Search

Abstract Wind power DC collection system, as a crucial component of wind farms, plays a vital role in ensuring the safe and stable operation of the entire wind farm. This paper proposes a reliability assessment method for wind power DC collection systems based on MLFTA-SMC. Firstly, it analyzes the topology and key equipment of wind power DC collection systems. Secondly, based on the topology of different wind power DC collection systems, it constructs multi-level fault tree models to calculate the comprehensive importance of different events, thus providing data support for subsequent reliability assessment. Then, it utilizes the MLFTA-SMC method to assess and analyze the reliability of different wind power DC collection system topologies. Finally, taking a 100 MW wind farm in Northwest China as an example, the proposed reliability assessment method is verified through simulation. The results indicate that this method exhibits good effectiveness and superiority.

The All-DC wind power generation system can effectively solve the harmonic resonance problem caused by large-scale wind power AC collection and transmission, gradually becoming a hot topic in future research. However, there are few studies on the relay protection of the All-DC wind power transmission systems. In the All-DC wind power systems, there are differences in topology, control strategies, fault characteristics, boundary characteristics, etc. At the same time, existing pilot protection has problems such as strict information synchronization requirements and selection of setting values through simulation. Therefore, the proposed pilot protection based on the similarity of the traveling wave between the quadratic fitting curve and the calculated curve for the All-DC wind power system transmission lines. Firstly, the difference of the 1-mode initial current waveform characteristics is analyzed in the time domain at both ends. Then, the least squares method is introduced to fit the differential curves of the current forward and backward traveling waves at two terminals. The differential characteristics of the differences between the fitted curve, the calculated curve, and the measured curve under the influence of internal and external errors are analyzed. Finally, the calculated value of traveling wave current during external metallicity fault is used as the standard. By comparing the fitting curve of current traveling waves and the difference in the calculation curve to distinguish internal faults from external faults. The simulation results show that the proposed protection principle can fault resistance of 2200 Ω and noise interference of 20 dB. In addition, the proposed method does not require strict data synchronization, higher sampling frequency, and setting value through simulation.

The modular multilevel converters (MMC) based high-voltage direct-current (HVDC) grids have a fast rise rate and higher peak value of fault current. The active current limiting control (ACLC) of MMC can effectively reduce the breaking current of DC circuit breakers (DCCB) and reduce the requirement for rapid fault identification in the line protection. However, the current ACLC based on changing the MMC control strategy cannot meet the selectivity requirements. The lack of selectivity may lead to power transmission on non-faulty lines in a multi-terminal DC power grid, thus expanding the range of fault influence. Therefore, this paper proposed a selective and staged ACLC. Firstly, the starting range of ACLC is determined according to the selective requirement and purpose of ACLC in the flexible DC power grid. Secondly, a staged ACLC approach was proposed to balance the speed and selectivity of ACLC. In this approach, the single end and both terminals electrical quantities of the line are fully utilized as the starting criteria for staged ACLC, and corresponding ACLC principles are proposed. Finally, the AC side current and MMC bridge arm current under ACLC were analyzed, and an ACLC scheme was proposed in coordination with DC circuit breakers (DCCB) and fault identification. The simulation results show that the proposed ACLC guarantees the current limiting ability and the MMC does not block during the whole fault period, and has a certain adaptability to the fault type, fault resistance, and noise.

Abstract Human cardiac and other organoids have recently emerged as a groundbreaking tool for advancing our understanding the developmental biology of human organs. A recent paper from Sasha Mendjan’s laboratory published in the journal Cell on December 7, 2023, reported the generation of multi-chamber cardioids from human pluripotent stem cells, a transformative technology in the field of cardiology. In this short highlight paper, we summarize their findings. Their cardioids remarkably recapitulate the complexity of the human embryonic heart, including tissue architecture, cellular diversity, and functionality providing an excellent in vitro model for investigation of human heart development, disease modeling, precision medicine, and regenerative medicine. Thus, generating cardioids is an important step forward for understanding human heart development and developing potential therapies for heart diseases.

We proposed a novel decision-making method, the large-scale group consensus multi-attribute decision-making method based on probabilistic dual hesitant fuzzy sets, to address the challenge of large-scale group multi-attribute decision-making in fuzzy environments. This method concurrently accounted for the membership and non-membership degrees of decision-making experts in fuzzy environments and the corresponding probabilistic value to quantify expert decision information. Furthermore, it applied to complex scenarios involving groups of 20 or more decision-making experts. We delineated five major steps of the method, elaborating on the specific models and algorithms used in each phase. We began by constructing a probabilistic dual hesitant fuzzy information evaluation matrix and determining attribute weights. The following steps involved classifying large-scale decision-making expert groups and selecting the optimal classification scheme based on effectiveness assessment criteria. A global consensus degree threshold was established, followed by implementing a consensus-reaching model to synchronize opinions within the same class of expert groups. Decision information was integrated within and between classes using an information integration model, leading to a comprehensive decision matrix. Decision outcomes for the objects were then determined through a ranking method. The method's effectiveness and superiority were validated through a case study on urban emergency capability assessment, and its advantages were further emphasized in comparative analyses with other methods.

Abstract Background Astroviruses (AstVs) are single-stranded RNA viruses that have been detected in a wide range of mammals and birds. They are associated with numerous interspecies transmissions and viral recombination events, posing a threat to human and animal health. Methods We collected 1,333 samples from wild animals, including bats, rodents, wild boars, and birds, from various states and cities in the Yunnan Province, China, between 2020 and 2023 to investigate the presence of AstVs. AstVs were detected using a polymerase chain reaction targeting the RdRp gene. Finally, the Molecular Evolutionary Genetics Analysis software was used to construct the phylogenetic tree. Results The overall positivity rate for AstVs was 7.12% in four species, indicating their widespread occurrence in the region. High genetic diversity among AstVs was observed in different animal species, suggesting the potential for interspecies transmission, particularly among rodents and birds. Additionally, we identified a novel AstV strain and, for the first time, provided information on the presence of bastroviruses in Yunnan, China. Conclusions The widespread distribution and high genetic diversity of AstVs, along with the observed potential for interspecies transmission, highlight the importance of further investigation and surveillance in the region. The findings emphasize the need for increased attention to AstVs and their potential impact on human and animal health in Yunnan and other regions.

Abstract Chiglitazar is a novel peroxisome proliferator-activated receptor (PPAR) pan-agonist, which passed phase III clinical trials and was newly approved in China for use as an adjunct to diet and exercise in glycemic control in adult patients with Type 2 Diabetes (T2D). To explore the circulating protein signatures associated with the administration of chiglitazar in T2D patients, we conducted a comparative longitudinal study using plasma proteome profiling. Of the 157 T2D patients included in the study, we administered chiglitazar to a specific group, while the controls were given either placebo or sitagliptin. The plasma proteomes were profiled at baseline and 12 and 24 weeks post-treatment using data-independent acquisition mass spectrometry (DIA-MS). Our study indicated that 13 proteins were associated with chiglitazar treatment in T2D patients, including 10 up-regulated proteins (SHBG, TF, APOA2, APOD, GSN, MBL2, CFD, PGLYRP2, A2M, and APOA1) and 3 down-regulated proteins (PRG4, FETUB, and C2) after treatment, which were implicated in the regulation of insulin sensitivity, lipid metabolism, and inflammation response. Our study provides insight into the response of chiglitazar treatment from a proteome perspective and demonstrates the multi-faceted effects of chiglitazar in T2D patients, which will help the clinical application of chiglitazar and further study of its action mechanism.

Background: Ulcerative colitis (UC) is a common and progressive inflammatory bowel disease primarily affecting the colon and rectum. Prolonged inflammation can lead to colitis-associated colorectal cancer (CAC). While the exact cause of UC remains unknown, this study aims to investigate the role of the TWIST1 gene in UC.Methods: Second-generation sequencing data from adult UC patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and characteristic genes were selected using machine learning and Lasso regression. The Receiver Operating Characteristic (ROC) curve assessed TWIST1’s potential as a diagnostic factor (AUC score). Enriched pathways were analyzed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA). Functional mechanisms of marker genes were predicted, considering immune cell infiltration and the competing endogenous RNA (ceRNA) network.Results: We found 530 DEGs, with 341 upregulated and 189 downregulated genes. TWIST1 emerged as one of four potential UC biomarkers via machine learning. TWIST1 expression significantly differed in two datasets, GSE193677 and GSE83687, suggesting its diagnostic potential (AUC = 0.717 in GSE193677, AUC = 0.897 in GSE83687). Enrichment analysis indicated DEGs associated with TWIST1 were involved in processes like leukocyte migration, humoral immune response, and cell chemotaxis. Immune cell infiltration analysis revealed higher rates of M0 macrophages and resting NK cells in the high TWIST1 expression group, while TWIST1 expression correlated positively with M2 macrophages and resting NK cell infiltration. We constructed a ceRNA regulatory network involving 1 mRNA, 7 miRNAs, and 32 long non-coding RNAs (lncRNAs) to explore TWIST1’s regulatory mechanism.Conclusion: TWIST1 plays a significant role in UC and has potential as a diagnostic marker. This study sheds light on UC’s molecular mechanisms and underscores TWIST1’s importance in its progression. Further research is needed to validate these findings in diverse populations and investigate TWIST1 as a therapeutic target in UC.

Abstract A fault identification scheme for protection and adaptive reclosing is proposed for a hybrid multi-terminal HVDC system to increase the reliability of fault isolation and reclosing. By analyzing the "zero passing" characteristic of current at the local end during the converter capacitor discharge stage, the fault identification scheme is proposed. The distributed parameter-based fault location equation, which incorporates fault distance and fault impedance, is developed with the injection signal and the distributed parameter model during the adaptive reclosing stage. The fault distance is determined using a trust region reflection algorithm to identify the permanent fault, and a fault identification scheme for adaptive reclosing is developed. Simulation results show that the proposed scheme is suitable for long-distance transmission lines with strong anti-fault impedance and anti-interference performance. Also, it is less affected by communication delay and DC boundary strength than existing methods.

Abstract Myocardial Brg1 is essential for heart regeneration in zebrafish, but it remains unknown whether and how endothelial Brg1 plays a role in heart regeneration. Here, we found that both brg1 mRNA and protein were induced in cardiac endothelial cells after ventricular resection and endothelium-specific overexpression of dominant-negative Xenopus Brg1 (dn-xbrg1) inhibited myocardial proliferation and heart regeneration and increased cardiac fibrosis. RNA-seq and ChIP-seq analysis revealed that endothelium-specific overexpression of dn-xbrg1 changed the levels of H3K4me3 modifications in the promoter regions of the zebrafish genome and induced abnormal activation of Notch family genes upon injury. Mechanistically, Brg1 interacted with lysine demethylase 7aa (Kdm7aa) to fine-tune the level of H3K4me3 within the promoter regions of Notch family genes and thus regulated notch gene transcription. Together, this work demonstrates that the Brg1-Kdm7aa-Notch axis in cardiac endothelial cells, including the endocardium, regulates myocardial proliferation and regeneration via modulating the H3K4me3 of the notch promoters in zebrafish.

Mulberry leaves, a common traditional Chinese medicine, represent a potential nutritional strategy to improve the fat profile, also known as the lipo-nutrition, of pork. However, the effects of mulberry leaves on pork lipo-nutrition and the microorganisms and metabolites in the porcine gut remain unclear. In this study, multi-omics analysis was employed in a Yuxi black pig animal model to explore the possible regulatory mechanism of mulberry leaves on pork quality. Sixty Yuxi black pigs were divided into two groups: the control group (n = 15) was fed a standard diet, and the experimental group (n = 45) was fed a diet supplemented with 8% mulberry leaves. Experiments were performed in three replicates (n = 15 per replicate); the two diets were ensured to be nutritionally balanced, and the feeding period was 120 days. The results showed that pigs receiving the diet supplemented with mulberry leaves had significantly reduced backfat thickness (p < 0.05) and increased intramuscular fat (IMF) content (p < 0.05) compared with pigs receiving the standard diet. Lipidomics analysis showed that mulberry leaves improved the lipid profile composition and increased the proportion of triglycerides (TGs). Interestingly, the IMF content was positively correlated with acyl C18:2 and negatively correlated with C18:1 of differential TGs. In addition, the cecal microbiological analysis showed that mulberry leaves could increase the abundance of bacteria such as UCG-005, Muribaculaceae_norank, Prevotellaceae_NK3B31_group, and Limosilactobacillus. Simultaneously, the relative levels of L-tyrosine-ethyl ester, oleic acid methyl ester, 21-deoxycortisol, N-acetyldihydrosphingosine, and mulberrin were increased. Furthermore, we found that mulberry leaf supplementation significantly increased the mRNA expression of lipoprotein lipase, fatty acid-binding protein 4, and peroxisome proliferators-activated receptor γ in muscle (p < 0.01). Mulberry leaf supplementation significantly increased the mRNA expression of diacylglycerol acyltransferase 1 (p < 0.05) while significantly decreasing the expression of acetyl CoA carboxylase in backfat (p < 0.05). Furthermore, mulberry leaf supplementation significantly upregulated the mRNA expression of hormone-sensitive triglyceride lipase and peroxisome proliferator-activated receptor α (p < 0.05) in backfat. In addition, mulberry leaf supplementation led to increased serum leptin and adiponectin (p < 0.01). Collectively, this omic profile is consistent with an increased ratio of IMF to backfat in the pig model.

Summary: Inter-organelle membrane contacts are highly dynamic and act as central hubs for many biological processes, but the protein compositions remain largely unknown due to the lack of efficient tools. Here, we developed BiFCPL to analyze the contact proteome in living cells by a bimolecular fluorescence complementation (BiFC)-based proximity labeling (PL) strategy. BiFCPL was applied to study mitochondria-endoplasmic reticulum contacts (MERCs) and mitochondria-lipid droplet (LD) contacts. We identified 403 highly confident MERC proteins, including many transiently resident proteins and potential tethers. Moreover, we demonstrated that mitochondria-LD contacts are sensitive to nutrient status. A comparative proteomic analysis revealed that 60 proteins are up- or downregulated at contact sites under metabolic challenge. We verified that SQLE, an enzyme for cholesterol synthesis, accumulates at mitochondria-LD contact sites probably to utilize local ATP for cholesterol synthesis. This work provides an efficient method to identify key proteins at inter-organelle membrane contacts in living cells.

Abstract Background Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. P21 activated kinase 4 (PAK4) and Breast cancer anti-estrogen resistance 3 (BCAR3) have been reported to be involved in numerous aspects in tumorous progression. In this study, we propose to screen multi-targeted microRNAs. (miRNAs), which simultaneously inhibit neoplastic evolution through suppressing the transcription of target genes. Methods MTT and Colony formation assays measured cell’s viability and proliferation. Scratch wound and Transwell assays detected the ability in migration and invasion for SW116 cells. The multi-targeted microRNAs of PAK4 and BCAR3 were predicted using bioinformatics analysis and verified by conducting dual luciferase reporter assay, western blot and qRT-PCR that could detect the expression levels of miR-199a/b-3p. Results The knockdown of PAK4 significantly impeded proliferation and colony formation of SW1116 cells when the knockdown of BCAR3 hindered migration and invasion of SW1116 cells. MiR-199a/b-3p directly targeted the 3'-UTR of PAK4 and BCAR3, further effected proliferation, colony formation, migration, and invasion of SW1116 cells. PAK4 or BCAR3 overexpression could partially reversed inhibitory effects of miR-199a/b-3p. Conclusions These results provided a new multi-targeted cite for cancerous suppressant to improve the prognosis of CRC inpatients.

Activation of mTORC1 is essential for anti-tumor function of iNKT cells. The mechanisms underlying impaired mTORC1 activation in intratumoral iNKT cells remain unclear. Via generating Vam6+/- mice and using flow cytometry, image approach, and RNA sequencing, we studied the role of Vam6 in controlling mTORC1 activation and intratumoral iNKT cell functions. Here, we find that increased Vam6 expression in intratumoral iNKT cells leads to impaired mTORC1 activation and IFN-γ production. Mechanistically, Vam6 in iNKT cells is essential for Rab7a-Vam6-AMPK complex formation and thus for recruitment of AMPK to lysosome to activate AMPK, a negative regulator of mTORC1. Additionally, Vam6 relieves inhibitory effect of VDAC1 on Rab7a-Vam6-AMPK complex formation at mitochondria-lysosome contact site. Moreover, we report that lactic acid produced by tumor cells increases Vam6 expression in iNKT cells. Given the key roles of increased Vam6 in promoting AMPK activation in intratumoral iNKT cells, reducing Vam6 expression signifificantly enhances the mTORC1 activation in intratumoral iNKT cells as well as their anti-tumor effificacy. Together, we propose Vam6 as a target for iNKT cell-based immunotherapy.

Abstract Organ regeneration is an important, fascinating, and old topic while much remains unknown in spite of extensive investigations for decades. From March 25th to 27th, 2021, the Third Chinese Symposium on Organ Regeneration took place in the beautiful ocean city of Zhoushan, Zhejiang, China. This biennial conference attracted ~ 300 academic attendees: students, postdoctoral fellows, and principal investigators, in addition to few industrial investigators. The mixed live and virtual talks covered the broad field of organ regeneration from different animal organisms to human organoids, and concluded with some impressive advances on inflammatory signaling, regenerative signaling mechanisms, new technologies, and applications for organ regeneration.

Abstract Hybrid HVDC consisting of line commutated converter (LCC) on rectifier side and modular multilevel converter (MMC) on inverter side is the favourable future trend of HVDC projects utilising the advantages of both converter technologies. However, fault clearing and adaptive restart capabilities of such hybrid HVDC are not much investigated in the literature. In this paper fault clearing of hybrid HVDC transmission line is investigated using mechanical DC breaker at MMC terminal and forced firing angle retardation at LCC terminal. Then, the characteristics of the line voltages are analysed during the fault blocking states of converters to clear the transient fault. Characteristics of the line voltages of LLC rectifier have shown prominent difference under permanent and temporary fault states, and also dependent on the severity of fault. Therefore, based on line voltage characteristics, a restart strategy is proposed for hybrid HVDC which is adaptive to the fault characteristics. Several PSCAD/EMTDC based simulation studies are conducted to validate the proposed fault recovery strategy. Simulation results validate the effectiveness of the proposed strategy for hybrid HVDC under DC line faults.

Abstract Background Biomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotated datasets, especially the limited knowledge contained in them. Methods To remedy the above issue, we propose a novel Biomedical Named Entity Recognition (BioNER) framework with label re-correction and knowledge distillation strategies, which could not only create large and high-quality datasets but also obtain a high-performance recognition model. Our framework is inspired by two points: (1) named entity recognition should be considered from the perspective of both coverage and accuracy; (2) trustable annotations should be yielded by iterative correction. Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset. For accuracy, we then filter it by utilizing multiple knowledge bases to generate another weakly labeled dataset. Next, the two datasets are revised by a label re-correction strategy to construct two high-quality datasets, which are used to train two recognition models, respectively. Finally, we compress the knowledge in the two models into a single recognition model with knowledge distillation. Results Experiments on the BioCreative V chemical-disease relation corpus and NCBI Disease corpus show that knowledge from large-scale datasets significantly improves the performance of BioNER, especially the recall of it, leading to new state-of-the-art results. Conclusions We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for BioNER.

The increasing demands for metasurfaces have led researchers to seek effective inverse design methods, which are counting on the developments in the optimization theory and deep learning techniques. Early approaches of the inverse design based on deep learning established a unique mapping between the device’s geometry parameters and its designated EM characteristics. However, the generated solution based on the traditional inverse design method may not be applicable due to practical fabrication conditions. The designers sometimes want to choose the most practical one from multiple schemes which can all meet the requirements of the given EM indicators. A fuzzy inverse design method is quite in demand. In this study, we proposed a fuzzy inverse design method for metamaterial absorbers based on the generative adversarial network (GAN). As a data-driven method, self-built data sets are constructed and trained by the GAN, which contain the absorber’s design parameters and their corresponding spectral response. After the training process is finished, it can generate multiple possible schemes which can satisfy the customized absorptivity and frequency bands for absorbers. The parameters generated by this model include structure sizes and impedance values, which indicates that it has the ability to learn a variety of features. The effectiveness and robustness of the proposed method have been verified by several examples for the design of both narrowband and broadband metamaterial absorbers. Our work proves the feasibility of using deep learning methods to break the limits of one-to-one mapping for the traditional inverse design method. This method may have profound usage for more complex EM device design problems in the future.

Abstract This paper aims to investigate the feasibility of smart metering system based on narrowband power line communications (NB‐PLC) by using low‐voltage (LV) and medium‐voltage (MV) power line channels. The LV and MV networks of two different sites are selected to carry out comprehensive field measurements to investigate the characteristics of NB‐PLC channels for narrowband frequencies. The NB‐PLC channels of both sites are classified into two categories, that is, bad and good channels of LV and MV networks by considering their characteristics and performance. On the basis of measured results, the simulation models for the modeling of LV and MV NB‐PLC channels are developed and thus proposed. The measured and simulated results are compared to validate the efficiency of proposed methods. A state of the art mechanism for the modelling of capacitative coupling device and impedances of LV and MV networks is developed. It is proposed that simulation‐based NB‐PLC channel models and analyses can play a vital role in examining the residential and industrial types of channels. This research work will pave the way to standardize the future smart metering equipment and sensing devices of power system protection.

Abstract Considering the advantages and limitations of traditional identification method, combined with the strategy of active detection, the principle of DC grid pilot protection based on active detection is proposed to improve the sensitivity and reliability of hybrid MMC DC grid protection, and to ensure the accurate identification of fault areas in DC grid. By using the DC fault ride-through control strategy of the hybrid sub-module MMC, the fault current at the converter station DC terminal is limited. Based on the high controllability of hybrid MMC, sinusoidal fault detection signals with the same frequency are injected into the line at each converter station. Based on model recognition, the capacitance model condition is satisfied by the detected signals at both ends during external faults whereas not satisfied during internal faults. The Spearman correlation coefficients is then introduced, and the correlation discriminant of capacitance model is constructed to realize fault area discrimination of DC grid. The simulation results show that the active detection protection scheme proposed in this paper can accurately identify the fault area of DC grid, and is not affected by fault impedance and has low sampling rate requirement.

The SKA (Square Kilometre Array) radio telescope will become the most sensitive telescope by correlating a large number of antenna nodes to form a giant antenna array. The data generated from such a large number of antenna nodes will pose a huge storage problem and require real-time data processing to make the best use of data, and the SKA Scientific Data Processing becomes the bottleneck of the whole processing flow. However, the existing high-performance CPU- and GPU (Graphics Processing Unit)-based solutions cannot satisfy the performance requirements and power budget requirements well [1]. Due to the consideration of the high energy efficiency of hardware accelerators and the flexibility and cost of prototype design, in this paper, we explore the FPGA(Field Programmable Gate Array)-based prototype of one of the most computationally demanding procedures in SKA scientific data processing: degridding. Through the analysis of algorithm behavior and bottlenecks, we design and optimize the memory architecture and computing logic of an FPGA-based prototype. Besides, with the consideration of the relations between the required data of processing multiple spectral channels, we reuse the shared data in processing neighboring spectral channels, and the performance further improves. The functionality and performance of our design have been verified on the target FPGA board, and the software-based benchmarks were also measured on comparable CPU and GPU platforms, indicating that the FPGA-based prototype achieves 2.74 times and 2.03 times speedup, 7.64 times and 7.42 times energy efficiency than the MPI(Message Passing Interface)-based CPU benchmark and the CUDA (Compute Unified Device Architecture)-based GPU benchmark, respectively.

The emergence of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship between humans, computers and the physical environment. CPSS extend cyber-physical systems (CPS) to include the social domain, which introduces a challenge of massive data processing. As a typically giant CPS, the Five-hundred-meter Aperture Spherical radio Telescope (FAST), the world's largest filled-aperture radio telescope, generates massive volume of data which poses a huge storage problem that CPSS face likewise and requires real-time data compressing to reduce data storage and movement overhead. The recently introduced Bitshuffle preprocessing algorithm is a novel approach towards exploiting spatial redundancy incorporation to improve the compression ratio with a specific compressor. However, the existing high-performance CPU-based solutions cannot satisfy the performance requirement and power budget requirement simultaneously. In the paper, we propose the implementation of this algorithm on Field Programmable Gate Array (FPGA) and present an unique data transformation strategy to turn raw FAST data in classic FITS format into another format to support huge file sizes, i.e. Hierarchical Data Format (HDF5). Evaluation results show that our implementation can achieve 3.2Gbyte/s throughput which can be equipped with LZ4 compressor to be high performance compressor. This makes Bitshuffle on FPGAs a candidate for meeting the computational and energy efficiency constraints of radio telescopes and provide reference for CPSS facing the same situation.

Metamaterials (MMs) have already achieved wide applications in academia and industry. The traditional design approach for MMs highly relies on full-wave numerical simulations and trial-and-error methods. It is time-consuming and laborious to obtain the optimal design parameters. Recently, extensive researches have shown advantages and superiority of the deep learning method in solving non-intuitive problem. Several attempts have been made to demonstrate Artificial Intelligence (AI) usage in the electromagnetic field. In this article, a target-driven method empowered by deep learning to realize customized metamaterial absorber (MMA) design has been proposed and demonstrated numerically. Unlike previous deep-learning-based design methods for MMs which directly use the spectrum response to generate the MM's design parameters, this method takes the frequency domain response of the absorber as the intermediary bridge to establish the mapping between MMAs geometry/material parameters and customized figure-of-merits. The proposed design framework greatly simplified the design process of MMAs, and it can also be generalized to realize automatic inverse design for other kinds of metasurfaces.

Aluminum (Al) toxicity is a major yield-limiting factor for crops in acidic soils. In this work, we have investigated the potential role of spermidine (Spd) on Al toxicity in rice chloroplasts. Exogenous Spd markedly reduced Al concentration and elevated other nutrient elements such as Mn, Mg, Fe, K, Ca, and Mo in chloroplasts of Al-treated plants. Meanwhile, Spd further activated arginine decarboxylase (ADC) activity of key enzyme in polyamine (PA) synthesis, and enhanced PA contents in chloroplasts. Spd application dramatically addressed Al-induced chlorophyll (Chl) losses, inhibited thylakoid membrane protein complexes degradation, especially photosystem II (PSII), and significantly depressed the accumulations of superoxide radical (O2·-), hydrogen peroxide (H2O2), and malondialdehyde (MDA) in chloroplasts. Spd addition activated antioxidant enzyme activities and decreased soluble sugar content in chloroplasts compared with Al treatment alone. Spd not only reversed the inhibition of photosynthesis-related gene transcript levels induced by Al toxicity, but diminished the increased expression of Chl catabolism-related genes. Furthermore, Chl fluorescence analysis showed that Spd protected PSII reaction centers and photosynthetic electron transport chain under Al stress, thus improving photosynthetic performance. These results suggest that PAs are involved in Al tolerance in rice chloroplasts and can effectively protect the integrity and function of photosynthetic apparatus, especially PSII, by mitigating oxidative damage induced by Al toxicity.

Background: The 5 m gait speed (5MGS), a simple and reliable performance metric and surrogate indicator of frailty, consistently predicts adverse events in elders. Additionally, MELD-Na (model for end-stage liver disease-sodium) scores fail to capture nutritional and functional decline of cirrhotic patients that may confer excess mortality. We hypothesized that 5MGS might be associated with all-cause mortality, and that inclusion of frailty assessment within MELD-Na could improve the prediction of mortality in cirrhosis. Methods: 5MGS was measured at baseline in 113 hospitalized cirrhotic patients. Survival status over 2 years and cirrhosis-related complications were recorded. We evaluated the prognostic value of 5MGS (as a continuous variable and as a dichotomous variable). The definition of slow versus preserved 5MGS was 0.8 ms −1 based on previous publication. Using Cox proportional hazards regression, a novel MELDNa-5MGS score was derived. Receiver operating characteristics (ROC) curves estimated discrimination between the new score model and established prognostic indices. Results: The continuous 5MGS and slow 5MGS were independent predictors of all-cause mortality [5MGS: hazard ratio (HR) 0.133 (0.047–0.347), p

Scientific workflow is a valuable tool for various complicated large-scale data processing applications. In recent years, the increasingly growing number of scientific processes available necessitates the development of recommendation techniques to provide automatic support for modelling scientific workflows. In this paper, with the help of heterogeneous information network (HIN) and tags of scientific workflows, we organize scientific workflows as a HIN and propose a novel scientific workflow similarity computation method based on metapath. In addition, the density peak clustering (DPC) algorithm is introduced into the recommendation process and a scientific workflow recommendation approach named HDSWR is proposed. The effectiveness and efficiency of our approach are evaluated by extensive experiments with real-world scientific workflows.

Ransomwares on Android have become a challenging threat, performing tasks such as hijacking screen resources, locking devices, and encrypting files. Even worse, with the evolution of ransomwares, many ransomwares can disable USB interfaces of mobile devices. It is difficult for users to recover their devices or decrypt files with the help of other equipment and gives monetary damages to victims. In this paper, we analyse the symmetry between the ransom behaviours and the source code of screen resource hijacked ransomwares, devices locked ransomwares and files encrypted ransomwares. We also propose strategies of recovering hijacked resources, recovering hijacked devices and decrypting encrypted files. To protect mobile devices and private files from ransomwares, we design and implement an automatic recovery application—KRRecover—which is used to recover the hijacked devices and decrypt encrypted files on Android.

Controlled differentiation of human embryonic stem cells (hESCs) can be utilized for precise analysis of cell type identities during early development. We established a highly efficient neural induction strategy and an improved analytical platform, and determined proteomic and phosphoproteomic profiles of hESCs and their specified multipotent neural stem cell derivatives (hNSCs). This quantitative dataset (nearly 13,000 proteins and 60,000 phosphorylation sites) provides unique molecular insights into pluripotency and neural lineage entry. Systems-level comparative analysis of proteins (e.g., transcription factors, epigenetic regulators, kinase families), phosphorylation sites, and numerous biological pathways allowed the identification of distinct signatures in pluripotent and multipotent cells. Furthermore, as predicted by the dataset, we functionally validated an autocrine/paracrine mechanism by demonstrating that the secreted protein midkine is a regulator of neural specification. This resource is freely available to the scientific community, including a searchable website, PluriProt.

The SKA (Square Kilometer Array) radio telescope will become the most sensitive telescope by correlating a huge number of antenna nodes to form a vast array of sensors in a region over one hundred kilometers. Faceting, the wide-field imaging algorithm, is a novel approach towards solving image construction from sensing data where earth surface curves cannot be ignored. However, the traditional processor of cloud computing, even if the most sophisticated supercomputer is used, cannot meet the extremely high computation performance requirement. In this paper, we propose the design and implementation of high-efficiency FPGA (Field Programmable Gate Array) -based hardware acceleration of the key algorithm, faceting in SKA by focusing on phase rotation and gridding, which are the most time-consuming phases in the faceting algorithm. Through the analysis of algorithm behavior and bottleneck, we design and optimize the memory architecture and computing logic of the FPGA-based accelerator. The simulation and tests on FPGA are done to confirm the acceleration result of our design and it is shown that the acceleration performance we achieved on phase rotation is 20× the result of the previous work. We then further designed and optimized an efficient microstructure of loop unrolling and pipeline for the gridding accelerator, and the designed system simulation was done to confirm the performance of our structure. The result shows that the acceleration ratio is 5.48 compared to the result tested on software in gridding parts. Hence, our approach enables efficient acceleration of the faceting algorithm on FPGAs with high performance to meet the computational constraints of SKA as a representative vast sensor array.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.