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The nuclear pore complex (NPC) is the sole mediator of nucleocytoplasmic transport. Despite great advances in understanding its conserved core architecture, the peripheral regions can exhibit considerable variation within and between species. One such structure is the cage-like nuclear basket. Despite its crucial roles in mRNA surveillance and chromatin organization, an architectural understanding has remained elusive. Using in-cell cryo-electron tomography and subtomogram analysis, we explored the NPC's structural variations and the nuclear basket across fungi (yeast; S. cerevisiae), mammals (mouse; M. musculus), and protozoa (T. gondii). Using integrative structural modeling, we computed a model of the basket in yeast and mammals that revealed how a hub of nucleoporins (Nups) in the nuclear ring binds to basket-forming Mlp/Tpr proteins: the coiled-coil domains of Mlp/Tpr form the struts of the basket, while their unstructured termini constitute the basket distal densities, which potentially serve as a docking site for mRNA preprocessing before nucleocytoplasmic transport.

Qian Wu,1,* Qing-Qing Dong,1,* Si-Hui Wang,1 Yi Lu,1 Yi Shi,1 Xiao-Ling Xu,2 Wei Chen1 1ICU, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 2Shulan International Medical College, Zhejiang Shuren University, Hangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei Chen, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, 725 South WanPing Road, Shanghai, 200032, People’s Republic of China, Tel +86-21-64385700-3522, Email cwdoctor@shutcm.edu.cn Xiao-Ling Xu, Shulan International Medical College, Zhejiang Shuren University, 8 Shuren Street, Hangzhou, 310015, People’s Republic of China, Email ziyao1988@zju.edu.cnBackground: Sepsis continues to exert a significant impact on morbidity and mortality in clinical settings, with immunosuppression, multi-organ failure, and disruptions in gut microbiota being key features. Although rheinic acid and tanshinone IIA show promise in mitigating macrophage apoptosis in sepsis treatment, their precise targeting of macrophages remains limited. Additionally, the evaluation of intestinal flora changes following treatment, which plays a significant role in subsequent cytokine storms, has been overlooked. Leveraging the innate inflammation chemotaxis of tumor cell-derived exosomes allows for their rapid recognition and uptake by activated macrophages, facilitating phenotypic changes and harnessing anti-inflammatory effects.Methods: We extracted exosomes from H1299 cells using a precipitation method. Then we developed a tumor cell-derived exosomal hybrid nanosystem loaded with rhubarbic acid and tanshinone IIA (R+T/Lipo/EXO) for sepsis treatment. In vitro studies, we verify the anti-inflammatory effect and the mechanism of inhibiting cell apoptosis of nano drug delivery system. The anti-inflammatory effects, safety, and modulation of intestinal microbiota by

To facilitate dynamic spectrum sharing, the FCC has designated certified SAS administrators to implement their own spectrum access systems (SASs) that manage the shared spectrum usage in the novel CBRS band. As a premise, different SAS servers must conduct periodic inter-SAS coordination to synchronize service states and avoid allocation conflicts. However, SAS servers may inevitably stop service for regular upgrades, crash down, or even perform maliciously that deviate from the normal routines, posing a fundamental operation security problem — the system shall be robust against these faults to guarantee secure and efficient spectrum sharing service. Unfortunately, the incumbent inter-SAS coordination mechanism, CPAS, is prone to SAS failures and does not support real-time allocation. Recent proposals that rely on blockchain smart contracts or state machine replication mechanisms to realize faulttolerant inter-SAS coordination require all SASs to follow a unified allocation algorithm. They however face performance bottlenecks and cannot accommodate the current fact that different SASs hold their own proprietary allocation algorithms. In this work, we propose TriSAS—a novel inter-SAS coordination mechanism to facilitate secure, efficient, and dependable spectrum allocation that is fully compatible with the existing SAS infrastructure. TriSAS decomposes the coordination process into two phases including input synchronization and decision finalization. The first phase ensures participants share a common input set while the second one fulfills a fair and verifiable spectrum allocation selection, which is generated efficiently via SAS proposers’ proprietary allocation algorithms and evaluated by a customized designed allocation evaluation algorithm (AEA), in the face of no more than one-third of malicious participants. We implemented a prototype of TriSAS on the AWS cloud computing platform and evaluated its throughput and latency performance. The results show that TriSA

Background: The Basic Public Health Service (BPHS), a recently announced free healthcare program, aims to combat the most prevalent Noncommunicable Disease-“Hypertension” (HTN)-and its risk factors on a nationwide scale. In China, there is a rife that HTN less impacts women during their lifetime. We, therefore, aimed to evaluate the sex disparity in hypertension patients with comorbidities among south-west Chinese and the contribution of BPHS to address that concern. Methods: We have opted for a multistage stratified random sampling method to enroll hypertensive patients of 35 years and older, divided them into BPHS and non-BPHS groups. We assessed the sex disparity in HTN patients with four major comorbidities- Dyslipidemia, Diabetes Mellitus (DM), Cardiovascular Disease (CVD), and Chronic Kidney Disease (CKD), and descriptive data were compiled. Odds ratios from logistic regression models estimated the effectiveness of BPHS in the management of HTN with comorbidities. Results: Among 1521 hypertensive patients,1011(66.5%) were managed in the BPHS group. The proportion of patients who had at least one comorbidity was 70.7% (95% confidence interval [CI]: 66.3-76.8%), patients aged 65 years and older were more likely to have coexisting comorbidities. Participants who received the BPHS showed significant blood pressure (BP) control with two comorbidities (odds ratio [OR] = 2.414, 95% CI: 1.276–4.570), three or more (OR = 5.500, 95%CI: 1.174–25.756). Patients with dyslipidemia and DM also benefited from BPHS in controlling BP (OR = 2.169, 95% CI: 1.430–3.289) and (OR = 2.785, 95%CI: 1.242–6.246), respectively. In certain high-income urban survey centers, there was sex differences in the HTN management provided by BPHS, with men having better BP control rates than women. Conclusions: Perhaps this is the first study in China to succinctly show the effectiveness and sex disparity regarding “management of hypertensive comorbidities”. This supports that the BPHS program p

Wireless baseband processing (WBP) is a key element of wireless communications, with a series of signal processing modules to improve data throughput and counter channel fading. Conventional hardware solutions, such as digital signal processors (DSPs) and more recently, graphic processing units (GPUs), provide various degrees of parallelism, yet they both fail to take into account the cyclical and consecutive character of WBP. Furthermore, the large amount of data in WBPs cannot be processed quickly in symmetric multiprocessors (SMPs) due to the unpredictability of memory latency. To address this issue, we propose a hierarchical dataflow-driven architecture to accelerate WBP. A pack-and-ship approach is presented under a non-uniform memory access (NUMA) architecture to allow the subordinate tiles to operate in a bundled access and execute manner. We also propose a multi-level dataflow model and the related scheduling scheme to manage and allocate the heterogeneous hardware resources. Experiment results demonstrate that our prototype achieves $2\times$ and $2.3\times$ speedup in terms of normalized throughput and single-tile clock cycles compared with GPU and DSP counterparts in several critical WBP benchmarks. Additionally, a link-level throughput of $288$ Mbps can be achieved with a $45$-core configuration., Comment: 7 pages, 7 figures, conference

The fatigue performance of AlSi10Mg composites decorated with nano-TiB2 particles fabricated through laser powder bed fusion (LPBF) was first experimentally investigated at room temperature and it exhibited superior fatigue resistance compared with other LPBF AlSi10Mg alloys and other reinforced AlSi10Mg composites. Then the fractography was examined and a fracture mechanics-based life prediction method was proposed to correlate the critical defect information (size and location) and fatigue life. To assess the crack growth driving force for critical defects, we employed Murakami's concept of the maximum stress intensity factor (SIF). The modification was made to include the effect of specimen cross-sectional geometry and initiation location since Murakami’s method would underestimate the maximum SIF in some cases. Meanwhile, the process of small crack propagation was also considered in modeling, and a unified model to describe crack arrest, small crack growth, and long crack growth was established. By inputting information about defect size and location and external loading conditions, the fatigue life could be evaluated and our results demonstrated good estimation within the [ scatter band. Furthermore, an unbroken specimen subjected to the highest external loading was examined using X-ray computed tomography (CT) to characterize the defect population. By incorporating the CT statistical results into our life prediction model, outcomes such as non-propagating cracks or small crack growth without leading to ultimate failure could be obtained, which was consistent with CT findings. Concurrently, CT analysis revealed that the detrimental effect of large inner defects could be less significant than that of small surface defects. Therefore, we propose a novel strategy that combines defect size and location to enhance defect tolerance in additive manufacturing (AM) materials., Peer Reviewed, Postprint (author's final draft)

In this paper, we propose SpotNet: a fast, single stage, image-centric but LiDAR anchored approach for long range 3D object detection. We demonstrate that our approach to LiDAR/image sensor fusion, combined with the joint learning of 2D and 3D detection tasks, can lead to accurate 3D object detection with very sparse LiDAR support. Unlike more recent bird's-eye-view (BEV) sensor-fusion methods which scale with range $r$ as $O(r^2)$, SpotNet scales as $O(1)$ with range. We argue that such an architecture is ideally suited to leverage each sensor's strength, i.e. semantic understanding from images and accurate range finding from LiDAR data. Finally we show that anchoring detections on LiDAR points removes the need to regress distances, and so the architecture is able to transfer from 2MP to 8MP resolution images without re-training.

In this paper, we touch on the problem of markerless multi-modal human motion capture especially for string performance capture which involves inherently subtle hand-string contacts and intricate movements. To fulfill this goal, we first collect a dataset, named String Performance Dataset (SPD), featuring cello and violin performances. The dataset includes videos captured from up to 23 different views, audio signals, and detailed 3D motion annotations of the body, hands, instrument, and bow. Moreover, to acquire the detailed motion annotations, we propose an audio-guided multi-modal motion capture framework that explicitly incorporates hand-string contacts detected from the audio signals for solving detailed hand poses. This framework serves as a baseline for string performance capture in a completely markerless manner without imposing any external devices on performers, eliminating the potential of introducing distortion in such delicate movements. We argue that the movements of performers, particularly the sound-producing gestures, contain subtle information often elusive to visual methods but can be inferred and retrieved from audio cues. Consequently, we refine the vision-based motion capture results through our innovative audio-guided approach, simultaneously clarifying the contact relationship between the performer and the instrument, as deduced from the audio. We validate the proposed framework and conduct ablation studies to demonstrate its efficacy. Our results outperform current state-of-the-art vision-based algorithms, underscoring the feasibility of augmenting visual motion capture with audio modality. To the best of our knowledge, SPD is the first dataset for musical instrument performance, covering fine-grained hand motion details in a multi-modal, large-scale collection., Comment: SIGGRAPH2024

Nuclear pore complexes (NPCs) mediate the nucleocytoplasmic transport of macromolecules. Here we provide a structure of the isolated yeast NPC in which the inner ring is resolved by cryo-EM at sub-nanometer resolution to show how flexible connectors tie together different structural and functional layers. These connectors may be targets for phosphorylation and regulated disassembly in cells with an open mitosis. Moreover, some nucleoporin pairs and transport factors have similar interaction motifs, which suggests an evolutionary and mechanistic link between assembly and transport. We provide evidence for three major NPC variants that may foreshadow functional specializations at the nuclear periphery. Cryo-electron tomography extended these studies, providing a model of the in situ NPC with a radially expanded inner ring. Our comprehensive model reveals features of the nuclear basket and central transporter, suggests a role for the lumenal Pom152 ring in restricting dilation, and highlights structural plasticity that may be required for transport.

Fang-fang Fan, Xiao Yu, Zi-wei Shuai, Xiao-yun Hu, Min Pang, Yi-wei Shi NHC Key Laboratory of Pneumoconiosis, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, People’s Republic of ChinaCorrespondence: Min Pang; Yi-wei Shi, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, No. 85, Jiefang South Road, Taiyuan, Shanxi, 030001, People’s Republic of China, Tel +86-18734890328, Email pangmin2009@126.com; chinasyw225@163.comIntroduction: Although Legionella is not the most common pathogen of community-acquired pneumonia, the epidemiological distribution of pneumonia pathogens has changed in recent years, with a gradual increase in some rare pathogens. For example, pneumonia that occurs after water source contamination is mostly caused by Legionella infection. This paper reports the diagnosis and treatment process of a patient after Legionella infection, who had misdiagnosis at the beginning, rapidly progressed to severe disease and combined with fungal infection. This article focuses on the timely and effective treatment of rapidly progressing Legionella pneumonia, in anticipation of a better understanding of the diagnosis and treatment of the disease.Case Presentation: Here, we report a case of legionella infection with the nausea, vomiting as the first symptoms accompanied by weakness, chills, dizziness, abdominal discomfort in a 75-year-old female. The patient had a history of type 2 diabetes for 30 years, diabetic peripheral neuropathy for more than 20 years, arterial hypertension for 10 years, bone hyperplasia for more than 5 years, resection of right-sided thyroid cystadenoma in 1990. The patient had firstly been diagnosed with cholecystitis and gallbladder neck stones, diet abstinence, metronidazole, cefoperazone sulbactam, and rehydration were given. The patient responded poorly to these empiric treatments. The patient was given moxifloxacin in combin

Notch has been implicated in human cancers and is a putative therapeutic target. However, the regulation of Notch activation in the nucleus remains largely uncharacterized. Therefore, characterizing the detailed mechanisms governing Notch degradation will identify attractive strategies for treating Notch-activated cancers. Here, we report that the long noncoding RNA (lncRNA) BREA2 drives breast cancer metastasis by stabilizing the Notch1 intracellular domain (NICD1). Moreover, we reveal WW domain containing E3 ubiquitin protein ligase 2 (WWP2) as an E3 ligase for NICD1 at K1821 and a suppressor of breast cancer metastasis. Mechanistically, BREA2 impairs WWP2-NICD1 complex formation and in turn stabilizes NICD1, leading to Notch signaling activation and lung metastasis. BREA2 loss sensitizes breast cancer cells to inhibition of Notch signaling and suppresses the growth of breast cancer patient-derived xenograft tumors, highlighting its therapeutic potential in breast cancer. Taken together, these results reveal the lncRNA BREA2 as a putative regulator of Notch signaling and an oncogenic player driving breast cancer metastasis.

Dan Luo,1,2,* Bo-Yi Yang,3,* Kai Qin,4 Chong-Yu Shi,3 Nian-Sa Wei,4 Hai Li,1 Yi-Xiang Qin,1 Gang Liu,1 Xiao-Ling Qin,1 Shi-Yi Chen,1 Xiao-Jing Guo,1 Li Gan,1 Ruo-Lan Xu,1 Bai-Qing Dong,1 Jing Li5 1Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China; 2Guangxi Key Laboratory of Translational Medicine for Treating High-Incidence Infectious Diseases with Integrative Medicine, Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China; 3The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 4The Second Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 5Deparment of Physiology, School of Basic Medical Sciences of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bai-Qing Dong, Guangxi University of Chinese Medicine, 13 Wuhe Road, Nanning, Guangxi, 530200, People’s Republic of China, Tel/Fax +86-771-4928360, Email 764937318@qq.com Jing Li, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, 530021, People’s Republic of China, Tel/Fax +86-771-5358212, Email lijing@gxmu.edu.cnPurpose: Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is important to control the prevalence of tuberculosis; however, there is currently no effective method. The aim of this study was to discover specific metabolites through fecal untargeted metabolomics to discriminate ATB, individuals with LTBI, and healthy controls (HC) and to probe the metabolic perturbation associated with the progression of tuberculosis.Patients and Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed to comprehensively detect compounds in fecal samples from HC, LTBI, and ATB patients. Differential metabolites

Liu L, Yuan M, Sun S, et al. Infect Drug Resist. 2022;15:5645–5653. The authors have advised that affiliation 1 on page 5645 is incorrect. The correct affiliation should read “The First School of Clinical Medicine, Southern Medical University, Guangzhou, China”. The authors apologize for this error.

Shi Chen,1 Yi Shi,1 Bingzhu Hu,1 Jie Huang2 1Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of Jianghan University, Wuhan City, Hubei, 430000, People’s Republic of China; 2Department of Respiratory Medicine, Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, 200031, People’s Republic of ChinaCorrespondence: Jie Huang, Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, 966 Huaihai Zhong Lu, Xuhui District, Shanghai, Tel +86-13585823471, Email syarhuang@163.comPurpose: To explore the association between red cell distribution width (RDW)-to-platelet ratio (RPR) and in-hospital mortality of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) patients and establish a prediction model based on RPR and other predictors.Material and Methods: This cohort study included 1922 AECOPD patients aged ≥ 18 years in the Medical Information Mart for Intensive Care (MIMIC)-III and MIMIC-IV as well as 1738 AECOPD patients from eICU Collaborative Research Database (eICU-CRD). Possible confounding factors were screened out by univariate logistic regression, and multivariable logistic regression was applied to evaluate the association between RPR and in-hospital mortality of AECOPD patients. The area under the curve (AUC), calibration curve and decision curve analysis (DCA) curve were plotted to evaluate the predictive value of the model. The median follow-up time was 3.14 (1.87, 6.25) day.Results: At the end of follow-up, there were 1660 patients survived and 262 subjects died. After adjusting for confounders, we found that Log (RPR× 1000) was linked with elevated risk of in-hospital mortality of AECOPD patients [odds ratio (OR)=1.36, 95% confidence interval (CI): 1.01– 1.84]. The prediction model was constructed using predictors including Log (RPR× 1000), age, malignant cancer, atrial fibrillation, ventilation, renal failure, diastolic blood pressure (DBP), temperature, Glasgow Coma Scale (G

Nuclear pore complexes (NPCs) direct the nucleocytoplasmic transport of macromolecules. Here, we provide a composite multiscale structure of the yeast NPC, based on improved 3D density maps from cryogenic electron microscopy and AlphaFold2 models. Key features of the inner and outer rings were integrated into a comprehensive model. We resolved flexible connectors that tie together the core scaffold, along with equatorial transmembrane complexes and a lumenal ring that anchor this channel within the pore membrane. The organization of the nuclear double outer ring reveals an architecture that may be shared with ancestral NPCs. Additional connections between the core scaffold and the central transporter suggest that under certain conditions, a degree of local organization is present at the periphery of the transport machinery. These connectors may couple conformational changes in the scaffold to the central transporter to modulate transport. Collectively, this analysis provides insights into assembly, transport, and NPC evolution.

IMPORTANCE: Improvements in cancer outcomes have led to a need to better understand long-term oncologic and nononcologic outcomes and quantify cancer-specific vs noncancer-specific mortality risks among long-term survivors. OBJECTIVE: To assess absolute and relative cancer-specific vs noncancer-specific mortality rates among long-term survivors of cancer, as well as associated risk factors. DESIGN, SETTING, AND PARTICIPANTS: This cohort study included 627 702 patients in the Surveillance, Epidemiology, and End Results cancer registry with breast, prostate, or colorectal cancer who received a diagnosis between January 1, 2003, and December 31, 2014, who received definitive treatment for localized disease and who were alive 5 years after their initial diagnosis (ie, long-term survivors of cancer). Statistical analysis was conducted from November 2022 to January 2023. MAIN OUTCOMES AND MEASURES: Survival time ratios (TRs) were calculated using accelerated failure time models, and the primary outcome of interest examined was death from index cancer vs alternative (nonindex cancer) mortality across breast, prostate, colon, and rectal cancer cohorts. Secondary outcomes included subgroup mortality in cancer-specific risk groups, categorized based on prognostic factors, and proportion of deaths due to cancer-specific vs noncancer-specific causes. Independent variables included age, sex, race and ethnicity, income, residence, stage, grade, estrogen receptor status, progesterone receptor status, prostate-specific antigen level, and Gleason score. Follow-up ended in 2019. RESULTS: The study included 627 702 patients (mean [SD] age, 61.1 [12.3] years; 434 848 women [69.3%]): 364 230 with breast cancer, 118 839 with prostate cancer, and 144 633 with colorectal cancer who survived 5 years or more from an initial diagnosis of early-stage cancer. Factors associated with shorter median cancer-specific survival included stage III disease for breast cancer (TR, 0.54; 95% CI, 0.53-0.55

Aiming at the problem that GNSS-RTK technology cannot effectively monitor landslides due to gross errors and high-frequency noise during landslide monitoring, a GNSS-RTK landslide monitoring system based on the improved Raida criterion(3{\sigma}) was designed. The system uses Raspberry Pi as the control core, GNSS-RTK technology to complete deformation data collection, and combines NB-IoT to achieve data transmission and cloud storage. To further improve the monitoring accuracy, real-time gross error detection and high-frequency noise removal method based on the improved Raida criterion(3{\sigma}) and Butterworth low-pass filtering is proposed, combined with edge computing devices to complete real-time data processing, and reduce the pressure of cloud computing. The experimental results show that the system's data transmission is reliable and efficient. After gross error elimination and noise processing, real-time deformation monitoring of 10 mm in the horizontal and vertical directions is achieved; simultaneously, the monitoring error of the GNSS combined system is smaller than that of the Single-GPS system., Comment: 6 pages

Decentralized federated learning (DFL) is an effective approach to train a deep learning model at multiple nodes over a multi-hop network, without the need of a server having direct connections to all nodes. In general, as long as nodes are connected potentially via multiple hops, the DFL process will eventually allow each node to experience the effects of models from all other nodes via either direct connections or multi-hop paths, and thus is able to train a high-fidelity model at each node. We consider an effective attack that uses jammers to prevent the model exchanges between nodes. There are two attack scenarios. First, the adversary can attack any link under a certain budget. Once attacked, two end nodes of a link cannot exchange their models. Secondly, some jammers with limited jamming ranges are deployed in the network and a jammer can only jam nodes within its jamming range. Once a directional link is attacked, the receiver node cannot receive the model from the transmitter node. We design algorithms to select links to be attacked for both scenarios. For the second scenario, we also design algorithms to deploy jammers at optimal locations so that they can attack critical nodes and achieve the highest impact on the DFL process. We evaluate these algorithms by using wireless signal classification over a large network area as the use case and identify how these attack mechanisms exploits various learning, connectivity, and sensing aspects. We show that the DFL performance can be significantly reduced by jamming attacks launched in a wireless network and characterize the attack surface as a vulnerability study before the safe deployment of DFL over wireless networks.

Yi Lu,1,* Yi Shi,1,* Qian Wu,1 Xin Sun,1 Wei-Zhen Zhang,1 Xiao-Ling Xu,2 Wei Chen1 1ICU, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 2Shulan International Medical College, Zhejiang Shuren University, Hangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei Chen, ICU, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, 725 South WanPing Road, Shanghai, 200032, People’s Republic of China, Tel +86-21-64385700-3522, Email cwdoctor@shutcm.edu.cn Xiao-Ling Xu, Shulan International Medical College, Zhejiang Shuren University, 8 Shuren Street, Hangzhou, 310015, People’s Republic of China, Email ziyao1988@zju.edu.cnAbstract: Sepsis, which is a systemic inflammatory response syndrome caused by infection, has high morbidity and mortality. Sepsis-related liver injury is one of the manifestations of sepsis-induced multiple organ syndrome. To date, an increasing number of studies have shown that the hepatic inflammatory response, oxidative stress, microcirculation coagulation dysfunction, and bacterial translocation play extremely vital roles in the occurrence and development of sepsis-related liver injury. In the clinic, sepsis-related liver injury is mainly treated by routine empirical methods on the basis of the primary disease. However, these therapies have some shortcomings, such as serious side effects, short duration of drug effects and lack of specificity. The emergence of drug delivery nanosystems can significantly improve drug bioavailability and reduce toxic side effects. In this paper, we reviewed drug delivery nanosystems designed for the treatment of sepsis-related liver injury according to their mechanisms (hepatic inflammation response, oxidative stress, coagulation dysfunction in the microcirculation, and bacterial translocation). Although much promising progress has been achiev

Generating realistic and controllable motions for virtual characters is a challenging task in computer animation, and its implications extend to games, simulations, and virtual reality. Recent studies have drawn inspiration from the success of diffusion models in image generation, demonstrating the potential for addressing this task. However, the majority of these studies have been limited to offline applications that target at sequence-level generation that generates all steps simultaneously. To enable real-time motion synthesis with diffusion models in response to time-varying control signals, we propose the framework of the Controllable Motion Diffusion Model (COMODO). Our framework begins with an auto-regressive motion diffusion model (A-MDM), which generates motion sequences step by step. In this way, simply using the standard DDPM algorithm without any additional complexity, our framework is able to generate high-fidelity motion sequences over extended periods with different types of control signals. Then, we propose our reinforcement learning-based controller and controlling strategies on top of the A-MDM model, so that our framework can steer the motion synthesis process across multiple tasks, including target reaching, joystick-based control, goal-oriented control, and trajectory following. The proposed framework enables the real-time generation of diverse motions that react adaptively to user commands on-the-fly, thereby enhancing the overall user experience. Besides, it is compatible with the inpainting-based editing methods and can predict much more diverse motions without additional fine-tuning of the basic motion generation models. We conduct comprehensive experiments to evaluate the effectiveness of our framework in performing various tasks and compare its performance against state-of-the-art methods.

Tobacco (Nicotiana tabacum L.) is a potential phytoremediator that can reduce soil cadmium (Cd) contamination. Pot and hydroponic experiments were conducted to investigate the difference in absorption kinetics, translocation patterns, accumulation capacity, and extraction amounts between two leading tobacco cultivars in China. We studied the chemical forms and subcellular distribution of Cd in the plants to understand the diversity of the detoxification mechanism of the cultivars. The concentration-dependent kinetics of Cd accumulation in leaves, stems, roots, and xylem sap for cultivars Zhongyan 100 (ZY100) and K326, fitted well with the MichaelisMenten equation. K326 exhibited high biomass, Cd tolerance, Cd translocation, and phytoextraction abilities. The acetic acid, sodium chloride, and water-extractable fractions accounted for > 90% of Cd in all ZY100 tissues but only in K326 roots and stems. Moreover, the acetic acid and NaCl fractions were the predominant storage forms, while the water fraction was the transport form. The ethanol fraction also contributed significantly to Cd storage in K326 leaves. As the Cd treatment increased, more NaCl and water fractions were found in K326 leaves, while only NaCl fractions increased in ZY100 leaves. For subcellular distribution, > 93% Cd proportions were primarily stored in both cultivars' soluble or cell wall fraction. The proportion of Cd in the cell wall fraction of ZY100 roots was less than that of K326, while that proportion in the soluble fraction in ZY100 leaves was higher than in K326 leaves. These findings demonstrate that Cd accumulation patterns, detoxification, and storage strategies differ between the cultivars, providing a deeper understanding of Cd tolerance and accumulation mechanism in tobacco plants. It also guides the screening of germplasm resources or gene modification to improve the Cd phytoextraction efficiency of tobacco.

This paper studies the poisoning attack and defense interactions in a federated learning (FL) system, specifically in the context of wireless signal classification using deep learning for next-generation (NextG) communications. FL collectively trains a global model without the need for clients to exchange their data samples. By leveraging geographically dispersed clients, the trained global model can be used for incumbent user identification, facilitating spectrum sharing. However, in this distributed learning system, the presence of malicious clients introduces the risk of poisoning the training data to manipulate the global model through falsified local model exchanges. To address this challenge, a proactive defense mechanism is employed in this paper to make informed decisions regarding the admission or rejection of clients participating in FL systems. Consequently, the attack-defense interactions are modeled as a game, centered around the underlying admission and poisoning decisions. First, performance bounds are established, encompassing the best and worst strategies for attackers and defenders. Subsequently, the attack and defense utilities are characterized within the Nash equilibrium, where no player can unilaterally improve its performance given the fixed strategies of others. The results offer insights into novel operational modes that safeguard FL systems against poisoning attacks by quantifying the performance of both attacks and defenses in the context of NextG communications.

Federated learning is known for its capability to safeguard participants' data privacy. However, recently emerged model inversion attacks (MIAs) have shown that a malicious parameter server can reconstruct individual users' local data samples through model updates. The state-of-the-art attacks either rely on computation-intensive search-based optimization processes to recover each input batch, making scaling difficult, or they involve the malicious parameter server adding extra modules before the global model architecture, rendering the attacks too conspicuous and easily detectable. To overcome these limitations, we propose Scale-MIA, a novel MIA capable of efficiently and accurately recovering training samples of clients from the aggregated updates, even when the system is under the protection of a robust secure aggregation protocol. Unlike existing approaches treating models as black boxes, Scale-MIA recognizes the importance of the intricate architecture and inner workings of machine learning models. It identifies the latent space as the critical layer for breaching privacy and decomposes the complex recovery task into an innovative two-step process to reduce computation complexity. The first step involves reconstructing the latent space representations (LSRs) from the aggregated model updates using a closed-form inversion mechanism, leveraging specially crafted adversarial linear layers. In the second step, the whole input batches are recovered from the LSRs by feeding them into a fine-tuned generative decoder. We implemented Scale-MIA on multiple commonly used machine learning models and conducted comprehensive experiments across various settings. The results demonstrate that Scale-MIA achieves excellent recovery performance on different datasets, exhibiting high reconstruction rates, accuracy, and attack efficiency on a larger scale compared to state-of-the-art MIAs.

Nuclear pore complexes (NPCs) mediate the nucleocytoplasmic transport of macromolecules. Here we provide a structure of the isolated yeast NPC in which the inner ring is resolved by cryo-EM at sub-nanometer resolution to show how flexible connectors tie together different structural and functional layers. These connectors may be targets for phosphorylation and regulated disassembly in cells with an open mitosis. Moreover, some nucleoporin pairs and transport factors have similar interaction motifs, which suggests an evolutionary and mechanistic link between assembly and transport. We provide evidence for three major NPC variants that may foreshadow functional specializations at the nuclear periphery. Cryo-electron tomography extended these studies, providing a model of the in situ NPC with a radially expanded inner ring. Our comprehensive model reveals features of the nuclear basket and central transporter, suggests a role for the lumenal Pom152 ring in restricting dilation, and highlights structural plasticity that may be required for transport.

Nuclear pore complexes (NPCs) mediate the nucleocytoplasmic transport of macromolecules. Here we provide a structure of the isolated yeast NPC in which the inner ring is resolved by cryo-EM at sub-nanometer resolution to show how flexible connectors tie together different structural and functional layers. These connectors may be targets for phosphorylation and regulated disassembly in cells with an open mitosis. Moreover, some nucleoporin pairs and transport factors have similar interaction motifs, which suggests an evolutionary and mechanistic link between assembly and transport. We provide evidence for three major NPC variants that may foreshadow functional specializations at the nuclear periphery. Cryo-electron tomography extended these studies, providing a model of the in situ NPC with a radially expanded inner ring. Our comprehensive model reveals features of the nuclear basket and central transporter, suggests a role for the lumenal Pom152 ring in restricting dilation, and highlights structural plasticity that may be required for transport.

In the post-industrial period, traditional Chinese enterprises are facing the awkward situation of being ‘big but not strong’, with their core technologies being controlled by others. The digital transformation of enterprises has not only reshaped business models and industrial boundaries, but also boosted the high-quality development of China’s economy. This paper reviews the existing literature and discovers that digital technology promotes enterprise production efficiency through cost reduction, efficiency improvement, and innovation. Based on the data of listed manufacturing companies in the Shanghai and Shenzhen stock exchanges from 2009 to 2017, this paper constructs a differences in differences (DID) model to empirically study the relationship between digital transformation and production efficiency. The results revealed that the implementation of digital transformation plays a significant role in promoting economic benefits and the results of the lag regression method are still robust. Based on this, combined with the actual situation of Chinese enterprises, this paper proposes countermeasures and suggestions to promote the development of enterprise digital transformation. The conclusion is of great significance for Chinese enterprises to occupy a dominant position in the new wave of global industrial revolution.

Li Liu,1,2 Mingjuan Yuan,3 Siqing Sun,4 Jinrong Wang,5 Yi Shi,6 Yamin Yu,7 Xin Su1,6 1Department of Respiratory and Critical Care Medicine, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, People’s Republic of China; 2Department of Infectious Disease, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan Province, People’s Republic of China; 3Department of Infectious Disease, The Central Hospital of Yueyang, Yueyang, Hunan Province, People’s Republic of China; 4Department of General Medicine, The Second Hospital of Nanjing, Nanjing Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 5Department of Respiratory and Critical Care Medicine, Hengshui People’s hospital, Hengshui, Hebei Province, People’s Republic of China; 6Department of Respiratory and Critical Care Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, People’s Republic of China; 7Department of Nephrology, Ningxiang People’s Hospital, Changsha, Hunan Province, People’s Republic of ChinaCorrespondence: Xin Su, Email suxinjs@163.comBackground: For acquired immunodeficiency syndrome (AIDS) patients with suspected opportunistic infections, the rapid and accurate identification of pathogens remains a challenge. Metagenomic next-generation sequencing (mNGS) has emerged as a pan-pathogen assay for infectious diseases diagnosis, but its guiding significance for diagnosis and antimicrobials treatment in AIDS patients with suspected opportunistic infections is still not well established. In this study, we compared the microbiological diagnostic value of mNGS with that of conventional microbiological tests (CMTs) in AIDS patients with suspected opportunistic infections.Methods: From January 2018 to February 2021, a retrospective study was performed at four tertiary teaching hospitals in China and data of 86 AIDS patients with suspected o

Stakeholders' pressure for environmental sustainability asks firms to develop ecosystem-based business models (EBMs) driven by a disruptive technology that strategically prioritizes green innovation. Given the infancy of the relevant issues, there is scarce empirical evidence addressing how blockchain technology (BT) as a typical disruptive technology triggers the formation of EBMs with higher levels of platformisation and stakeholder diversity. To address this need, from the dynamic capability view, we identify value appropriation capability (VAC) as a vital catalyst translating BT into realizing green innovation in EBMs. Our results indicate that BT and VAC positively affect green innovation performance, while VAC also mediates the BT-green innovation relationship in EBMs. The main contribution is to offer new insight into the mechanisms between disruptive technology and the evolution towards EBMs through the lens of a previously under-researched dynamic capability, VAC. We also provide feasible, practical guidance for organizations to inspire green innovation and establish sustainable EBMs.

Stakeholders' pressure for environmental sustainability asks firms to develop ecosystem-based business models (EBMs) driven by a disruptive technology that strategically prioritizes green innovation. Given the infancy of the relevant issues, there is scarce empirical evidence addressing how blockchain technology (BT) as a typical disruptive technology triggers the formation of EBMs with higher levels of platformisation and stakeholder diversity. To address this need, from the dynamic capability view, we identify value appropriation capability (VAC) as a vital catalyst translating BT into realizing green innovation in EBMs. Our results indicate that BT and VAC positively affect green innovation performance, while VAC also mediates the BT-green innovation relationship in EBMs. The main contribution is to offer new insight into the mechanisms between disruptive technology and the evolution towards EBMs through the lens of a previously under-researched dynamic capability, VAC. We also provide feasible, practical guidance for organizations to inspire green innovation and establish sustainable EBMs.

Spectrum coexistence is essential for next generation (NextG) systems to share the spectrum with incumbent (primary) users and meet the growing demand for bandwidth. One example is the 3.5 GHz Citizens Broadband Radio Service (CBRS) band, where the 5G and beyond communication systems need to sense the spectrum and then access the channel in an opportunistic manner when the incumbent user (e.g., radar) is not transmitting. To that end, a high-fidelity classifier based on a deep neural network is needed for low misdetection (to protect incumbent users) and low false alarm (to achieve high throughput for NextG). In a dynamic wireless environment, the classifier can only be used for a limited period of time, i.e., coherence time. A portion of this period is used for learning to collect sensing results and train a classifier, and the rest is used for transmissions. In spectrum sharing systems, there is a well-known tradeoff between the sensing time and the transmission time. While increasing the sensing time can increase the spectrum sensing accuracy, there is less time left for data transmissions. In this paper, we present a generative adversarial network (GAN) approach to generate synthetic sensing results to augment the training data for the deep learning classifier so that the sensing time can be reduced (and thus the transmission time can be increased) while keeping high accuracy of the classifier. We consider both additive white Gaussian noise (AWGN) and Rayleigh channels, and show that this GAN-based approach can significantly improve both the protection of the high-priority user and the throughput of the NextG user (more in Rayleigh channels than AWGN channels).

In the post-industrial period, traditional Chinese enterprises are facing the awkward situation of being ‘big but not strong’, with their core technologies being controlled by others. The digital transformation of enterprises has not only reshaped business models and industrial boundaries, but also boosted the high-quality development of China’s economy. This paper reviews the existing literature and discovers that digital technology promotes enterprise production efficiency through cost reduction, efficiency improvement, and innovation. Based on the data of listed manufacturing companies in the Shanghai and Shenzhen stock exchanges from 2009 to 2017, this paper constructs a differences in differences (DID) model to empirically study the relationship between digital transformation and production efficiency. The results revealed that the implementation of digital transformation plays a significant role in promoting economic benefits and the results of the lag regression method are still robust. Based on this, combined with the actual situation of Chinese enterprises, this paper proposes countermeasures and suggestions to promote the development of enterprise digital transformation. The conclusion is of great significance for Chinese enterprises to occupy a dominant position in the new wave of global industrial revolution.

The opacity of FRTE depends on not only the material temperature but also the frequency, whose values may vary several orders of magnitude for different frequencies. The gray radiation diffusion and frequency-dependent diffusion equations are two simplified models that can approximate the solution to FRTE in the thick opacity regime. The frequency discretization for the two limit models highly affects the numerical accuracy. However, classical frequency discretization for FRTE considers only the absorbing coefficient. In this paper, we propose a new decomposed multi-group method for frequency discretization that is not only AP in both gray radiation diffusion and frequency-dependent diffusion limits, but also the frequency discretization of the limiting models can be tuned. Based on the decomposed multi-group method, a full AP scheme in frequency, time, and space is proposed. Several numerical examples are used to verify the performance of the proposed scheme., Comment: 36 pages, 14 figures

Multi-scale detection plays an important role in object detection models. However, researchers usually feel blank on how to reasonably configure detection heads combining multi-scale features at different input resolutions. We find that there are different matching relationships between the object distribution and the detection head at different input resolutions. Based on the instructive findings, we propose a lightweight traffic object detection network based on matching between detection head and object distribution, termed as MHD-Net. It consists of three main parts. The first is the detection head and object distribution matching strategy, which guides the rational configuration of detection head, so as to leverage multi-scale features to effectively detect objects at vastly different scales. The second is the cross-scale detection head configuration guideline, which instructs to replace multiple detection heads with only two detection heads possessing of rich feature representations to achieve an excellent balance between detection accuracy, model parameters, FLOPs and detection speed. The third is the receptive field enlargement method, which combines the dilated convolution module with shallow features of backbone to further improve the detection accuracy at the cost of increasing model parameters very slightly. The proposed model achieves more competitive performance than other models on BDD100K dataset and our proposed ETFOD-v2 dataset. The code will be available., Comment: 26 pages, 4 figures, 7 tables

Two iso-singlet hadron states $X_0(2900)$ and $X_1(2900)$ with $J=0$ and 1 respectively, discovered by the LHCb collaboration in 2020, were identified as molecular bound states of $\bar D^*K^*$. Recently two structures $T^a_{cs0}(2900)^0$ and $T^a_{cs0}(2900)^{++}$ have been observed at the hadron spectra, one would suspect if they also are molecular states of $D^*$ and $K^*$. As long as they were of the molecular structures of $D^*K^*$, the hadron states must be in an iso-vector, namely $T^a_{cs0}(2900)^0$ and $T^a_{cs0}(2900)^{++}$ were $I_3=-1, 1$ components of the iso-vector. If it is the case, the corresponding $T^a_{cs0}(2900)^+$ of ($I=1,I_3=0$) and $T^{'a}_{cs0}(2900)^{+}$ of $I=0,I_3=0$ so far evade experimental observation, but should be found by the future experiments. To testify this ansatz, in this paper we study the possible molecular structures of $\bar D^{*}K^{*}$ and $D^{*}K^{*}$ within the Bethe-Salpeter (B-S) framework. With reasonable input parameters it is found that $\bar D^{*}K^{*}$ iso-scalar systems with $J^P=0^+$ and $1^+$ are solutions. The result supports the ansatz of $X_0(2900)$ ($X_1(2900)$) being molecular states of $\bar D^*K^{*}$. Whereas for the system of $ D^{*}K^{*}$ with $I=1$ the corresponding B-S equation has no solution. Thus we can draw a clear conclusion that $T^a_{cs0}(2900)^0$ and $T^a_{cs0}(2900)^{++}$ should not be bound states of $ D^{*}$ and $K^{*}$. The two structures observed by the LHCb collaboration may be caused by dynamics, such as the well-recognized triangle anomalies or other mechanisms., Comment: 14 pages, 2 figures and 5 tables. Accepted by PRD. arXiv admin note: text overlap with arXiv:2112.14142

Rich semantics inside an image result in its ambiguous relationship with others, i.e., two images could be similar in one condition but dissimilar in another. Given triplets like "aircraft" is similar to "bird" than "train", Weakly Supervised Conditional Similarity Learning (WS-CSL) learns multiple embeddings to match semantic conditions without explicit condition labels such as "can fly". However, similarity relationships in a triplet are uncertain except providing a condition. For example, the previous comparison becomes invalid once the conditional label changes to "is vehicle". To this end, we introduce a novel evaluation criterion by predicting the comparison's correctness after assigning the learned embeddings to their optimal conditions, which measures how much WS-CSL could cover latent semantics as the supervised model. Furthermore, we propose the Distance Induced Semantic COndition VERification Network (DiscoverNet), which characterizes the instance-instance and triplets-condition relations in a "decompose-and-fuse" manner. To make the learned embeddings cover all semantics, DiscoverNet utilizes a set module or an additional regularizer over the correspondence between a triplet and a condition. DiscoverNet achieves state-of-the-art performance on benchmarks like UT-Zappos-50k and Celeb-A w.r.t. different criteria., Comment: CVPR2022

NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users and support various spectrum monitoring tasks such as anomaly detection, fault diagnostics, user equipment identification, and authentication. A network of wireless sensors is needed to monitor the spectrum for signal transmissions of interest over a large deployment area. Each sensor receives signals under a specific channel condition depending on its location and trains an individual model of a deep neural network (DNN) accordingly to classify signals. To improve the accuracy, individual sensors may exchange sensing data or sensor results with each other or with a fusion center (such as in cooperative spectrum sensing). In this paper, distributed federated learning over a multi-hop wireless network is considered to collectively train a DNN for signal identification. In distributed federated learning, each sensor broadcasts its trained model to its neighbors, collects the DNN models from its neighbors, and aggregates them to initialize its own model for the next round of training. Without exchanging any spectrum data, this process is repeated over time such that a common DNN is built across the network while preserving the privacy associated with signals collected at different locations. Signal classification accuracy and convergence time are evaluated for different network topologies (including line, star, ring, grid, and random networks) and packet loss events. Then, the reduction of communication overhead and energy consumption is considered with random participation of sensors in model updates. The results show the feasibility of extending cooperative spectrum sensing over a general multi-hop wireless network through federated learning and indicate its robustness to wireless network effects, thereby sustaining high accuracy with low communication overhead and energy consumption.

Federated learning (FL) offers a decentralized learning environment so that a group of clients can collaborate to train a global model at the server, while keeping their training data confidential. This paper studies how to launch over-the-air jamming attacks to disrupt the FL process when it is executed over a wireless network. As a wireless example, FL is applied to learn how to classify wireless signals collected by clients (spectrum sensors) at different locations (such as in cooperative sensing). An adversary can jam the transmissions for the local model updates from clients to the server (uplink attack), or the transmissions for the global model updates the server to clients (downlink attack), or both. Given a budget imposed on the number of clients that can be attacked per FL round, clients for the (uplink/downlink) attack are selected according to their local model accuracies that would be expected without an attack or ranked via spectrum observations. This novel attack is extended to general settings by accounting different processing speeds and attack success probabilities for clients. Compared to benchmark attack schemes, this attack approach degrades the FL performance significantly, thereby revealing new vulnerabilities of FL to jamming attacks in wireless networks.

Background: To alleviate the rising mortality burden due to hypertension and other non-communicable diseases, a new public health policy initiative in 2009 called the Basic Public Health Services (BPHS). Program was introduced by the Chinese government. The goal of the study is to assess the feasibility and impact of a nationwide health care service—the “BPHS”. Methods: From January to December 2021, a stratified multistage random sampling method in the survey was conducted to select 6,456 people from 8 cities/districts in Yunnan Province, China, who were above the age of 35 years. 1,521 hypertensive patients were previously aware of their high blood pressure status were matched to the BPHS program database based on ID number and then further divided into BPHS group and non-BPHS (control) group. The results of the current study are based on their responses to a short structured questionnaire, a physical examination, and laboratory tests. The association between BPHS management and its effect on the control of hypertension was estimated using multivariable logistic regression models. We evaluated the accessibility and efficacy of BPHS health care services by analyzing various variables such as blood pressure, BMI, lifestyle modification, anti-hypertensive drugs taken, and cardiovascular risk factors. Results: Among the 1,521 hypertensive patients included in this study, 1,011 (66.5%) were managed by BPHS programme. The multivariable logistic regression model demonstrated that the BPHS facilitated hypertension control (OR = 1.640, 95% CI: 1.237–2.175). A higher proportion of participants receiving lifestyle guidance from the BPHS management showed lowering of total cholesterol. In comparison to the non-BPHS group, those under BPHS management adhered better to antihypertensive medications either single drug (54.3%) or in combination (17.3%) of drugs. Additionally, we also noticed that urban areas with centralized and well-established digital information management s

Manar Atyah,1,* Chenhao Zhou,1,* Qiang Zhou,1 Wanyong Chen,1 Jialei Weng,1 Pengcheng Wang,1 Yi Shi,2 Qiongzhu Dong,3,4 Ning Ren1,3 1Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, 200032, People’s Republic of China; 2Biomedical Research Centre, Zhongshan Hospital, Fudan University, Shanghai, 200032, People’s Republic of China; 3Institute of Fudan Minhang Academic Health System, and Key Laboratory of Whole-period Monitoring and Precise Intervention of Digestive Cancer (SMHC), Minhang Hospital & AHS, Fudan University, Shanghai, 201199, People’s Republic of China; 4Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ning Ren, Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, 200032, People’s Republic of China, Tel +86-21-64041990, Email ren.ning@zs-hospital.sh.cn; Qiongzhu Dong, Institute of Fudan Minhang Academic Health System, and Key Laboratory of Whole-period Monitoring and Precise Intervention of Digestive Cancer (SMHC), Minhang Hospital & AHS, Fudan University, Shanghai, 201199, People’s Republic of China, Tel +86-21- 64923400, Email qzhdong@fudan.edu.cnBackground: Nuclear factor (erythroid-derived 2)-like 2 (NRF2) functions decline with age; however, cancer cells can hijack its pathways to ensure survival and aggressiveness. Yet, the role of NRF2 in hepatocellular carcinoma (HCC) is rarely investigated in an age-specific manner. This study investigates the expression of NRF2 and its activator (MAPK10) in different age groups of HCC patients, in addition to the age-specific features of NRF2 an

BACKGROUND: Despite standard curative-intent treatment with neoadjuvant cisplatin-based chemotherapy, followed by radical surgery in eligible patients, muscle-invasive urothelial carcinoma has a high recurrence rate and no level 1 evidence for adjuvant therapy. We aimed to evaluate atezolizumab as adjuvant therapy in patients with high-risk muscle-invasive urothelial carcinoma. METHOD: In the IMvigor010 study, a multicentre, open-label, randomised, phase 3 trial done in 192 hospitals, academic centres, and community oncology practices across 24 countries or regions, patients aged 18 years and older with histologically confirmed muscle-invasive urothelial carcinoma and an Eastern Cooperative Oncology Group performance status of 0, 1, or 2 were enrolled within 14 weeks after radical cystectomy or nephroureterectomy with lymph node dissection. Patients had ypT2-4a or ypN+ tumours following neoadjuvant chemotherapy or pT3-4a or pN+ tumours if no neoadjuvant chemotherapy was received. Patients not treated with neoadjuvant chemotherapy must have been ineligible for or declined cisplatin-based adjuvant chemotherapy. No post-surgical radiotherapy or previous adjuvant chemotherapy was allowed. Patients were randomly assigned (1:1) using a permuted block (block size of four) method and interactive voice-web response system to receive 1200 mg atezolizumab given intravenously every 3 weeks for 16 cycles or up to 1 year, whichever occurred first, or to observation. Randomisation was stratified by previous neoadjuvant chemotherapy use, number of lymph nodes resected, pathological nodal status, tumour stage, and PD-L1 expression on tumour-infiltrating immune cells. The primary endpoint was disease-free survival in the intention-to-treat population. Safety was assessed in patients who either received at least one dose of atezolizumab or had at least one post-baseline safety assessment. This trial is registered with ClinicalTrials.gov, NCT02450331, and is ongoing but not recruiting

Edge computing, which sinks a large number of complex calculations into edge servers, can effectively meet the requirement of low latency and bandwidth efficiency and can be conducive to the development of the Internet of Vehicles (IoV). However, a large number of edge servers mean a big cost, especially for the 5G scenario in IoV, because of the small coverage of 5G base stations. Fortunately, coherent beamforming (CB) technology enables fast and long-distance transmission, which gives us a possibility to reduce the number of 5G base stations without losing the whole network performance. In this paper, we try to adopt the CB technology on the IoV 5G scenario. We suppose we can arrange roadside nodes for helping transferring tasks of vehicles to the base station based on the CB technology. We first give the mathematical model and prove that it is a NP-hard model that cannot be solved directly. Therefore, we design a heuristic algorithm for an Iterative Coherent Beamforming Node Design (ICBND) algorithm to obtain the approximate optimal solution. Simulation results show that this algorithm can greatly reduce the cost of communication network infrastructure.

Yi Shi,1,* Meng-Lu Zhu,2,* Qian Wu,1 Yi Huang,1 Xiao-Ling Xu,3 Wei Chen1 1ICU, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, People’s Republic of China; 2Department of Pharmacy, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, 310006, People’s Republic of China; 3Shulan International Medical College, Zhejiang Shuren University, Hangzhou, 310004, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei ChenICU, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, 725 South WanPing Road, Shanghai, 200032, People’s Republic of ChinaTel +86-021-64385700-3522Email cwdoctor@shutcm.edu.cnXiao-Ling XuShulan International Medical College, Zhejiang Shuren University, 8 Shuren Street, Hangzhou, 310004, People’s Republic of ChinaEmail ziyao1988@zju.edu.cnAbstract: Sepsis is a major immune response disorder caused by infection, with very high incidence and mortality rates. In the clinic, sepsis and its complications are mainly controlled and treated with antibiotics, anti-inflammatory, and antioxidant drugs. However, these treatments have some shortcomings, such as rapid metabolism and severe side effects. The emergence of drug delivery nanosystems can significantly improve tissue permeability, prolong drugs’ circulation time, and reduce side effects. In this paper, we reviewed recent drug delivery nanosystems designed for sepsis treatment based on their mechanisms (anti-bacterial, anti-inflammatory, and antioxidant). Although great progress has been made recently, clinical practice transformation is still very difficult. Therefore, we also discussed key obstacles, including tissue distribution, overcoming bacterial resistance, and single treatment modes. Finally, a rigorous optimization of drug delivery nanosystems is expected to present great potential for sepsis therapy.Keywords: sepsis, anti-b

Recent development in brain-machine interface technology has made seizure prediction possible. However, the communication of large volume of electrophysiological signals between sensors and processing apparatus and related computation become two major bottlenecks for seizure prediction systems due to the constrained bandwidth and limited computation resource, especially for wearable and implantable medical devices. Although compressive sensing (CS) can be adopted to compress the signals to reduce communication bandwidth requirement, it needs a complex reconstruction procedure before the signal can be used for seizure prediction. In this paper, we propose C$^2$SP-Net, to jointly solve compression, prediction, and reconstruction with a single neural network. A plug-and-play in-sensor compression matrix is constructed to reduce transmission bandwidth requirement. The compressed signal can be used for seizure prediction without additional reconstruction steps. Reconstruction of the original signal can also be carried out in high fidelity. Prediction accuracy, sensitivity, false prediction rate, and reconstruction quality of the proposed framework are evaluated under various compression ratios. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines by a large margin in prediction accuracy. In particular, our proposed method produces an average loss of 0.35 % in prediction accuracy with a compression ratio ranging from 1/2 to 1/16.

As the acquisition cost of the graphics processing unit (GPU) has decreased, personal computers (PC) can handle optimization problems nowadays. In optimization computing, intelligent swarm algorithms (SIAs) method is suitable for parallelization. However, a GPU-based Simplified Swarm Optimization Algorithm has never been proposed. Accordingly, this paper proposed Parallel Simplified Swarm Optimization (PSSO) based on the CUDA platform considering computational ability and versatility. In PSSO, the theoretical value of time complexity of fitness function is O (tNm). There are t iterations and N fitness functions, each of which required pair comparisons m times. pBests and gBest have the resource preemption when updating in previous studies. As the experiment results showed, the time complexity has successfully reduced by an order of magnitude of N, and the problem of resource preemption was avoided entirely.

Network systems are commonly used in various fields, such as power grid, Internet of Things (IoT), and gas networks. Reliability redundancy allocation problem (RRAP) is a well-known reliability design tool, which needs to be developed when the system is extended from the series-parallel structure to a more general network structure. Therefore, this study proposes a novel RRAP called General RRAP (GRRAP) to be applied to network systems. The Binary Addition Tree Algorithm (BAT) is used to solve the network reliability. Since GRRAP is an NP-hard problem, a new algorithm called Binary-addition simplified swarm optimization (BSSO) is also proposed in this study. BSSO combines the accuracy of the BAT with the efficiency of SSO, which can effectively reduce the solution space and speed up the time to find high-quality solutions. The experimental results show that BSSO outperforms three well-known algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Swarm Optimization (SSO), on six network benchmarks.

Edge computing, which sinks a large number of complex calculations into edge servers, can effectively meet the requirement of low latency and bandwidth efficiency and can be conducive to the development of the Internet of Vehicles (IoV). However, a large number of edge servers mean a big cost, especially for the 5G scenario in IoV, because of the small coverage of 5G base stations. Fortunately, coherent beamforming (CB) technology enables fast and long-distance transmission, which gives us a possibility to reduce the number of 5G base stations without losing the whole network performance. In this paper, we try to adopt the CB technology on the IoV 5G scenario. We suppose we can arrange roadside nodes for helping transferring tasks of vehicles to the base station based on the CB technology. We first give the mathematical model and prove that it is a NP-hard model that cannot be solved directly. Therefore, we design a heuristic algorithm for an Iterative Coherent Beamforming Node Design (ICBND) algorithm to obtain the approximate optimal solution. Simulation results show that this algorithm can greatly reduce the cost of communication network infrastructure.

The heavy metals, including cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), zinc (Zn), and the metalloid arsenic (As) were detected in surface and core soil samples collected from a tobacco growing region in Shandong Peninsula on the east coast of China to evaluate their pollution levels, ecological and health risks, and to analyze their spatial and vertical distributions. The heavy metal sources were identified quantitatively using the positive matrix factorization (PMF) receptor model. In accordance, most of the soils did not have accumulations and were not contaminated by As, Cr, Cu, Ni, Pb, and Zn. High accumulations of Cd and Hg occurred in the soils, posing an ecological risk to the local agricultural environment, while Cr and Ni levels presented a carcinogenic health risk to humans. Four main sources of heavy metals in the soils were identified. Correspondingly Ni and Cr were mainly originated from natural sources, Hg from coal combustion, Cd from agricultural practices, Cu, Pb, and Zn from agricultural practices and industrial activities, and As from industrial activities.

We consider adversarial machine learning based attacks on power allocation where the base station (BS) allocates its transmit power to multiple orthogonal subcarriers by using a deep neural network (DNN) to serve multiple user equipments (UEs). The DNN that corresponds to a regression model is trained with channel gains as the input and returns transmit powers as the output. While the BS allocates the transmit powers to the UEs to maximize rates for all UEs, there is an adversary that aims to minimize these rates. The adversary may be an external transmitter that aims to manipulate the inputs to the DNN by interfering with the pilot signals that are transmitted to measure the channel gain. Alternatively, the adversary may be a rogue UE that transmits fabricated channel estimates to the BS. In both cases, the adversary carefully crafts adversarial perturbations to manipulate the inputs to the DNN of the BS subject to an upper bound on the strengths of these perturbations. We consider the attacks targeted on a single UE or all UEs. We compare these attacks with a benchmark, where the adversary scales down the input to the DNN. We show that the adversarial attacks are much more effective than the benchmark attack in terms of reducing the rate of communications. We also show that adversarial attacks are robust to the uncertainty at the adversary including the erroneous knowledge of channel gains and the potential errors in exercising the attacks exactly as specified.

An over-the-air membership inference attack (MIA) is presented to leak private information from a wireless signal classifier. Machine learning (ML) provides powerful means to classify wireless signals, e.g., for PHY-layer authentication. As an adversarial machine learning attack, the MIA infers whether a signal of interest has been used in the training data of a target classifier. This private information incorporates waveform, channel, and device characteristics, and if leaked, can be exploited by an adversary to identify vulnerabilities of the underlying ML model (e.g., to infiltrate the PHY-layer authentication). One challenge for the over-the-air MIA is that the received signals and consequently the RF fingerprints at the adversary and the intended receiver differ due to the discrepancy in channel conditions. Therefore, the adversary first builds a surrogate classifier by observing the spectrum and then launches the black-box MIA on this classifier. The MIA results show that the adversary can reliably infer signals (and potentially the radio and channel information) used to build the target classifier. Therefore, a proactive defense is developed against the MIA by building a shadow MIA model and fooling the adversary. This defense can successfully reduce the MIA accuracy and prevent information leakage from the wireless signal classifier., Comment: arXiv admin note: substantial text overlap with arXiv:2006.14576