Search

ObjectiveIn order to meet the development trend of low weight and high strength in the design of compression shell structures for deep-sea submersibles, this paper proposes a structural enhancement method which involves winding a circumferential internal corrugated compression shell with a carbon fiber reinforced polymer (CFRP), then analyzes and optimizes the CFRP layering scheme. MethodFinite element simulation software ABAQUS is used to change the layering parameters of the CFRP according to the stress distribution of the composite layers, and the optimal winding angle under 14 layers of CFRP is explored to obtain the best layering scheme. The relevant strength failure criteria are then combined to check the intensity. ResultsThe results indicate that 40° is the best CFRP winding angle to enhance the circumferential internal corrugated compression shell. When the layering mode is (±40°2/±55°2/±70°2/90°2), the stress distribution between the layers is relatively uniform, meaning that the structure has a lighter weight while its strength fulfills the actual use requirements. ConclusionThe proposed structure can reduce the overall weight of deep-sea compression shells and push the development of the field toward lightweight design.

[18F]-Florbetazine ([18F]-92) is a selective PET tracer for β-amyloid (Aβ) depositions with a novel diaryl-azine scaffold to reduce lipophilicity and to achieve higher gray-to-white matter contrast. We aimed to assess its diagnostic value in Alzheimer's disease (AD) and pharmacokinetics characteristics in human subjects. Methods: Six healthy controls (HCs) and nine AD patients underwent dynamic PET examination with [18F]-Florbetazine and a structural MRI scan. The time-activity-curves (TACs) for volumes of interest (VOIs) in cerebral cortex, cerebellar cortex and cerebral white matter was depicted and their standardized uptake value ratios (SUVRs) with cerebellar cortex as reference were compared between HCs and AD patients. The cerebral gray-to-white matter SUV ratio (GWR) was also calculated. Results: In HCs, radioactivities in the cerebral cortex VOIs were homogeneously low and at the same level as in cerebellar cortex, while in AD patients, cortical VOIs expected to contain Aβ exhibited high radioactivity. Cerebral cortex SUVRs remain relatively low in HCs while keep increasing along with time in AD patients. After 15 min, the cerebral cortex SUVRs became significant higher in AD patients compared to HCs with 100 % discrimination accuracy. In AD patients, GWR remained over 1.3 for all time intervals and visual inspection showed lower uptake in cerebral white matter compared to cerebral cortex. Conclusion: [18F]-Florbetazine PET showed high uptake on Aβ plaques and high gray-to-white contrast in AD patients that are favorable in visual read. [18F]-Florbetazine can be potentially used for detection and quantification of Aβ depositions in the living human brain.

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.

Summary: 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.

Abstract Background Glycogen storage disease type 1a (GSD1a) is an inborn genetic disease caused by glucose-6-phosphatase-α (G6Pase-α) 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. Case presentation Here we describe a case of a 25-year-old Chinese female patient with GSD1a, who developed uncontrolled type 2 diabetes mellitus (T2DM) as a young adult. The patient was diagnosed with GSD1a disease at the age of 10 and was subsequently treated with an uncooked cornstarch diet. Recently, the patient was treated in our hospital for vomiting and electrolyte imbalance and was subsequently diagnosed with T2DM. Owing to the impaired secretory function of the patient’s pancreatic islets, liver dysfunction, hypothyroidism, severe hyperlipidemia, and huge hepatic adenoma, we adopted diet control, insulin therapy, and hepatic adenoma resection to alleviate this situation. The WES discovered compound heterozygous mutations at the exon 5 of G6PC gene at 17th chromosome in the patient, c.648G>T (p.L216 L, NM_000151.4, rs80356484) in her father and c.674T>C (p.L225 P, NM_000151.4, rs1555560128) in her mother. c.648G>T is a well-known splice-site mutation, which causes CTG changing to CTT at protein 216 and creates a new splicing site 91 bp downstream of the authentic splice site, though both codons encode leucine. c.674T>C is a known missense mutation that causes TGC to become CGC at protein 225, thereby changing from coding for leucine to coding for proline. Conclusion 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.

To solve the problem of low polishing efficiency of silicon carbide crystal, electrochemical mechanical polishing (ECMP) of silicon carbide was carried out to study the effect of NaOH, NaNO3 and H3PO4 electrolytes on electrochemical oxidation of silicon carbide. NaNO3 of 0.6 mol/L was selected as the electrolyte in the ECMP process and so were the diamond-alumina mixed abrasive particles. The influence of load, rotational speed, voltage and particle size on the surface quality and material removal rate of ECMP silicon carbide was studied by using orthogonal experiment. With the optimized processing parameters, the combined polishing experiment can achieve a high-efficiency material removal rate of 20.259 μm/h in the rough polishing stage, and finally obtain the surface roughness of Sa 0.408 nm through precision polishing.

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.

Abstract Objective To study the value of multitime point salivary pepsin testing (MTPSPT) for the diagnosis of laryngopharyngeal reflux (LPR). Study Design Prospective noncontrolled. Methods For patients who met the enrollment criteria, the reflux symptom index (RSI) and reflux finding score (RFS) were calculated and salivary pepsin testing was performed. The pepsin test was performed every hour from 7:00 a.m. to 6:00 p.m. by collecting fresh saliva samples. A single positive test result was needed for the diagnosis of LPR. The consistency in the diagnosis of LPR between the two methods was compared with the weighted Cohen's kappa statistic. Results A total of 204 patients were included. The kappa value between the two methods was 0.566 (p = .00). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MTPSPT were 76.43%, 85.94%, 92.24%, and 62.5%, respectively. We also compared a single pepsin measure at 7 a.m. with the screening results based on the RSI and RFS, and found a much lower kappa agreement value (0.223, p = .00). The sensitivity, specificity, PPV, NPV, and false‐negative rate of pepsin testing at 7 a.m. (fasting) were 37.86%, 92.18%, 91.38%, 40.41%, and 58.57%, respectively. Conclusion The use of the result of a single salivary pepsin test in the morning yields a relatively higher rate of missed diagnosis of LPR, and multitime point testing through a day increased the accuracy and sensitivity of detection of LPR twofold compared to a single morning fasting sample. Level of Evidence 3

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.

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.

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.

Electric power ICT network is an important part of power system,which plays a vital role in the security,stability and economic operation of power network.A diagnostic index system of electric power ICT network was established and a multi-objective layered diagnosis model of electric power ICT network system based on extension rating was proposed.This evaluation method could not only evaluate the impact factors of the electric power ICT network,but also diagnose and rate the whole electric power ICT network.

As the coupling relationship between information systems and physical power grids is getting closer, various types of cyber attacks have increased the operational risks of a power cyber-physical System (CPS). In order to effectively evaluate this risk, this paper proposed a method of cross-domain propagation analysis of a power CPS risk based on reinforcement learning. First, the Fuzzy Petri Net (FPN) was used to establish an attack model, and Q-Learning was improved through FPN. The attack gain was defined from the attacker’s point of view to obtain the best attack path. On this basis, a quantitative indicator of information-physical cross-domain spreading risk was put forward to analyze the impact of cyber attacks on the real-time operation of the power grid. Finally, the simulation based on Institute of Electrical and Electronics Engineers (IEEE) 14 power distribution system verifies the effectiveness of the proposed risk assessment method.

For energy-limited wireless sensor networks (WSNs), we propose a multiparameter fusion decision routing (MPFDR) algorithm in this study. This algorithm gives a comprehensive account of the residual energy and forward distance, single-hop transmission ratio, cache queue, and energy equilibrium degree. It calculates the routing evaluation parameters of the forward neighbors, realizing a multidirectional reflection of the network status. Simultaneously, combined with the defined routing selection strategy based on the parameter contribution degree and fuzzy contribution degree, the fusion contribution degree of each forward neighbor is obtained. Then, the node with the most considerable fusion contribution degree is selected as the next hop. Finally, the performance of the MPFDR algorithm is simulated and compared with other algorithms. Simulation results indicate that our algorithm has good congestion control ability in energy-limited wireless sensor networks and can significantly reduce the packet loss rate and average hops.

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.

Recent progress in wireless charging technologies has greatly promoted the development of rechargeable wireless sensor networks (RWSN). The network lifetime of RWSN can be commonly extended through routing strategy and wireless charging technology. However, the node accepts the relay request of its neighbor unconditionally, and it cannot remove its overload on its own in a timely manner in traditional routing strategies. The energy balancing efficiency of the network may be limited by this passive mechanism, which poses a great challenge to obtaining optimal joint efficiency of routing and charging strategies. In this paper, we propose an autonomous load regulation mechanism-based energy balanced routing algorithm (ALRMR) for RWSN. In addition to an efficient framework of joint wireless energy transfer and multi-hop routing where the routing strategy is adapted to the charging scheme, an innovative load regulation mechanism is proposed. Under this mechanism, each node can actively adjust its own load by controlling its relay radius. The simulation demonstrates the advantages of our algorithm for energy balance efficiency and improving the network lifetime through the charging scheme and the innovative mechanism.

The Hybrid energy supply (HES) wireless relay system is a new green network technology, where the source node is powered by the grid and relay is powered by harvested renewable energy. However, the network’s performance may degrade due to the intermittent nature of renewable energy. In this paper, our purpose is to minimize grid energy consumption and maximize throughput. However, improving the throughput requires increasing the transmission power of the source node, which will lead to a higher grid energy consumption. Linear weighted summation method is used to turn the two conflicting objectives into a single objective. Link assignment and a power control strategy are adopted to maximize the total reward of the network. The problem is formulated as a discrete Markov decision model. In addition, a backwards induction method based on state deletion is proposed to reduce the computational complexity. Simulation results show that the proposed algorithm can effectively alleviate performance degradation caused by the lack of renewable energy, and present the trade-off between energy consumption and throughput.

The network lifetime of wireless rechargeable sensor network (WRSN) is commonly extended through routing strategy or wireless charging technology. In this paper, we propose an optimization algorithm from the aspects of both charging and routing process. To balance the network energy in charging part, node’s charging efficiency is balanced by dynamically planning charging point positions and the charging time is allocated according to the energy consumption rate of nodes. Moreover, the routing method is adapted to the node’s charging efficiency. The adaptive routing strategy assigns more forwarding tasks to nodes that can get more energy during the charging phase, and makes the data packets transmit farther away, thus reducing the average hops and energy consumption of the network. Finally, the simulation results reveal that the proposed algorithm has certain advantages in prolonging the network lifetime, reducing the average hop counts and balancing the energy of each node.

Researches on blast-resistant measures for underground structures such as tunnels and underground shopping malls are of great importance for their significant role in economic and social development. In this paper, a new blast-resistant method based on wave converters with spring oscillator for underground structures was put forward, so as to convert the shock wave with high frequency and high peak pressure to the periodic stress wave with low frequency and low peak pressure. The conception and calculation process of this new method were introduced. The mechanical characteristics and motion evolution law of wave converters were deduced theoretically. Based on the theoretical deduction results and finite difference software FLAC3D, the dynamic responses of the new blast-resistant structure and the traditional one were both calculated. Results showed that, after the deployment of wave converters, the peak absolute values of the bending moment, shear force, and axial force of the structure decreased generally, which verified the good blast-resistant effect of the new blast-resistant method.