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Abstract Background The chemotherapy regimens recommended for both rhabdomyosarcoma (RMS) and Ewing sarcoma (ES) patients are myelosuppressive and can reduce the absolute neutrophil count (ANC) and subsequently increase the risk of febrile neutropenia (FN). However, only a few studies have focused on the efficacy and safety of granulocyte-colony stimulating factor (G-CSF) drugs in pediatric and adolescent patients with RMS and ES. Our objective was to investigate the efficacy and safety of mecapegfilgrastim, a biosimilar of pegfilgrastim, in prophylaxis of FN for pediatric and adolescent patients with RMS or ES. Methods In this single-arm, single-center, prospective study, pediatric and adolescent patients with RMS or ES were enrolled to receive either VAC (vincristine, cyclophosphamide, dactinomycin) regimen or VDC (vincristine, cyclophosphamide, doxorubicin) regimen in a 3-week cycle, followed by treatment with mecapegfilgrastim (100 μg/kg, maximum 6 mg) given at 24 h after completing chemotherapy. The primary endpoint was the incidence rate of FN. Secondary endpoints included the incidence rate of grade 4 neutropenia, duration of ANC ≤ 0.5 × 109/L, incidence rate of chemotherapy delay or reduction, use of antibiotics, and safety profile. Results In total, 2 of the 30 (6.7%, 95% CI: 0.82–22.07) patients experienced FN after the first cycle of chemotherapy. Eight (26.7%, 95% CI: 12.28–45.89) patients experienced grade 4 neutropenia after receiving prophylactic mecapegfilgrastim. Eight patients experienced ANC ≤ 0.5 × 109/L with a median duration of 4.5 days; among them, 6 patients reached the lowest point of their ANC level on day 7, and 5 of them recovered by day 10. No dose reductions, delays, or discontinuation of chemotherapy was reported. Twenty-one (70.0%) patients received antibiotics during the treatment period. No patient experienced FN in the 0–5 years and the 13–18 years groups, and 2 patients experienced FN in the 6–12 years group. Two patients, 6 patients, and no patient experienced grade 4 neutropenia in the 0–5 years, 6–12 years, and 13–18 years groups, respectively. Conclusion Mecapegfilgrastim showed acceptable efficacy and safety profile in pediatric and adolescent patients with RMS or ES. Further randomized studies with large sample size are warranted. Trial registration This clinical trial was registered at Chictr.org.cn (No.ChiCTR1900022249). Registered on March 31, 2019.

Abstract Background Small-cell lung cancer (SCLC) is a leading cause of cancer-related death. However, the prognostic value of the tumor shrinkage rate (TSR) after chemotherapy for SCLC is still unknown. Methods We performed a retrospective analysis of 235 patients with SCLC. The TSR cutoff was determined based on receiver-operating characteristic curve analysis. The associations of TSR with progression-free survival (PFS) and overall survival (OS) were assessed using univariate and multivariate Cox proportional hazards models. Survival curves were obtained by the Kaplan–Meier method and compared using the log-rank test. Recurrence patterns after first-line treatment were summarized in a pie chart. A nomogram was constructed to validate the predictive role of the TSR in SCLC. Results The TSR cutoff was identified to be − 6.6%. Median PFS and OS were longer in the group with a TSR − 6.6%. Brain metastasis-free survival was better in the group with a TSR

Abstract Thallium (Tl), vanadium (V) and arsenic (As) are considered as typical toxic elements of increased interest. Their accumulation in soils can pose a substantial health threat to human beings. In this study, Fe–Mn modified biochar (FMBC) was chemically constructed to immobilize Tl, V and As in contaminated soils. The results showed that compared with pristine biochar (BC), FMBC can achieve significantly higher passivation effects for the studied contaminated soils, which reduced the bioavailable Tl, V and As contents by 83.9%, 71.09% and 71.92%, respectively. The passivation of Tl, As, and V via FMBC application was partially attributed to a notable increase in pH, which enhances the availability of adsorptive sites. Further, the newly formed minerals, including cancrinite, gibbsite and Fe–Mn (hydr)oxides, serve as additional adsorbents, substantially reducing the mobility of Tl, V and As. Additionally, the oxidation of Tl(I) to Tl(III) by the Fe–Mn (hydr)oxide of FMBC significantly enhanced Tl immobilization, consequently diminishing its bioavailability. The findings suggest that significant environmental threats could be alleviated through the potential application of FMBC in treating Tl-As-V dominated contamination in soils, providing a new perspective for the sustainable utilization of industrially polluted soils. Graphical Abstract

Abstract Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Dynamic recommendation systems aim to achieve real-time updates and dynamic migration of user interests, primarily utilizing user-item interaction sequences with timestamps to capture the dynamic changes in user interests and item attributes. Recent research has mainly centered on two aspects. First, it involves modeling the dynamic interaction relationships between users and items using dynamic graphs. Second, it focuses on mining their long-term and short-term interaction patterns. This is achieved through the joint learning of static and dynamic embeddings for both users and items. Although most existing methods have achieved some success in modeling the historical interaction sequences between users and items, there is still room for improvement, particularly in terms of modeling the long-term dependency structures of dynamic interaction histories and extracting the most relevant delayed interaction patterns. To address this issue, we proposed a Dynamic Context-Aware Recommendation System for dynamic recommendation. Specifically, our model is built on a dynamic graph and utilizes the static embeddings of recent user-item interactions as dynamic context. Additionally, we constructed a Gated Multi-Layer Perceptron encoder to capture the long-term dependency structure in the dynamic interaction history and extract high-level features. Then, we introduced an Attention Pooling network to learn similarity scores between high-level features in the user-item dynamic interaction history. By calculating bidirectional attention weights, we extracted the most relevant delayed interaction patterns from the historical sequence to predict the dynamic embeddings of users and items. Additionally, we proposed a loss function called the Pairwise Cosine Similarity loss for dynamic recommendation to jointly optimize the static and dynamic embeddings of two types of nodes. Finally, extensive experiments on two real-world datasets, LastFM, and the Global Terrorism Database showed that our model achieves consistent improvements over state-of-the-art baselines.

BackgroundSarcopenia is a progressive geriatric syndrome that impacts older adults’ quality of life. Insufficient focus has been given to sarcopenia among Chinese residents, resulting in low level of sarcopenia awareness. This study aims to investigate awareness of sarcopenia and its influencing factors and the influencing pathways among older adults in Hangzhou.MethodsA stratified random sample of 942 community-dwelling older adults was evaluated using the SARC-CalF screening tool, along with a questionnaire based on health ecology theory to assess awareness of sarcopenia and its influencing factors and the influencing pathways. Descriptive statistics, linear regression analysis, and path analysis were conducted using SPSS 25.0 and Amos 23.0 to analyze the data.ResultsThe mean awareness score for sarcopenia was 60.26 ± 7.31. Self-rated physical health, daily intake of high-quality protein, exercise frequency, smoking status, self-efficacy, religious beliefs, social support, education level, occupation, participation in community free medical examinations, and awareness of nutrition policy were all factors affecting scores for sarcopenia awareness (p

Straw cellulose is an abundant renewable resource in nature. In recent years, the conversion of cellulose from waste straw into biofuel by specific microorganisms’ fragmentation has attracted extensive attention. Although many bacteria with the ability to degrade cellulose have been identified, comprehensive bioinformatics analyses of these bacteria remain limited, and research exploring optimal fragmentation conditions is scarce. Our study involved the isolation and screening of bacteria from various locations in Yangzhou using carboxymethyl cellulose (CMC) media. Then, the cellulose-degrading bacteria were identified using 16S rRNA and seven candidate bacterial strains with cellulose degrading ability were identified in Yangzhou city for the first time. The cellulase activity was determined by the 3,5-dinitrosalicylic acid (DNS) method in different fragmentation conditions, and finally two bacteria strains with the strongest cellulose degradation ability were selected for whole genome sequencing analysis. Sequencing results revealed that the genome sizes of Rhodococcus wratislaviensis YZ02 and Pseudomonas Xanthosomatis YZ03 were 8.51 Mb and 6.66 Mb, containing 8,466 and 5,745 genes, respectively. A large number of cellulose degradation-related genes were identified and annotated using KEGG, GO and COG analyses. In addition, genomic CAZyme analysis indicated that both R. wratislaviensis YZ02 and P. Xanthosomatis YZ03 harbor a series of glycoside hydrolase family (GH) genes and other genes related to cellulose degradation. Our finding provides new options for the development of cellulose-degrading bacteria and a theoretical basis for improving the cellulose utilization of straw.

Abstract With the large‐scale deployment of communication and computation units, the conventional radio access network has been gradually transformed into a sophisticated Cyber‐Physical System. Since the access network is put forward higher expectations for ultra‐reliable and low‐latency services, Short‐Packet Communications (SPC) based on Finite Blocklength Codes can be adopted to further decrease network latency and enhance communication reliability. Considering the interaction among the service provider, the network operator, and users, a three‐stage Stackelberg game is constructed to dynamically arrange communication and computation resources. In this paper, the tripartite interaction problem is modelled as a generalized Nash equilibrium problem, and the reverse induction method is employed to achieve the sub‐game equilibrium. To solve the problem of maximizing tripartite utilities, the authors propose an iterative algorithm for jointly managing resource allocation and tripartite pricing. Simulation results show that compared with the benchmark, the proposed scheme can effectively improve the utilities of users, the service provider, and the network operator and achieve a win–win situation.

Summary: Background: Currently, the diagnostic testing for the primary origin of liver metastases (LMs) can be laborious, complicating clinical decision-making. Directly classifying the primary origin of LMs at computed tomography (CT) images has proven to be challenging, despite its potential to streamline the entire diagnostic workflow. Methods: We developed ALMSS, an artificial intelligence (AI)-based LMs screening system, to provide automated liver contrast-enhanced CT analysis for distinguishing LMs from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), as well as subtyping primary origin of LMs as six organ systems. We processed a CECT dataset between January 1, 2013 and June 30, 2022 (n = 3105: 840 HCC, 354 ICC, and 1911 LMs) for training and internally testing ALMSS, and two additional cohorts (n = 622) for external validation of its diagnostic performance. The performance of radiologists with and without the assistance of ALMSS in diagnosing and subtyping LMs was assessed. Findings: ALMSS achieved average area under the curve (AUC) of 0.917 (95% confidence interval [CI]: 0.899–0.931) and 0.923 (95% [CI]: 0.905–0.937) for differentiating LMs, HCC and ICC on both the internal testing set and external testing set, outperformed that of two radiologists. Moreover, ALMSS yielded average AUC of 0.815 (95% [CI]: 0.794–0.836) and 0.818 (95% [CI]: 0.790–0.842) for predicting six primary origins on both two testing sets. Interestingly, ALMSS assigned origin diagnoses for LMs with pathological phenotypes beyond the training categories with average AUC of 0.761 (95% [CI]: 0.657–0.842), which verify the model's diagnostic expandability. Interpretation: Our study established an AI-based diagnostic system that effectively identifies and characterizes LMs directly from multiphasic CT images. Funding: National Natural Science Foundation of China, Guangdong Provincial Key Laboratory of Medical Image Processing.

A two-stage adaptive non-orthogonal/orthogonal multiple access transmission scheme was designed for content delivery scenarios in wireless caching networks.For the scheme, a high-reliability user scheduling strategy was proposed, and the analytical expression of the outage probability and its asymptotic expression in high signal-to-noise ratio regime were derived by theoretical analysis.The analysis results indicate that the proposed strategy can achieve full spatial diversity gain.Simulation results demonstrate that compared with the instantaneous channel state information-based scheduling strategy, the random scheduling strategy, and the conventional orthogonal multiple access transmission strategy, the proposed strategy can achieve better content delivery reliability than existing strategies by obtaining the spatial diversity gain.

Abstract Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing framework exhibit a shallow structure, which hampers their ability to efficiently transmit information between distant nodes. To address this, we aim to propose a novel message-passing framework, enabling the construction of GNN models with deep architectures akin to convolutional neural networks (CNNs), potentially comprising dozens or even hundreds of layers. (2) Existing models often approach the learning of edge and node features as separate tasks. To overcome this limitation, we aspire to develop a deep graph convolutional neural network learning framework capable of simultaneously acquiring edge embeddings and node embeddings. By utilizing the learned multi-dimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features. To address these challenges, we propose the Co-embedding of Edges and Nodes with Deep Graph Convolutional Neural Networks (CEN-DGCNN). In our approach, we propose a novel message-passing framework that can fully integrate and utilize both node features and multi-dimensional edge features. Based on this framework, we develop a deep graph convolutional neural network model that prevents over-smoothing and obtains node non-local structural features and refined high-order node features by extracting long-distance dependencies between nodes and utilizing multi-dimensional edge features. Moreover, we propose a novel graph convolutional layer that can learn node embeddings and multi-dimensional edge embeddings simultaneously. The layer updates multi-dimensional edge embeddings across layers based on node features and an attention mechanism, which enables efficient utilization and fusion of both node and edge features. Additionally, we propose a multi-dimensional edge feature encoding method based on directed edges, and use the resulting multi-dimensional edge feature matrix to construct a multi-channel filter to filter the node information. Lastly, extensive experiments show that CEN-DGCNN outperforms a large number of graph neural network baseline methods, demonstrating the effectiveness of our proposed method.

To improve the throughput of multiuser simultaneous wireless information and power transfer systems, a buffer-aided cooperative non-orthogonal multiple access scheme with power transfer was designed to maximize the average throughput under the constraints of average/peak power, user’s rate, and the buffer stability.To reduce the optimization complexity, the Lyapunov’s method was introduced to convert the long-term average optimization problem into a series of time-discrete subproblems, and an adaptive transmission and resource allocation optimization algorithm was proposed, where the working mode, the user scheduling and the power allocation were dynamically optimized according to the time-varying channel/buffer state.Simulation results demonstrate that compared with the existing schemes, the proposed scheme can significantly enhance the average throughput whilst achieving the tradeoff between the delay and throughput.

Abstract At present, terrorism has become an important factor affecting world peace and development. As the time series data of terrorist attacks usually show a high degree of spatial–temporal correlation, the spatial–temporal prediction of casualties in terrorist attacks is still a significant challenge in the field of counter-terrorism. Most of the existing terrorist attack prediction methods lack the ability to model the spatial–temporal dynamic correlation of the time series data of terrorist attacks, so they cannot yield satisfactory prediction results. In this paper, we propose a novel Attention-based spatial–temporal multi-graph convolutional network (AST-MGCN) for casualty prediction of terrorist attacks. Specifically, we construct the spatial adjacency graph and spatial diffusion graph based on the different social-spatial dynamic relationships of terrorist attacks and determine the multi-scale period of time series data of terrorist attacks by using wavelet transform to model the temporal trend, period and closeness properties of terrorist attacks. The AST-MGCN mainly consists of spatial multi-graph convolution for extracting social-spatial features in multi-views and temporal convolution for capturing the transition rules. In addition, we also use the spatial–temporal attention mechanism to effectively capture the most relevant spatial–temporal dynamic information. Experiments on public datasets demonstrate that the proposed model outperforms the state-of-the-art baselines.

The match and mismatch between the supply and demand of ecosystem services (ESs) are closely related to watershed sustainable development. Previous studies have mainly focused on annual or interdecadal matching relationships, and little is known about the seasonal variations of the ES supply–demand relationship. Based on the SWAT (Soil and Water Assessment Tool) model and statistical data, we took the Guanting Reservoir Basin, a transitional ecosystem from semi-arid areas to arid areas, as the study area to analyze the supply and demand of water provisioning services and soil conservation services and identified their matching relationship on the seasonal scale. The results showed that the water provisioning services was in short supply, with a supply–demand ratio of 0.81, whereas the supply of soil conservation services was in excess of demand. In terms of seasonal variation, the mismatch of water provisioning services was particularly severe in winter, followed by that in spring. The mismatch of soil conservation services was relatively severe in winter, followed by that in summer. Such mismatches imply that we must pay attention to the misallocation of resources caused by seasonal variations. It is crucial that policy-makers consider the role of reservoirs and water transfer in providing seasonal water sources for sustaining water resources in transitional areas, and optimize land management to prevent the aggravation of soil erosion in winter.

Abstract This paper investigates low‐latency offloading strategy in a non‐orthogonal multiple access aided mobile edge computing (NOMA‐MEC) system consisting of K edge servers, one mobile user and one cloud server. An intelligent edge server selection strategy (IESSS) based on Markov decision process (MDP) is proposed to select an edge server, in order to reduce the task completion latency of this system. When an edge server is selected by the proposed IESSS, a joint optimization problem of power allocation and task scheduling factors is formulated to minimize the task completion latency of the hybrid NOMA‐MEC system. To solve the formulated non‐convex optimization problem with coupled variables, a low‐complexity adaptive power‐task resource allocation iterative (APTRAI) algorithm is proposed. Simulation results demonstrate the advantages of the proposed IESSS and verify the convergence and time complexity of the proposed APTRAI algorithm.

Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m−2 a−1, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle.

IntroductionMucosal-associated invariant T (MAIT) cells are a population of innate-like T cells, which mediate host immunity to microbial infection by recognizing metabolite antigens derived from microbial riboflavin synthesis presented by the MHC-I-related protein 1 (MR1). Namely, the potent MAIT cell antigens, 5-(2-oxopropylideneamino)-6-D-ribitylaminouracil (5-OP-RU) and 5-(2-oxoethylideneamino)-6-D-ribitylaminouracil (5-OE-RU), form via the condensation of the riboflavin precursor 5-amino-6-D-ribitylaminouracil (5-A-RU) with the reactive carbonyl species (RCS) methylglyoxal (MG) and glyoxal (G), respectively. Although MAIT cells are abundant in humans, they are rare in mice, and increasing their abundance using expansion protocols with antigen and adjuvant has been shown to facilitate their study in mouse models of infection and disease. MethodsHere, we outline three methods to increase the abundance of MAIT cells in C57BL/6 mice using a combination of inflammatory stimuli, 5-A-RU and MG.ResultsOur data demonstrate that the administration of synthetic 5-A-RU in combination with one of three different inflammatory stimuli is sufficient to increase the frequency and absolute numbers of MAIT cells in C57BL/6 mice. The resultant boosted MAIT cells are functional and can provide protection against a lethal infection of Legionella longbeachae.ConclusionThese results provide alternative methods for expanding MAIT cells with high doses of commercially available 5-A-RU (± MG) in the presence of various danger signals.

Abstract With the rapid development of Internet‐of‐Things (IoT) and mobile devices, the IoT applications become more computation‐intensive and latency‐sensitive, which bring severe challenges to the resource‐limited devices. Mobile Edge Computing has served as a key promising method to enhance the network's computing capability by enabling resource‐constrained devices to offload tasks to the edge servers. A major challenge, which has been overlooked by most existing works on task offloading, is the dependencies among tasks and subtasks. In this paper, the subtask offloading with logical dependency for IoT applications is focused on. Specifically, subtask dependent graphs are employed to explore the dependency of subtasks and consider the priority of task scheduling. Further, an offloading scheme is put forward for minimizing both task latency and energy consumption of the device with dependency guarantees for all IoT tasks in multi‐server edge networks. Finaly, the simulation results demonstrate that the overall reduction rate is around 14% and relatively stable can effectively reduce task latency in multi‐server edge networks.

Brensocatib is a novel, oral, selective, reversible inhibitor of dipeptidyl peptidase 1 (DPP1), which activates several neutrophil serine proteases (NSPs), including neutrophil elastase (NE), proteinase 3 (PR3), and cathepsin G (CatG) in the bone marrow during the early stage of neutrophil maturation. These NSPs are associated with pathogen destruction and inflammatory mediation; their dysregulated activation can result in excess secretion of active NSPs causing damaging inflammation and contributing to neutrophil-mediated inflammatory and autoimmune diseases. Pharmacological inhibition of DPP1 in the bone marrow could therefore represent an attractive strategy for these neutrophil-driven diseases. A completed Phase 2 trial in non-cystic fibrosis bronchiectasis patients (ClinicalTrials.gov number NCT03218917; EudraCT number: 2017-002533-32) indeed demonstrated that administration of brensocatib attenuated the damaging effects of chronic inflammation by inhibiting the downstream activation of NSPs. To support a range of preclinical programs and further understand how rodent species and strains may affect brensocatib’s pharmacokinetic (PK) profile and its pharmacodynamic (PD) effects on NE, PR3, and CatG, an extensive naïve dosing study with brensocatib at different dosing levels, frequencies, and durations was undertaken. Dose-dependent PK exposure responses (AUC and Cmax) were observed regardless of the rodent species and strain. Overall, mice showed greater reduction in NSP activities compared to rats. Both mice and rats dosed once daily (QD) had equivalent NSP activity reduction compared to BID (twice a day) dosing when the QD dose was 1.5-times the BID daily dose. For both mouse strains, CatG activity was reduced the most, followed by NE, then PR3; whereas, for both rat strains, PR3 activity was reduced the most, followed by CatG, and then NE. Maximum reduction in NSP activities was observed after ∼7 days and recoveries were nearly symmetrical. These results may facilitate future in vivo brensocatib study dosing considerations, such as the timing of prophylactic or therapeutic administration, choice of species, dosage and dosing frequency.

BackgroundNeutrophil extracellular traps (NETs) have been shown to play a pivotal role in promoting metastasis and immune escape in hepatocellular carcinoma (HCC). Therefore, noninvasive tests to detect the formation of NETs in tumors can have significant implications for the treatment and prognoses of patients. Here, we sought to develop and validate a computed tomography (CT)-based radiomics model to predict the gene expression profiles that regulate the formation of NETs in HCC.MethodsThis study included 1133 HCC patients from five retrospective cohorts. Based on the mRNA expression levels of 69 biomarkers correlated with NET formation, a 6-gene score (NETs score, NETS) was constructed in cohort 1 from TCIA database (n=52) and validated in cohort 2 (n=232) from ICGC database and cohort 3 (n=365) from TCGA database. And then based on the radiomics features of CT images, a radiomics signature (RNETS) was developed in cohort 1 to predict NETS status (high- or low-NETS). We further employed two cohorts from Nanfang Hospital (Guangzhou, China) to evaluate the predictive power of RNETS in predicting prognosis in cohort 4 (n=347) and the responses to PD-1 inhibitor of HCC patients in cohort 5 (n=137).ResultsFor NETS, in cohort 1, the area under the curve (AUC) values predicting 1, 2, and 3-year overall survival (OS) were 0.836, 0.879, and 0.902, respectively. The low-NETS was associated with better survival and higher levels of immune cell infiltration. The RNETS yielded an AUC value of 0.853 in distinguishing between high-NETS or low-NETS and patients with low-RNETS were associated with significantly longer survival time in cohort 1 (P

Neutrophils have been implicated in initiating and perpetuating systemic lupus erythematosus and the resultant kidney damage in lupus nephritis (LN) patients, in part through an excessive release of neutrophil serine proteases (NSPs). NSP zymogens are activated by dipeptidyl peptidase 1 (DPP1) during neutrophil maturation and released by mature neutrophils in response to inflammatory stimuli. Thus, a potential strategy to attenuate disease progression in LN would be to inhibit DPP1. We tested whether brensocatib, a highly selective and reversible DPP1 inhibitor, could mitigate LN progression in an interferon-alpha (IFNα)-accelerated NZB/W F1 mouse model. To confirm brensocatib’s pharmacodynamic effect on NSPs in this mouse strain, repeated dose studies were conducted for 7 and 14 days in naïve NZB/W F1 mice via oral gavage twice a day. Brensocatib at 2 and 20 mg/kg/day achieved a significant reduction in bone marrow NSP activities after 7 days of daily administration. To initiate LN disease progression, the mice were injected with an IFNα-expressing adenovirus. After 2 weeks, three brensocatib doses (or vehicle) were administered for 6 more weeks. Throughout the 8-week study, brensocatib treatment (20 mg/kg/day) significantly reduced the occurrence of severe proteinuria compared to the vehicle control. Brensocatib treatment also entailed a significant reduction in the urine albumin-to-creatinine ratio, indicating decreased kidney damage, as well as a significant reduction in blood urea nitrogen level, suggesting improved renal function. Based on kidney histopathology analysis, brensocatib treatment significantly lowered both the renal tubular protein score and the nephropathy score compared to the vehicle group. A trend towards reduced glomerulonephritis score with brensocatib treatment was also observed. Lastly, brensocatib significantly reduced LN mouse kidney infiltration by various inflammatory cells. In conclusion, these results suggest that brensocatib alters disease progression in LN mice and warrant further evaluation of DPP1 inhibition in LN.

Theta oscillations in the primary visual cortex (VC) have been observed during running tasks, but the mechanism behind their generation is not well understood. Some studies have suggested that theta in the VC is locally generated, while others have proposed that it is volume conducted from the hippocampus. The present study aimed to investigate the relationship between hippocampal and VC LFP dynamics. Analysis of power spectral density revealed that LFP in the VC was similar to that in the hippocampus, but with lower overall magnitude. As running velocity increased, both the power and frequency of theta and its harmonics increased in the VC, similarly to what is observed in the hippocampus. Current source density analysis triggered to theta did not identify distinct current sources and sinks in the VC, supporting the idea that theta in the VC is conducted from the adjacent hippocampus. Phase coupling between theta, its harmonics, and gamma is a notable feature in the hippocampus, particularly in the lacunosum moleculare. While some evidence of coupling between theta and its harmonics in the VC was found, bicoherence estimates did not reveal significant phase coupling between theta and gamma. Similar results were seen in the cross-region bicoherence analysis, where theta showed strong coupling with its harmonics with increasing velocity. Thus, theta oscillations observed in the VC during running tasks are likely due to volume conduction from the hippocampus.

ABSTRACT Polymyxins are currently the last-resort antibiotics for the treatment of multidrug-resistant Gram-negative bacterial infections. To expand the understanding of the intrinsic resistance mechanism against polymyxins, a laboratory strain of Pseudomonas aeruginosa PAO1 was subjected to serial passage in the presence of sublethal doses of polymyxin B over a period of 30 days. By whole-genome sequencing of successively isolated polymyxin B-resistant isolates, we identified a frameshift mutation (L183fs) in the mvfR gene that further increased polymyxin resistance in the pmrB mutant background. A ΔmvfR mutation alone showed higher tolerance to polymyxin B due to altered lipopolysaccharide (LPS) on the surface of bacterial cells, which decreases its outer membrane permeability. In the ΔmvfR mutant, polymyxin B treatment caused the upregulation of rfaD, the gene involved in LPS core oligosaccharide synthesis, which is responsible for polymyxin tolerance. To the best of our knowledge, this is the first report of mvfR mutation conferring polymyxin resistance in P. aeruginosa via increased integrity of bacterial outer membrane. IMPORTANCE Antibiotic resistance imposes a considerable challenge for the treatment of P. aeruginosa infections. Polymyxins are the last-resort antibiotics for the treatment of multidrug-resistant P. aeruginosa infections. Understanding the development and mechanisms of bacterial resistance to polymyxins may provide clues for the development of new or improved therapeutic strategies effective against P. aeruginosa. In this study, using an in vitro evolution assay in combination with whole-genome sequencing, we demonstrated that MvfR controls tolerance to polymyxin B by regulating the rfaD gene in P. aeruginosa. Our results reveal a novel mechanism employed by P. aeruginosa in the defense against polymyxin antibiotics.

BackgroundLoneliness is an important problem afflicting the health of older adults, and has been proven to be associated with social capital. Previous research in China rarely investigated the differences of social capital and loneliness between older adults living in community dwellings and nursing homes. This study aims to examine the status of social capital and loneliness among older adults living in community dwellings and nursing homes, and analyze the relationship between them.MethodsA total of 1,278 older adults were recruited for the study from the cities of Hangzhou, Huzhou, and Lishui in Zhejiang Province of China from July to October 2021 by using multi-stage stratified random sampling. Questionnaires were used to collect data on the participants’ sociodemographic characteristics, social capital, and loneliness. Hierarchical multiple regression was used to examine the relationship between social capital and loneliness. The interaction of social capital and institutionalization on loneliness was also explored.ResultsCompared with community-dwelling older adults, institutionalized older adults had higher levels of loneliness and lower degrees of social support, social connection, trust, cohesion, and reciprocity. A further analysis of the social capital showed that low levels of social support, trust, and cohesion were related to high levels of loneliness among adults in both community dwellings and nursing homes. Social connection was negatively correlated with loneliness among older adults living in community dwellings. Institutionalization itself demonstrated a strong effect on loneliness.ConclusionHealth-related policies should help older adults gain more social support, trust and cohesion to alleviate their loneliness. This is particularly crucial for older adults living in nursing homes, as they have higher levels of loneliness and lower levels of social capital than noninstitutionalized older adults.

The dynamic recommender system realizes the real-time recommendation for users by learning the dynamic interest characteristics, which is especially suitable for the scenarios of rapid transfer of user interests, such as e-commerce and social media. The dynamic recommendation model mainly depends on the user-item history interaction sequence with timestamp, which contains historical records that reflect changes in the true interests of users and the popularity of items. Previous methods usually model interaction sequences to learn the dynamic embedding of users and items. However, these methods can not directly capture the excitation effects of different historical information on the evolution process of both sides of the interaction, i.e., the ability of events to influence the occurrence of another event. In this work, we propose a Dynamic Graph Hawkes Process based on Linear complexity Self-Attention (DGHP-LISA) for dynamic recommender systems, which is a new framework for modeling the dynamic relationship between users and items at the same time. Specifically, DGHP-LISA is built on dynamic graph and uses Hawkes process to capture the excitation effects between events. In addition, we propose a new self-attention with linear complexity to model the time correlation of different historical events and the dynamic correlation between different update mechanisms, which drives more accurate modeling of the evolution process of both sides of the interaction. Extensive experiments on three real-world datasets show that our model achieves consistent improvements over state-of-the-art baselines.

In the process of societal development, forest land categories often conflict with other land use types, leading to impacts on the ecological environment. Therefore, research on changes in forest land categories has increasingly become a globally focused topic. To anticipate potential forest ecological security issues under urbanization trends, studies on regional land use simulation become more important. This paper, based on land use data from the Ganjiang River basin, analyzes the distribution characteristics and changing trends of land use types from 2000 to 2020. Using the CA-Markov model, it predicts the land use pattern of the basin in 2040 and analyzes the transfer characteristics of forest land categories. The conclusions indicate that, between 2000 and 2020, the most significant trend in land use evolution was the transfer between various subcategories of forest land, especially frequent in the high-altitude mountainous areas in the southern and western parts of the basin. The land use pattern prediction model constructed in this paper has a kappa index of 0.92, indicating high accuracy and reliability of the predictions. In 2040, the most significant land evolution phenomenon would be from forest land to arable land to construction land, particularly pronounced around large cities. Over the next 20 years, the focus of land use evolution may shift from the southern part of the basin to the central and northern parts, with urban expansion possibly becoming the main driving force of land use changes during this period. Forest land restoration work is an effective method to compensate for the loss of forest land area in the Ganjiang River basin, with key areas for such work including Longnan, Yudu, Xingguo, Ningdu, Lianhua, and Yongxin counties.

Traditional villages, rich in historical and cultural value, hold a high level of preservation value. In the process of urbanization, traditional villages face the crisis of decline, making it difficult to perpetuate the carried cultural heritage. The Ganjiang River Basin hosts numerous traditional villages with rich research value, making the study of their preservation and development in this region a significant topic. This paper, from the perspective of landscape ecology, employs geographic detectors to analyze the driving factors behind the emergence of traditional villages in the Ganjiang River Basin, summarizing the spatial distribution characteristics of traditional villages. A classification method based on village landscape features is adopted to categorize traditional villages in the Ganjiang River Basin, providing a reference for planning the preservation and development of traditional villages. The research results show that plain areas are more suitable for the continuation of traditional villages; a single suitable environmental element cannot provide an environment conducive to the development of traditional villages, which is the result of the combined effect of multiple suitable elements; the study has divided traditional village landscapes into nine types, with clear distribution differences among different types of villages; for different regions and types of traditional villages, it is necessary to balance development and protection tendencies and plan differently according to environmental characteristics.

We applied the time-series clustering method to analyze the trajectory data of rummy-nose tetra (Hemigrammus rhodostomus), with a particular focus on their spontaneous paired turning behavior. Firstly, an automated U-turn maneuver identification method was proposed to extract turning behaviors from the open trajectory data of two fish swimming in an annular tank. We revealed two distinct ways of pairwise U-turn swimming, named dominated turn and non-dominated turn. Upon comparison, the dominated turn is smoother and more efficient, with a fixed leader–follower relationship, i.e., the leader dominates the turning process. Because these two distinct ways corresponded to different patterns of turning feature parameters over time, we incorporated the Toeplitz inverse covariance-based clustering (TICC) method to gain deeper insights into this process. Pairwise turning behavior was decomposed into some elemental state compositions. Specifically, we found that the main influencing factor for a spontaneous U-turn is collision avoidance with the wall. In dominated turn, when inter-individual distances were appropriate, fish adjusted their positions and movement directions to achieve turning. Conversely, in closely spaced non-dominated turn, various factors such as changes in distance, velocity, and movement direction resulted in more complex behaviors. The purpose of our study is to integrate common location-based analysis methods with time-series clustering methods to analyze biological behavioral data. The study provides valuable insights into the U-turn behavior, motion characteristics, and decision factors of rummy-nose tetra during pairwise swimming. Additionally, the study extends the analysis of fish interaction features through the application of time-series clustering methods, offering a fresh perspective for the analysis of biological collective data.

BackgroundIntracorporeal anastomosis (IA) is a difficult but popular anastomotic approach for reconstruction of digestive tract after laparoscopic right hemicolectomy, which may reduce some limitations faced during extracorporeal anastomosis (EA).MethodsA retrospective review of 78 patients who underwent laparoscopic right hemicolectomy by a veteran surgeon in a high-volume public tertiary hospital, including 50 patients with IA and 28 patients with EA. The intraoperative-related factors and short-term results of the two anastomotic approaches were compared.ResultsThere was no significant difference in demographics and clinical characteristics between the two groups (P>0.05). The intraoperative blood loss was less (P=0.010) and the incision length was shorter (P

Abstract The measurement of mobility and threshold voltage in thin‐film transistors (TFTs) in which the mobility is a function of gate voltage or carrier density is usually done inaccurately. Herein, accurate mobility calculations within the framework of the gradual channel approximation are described. Conventionally, the derivative of drain current with respect to gate voltage is often used to calculate mobilities in the linear region. This procedure often leads to errors when the mobility is not constant. Using a first‐order finite difference‐based calculations, it is shown how the correct field‐effect mobility can be extracted. The corrected mobility can be smaller than the conventionally calculated field‐effect mobility by up to a factor of 2. It is also shown that the corrected field‐effect mobility is identical to the average mobility. A threshold voltage that is independent of gate voltage value and suitable for disordered semiconductors is used for more accurate mobility calculations. The mobility and threshold voltage calculations are illustrated with experimental data from multiple TFTs with indium gallium zinc oxide, zinc tin oxide, and molybdenum disulfide channel layers.

COVID-19 pandemic continues to spread throughout the world with an urgent demand for a safe and protective vaccine to effectuate herd protection and control the spread of SARS-CoV-2. Here, we report the development of a bacterial vector COVID-19 vaccine (aPA-RBD) that carries the gene for the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Live-attenuated strains of Pseudomonas aeruginosa (aPA) were constructed which express the recombinant RBD and effectively deliver RBD protein into various antigen presenting cells through bacterial type 3 secretion system (T3SS) in vitro. In mice, two-dose of intranasal aPA-RBD vaccinations elicited the development of RBD-specific serum IgG and IgM. Importantly, the sera from the immunized mice were able to neutralize host cell infections by SARS-CoV-2 pseudovirus as well as the authentic virus variants potently. T-cell responses of immunized mice were assessed by enzyme-linked immunospot (ELISPOT) and intracellular cytokine staining (ICS) assays. aPA-RBD vaccinations can elicit RBD-specific CD4+and CD8+T cell responses. T3SS-based RBD intracellular delivery heightens the efficiency of antigen presentation and enables the aPA-RBD vaccine to elicit CD8+T cell response. Thus, aPA vector has the potential as an inexpensive, readily manufactured, and respiratory tract vaccination route vaccine platform for other pathogens

The video-based commonsense captioning task aims to add multiple commonsense descriptions to video captions to understand video content better. This paper aims to consider the importance of cross-modal mapping. We propose a combined framework called Class-dependent and Cross-modal Memory Network considering SENtimental features (CCMN-SEN) for Video-based Captioning to enhance commonsense caption generation. Firstly, we develop class-dependent memory for recording the alignment between video features and text. It only allows cross-modal interactions and generation on cross-modal matrices that share the same labels. Then, to understand the sentiments conveyed in the videos and generate accurate captions, we add sentiment features to facilitate commonsense caption generation. Experiment results demonstrate that our proposed CCMN-SEN significantly outperforms the state-of-the-art methods. These results have practical significance for understanding video content better.

2D iodine structures under high pressures are more attractive and valuable due to their special structures and excellent properties. Here, electronic transport properties of such 2D iodine structures are theoretically studied by considering the influence of the metal-element doping. In equilibrium, metal elements in Group 1 can enhance the conductance dramatically and show a better enhancement effect. Around the Fermi level, the transmission probability exceeds 1 and can be improved by the metal-element doping for all devices. In particular, the device density of states explains well the distinctions between transmission coefficients originating from different doping methods. Contrary to the “big” site doping, the “small” site doping changes transmission eigenstates greatly, with pronounced electronic states around doped atoms. In non-equilibrium, the conductance of all devices is almost weaker than the equilibrium conductance, decreasing at low voltages and fluctuating at high voltages with various amplitudes. Under biases, K-big doping shows the optimal enhancement effect, and Mg-small doping exhibits the most effective attenuation effect on conductance. Contrastingly, the currents of all devices increase with bias linearly. The metal-element doping can boost current at low biases and weaken current at high voltages. These findings contribute much to understanding the effects of defects on electronic properties and provide solid support for the application of new-type 2D iodine materials in controllable electronics and sensors.

GSK2606414 is a new, effective, highly selective PERK inhibitor with adenosine-triphosphate-competitive characteristics. It can inhibit endoplasmic reticulum stress and has the possibility of treating periodontitis. However, owing to its strong hydrophobicity and side effects, highly efficient pharmaceutical formulations are urgently needed to improve the bioavailability and therapeutic efficacy of GSK2606414 in the treatment of periodontitis. Herein, a novel local GSK2606414 delivery system was developed by synthesizing GSK2606414 nanoparticles (NanoGSK) and further loading NanoGSK into a collagenase-responsive hydrogel. The drug release results showed that the drug-loaded hydrogels had outstanding enzymatic responsive drug release profiles under the local microenvironment of periodontitis. Furthermore, in vitro studies showed that the drug-loaded hydrogel exhibited good cellular uptake and did not affect the growth and proliferation of normal cells, while the drug-loaded hydrogel significantly improved the osteogenic differentiation of inflammatory cells. In the evaluations of periodontal tissue repair, the drug-loaded hydrogels showed a great effect on inflammation inhibition, as well as alveolar bone regeneration. Therefore, this work introduces a promising strategy for the clinical treatment of periodontitis.

Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to extract high-level features of nodes. There are two main reasons for this: 1) Overlaying too many graph convolution layers will lead to the problem of over-smoothing. 2) Graph convolution is a kind of localized filter, which is easily affected by local properties. To solve the above problems, we first propose a novel general framework for graph neural networks called Non-local Message Passing (NLMP). Under this framework, very deep graph convolutional networks can be flexibly designed, and the over-smoothing phenomenon can be suppressed very effectively. Second, we propose a new spatial graph convolution layer to extract node multiscale high-level node features. Finally, we design an end-to-end Deep Graph Convolutional Neural Network II (DGCNNII) model for graph classification task, which is up to 32 layers deep. And the effectiveness of our proposed method is demonstrated by quantifying the graph smoothness of each layer and ablation studies. Experiments on benchmark graph classification datasets show that DGCNNII outperforms a large number of shallow graph neural network baseline methods.

ABSTRACT Importance Pediatric palliative care (PPC) is an interdisciplinary collaboration that focuses on the prevention and relief of patient suffering. PPC has emerged as a critical field of medical expertise and practice. However, no information is available regarding the progress of PPC in the Chinese mainland. Objective This study investigated the geographic distribution, team structure, and services of PPC teams in the Chinese mainland. It also investigated the level of understanding and implementation among pediatric oncologists regarding PPC. Methods The PPC subspecialty group of the Pediatrics Society of the Chinese Medical Association included 45 PPC teams. The team structure and services were investigated using questionnaires mailed to the team leader of each PPC team. In addition, we sent questionnaires regarding the level of PPC understanding and implementation of PPC practices to 170 pediatric oncologists in 11 hospitals. Results The geographical distribution of PPC teams is uneven in China. Most PPC teams are concentrated in the eastern provincial capital of China. Most PPC teams had limited staff and services. The level of PPC understanding was considerably limited across all demographics; most pediatric oncologists reported “some understanding” (n = 71, 41.8%) or “poor understanding” (n = 50, 29.4%). Only 62.9% of pediatric oncologists had experience providing advice to family members regarding PPC matters. Interpretation China is currently experiencing a critical shortage of PPC resources. Most pediatric oncologists had a limited understanding of PPC and reported limited practical implementation of PPC, which leads to underutilization of PPC resources.

Abstract This paper investigates the reliable computation offloading in non‐orthogonal multiple access enabled mobile edge computing networks, where the short‐packet technique is adopted to meet the stringent latency requirements of delay‐sensitive computing services. To characterize the reliability of computation offloading with finite blocklength coding, a novel analytical framework is developed to approximate the average overall block error probability (AOBEP) by using a double‐case linearization. With the derived approximate expression for AOBEP, the joint optimization of computation workload and transmit power is further investigated, in order to minimize the AOBEP under the computation/communication delay constraints. To tackle the non‐convexity of the formulated problem, the formulated problem is first decomposed into the computation workload problem and the transmit power allocation problem. For the computing workload problem, a closed‐form solution is derived. Then, by applying the derived workload allocation, the power allocation is transformed into the convex form through some approximations and solved by the successive convex approximation technique. Finally, simulation results are provided to demonstrate the reliability enhancement of computation offloading and reveal the relationship between the workload/power allocation and the communication/computation delay.

Summary: Hippocampal theta and gamma rhythms are hypothesized to play a role in the physiology of higher cognition. Prior research has reported that an offset in theta cycles between the entorhinal cortex, CA3, and CA1 regions promotes independence of population activity across the hippocampus. In line with this idea, it has recently been observed that CA1 pyramidal cells can establish and maintain coordinated place cell activity intrinsically, with minimal reliance on afferent input. Counter to these observations is the contemporary hypothesis that CA1 neuron activity is driven by a gamma oscillation arising from the medial entorhinal cortex (MEC) that relays information by providing precisely timed synchrony between MEC and CA1. Reinvestigating this in rats during appetitive track running, we found that theta is the dominant frequency of cross-frequency coupling between the MEC and hippocampus, with hippocampal gamma largely independent of entorhinal gamma.

Rehmannia glutinosa is a top-grade traditional Chinese medicine, and also is an important planting medicinal material for Chinese poor farmers shaking off poverty. Rehmannia mosaic virus (ReMV) causes big economic loss of R. glutinosa in planting area. However, there is no effective methods for quick, accurate, and high-throughput detection for ReMV in Chinese production area. The preserved R. glutinosa samples carrying ReMV was taken for research material. The coat protein coding sequences (CPReMV) was cloned and sequenced. The target sequence was further placed into a prokaryotic expression vector to express the N-terminal-tagged recombinant CPReMV protein (His-CPReMV). Purified His-CPReMV was used as an antigen to immunize New Zealand white rabbits, and antiserum was obtained. The titers and sensitivities of the antisera were analyzed and evaluated. Polyclonal antibodies were purified from the antiserum, and the titers and sensitivity to the target His-CPReMV protein were evaluated. The results demonstrate that the obtained polyclonal antibodies against His-CPReMV could be successfully used for rapid, accurate, and high-throughput detection of ReMV from R. glutinosa planted in the wild. Our investigation established serological-based detection methods for ReMV for the first time, and provides a foundation for future exploration of the pathogenic mechanisms of ReMV in R. glutinosa.

Idiopathic spontaneous intraperitoneal hemorrhage (ISIH) is a phenomenon caused by spontaneous rupture of intra-abdominal visceral vessels, and vascular malformations (VMs) leading to ISIH are rare in previously reported cases. VMs of the gastric wall, which are commonly located in the mucosa and submucosa, mostly lead to upper gastrointestinal bleeding rather than intraperitoneal hemorrhage. To our knowledge, this is the first report of ISIH caused by VMs in gastric muscularis. In the current case, a 22-year-old male patient presented with sudden abdominal pain for 4 h, accompanied by tachycardia and hypotension. CT revealed a hematoma in the omental bursa and fluids in abdominopelvic cavities. Then intraperitoneal hemorrhage was confirmed after abdominal paracentesis. Furthermore, ultrasonic gastroscopy indicated that vascular malformation in the muscularis of the stomach probably led to intraperitoneal hemorrhage. The patient recovered after conservative treatment based on fluid resuscitation and remained stable for 12 months of follow-up. This case suggests that VMs located in the gastrointestinal tract may lead to ISIH and ultrasonic gastroscopy is helpful in the diagnosis of VMs in the gastrointestinal tract.

A 12-week feeding trial was conducted to determine the effect of dietary arachidonic acid (ARA) levels on growth performance, feed utilization, muscle fatty acid composition and body proximate composition for juvenile Chinese mitten crab, Eriocheir sinensis. Six semi-purified diets containing 0.11% (control group, G1), 1.20% (G2), 2.37% (G3), 3.52% (G4), 4.76% (G5) and 7.39% (G6) ARA of total lipid content were formulated to feed six groups of crabs (the initial weight, 1.01 ± 0.12 g), respectively. Crabs in G3 showed the highest final weight, weight gain rate (WGR) and specific growth rate (SGR). The highest feed conversion ratio (FCR) was found in crabs in G1. In addition, the fatty acid composition of muscle was significantly affected by dietary ARA levels. For ARA, 22:2 and 20:5n-3, with the dietary ARA level increased, their contents increased first and then decreased, the highest values were all found in crabs of G4. High dietary ARA significantly affected the fatty acid composition of muscle in E. sinensis by increasing the relative expression of Srebp, while decreasing the relative expression of FAS, FAD 6 and ELOVL 6. Based on the results of the quadratic regression analysis of SGR and FCR, the optimum level of ARA for E. sinensis was suggested to be 4.54% and 3.88% of total lipid content, respectively.

Abstract This study investigates the physical‐layer security of a terrestrial communication system in the presence of potential unmanned aerial vehicle (UAV) eavesdroppers (UEDs). Due to the line‐of‐sight channels of ground‐to‐air links and the broadcast characteristics of wireless channels, the UEDs have a better chance to eavesdrop terrestrial communication systems compared with the conventional ground eavesdroppers. It indicates that the emerging of UEDs brings new challenges to secure transmissions in terrestrial communication systems. In this study, a UAV is recruited to wisely jam the UEDs by adopting a dynamic three‐dimensional (3D) trajectory, in order to protect the legitimate transmission. To maximally improve the average secrecy rate, the joint optimization of the 3D trajectory of the UAV jammer and legitimate user scheduling as a non‐convex mixed‐integer optimization problem is formulated. To efficiently solve such a challenging problem, a novel enhanced genetic algorithm (EGA), where a combined encoding method and a fitness‐based crossover method are developed to improve the convergence performance, is proposed. Simulation results demonstrate the superior performance of the proposed EGA in terms of the average secrecy rate.

Different material supply-related decisions intensively affect the efficiency of manufacturers. To obtain a suitable supply-related protocol, this study proposes a supply selection model which considers both manufacturers’ development orientation and material ordering. In contrast to traditional approaches that rely on expert opinions, the proposed approach in this study allows the time series analysis (ARIMA) to forecast the trend in manufacturers’ development during the execution of the plan. Based on the predicted trend, taking the minimum of total material management cost as the objective function, the control function and optimization conditions are constructed to select the appropriate protocol. The dynamic prediction protocol is obtained by considering the variation in production and material costs by an evolutionary algorithm. The model enables users to determine material supply protocol in continuous time and autonomously adapt to changes in the manufacturers’ production goals within a lower convergence time.

BackgroundHepatocellular carcinoma (HCC) is one of the leading malignant tumors worldwide. Prognosis and long-term survival of HCC remain unsatisfactory, even after radical resection, and many non-invasive predictors have been explored for post-operative patients. Most prognostic prediction models were based on preoperative clinical characteristics and pathological findings. This study aimed to investigate the prognostic value of a newly constructed nomogram, which incorporated post-operative aspartate aminotransferase to lymphocyte ratio index (ALRI).MethodsA total of 771 HCC patients underwent radical resection from three medical centers were enrolled and grouped into the training cohort (n = 416) and validation cohort (n = 355). Prognostic prediction potential of ALRI was assessed by receiver operating curve (ROC) analysis. The Cox regression model was used to identify independent prognostic factors. Nomograms for overall survival (OS) and disease-free survival (DFS) were constructed and further validated externally.ResultsThe ROC analysis ranked ALRI as the most effective prediction marker for resected HCC patients, with the cut-off value determined at 22.6. Higher ALRI level positively correlated with larger tumor size, higher tumor node metastasis (TNM) stage, and inversely with lower albumin level and shorter OS and DFS. Nomograms for OS and DFS were capable of discriminating HCC patients into different risk-groups.ConclusionsPost-operative ALRI was of prediction value for HCC prognosis. This novel nomogram may categorize HCC patients into different risk groups, and offer individualized surveillance reference for post-operative patients.

Mn-doped ZnS quantum dots (QDs) with excellent optical properties have been explored in a wide range of fields. Their potential adverse effects on biological systems and human health should be evaluated before biological application. In the present study, we investigated the effect of Mn-doped ZnS QDs on the intestinal tract and gut microbiota structures at 2 h and 14 days (d) after 14 d repeated oral exposure in mice. Flame atomic absorption spectrophotometry (FAAS), histopathological examination, and transmission electron microscopy (TEM) were used to assess the absorption and toxicity of Mn-doped ZnS QDs on the intestinal tract. The 16S rRNA gene sequencing was used to evaluate the gut microbial communities. Mn-doped ZnS QDs did not accumulate in the duodenum, jejunum, ileum, or colon. The Zn content of feces was not significantly higher than in the control group. No major histological changes were found in these tissues. The intestinal microvilli remained regular, but swelling of mitochondria and endoplasmic reticulum was detected by TEM at 14 d after the last gavage. A total of 2,712 operational taxonomic units (OTUs) were generated. Mn-doped ZnS QDs treatment did not significantly change the α-diversity of Richness, Chao1, Shannon, and Simpson indexes. According to principal component analysis (PCA), Mn-doped ZnS QDs had no effect on the overall structure of the gut microbiota. No significant change occurred at the phylum level, while three genera were downregulated at 2 h and seven changed at 14 d after the last gavage. Our findings revealed that Mn-doped ZnS QDs had a little stimulation of the intestinal tract and gut microbiota, and oral administration may be a safe route for biological application (such as bioimaging and drug delivery).

PurposeThis study aimed to identify preoperative gadoxetic acid-enhanced MRI features and establish a nomogram for predicting early recurrence (≤ 2 years) of hepatocellular carcinoma (HCC) after ablation therapy.MethodsA total of 160 patients who underwent gadoxetic acid-enhanced MRI and ablation HCC therapy from January 2015 to June 2018, were included retrospectively and divided into a training cohort (n = 112) and a validation cohort (n = 48). Independent clinical risk factors and gadoxetic acid-enhanced MRI features associated with early recurrence were identified by univariate and multivariate logistic regression analysis and used for construction of a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.ResultsAlpha-fetoprotein (AFP) level, tumor number, arterial peritumoral enhancement, satellite nodule and peritumoral hypointensity at hepatobiliary phases in the training cohort were identified as independent risk factors for early recurrence after ablation. A new nomogram that was constructed with these five features showed an area under the curve (AUC) of 0.843 (95%CI 0.771-0.916) and 0.835 (95%CI 0.713-0.956) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had good consistency and clinical utility.ConclusionsA new nomogram that was constructed using four preoperative gadoxetic acid-enhanced MRI features and serum AFP level can predict the risk of early HCC recurrence after ablation therapy with AUC up to 0.843. The strong performance of this nomogram may help hepatologists to categorize patients’ recurrent risk to guide selecting treatment options and improve postoperative management.

The hippocampal local field potential (LFP) exhibits a strong correlation with behavior. During rest, the theta rhythm is not prominent, but during active behavior, there are strong rhythms in the theta, theta harmonics, and gamma ranges. With increasing running velocity, theta, theta harmonics and gamma increase in power and in cross-frequency coupling, suggesting that neural entrainment is a direct consequence of the total excitatory input. While it is common to study the parametric range between the LFP and its complementing power spectra between deep rest and epochs of high running velocity, it is also possible to explore how the spectra degrades as the energy is completely quenched from the system. Specifically, it is unknown whether the 1/f slope is preserved as synaptic activity becomes diminished, as low frequencies are generated by large pools of neurons while higher frequencies comprise the activity of more local neuronal populations. To test this hypothesis, we examined rat LFPs recorded from the hippocampus and entorhinal cortex during barbiturate overdose euthanasia. Within the hippocampus, the initial stage entailed a quasi-stationary LFP state with a power-law feature in the power spectral density. In the second stage, there was a successive erosion of power from high- to low-frequencies in the second stage that continued until the only dominant remaining power was

The aim of this study was to apply machine learning methods to deeply explore the risk factors associated with adverse drug events (ADEs) and predict the occurrence of ADEs in Chinese pediatric inpatients. Data of 1,746 patients aged between 28 days and 18 years (mean age = 3.84 years) were included in the study from January 1, 2013, to December 31, 2015, in the Children’s Hospital of Chongqing Medical University. There were 247 cases of ADE occurrence, of which the most common drugs inducing ADEs were antibacterials. Seven algorithms, including eXtreme Gradient Boosting (XGBoost), CatBoost, AdaBoost, LightGBM, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and TPOT, were used to select the important risk factors, and GBDT was chosen to establish the prediction model with the best predicting abilities (precision = 44%, recall = 25%, F1 = 31.88%). The GBDT model has better performance than Global Trigger Tools (GTTs) for ADE prediction (precision 44 vs. 13.3%). In addition, multiple risk factors were identified via GBDT, such as the number of trigger true (TT) (+), number of doses, BMI, number of drugs, number of admission, height, length of hospital stay, weight, age, and number of diagnoses. The influencing directions of the risk factors on ADEs were displayed through Shapley Additive exPlanations (SHAP). This study provides a novel method to accurately predict adverse drug events in Chinese pediatric inpatients with the associated risk factors, which may be applicable in clinical practice in the future.

Industrial activities tend to deteriorate adjacent agricultural lands due to accumulation of potentially toxic elements in soils and crops. However, better understanding of their distinctive source partitions and transfer process remains insufficient in steel-making area. The paper focuses on the pollution levels, health risks, and provenance identification of Tl, As, Pb, Cu, Ni, Co, Sb, Cd, Zn, Be, Cr, Fe, Mn, Mo, Sn, and V in common vegetables from different farmlands near a steel-making plant. The results showed that the Tl, As, Pb, Cd, Cr, Cu and Mn were of high-level contamination in soils and generally above the maximum permissible level (MPL). Calculation using hazard quotients (HQ) exhibited that consumption of the studied vegetables may entail significant health risks to residents, especially for children, resulting from the elevated contents of Tl, As and associated toxic elements. Calculation by binary mixing model using Pb isotopic compositions suggested that steel-making activities contributed to 35–80% of the contamination of Pb and As in vegetables. It is necessary to adopt appropriate remediation measures to mitigate the farmland contamination and ensure the food safety of the agricultural products.