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Abstract Background Management of anterior cervical corpectomy and fusion (ACCF)-derived adjacent segment disease (ASD) represented a challenge facing the surgeons. Methods A 41-year man diagnosed as C3-4 level ASD derived from C5-level ACCF surgery 13 years ago was admitted to the hospital for numbness and pain in the right shoulder and upper limb. Percutaneous full-endoscopic anterior transcorporeal cervical discectomy (PEATCD) was performed, and pre- and postoperative clinical and imaging data were collected. Results The operation was completed within 70 min, and no clinical or radiological complication was reported. The visual analog scale (VAS) score decreased from preoperative 5 points to postoperative 1 point. Numbness was relieved postoperatively and disappeared completely at postoperative 3 months. Imaging data indicated sufficient spinal cord decompression, good channel repairing and cervical alignment. Conclusions Channel-repairing PEATCD was successfully performed to treat ACCF-derived ASD, nevertheless, the long-term efficacy remained tracing and further clinical trials were needed to validate its efficacy.

BackgroundMinimal change disease (MCD) is a common cause of adult nephrotic syndrome. Most adults with MCD achieve complete remission (CR) after initial steroid therapy. However, approximately 30% of adults who respond to steroids experience frequent relapses, becoming steroid-dependent and potentially developing refractory MCD. Treating refractory MCD in adults poses a significant challenge.Main bodyA 37-year-old woman presented to the nephrology department with a 6-year history of MCD. The diagnosis of MCD was confirmed via renal biopsy. She initially achieved CR with steroid treatment but experienced relapse during steroid tapering. Subsequent CR was achieved with a regimen of steroids and tacrolimus although multiple relapses occurred. Rituximab led to another CR, but its maintenance lasted only 6 months. The response to subsequent rituximab treatments was unsatisfactory. Ultimately, obinutuzumab was selected, resulting in the induction and maintenance of CR for 12 months.ConclusionsThis case demonstrates the successful treatment of frequently relapsed, steroid-dependent, and rituximab-resistant MCD with obinutuzumab. Obinutuzumab is a promising therapeutic option for rituximab-resistant MCD.

The seamless integration of swift and precise topological analysis with state estimation is crucial for ensuring the dependability, stability, and efficiency of the power system. In response to this need, this paper introduced a novel approach to constructing a spatiotemporal “Power Grid One Graph” model using a graph database, enabling rapid topological analysis and state estimation. Initially, a spatiotemporal power grid model was created by merging grid topology with dynamically updated telemetry and telesignaling data. Subsequently, utilizing the graph model and entity mapping, the spatiotemporal node-breaker graph model was obtained and the corresponding bus-branch model was generated. Based on the node-breaker graph model, topological error identification was conducted, and a fast topological analysis optimization algorithm, considering component functionality, was applied to update the bus-branch graph model, facilitating graph-based state estimation. Finally, the proposed method was validated on a real power system, and its application, along with performance enhancements of the spatiotemporal power grid model considering topological changes, was investigated. The presented method provides both theoretical and practical support for the digital transformation of the power system and the advancement of the digital twin power grid.

The 6 GHz band plays a crucial role in the development of the 6G. A profound comprehension of channel reciprocity is essential for designing time division duplexing/frequency division duplexing (TDD/FDD) systems within this band. Firstly, in an indoor corridor scenario, precise and impartial measurements are conducted for both the uplink (UL) and downlink (DL) channels in the 6 GHz band; A denoising algorithm is proposed to extract multipath components (MPCs) from the measurement data, enabling a more equitable assessment of channel reciprocity; Then, a comprehensive analysis of channel reciprocity has been conducted, focusing on four aspects: path loss, delay spread, cluster-based correlation coefficient (CBCC), and multipath power dissimilarity (MPD). The findings indicate that TDD systems demonstrate nearly perfect reciprocity, whereas FDD systems exhibit partial reciprocity in indoor scenarios. Specifically, in TDD systems, the CBCCs between UL and DL exceed 95%, while in FDD systems, they fluctuate between 80% and 90%. Additionally, a model has been provided to depict the relationship between MPD and center frequency, as well as frequency interval; Finally, a comparative analysis of measured and ray-tracing simulated results reveals the presence of numerous public MPCs, which share the same propagation delay and spatial angle between the UL and DL in FDD systems, as well as private MPCs that exist exclusively in either the UL or DL. They collectively influence the channel reciprocity.

Abstract Sparse arrays with O(N) sensors can offer ON2 number of degrees of freedom by exploiting the difference co‐array that can be obtained from the second‐order statistics of received signals. In this article, a novel sparse array with seven subarrays (SA‐7S) by carefully assembling the subarrays is proposed, and the resulting SA‐7S can generate a hole‐free uniform difference co‐array. The sensor locations of the SA‐7S array are uniquely determined by a closed‐form expression and the number of achievable degrees of freedom is analytically presented as well. Theoretical analysis shows that the SA‐7S can provide a higher number of degrees of freedom than most sparse linear arrays. Simulations are performed to validate the advantage of the proposed array in terms of uDOFs, mutual coupling and direction of arrival estimation performance.

While digital twin networks (DTNs) can potentially estimate network strategy performance in pre-validation environments, they are still in their infancy for split learning (SL) tasks, facing challenges like unknown non-i.i.d. data distributions, inaccurate channel states, and misreported resource availability across devices. To address these challenges, this paper proposes a TransNeural algorithm for DTN pre-validation environment to estimate SL latency and convergence. First, the TransNeural algorithm integrates transformers to efficiently model data similarities between different devices, considering different data distributions and device participate sequence greatly influence SL training convergence. Second, it leverages neural network to automatically establish the complex relationships between SL latency and convergence with data distributions, wireless and computing resources, dataset sizes, and training iterations. Deviations in user reports are also accounted for in the estimation process. Simulations show that the TransNeural algorithm improves latency estimation accuracy by 9.3% and convergence estimation accuracy by 22.4% compared to traditional equation-based methods.

Abstract Background Townes-Brocks syndrome is a rare autosomal dominant genetic syndrome caused by mutations in SALL1. The clinical features of Townes-Brocks syndrome are highly heterogonous. Identification of new SALL1 mutations and study of the relation between SALL1 mutations and clinical features can facilitate diagnosis of Townes-Brocks syndrome. Methods We collected clinical data and blood samples of the two patients and their family members for whole-exome sequencing and Sanger sequencing. Prediction analysis of the SALL1variation protein structure was achieved using Alphafold. The clinical materials and gene sequencing results were analyzed. The clinical materials and gene sequencing results were analyzed. The related literature of Townes-Brocks syndrome were searched and the genotype-renal phenotype analysis was performed combined with this two cases. Results Based on the clinical features and gene sequencing results, the two patients were diagnosed as Townes-Brocks syndrome. Two novel SALL1 mutations (c.878-887del and c.1240G > T) were identified, both of which were pathogenic mutations. The correlation between genotypes and renal phenotypes in Townes-Brocks syndrome patients caused by SALL1 mutation were summarized. Conclusion This study identified two novel mutations and provided new insights into the correlation of genotypes and renal phenotypes of Townes-Brocks syndrome.

Mobile visible light communication (VLC) is key for integrating lighting and communication applications in the 6G era, yet there exists a notable gap in experimental research on mobile VLC. In this study, we introduce a mobile VLC system and investigate the impact of mobility speed on communication performance. Leveraging a laser-based light transmitter with a wide coverage, we enable a light fidelity (LiFi) system with a mobile receiving end. The system is capable of supporting distances from 1 m to 4 m without a lens and could maintain a transmission rate of 500 Mbps. The transmission is stable at distances of 1 m and 2 m, but an increase in distance and speed introduces interference to the system, leading to a rise in the Bit Error Rate (BER). The mobile VLC experimental system provides a viable solution to the issue of mobile access in the integration of lighting and communication applications, establishing a solid practical foundation for future research.

A novel multistatic integrated sensing and communication (ISAC) system based on macro–micro cooperation for the sixth-generation (6G) mobile network is proposed. Instead of using macrosites at both the transmitter and receiver sides, microsites are considered as receivers in cooperative sensing. This system is important since microsites can be deployed more flexibly to reduce their distances to the sensing objects, providing better coverage for sensing service. In this work, we first analyze the deployment problem of microsites, which can be deployed along the radius and azimuth angle to cover macrosite cells. The coverage area of each microsite is derived in terms of its position in the cell. Then, we describe an efficient estimating approach for obtaining the position and velocity of sensing objects in the macrosite cell. By choosing multiple microsites around the targeted sensing area, joint data processing with an efficient optimization method is also provided. Simulation results show that the multistatic ISAC system employing macro–micro cooperation can improve the position and velocity estimation accuracy of objects compared to systems employing macrosite cooperation alone, demonstrating the effectiveness and potential for implementing the proposed system in the 6G mobile network.

High-precision sensing is one of the basic capabilities of 6G mobile communication system to fulfill the demands of many application scenarios in the future, and the integrated design of sensing and communication (ISAC) is an important direction of 6G research.The works on ISAC mainly focus on improving the sensing performance.However, besides high sensing accuracy, 6G ISAC network still has a high communication rate requirement.Therefore, joint analysis and design of communication and sensing is necessary.First, three classic sensing algorithms were introduced that could achieve multi-target ranging and speed measurement, and the algorithms were analyzed from three aspects: sensing accuracy, communication performance and computational complexity.It is shown that the optimal sensing accuracy, sensing capacity and communication rate could not be achieved at the same time by using either one algorithm.Second, combined with the characteristics of different sensing algorithms, an adaptive sensing algorithm was proposed that the receiver selected the appropriate sensing algorithm according to the measured receive signal-to-interference-plus-noise ratio to realize the joint optimization of sensing and communication performance.Finally, the link-level simulation was carried out to verify that the proposed algorithm can obtain better sensing accuracy and communication capacity than any single algorithm.

Abstract In this letter, a dilating and embedding scheme for constructing sparse multiple‐input and multiple‐output radar arrays is addressed, which seeks to increase the uniform degrees of freedom (uDOFs) and reduce the mutual coupling effects for a given number of transmitters and receivers (Tx/Rx). According to this scheme, a dilated maximum inter‐element spacing constraint (MISC) array with an embedded thinned coprime array in the collocated MIMO radar for DOA estimation is proposed. Theoretical analysis shows that the proposed array can provide higher uDOFs and is robust in a scenario with heavy levels of mutual coupling. Numerical simulations are conducted to validate its aforementioned advantages in line with the design scheme.

Summary: Diabetic kidney disease (DKD) is the most prevalent chronic kidney disease. Macrophage infiltration in the kidney is critical for the progression of DKD. However, the underlying mechanism is far from clear. Cullin 4B (CUL4B) is the scaffold protein in CUL4B-RING E3 ligase complexes. Previous studies have shown that depletion of CUL4B in macrophages aggravates lipopolysaccharide-induced peritonitis and septic shock. In this study, using two mouse models for DKD, we demonstrate that myeloid deficiency of CUL4B alleviates diabetes-induced renal injury and fibrosis. In vivo and in vitro analyses reveal that loss of CUL4B suppresses migration, adhesion, and renal infiltration of macrophages. Mechanistically, we show that high glucose upregulates CUL4B in macrophages. CUL4B represses expression of miR-194-5p, which leads to elevated integrin α9 (ITGA9), promoting migration and adhesion. Our study suggests the CUL4B/miR-194-5p/ITGA9 axis as an important regulator for macrophage infiltration in diabetic kidneys.

The increasing use of rotary-wing UAVs poses security risks, which makes image detection of rotary-wing UAVs a critical issue. This paper proposes an object detection algorithm for rotary-wing UAVs based on a transformer network. A self-attention mechanism is used to utilize the local contextual information to extract the features of the rotary-wing UAV more effectively, which improves the accuracy of object detection. Meanwhile, a new self-attention mechanism is designed, in which the query vector and the key vector of the surrounding annular area are calculated separately and then concatenated by different heads of attention. Experimental results show that, compared with existing algorithms, the proposed algorithm improves the mean average precision by 1.7% on the proposed rotary-wing UAV dataset.

With the large-scale commercial launch of fifth generation (5G) mobile network, the development of new services and applications catering to the year 2030, along with the deep convergence of information, communication, and data technologies (ICDT), and the lessons and experiences from 5G practice will drive the evolution of the next generation of mobile networks. This article surveys the history and driving forces of the evolution of the mobile network architecture and proposes a logical function architecture for sixth generation (6G) mobile network. The proposed 6G network architecture is termed SOLIDS (related to the following basic features: soft, on-demand fulfillment, lite, native intelligence, digital twin, and native security), which can support self-generation, self-healing, self-evolution, and self-immunity without human involvement and address the primary issues in the legacy 5G network (e.g., high cost, high power consumption, and highly complicated operation and maintenance), significantly well.

Convergence of ICDT is one of the important technical trends in the evolution of wireless mobile communication.How to realize the convergence of wireless network and AI technology is an important topic in the evolution of wireless network.Based on the concept of native AI, AI was applied for wireless resources optimization.6G context-aware wireless network architecture was proposed and AI was introduced into RRM to form an intelligent controller (AI-driven RRM), which completed the intelligent wireless resource management.Driven by big data and artificial intelligence, intelligent management of wireless resources was realized.In the deployment, the intelligent controller is deployed according to its functions and supports dynamic function scaling, so that it can be deployed independently of base station to minimize the cost of hardware.

Since the outbreak of COVID-19, hundreds of millions of people have been infected, causing millions of deaths, and resulting in a heavy impact on the daily life of countless people. Accurately identifying patients and taking timely isolation measures are necessary ways to stop the spread of COVID-19. Besides the nucleic acid test, lung CT image detection is also a path to quickly identify COVID-19 patients. In this context, deep learning technology can help radiologists identify COVID-19 patients from CT images rapidly. In this paper, we propose a deep learning ensemble framework called VitCNX which combines Vision Transformer and ConvNeXt for COVID-19 CT image identification. We compared our proposed model VitCNX with EfficientNetV2, DenseNet, ResNet-50, and Swin-Transformer which are state-of-the-art deep learning models in the field of image classification, and two individual models which we used for the ensemble (Vision Transformer and ConvNeXt) in binary and three-classification experiments. In the binary classification experiment, VitCNX achieves the best recall of 0.9907, accuracy of 0.9821, F1-score of 0.9855, AUC of 0.9985, and AUPR of 0.9991, which outperforms the other six models. Equally, in the three-classification experiment, VitCNX computes the best precision of 0.9668, an accuracy of 0.9696, and an F1-score of 0.9631, further demonstrating its excellent image classification capability. We hope our proposed VitCNX model could contribute to the recognition of COVID-19 patients.

COVID-19 has caused enormous challenges to global economy and public health. The identification of patients with the COVID-19 infection by CT scan images helps prevent its pandemic. Manual screening COVID-19-related CT images spends a lot of time and resources. Artificial intelligence techniques including deep learning can effectively aid doctors and medical workers to screen the COVID-19 patients. In this study, we developed an ensemble deep learning framework, DeepDSR, by combining DenseNet, Swin transformer, and RegNet for COVID-19 image identification. First, we integrate three available COVID-19-related CT image datasets to one larger dataset. Second, we pretrain weights of DenseNet, Swin Transformer, and RegNet on the ImageNet dataset based on transformer learning. Third, we continue to train DenseNet, Swin Transformer, and RegNet on the integrated larger image dataset. Finally, the classification results are obtained by integrating results from the above three models and the soft voting approach. The proposed DeepDSR model is compared to three state-of-the-art deep learning models (EfficientNetV2, ResNet, and Vision transformer) and three individual models (DenseNet, Swin transformer, and RegNet) for binary classification and three-classification problems. The results show that DeepDSR computes the best precision of 0.9833, recall of 0.9895, accuracy of 0.9894, F1-score of 0.9864, AUC of 0.9991 and AUPR of 0.9986 under binary classification problem, and significantly outperforms other methods. Furthermore, DeepDSR obtains the best precision of 0.9740, recall of 0.9653, accuracy of 0.9737, and F1-score of 0.9695 under three-classification problem, further suggesting its powerful image identification ability. We anticipate that the proposed DeepDSR framework contributes to the diagnosis of COVID-19.

The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been proposed for different application scenarios; currently, there is no unified mobility model that can be adapted to all scenarios. The mobility of nodes is an essential characteristic of mobile ad hoc networks (MANETs), and the motion state of nodes significantly impacts the network’s performance. Currently, most related studies focus on the establishment of mathematical models that describe the motion and connectivity characteristics of the mobility models with limited universality. In this study, we use a backpropagation neural network (BPNN) to explore the relationship between the motion characteristics of mobile nodes and the performance of routing protocols. The neural network is trained by extracting five indicators that describe the relationship between nodes and the global features of nodes. Our model shows good performance and accuracy of classification on new datasets with different motion features, verifying the correctness of the proposed idea, which can help the selection of mobility models and routing protocols in different application scenarios having the ability to avoid repeated experiments to obtain relevant network performance. This will help in the selection of mobility models for drone networks and the setting and optimization of routing protocols in future practical application scenarios.

Numerous studies compared the effectiveness of various formats of video-based teaching, yet their focus has primarily been on relatively straightforward content, such as concepts and basic procedures. Research on the effectiveness of teaching complex content through different formats of videos remains limited. This study addresses this gap by conducting a well-controlled comparison between recorded video and narrated animation in the context of teaching physics problem-solving, a challenging content area with easily measurable difficulty levels. The study employed a controlled experimental design with a sample of 361 upper secondary school students who had been randomly assigned to seven classes within a selected secondary school by the school administrator. Data were collected using pre- and post-test assessments that measured students' problem-solving performance after video-based teaching. The results indicated that the effectiveness of recorded videos featuring the teacher's face was not significantly different from that of narrated animations that did not include the teacher's face, irrespective of the content's difficulty level. These findings provide valuable insights for educators in selecting appropriate teaching formats for teaching challenging content through video-based education. They contribute to our understanding of teaching strategies and have practical implications for educators seeking to optimise teaching approaches in similar contexts.

End-stage renal disease (ESRD) is a global health problem with a high incidence (1) and a steadily increasing prevalence (2). Secondary hyperparathyroidism (SHPT) is a common and serious complication of chronic renal failure (CRF) in dialysis patients (3). It is mainly manifested as parathyroid hyperplasia caused by abnormal calcium and phosphorus metabolism and active vitamin D resistance, resulting in excessive secretion of parathyroid hormone (PTH), which leads to complications such as bone deformity, osteoarthralgia, pruritus, ectopic calcification, and cardiovascular calcification in CKD patients, significantly reducing the quality of life in CKD patients (4, 5). In patients with chronic kidney disease, secondary parathyroid gland hyperplasia needs to be treated as early as possible (6). Currently, there are a variety of treatment options, including vitamin D receptor agonists, xenacax hydrochloride, parathyroidectomy and ablation techniques, etc. (7, 8). Medical treatment is the main choice among these treatments, but it is invalid in patients with severe hyperparathyroidism. So, parathyroidectomy is suggested to do in those patients (9). However, many dialysis patients who have severe cardiopulmonary dysfunction cannot tolerate the trauma caused by surgery as the concept of minimally invasive surgery has been gradually introduced into all fields of surgery and medical treatment. Traditional surgery is no longer the only option. Radiofrequency ablation has been widely applied due to its advantages of less trauma, simple operation, and good repeatability. It has been reported to achieve good effects in treating secondary hyperparathyroidism patients (8). This case reports that one severe secondary hyperparathyroidism patient gets good therapeutic results from parathyroid radiofrequency ablation.

The sequential method is easy to integrate with existing large-scale alternating current (AC) power flow solvers and is therefore a common approach for solving the power flow of AC/direct current (DC) hybrid systems. In this paper, a highperformance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is developed. The proposed approach is capable of speeding up the entire computation process without compromising the accuracy of result. First, the AC/DC network is intuitively represented by a graph and stored in a graph database (GDB) to expedite data processing. Considering the interconnection of AC grids via high-voltage direct current (HVDC) links, the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow analysis. For each area, the fast-decoupled power flow (FDPF) is employed with node-based parallel computing (NPC) and hierarchical parallel computing (HPC) to quickly identify system states. Furthermore, to reduce the alternate iterations in the sequential method, a new decoupled approach is utilized to achieve a good initial estimate for the New-ton-Raphson method. With the improved initial estimate, the sequential method can converge in fewer iterations. Consequently, the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power system. The performance is verified on standard IEEE 300-bus system, extended large-scale systems, and a practical 11119-bus system in China.

In TDOA passive location tasks, the geometric configuration can greatly affect the positioning precision due to the complicated characteristics of electromagnetic environment. How to find an appropriate path to a good geometry to locate the transmitter accurately is vital in practical location tasks. This paper proposes a novel geometry optimization method based on deep reinforcement learning. In the proposed method, stations are regarded as mobile agents that can receive wireless signals decide where to go. All agents are controlled by an actor-critic learner, which is trained on the experiences collected from executing the TDOA location task repeatedly. To evaluate the trained agents, a TDOA location simulator environment with complex electromagnetic characteristics is developed. The empirical results show that, the learner mastered useful strategies and navigated to optimal geometric configurations efficiently. A visual depiction of highlights of the learner’s behavior in TDOA passive location tasks can be viewed in the video provided in the supplementary material.

Reinforcement learning has recently made great progress in various challenging domains such as board game of Go and MOBA game of StarCraft II. Policy gradient based reinforcement learning method has become the mainstream due to its effectiveness and simplicity both in discrete and continuous scenarios. However, policy gradient methods commonly involve function approximation and work in an on-policy fashion, which leads to high variance and low sample efficiency. This paper introduces a novel policy gradient method to improve the sample efficiency via a pair of trajectory based prioritized replay buffers and reduce the variance in training with a target network whose weights are updated in a “soft” manner. We evaluate our method on the reinforcement learning suit of Open AI Gym tasks, and the results show that the proposed method can learn more steadily and achieve higher performance than existing methods.

The flotation and adsorption mechanism of wolframite with low-toxic copper ion in the presence of α-hydroxyoctyl phosphinic acid (HPA) were investigated to reduce the use of highly toxic lead nitrate. The adsorption mechanism and flotation performance were characterized by flotation test, time-of-flight secondary-ion mass spectrometry (TOF-SIMS), zeta-potential measurement and X-ray photoelectron spectrometer (XPS). The flotation test showed that the recovery of wolframite increased from 54.9% to 69.1% after Cu(II) modification at pH 9. Meanwhile, Sodium hexametaphosphate(SHP) performed well in separating wolframite from quartz in presence of Cu(II) and HPA. The results of TOF-SIMS, Zeta-potential and XPS indicated new Cu(I), Cu(II) and Fe(III) species were traced on the wolframite surface after Cu(II) treatment. HPA was chemisorbed on the surface of wolframite through M(Fe, Cu, Mn, W)-HPA complexes.

Coronavirus disease 2019 (COVID-19) is rapidly spreading. Researchers around the world are dedicated to finding the treatment clues for COVID-19. Drug repositioning, as a rapid and cost-effective way for finding therapeutic options from available FDA-approved drugs, has been applied to drug discovery for COVID-19. In this study, we develop a novel drug repositioning method (VDA-KLMF) to prioritize possible anti-SARS-CoV-2 drugs integrating virus sequences, drug chemical structures, known Virus-Drug Associations, and Logistic Matrix Factorization with Kernel diffusion. First, Gaussian kernels of viruses and drugs are built based on known VDAs and nearest neighbors. Second, sequence similarity kernel of viruses and chemical structure similarity kernel of drugs are constructed based on biological features and an identity matrix. Third, Gaussian kernel and similarity kernel are diffused. Forth, a logistic matrix factorization model with kernel diffusion is proposed to identify potential anti-SARS-CoV-2 drugs. Finally, molecular dockings between the inferred antiviral drugs and the junction of SARS-CoV-2 spike protein-ACE2 interface are implemented to investigate the binding abilities between them. VDA-KLMF is compared with two state-of-the-art VDA prediction models (VDA-KATZ and VDA-RWR) and three classical association prediction methods (NGRHMDA, LRLSHMDA, and NRLMF) based on 5-fold cross validations on viruses, drugs, and VDAs on three datasets. It obtains the best recalls, AUCs, and AUPRs, significantly outperforming other five methods under the three different cross validations. We observe that four chemical agents coming together on any two datasets, that is, remdesivir, ribavirin, nitazoxanide, and emetine, may be the clues of treatment for COVID-19. The docking results suggest that the key residues K353 and G496 may affect the binding energies and dynamics between the inferred anti-SARS-CoV-2 chemical agents and the junction of the spike protein-ACE2 interface. Integrating various biological data, Gaussian kernel, similarity kernel, and logistic matrix factorization with kernel diffusion, this work demonstrates that a few chemical agents may assist in drug discovery for COVID-19.

The flotation response of malachite, calcite and quartz to bis (2-ethylhexyl) phosphate (DEHPA), dibutyl phosphate (DBP) and tributyl phosphate (TBP) was evaluated by micro-flotation experiments. The results showed that DEHPA exhibited an impressive flotation selectivity for malachite against calcite/quartz in the pH range of 6.0–9.0 and achieved superior malachite flotation recovery over DBP and TBP. The flotation mechanism of DEHPA to malachite was farther investigated through adsorption, contact angle, zeta potential, Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The findings inferred that DEHPA reacted with the surface copper atom of malachite via its O atoms of POH and PO to form the hydrophobic surface complexes of Cu(II)-DEHPA. The double active center of the P(O)OH group might be contributed to the stronger affinity of DEHPA and DBP to malachite than that of TBP with the single active center of the PO group. TBP returned the lowest malachite recovery among the three phosphate surfactants, even it has more hydrophobic-carbon atoms than those of DBP. Keywords: Bis (2-ethylhexyl) phosphate, Malachite, Flotation, Adsorption

Maternal infection during pregnancy is an important factor involved in the pathogenesis of brain disorders in the offspring. Mounting evidence from maternal immune activation (MIA) animals indicates that microglial priming may contribute to neurodevelopmental abnormalities in the offspring. Because peroxisome proliferator-activated receptor gamma (PPARγ) activation exerts neuroprotective effects by regulating neuroinflammatory response, it is a pharmacological target for treating neurogenic disorders. We investigated the effect of PPARγ-dependent microglial activation on neurogenesis and consequent behavioral outcomes in male MIA-offspring. Pregnant dams on gestation day 18 received Poly(I:C) (1, 5, or 10 mg/kg; i.p.) or the vehicle. The MIA model that received 10 mg/kg Poly(I:C) showed significantly increased inflammatory responses in the maternal serum and fetal hippocampus, followed by cognitive deficits, which were highly correlated with hippocampal neurogenesis impairment in prepubertal male offspring. The microglial population in hippocampus increased, displayed decreased processes and larger soma, and had a higher expression of the CD11b, which is indicative of the M1 phenotype (classical activation). Activation of the PPARγ pathway by pioglitazone in the MIA offspring rescued the imbalance of the microglial activation and ameliorated the MIA-induced suppressed neurogenesis and cognitive impairments and anxiety behaviors. In an in vitro experiment, PPARγ-induced M2 microglia (alternative activation) promoted the proliferation and differentiation of neural precursor cells. These results indicated that the MIA-induced long-term changes in microglia phenotypes were associated with hippocampal neurogenesis and neurobehavioral abnormalities in offspring. Modulation of the microglial phenotypes was associated with a PPARγ-mediated neuroprotective mechanism in the MIA offspring and may serve as a potential therapeutic approach for prenatal immune activation-induced neuropsychiatric disorders.

The 5thgeneration mobile system (5G) was designed to fulfill the mobile market requirements for higher data rate,lower latency and higher connection density beyond 2020.3.5 GHz spectrum was a typical band for 5G deployment and could be a global band for 5G development.The performance of 3.5 GHz 5G would be very meaningful as a reference for global operators.Based on the first release of 5G specification frozen in June 2018 by 3rdgeneration partner project (3GPP),field trials of 5G network in typical urban area were conducted to validate the peak data rate,cell throughput and latency of 5G network.The trial results show that 5G NR system can achieved 1 Gbit/s peak data rate and 4 ms latency of single direction.

The cost functions and their performances of direct position determination (DPD) methods in the presence of multipath propagation are investigated. We first establish a general DPD (GDPD) model in the presence of multi-path propagation and point out that the existing cost functions cannot get the emitter positions correctly because of the singularity of the manifold matrix in a multipath propagation scenario. Eight cost functions are developed for the GDPD model and formulated in a unified subspace fitting (USF)-based framework, which provides insight into their algebraic and asymptotic relations. Moreover, we derive the closed-form expressions of the asymptotic distributions of the estimation errors, which are optimized by those cost functions. Besides, the optimal cost function for achieving an optimal asymptotic performance is derived based on the optimization theory. Finally, the numerical simulations and Cramér–Rao lower bound are provided to verify the analytical results and show that: 1) the cost functions which work well in the single-path DPD model cannot find the emitters correctly in a multipath scenario; 2) the signal subspace fitting cost functions and noise subspace fitting cost functions, which are proposed in this paper, find the emitters accurately in the multipath propagation scenarios; 3) the optimal-weighted-subspace-fitting cost function holds the best asymptotic performance under the USF framework; and 4) the asymptotic performance of a multiple dimension cost function is better than a 1D cost function.

The voltage stability is an essential security concern when a system is operating in peak load hours or subjected to an N-1 contingency. Among various voltage stability indices, Local Thevenin Index (LTI) has been a popular one, but mostly applied to the measurement-based framework. The reason is that it requires significant computing effort if applied to model-based approach, even though the model-based LTI calculation can provide more accurate results. In this paper, a new model-based LTI calculation is proposed using graph-computing-based power flow calculation, which is fast enough to fully utilize the advantage of model-based LTI. Then, when the voltage stability index of one or multiple buses exceeds the security limit, a demand response scheme is activated to increase the voltage security margin at the lowest cost. The simulation study of a thousand-level-bus system verifies the computation accuracy and efficiency of the proposed model-based LTI. It also demonstrates that the demand response action can enhance the system security effectively. Thus, the proposed work has advantages if applied to a large-scale Energy Management System (EMS).

A solution to the power flow problem is imperative for many power system applications and several iterative approaches are employed to achieve this objective. However, the chance of finding a solution is dependent on the choice of the initial point because of the non- convex feasibility region of this problem. In this paper, a non-iterative approach that leverages a convexified relaxed power flow problem is employed to verify the existence of a feasible solution. To ensure the scalability of the proposed convex relaxation, the problem is formulated as a sparse semi-definite programming problem. The variables associated with each maximal clique within the network form several positive semidefinite matrices. Perturbation and network reconfiguration schemes are employed to improve the tightness of the proposed convex relaxation in order to validate the existence of a feasible solution for the original non-convex problem. Multiple case studies including an ill-conditioned power flow problem are examined to show the effectiveness of the proposed approach to find a feasible solution.

Mutations in COL4A5 on chromosome Xq22 cause X-linked Alport syndrome (XLAS). In this study, we generated two human induced pluripotent stem cell (iPSC) lines from two male patients carrying mutation c.796C > T (p.R266X) in COL4A5 gene. The two iPSC lines retain the original mutation, possess normal karyotypes, express pluripotency markers and bear differentiation potential.

Alport syndrome (AS) is a hereditary kidney disease caused by mutations in COL4A3, COL4A4, or COL4A5 genes. Here we report the generation of an induced pluripotent stem cell line (iPSC) from an AS patient carrying compound heterozygote mutations (c.4243G > C and c.4216G > A) in COL4A3 gene using non-integrative reprogramming technology. The established iPSC line demonstrates hESC morphology, expresses pluripotency markers, has normal karyotype, and is capable of differentiating into all three germ layers in vitro.

Millimeter-wave (mmWave) communications and visible light communications (VLC) are proposed to form hybrid mmWave/VLC systems. Furthermore, channel modeling is the foundation of system design and optimization. In this paper, we compare the propagation characteristics, including path loss, root mean square (RMS) delay spread (DS), K-factor, and cluster characteristics, between mmWave and VLC bands based on a measurement campaign and ray tracing simulation in a conference room. We find that the optical path loss (OPL) of VLC channels is highly dependent on the physical size of the photodetectors (PDs). Therefore, an OPL model is further proposed as a function of the distance and size of PDs. We also find that VLC channels suffer faster decay than mmWave channels. Moreover, the smaller RMS DS in VLC bands shows a weaker delay dispersion than mmWave channels. The results of K-factor indicate that line-of-sight (LOS) components mainly account for more power for mmWave in LOS scenarios. However, non-LOS (NLOS) components can be stronger for VLC at a large distance. Furthermore, the K-Power-Means algorithm is used to perform clustering. The fitting cluster number is 5 and 6 for mmWave and VLC channels, respectively. The clustering results reveal the temporal sparsity in mmWave bands and show that VLC channels have a large angular spread.

Microbes with abnormal levels have important impacts on the formation and development of various complex diseases. Identifying possible Microbe-Disease Associations (MDAs) helps to understand the mechanisms of complex diseases. However, experimental methods for MDA identification are costly and time-consuming. In this study, a new computational model, RNMFMDA, was developed to find possible MDAs. RNMFMDA contains two main processes. First, Reliable Negative MDA samples were selected based on Positive-Unlabeled (PU) learning and random walk with restart on the heterogeneous microbe-disease network. Second, Logistic Matrix Factorization with Neighborhood Regularization (LMFNR) was developed to compute the association probabilities for all microbe-disease pairs. To evaluate the performance of the proposed RNMFMDA method, we compared RNMFMDA with five state-of-the-art MDA prediction methods based on five-fold cross-validations on microbes, diseases, and MDAs. As a result, RNMFMDA obtained the best AUCs of 0.6332, 0.8669, and 0.9081, respectively for the three five-fold cross validations, significantly outperforming other models. The promising prediction performance may be attributed to the following three features: highly quality negative MDA sample selection, LMFNR-based MDA prediction model, and various biological information integration. In addition, a few predicted microbe-disease pairs with high association scores are worthy of further experimental validation.

Wogonin, an active component derived from Scutellaria baicalensis, has shown anti-tumor activities in several malignancies. However, the roles of wogonin in RCC cells remain elusive. Here, we explored the effects of wogonin on RCC cells and the underlying mechanisms. We found that wogonin showed significant cytotoxic effects against RCC cell lines 786-O and OS-RC-2, with much lower cytotoxic effects on human normal embryonic kidney cell line HEK-293 cells. Wogonin treatment dramatically inhibited the proliferation, migration, and invasion of RCC cells. We further showed that by inhibiting CDK4-RB pathway, wogonin transcriptionally down-regulated CDC6, disturbed DNA replication, induced DNA damage and apoptosis in RCC cells. Moreover, we found that the levels of p-RB, CDK4, and Cyclin D1 were up-regulated in sunitinib resistant 786-O, OS-RC-2, and TK-10 cells, and inhibition of CDK4 by palbociclib or wogonin effectively reversed the sunitinib resistance, indicating that the hyperactivation of CDK4-RB pathway may at least partially contribute to the resistance of RCC to sunitinib. Together, our findings demonstrate that wogonin could induce apoptosis and reverse sunitinib resistance of RCC cells via inhibiting CDK4-RB pathway, thus suggesting a potential therapeutic implication in the future management of RCC patients.

Abstract Purpose Glutamine, as an essential part of enteral nutrition and parenteral nutrition agent, has been widely recognized to be a kind of important intestinal mucosa protectant in clinical practice and experimental research. However, the mechanisms of its protective effects are still not fully understand. Consequently, this study aimed to explore the potential mechanism of glutamine on ischemia-reperfusion (I/R) injury induced endoplasmic reticulum (ER) stress in intestine. Methods An experimental model of intestinal I/R in rats was established by 1 hour occlusion of the superior mesenteric artery followed by 3 hours of reperfusion. Morphologic changes of intestinal mucosa, apoptosis of epithelial cells, and expression of intestinal Grp78, Gadd153, Caspase-12, ATF4, PERK phosphorylation (P-PERK) and elF2αphosphorylation(P-elF2α) were determined. Results After I/R, the apoptotic index of intestinal mucosa epithelial cells observably increased with notable necrosis of intestinal mucosa, and the expressions of Grp78, Gadd153, Caspase-12, ATF4, P-PERK and P-elF2αall were increased. However, treatment with glutamine could significantly relieve intestinal I/R injury and apoptosis index. Moreover, glutamine could clearly up-regulate the expression of Grp78, restrain P-PERK and P-elF2α, and reduce ATF4, Gadd153 and Caspase-12 expressions. Conclusion Glutamine may be involved in alleviating ER stress induced intestinal mucosa cells apoptosis.

SOX4 is highly expressed in gastric cancer (GC) and is associated with tumor grade, metastasis and prognosis, however the mechanism is not clear. We report herein that SOX4 was upregulated and overexpression of SOX4 was associated with increased expression of the markers of Epithelial–mesenchymal transition (EMT) and stemness in clinic patient samples. In vitro, overexpression of SOX4 promoted the invasion as showed by Transwell assay and stemness of GC cells as assessed by sphere formation assay, which was suppressed by silencing SOX4 with shRNA. Further studies showed that SOX4 up-regulated the expression of EMT transcription factors Twist1, snail1 and zeb1 and stemness transcription factors SOX2 and OCT4, and promoted the nuclear translocation of β-catenin. Moreover, we revealed that TGF-β treatment significantly up-regulated the expression of SOX4 and silencing SOX4 reversed TGF-β induced invasion and sphere formation ability of GC cells. Finally, we showed that SOX4 promoted the lung metastasis and tumor formation ability of gastric cancer cells in nude mice. Our results suggest that SOX4 is a target TGF-β signaling and mediates TGF-β-induced EMT and stem cell characteristics of GC cells, revealing a novel role of TGF-β/SOX4 axis in the regulation of malignant behavior of GC.

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the topology of distribution grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.

Under the complicated environment of large wind turbines, the vibration signal of a wind turbine has the characteristics of coupling and nonlinearity. The traditional feature extraction method for the signal is hard to accurately extract fault information, and there is a serious problem of information redundancy in fault diagnosis. Therefore, this paper proposed a multidomain feature fault diagnosis method based on complex empirical mode decomposition (CEMD) and random forest theory (RF). Firstly, this paper proposes a novel method of complex empirical mode decomposition by using the correlation information between two-dimensional signals and utilizing the idea of ensemble empirical mode decomposition (EEMD) by adding white noise to suppress the problem mode mixing in empirical mode decomposition (EMD). Secondly, the collected vibration signals are decomposed into IMFs by CEMD. Then, calculate 11 time domain characteristic parameters and 13 frequency domain characteristic parameters of the vibration signal, and calculate the energy and energy entropy of each IMF components. Make all the characteristic parameters as the multidomain feature vectors of wind turbines. Finally, the redundant feature vectors are eliminated by the importance of each feature vector which has been calculated, and the feature vectors selected are input to the random forest classifier to achieve the fault diagnosis of large wind turbines. Simulation and experimental results show that this method can effectively extract the fault feature of the signal and achieve the fault diagnosis of wind turbines, which has a higher accuracy of fault diagnosis than the traditional classification methods.

It is common to operate more than one RAT of operators，who are confronted with complicated cooperation among multiple RATs，and relative low multi-RAT cooperating efficiency. How to facilitate efficient multi-RAT coordination to improve cooperation efficiency and user experience has become an important research topic with broad application prospects. The research supplements each other with 5G network architecture design. On the basis of analyzing background and research status，key technologies were addressed. A paradigm of logical architecture was given and discussed from multi-RAT point of view. And further solutions of user/core agnostic multi-RAT unified access and seamless mobility were given to shorten cooperation path，reduce complexity of cooperation，and improve cooperating efficiency as well as user experience.

The interaction mechanism of sodium diisobutyl dithiophosphinate (DTPINa) with chalcopyrite, pyrite, galena and sphalerite was investigated by single mineral flotation experiment, adsorption measurement and FTIR spectrum analysis. Single mineral flotation experiments showed that sodium diisobutyl dithiophosphinate exhibited a strong ability to collect chalcopyrite and galena. For chalcopyrite and pyrite, the recovery of chalcopyrite can reach 96.2% when the dosage is 12 mg/L and pH value is 8. In the same situation, the recovery of pyrite is as low as 13.5%. For galena and sphalerite, the recovery of galena reached 91.7% when the dosage was 50 mg/L at pH 11, and the recovery of sphalerite was only 16.9%. DTPINa adsorbed on chalcopyrite and galena surfaces are more than that on pyrite and sphalerite surfaces. The adsorption capacity of DTPINa on the minerals surface is proportional to its dosage. The FTIR spectrum analysis results showed that the adsorption of DTPINa on sulfide minerals surface is chemical and S atoms in PS and PS may have taken part in the reaction. The natural ores experiments also confirmed the excellent performance of sodium diisobutyl dithiophosphinate.

Big data analytics applied to signaling, traffic, and wireless environment data in mobile communication networks can help realize autonomous network optimization and build big data-based network operation. In this paper, a signaling-based intelligent network optimization scheme is introduced and applied to the current mobile communication networks, such as 4G Long Term Evolution. In 5G era, big data analytics can help mine user and service requirements from the radio access network level, thus allowing a more efficient 5G design and operation. This paper illustrates how it would significantly facilitate local content provision, dynamical network and functionality deployment, user behavior awareness, fine-tuned network operation, and globally optimized energy saving solutions. It is anticipated that the big data-based 5G network design, and the operation will be greener and softer, and better meet the ever increasing user-centric requirements of mobile communication.

Existing clustering algorithms of data gathering in wireless sensor networks neglect the impact of event source on the data spatial correlation. In this article, we proposed a compressed sensing–based dynamic clustering algorithm centred on event source. The main challenges of the prescribed scheme are how to model the impact of event source on spatial correlation and how to obtain the location of event source. To solve both the problems, we first formulate the Euclidean distance spatial correlation model and employ joint sparsity model-1 to describe the impact on the spatial correlation caused by event source. Based on these models, we conceive an efficient clustering scheme, which exploits the compressive data for computing the location of event source and for dynamic clustering. Simulation results show that the proposed compressed sensing–based dynamic clustering algorithm centred on event source outperforms the existing data gathering algorithms in decreasing the communication cost, saving the network energy consumption as well as extending the network survival time under a same accuracy. Additionally, the three performance affecting factors, namely, the attenuation coefficient of event sources, the distance between event sources and the number of event sources, are investigated and provided for constituting the application condition of the compressed sensing–based dynamic clustering algorithm centred on event source. The proposed scheme is potential in large-scale wireless sensor networks such as sensor-based IoT application.