development of the 5G power Internet of Things (IoT), new power systems have higher requirements for data transmission rates, latency, reliability, and energy efficiency. Specifically, ...the hybrid service of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) has brought new challenges to the differentiated service of the 5G power IoT. To solve the above problems, this paper first constructs a power IoT model based on NOMA for the mixed service of URLLC and eMBB. Considering the shortage of resource utilization in eMBB and URLLC hybrid power service scenarios, the problem of maximizing system throughput through joint channel selection and power allocation is proposed. The channel selection algorithm based on matching as well as the power allocation algorithm based on water injection are developed to tackle the problem. Both theoretical analysis and experimental simulation verify that our method has superior performance in system throughput and spectrum efficiency.
Gluconeogenesis is the main process for endogenous glucose production during prolonged fasting, or certain pathological conditions, which occurs primarily in the liver. Hepatic gluconeogenesis is a ...biochemical process that is finely controlled by hormones such as insulin and glucagon, and it is of great importance for maintaining normal physiological blood glucose levels. Dysregulated gluconeogenesis induced by obesity is often associated with hyperglycemia, hyperinsulinemia, and type 2 diabetes (T2D). Long noncoding RNAs (lncRNAs) are involved in various cellular events, from gene transcription to protein translation, stability, and function. In recent years, a growing number of evidences has shown that lncRNAs play a key role in hepatic gluconeogenesis and thereby, affect the pathogenesis of T2D. Here we summarized the recent progress in lncRNAs and hepatic gluconeogenesis.
To satisfy the continuously high energy consumption and high computational capacity requirements for IoT applications, such as video monitoring, we integrate solar harvesting and multi-access edge ...computing (MEC) technologies to develop a solar-powered MEC system. Considering the stochastic nature of solar arrivals and channel conditions, we formulate a stochastic optimization problem to maximize network energy efficiency under the constraints of energy queue stability, task queue stability, peak transmission power, and maximum CPU frequency of each sensor. To solve the long-term stochastic optimization problem, we propose a Lyapunov-based online joint computational offloading and resource scheduling optimization algorithm, transforming the long-term stochastic problem into a series of deterministic subproblems in each time slot. Simulation results show that the proposed algorithm can find the optimal solution to tradeoff long-term energy efficiency and queueing backlog without requiring a priori knowledge of the channel state and energy arrival, which is a more realistic solution for practical solar-powered MEC systems.
Glycogen storage disease type 1a (GSD1a) is an inborn genetic disease caused by glucose-6-phosphatase-alpha (G6Pase-alpha) deficiency and is often observed to lead to endogenous glucose production ...disorders manifesting as hypoglycemia, hyperuricemia, hyperlipidemia, lactic acidemia, hepatomegaly, and nephromegaly. The development of GSD1a with diabetes is relatively rare, and the underlying pathogenesis remains unclear. We report a rare case of GSD1a with T2DM. On the basis of the pathogenesis of GSD1a, we recommend attentiveness to possible development of fasting hypoglycemia caused by GSD and postprandial hyperglycemia from diabetes. As the disease is better identified and treated, and as patients with GSD live longer, this challenge may appear more frequently. Therefore, it is necessary to have a deeper and more comprehensive understanding of the pathophysiology of the disease and explore suitable treatment options.
This paper explores how sports-induced bad mood affects the sentiment and behavior of sell-side financial analysts. I construct the Sports Mood Index (SMI) of metropolitan areas in the U.S. based on ...the performance of Big 4 professional sports teams. In sports-induced bad mood settings, sell-side analysts tend to issue more pessimistic forecasts in both earnings forecast and price target samples. Sports-induced bad mood also leads to inattention, associated with larger forecast errors, and analysts are slower or less likely to respond to earnings announcements. The results are robust to various measurements of pessimism, forecast errors and activity levels.
•I construct the Sports Mood Index (SMI) of metropolitan areas in the U.S.•SMI is based on the performance of Big 4 professional sports teams.•In sports-induced bad mood settings, sell-side analysts tend to issue more pessimistic forecasts.•Sports-induced bad mood leads to inattention, which is associated with larger forecast errors.•In sports-induced bad mood settings, analysts are slower or less likely to respond to earnings announcements.
Cancer cells usually exhibit shortened 3′ untranslated regions (UTRs) due to alternative polyadenylation (APA) to promote cell proliferation and migration. Upregulated CPSF6 leads to a systematic ...prolongation of 3′ UTRs, but CPSF6 expression in tumors is typically higher than that in healthy tissues. This contradictory observation suggests that it is necessary to investigate the underlying mechanism by which CPSF6 regulates APA switching in cancer. Here, we find that CPSF6 can undergo liquid-liquid phase separation (LLPS), and elevated LLPS is associated with the preferential usage of the distal poly(A) sites. CLK2, a kinase upregulated in cancer cells, destructs CPSF6 LLPS by phosphorylating its arginine/serine-like domain. The reduction of CPSF6 LLPS can lead to a shortened 3′ UTR of cell-cycle-related genes and accelerate cell proliferation. These results suggest that CPSF6 LLPS, rather than its expression level, may be responsible for APA regulation in cancer cells.
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•CPSF6 undergoes liquid-liquid phase separation (LLPS) and recruits CPSF5 into foci•CPSF6 LLPS is disrupted by phosphorylation mediated through CLK2 in cancer cells•CPSF6 LLPS is associated with APA regulation and cell proliferation in cancer cells
Liu et al. show that the reduction of CPSF6 liquid-liquid phase separation (LLPS) through CLK2-mediated phosphorylation is associated with the shortened 3′ UTR and accelerated proliferation of cancer cells, dissecting a mechanism of APA regulation involving the alteration of CPSF6 LLPS through post-translational modification.
Great improvement recently appeared in terms of efficient service delivery in wireless sensor networks (WSNs) for Internet of things (IoT). The IoT is mainly dependent on optimal routing of ...energy-aware WSNs for gathering data. In addition, as the wireless charging technology develops in leaps and bounds, the performance of rechargeable wireless sensor networks (RWSNs) is greatly ameliorated. Many researches integrated wireless energy transfer into data gathering to prolong network lifetime. However, the mobile collector cannot visit all nodes under the constraints of charging efficiency and gathering delay. Thus, energy consumption differences caused by different upload distances to collectors impose a great challenge in balancing energy. In this paper, we propose an adaptive dual-mode routing-based mobile data gathering algorithm (ADRMDGA) in RWSNs for IoT. The energy replenishment capability is reasonably allocated to low-energy nodes according to our objective function. Furthermore, the innovative adaptive dual-mode routing allows nodes to choose direct or multi-hop upload modes according to their relative upload distances. The empirical study confirms that ADRMDGA has excellent energy equilibrium and effectively extends the network lifetime.
Nosiheptide is a prototypal thiopeptide antibiotic, containing an indole side ring in addition to its thiopeptide-characteristic macrocylic scaffold. This indole ring is derived from ...3-methyl-2-indolic acid (MIA), a product of the radical S-adenosylmethionine enzyme NosL, but how MIA is incorporated into nosiheptide biosynthesis remains to be investigated. Here we report functional dissection of a series of enzymes involved in nosiheptide biosynthesis. We show NosI activates MIA and transfers it to the phosphopantetheinyl arm of a carrier protein NosJ. NosN then acts on the NosJ-bound MIA and installs a methyl group on the indole C4, and the resulting dimethylindolyl moiety is released from NosJ by a hydrolase-like enzyme NosK. Surface plasmon resonance analysis show that the molecular complex of NosJ with NosN is much more stable than those with other enzymes, revealing an elegant biosynthetic strategy in which the reaction flux is controlled by protein-protein interactions with different binding affinities.Thiopeptides such as nosiheptide are clinically-interesting antimicrobial natural products. Here the authors show the functional dissection of a series of enzymes involved in nosiheptide biosynthesis, revealing a unique biosynthetic pathway that centers on a previously-unknown carrier protein.
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