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Abstract Diabetic retinopathy (DR), a leading cause of blindness in diabetic patients, necessitates the precise segmentation of lesions for the effective grading of lesions. DR multi-lesion segmentation faces the main concerns as follows. On the one hand, retinal lesions vary in location, shape, and size. On the other hand, the currently available multi-lesion region segmentation models are insufficient in their extraction of minute features and are prone to overlooking microaneurysms. To solve the above problems, we propose a novel deep learning method: the Multi-Scale Spatial Attention Gate (MSAG) mechanism network. The model inputs images of varying scales in order to extract a range of semantic information. Our innovative Spatial Attention Gate merges low-level spatial details with high-level semantic content, assigning hierarchical attention weights for accurate segmentation. The incorporation of the modified spatial attention gate in the inference stage enhances precision by combining prediction scales hierarchically, thereby improving segmentation accuracy without increasing the associated training costs. We conduct the experiments on the public datasets IDRiD and DDR, and the experimental results show that the proposed method achieves better performance than other methods.

Objective: To develop a deep learning model to predict lymph node (LN) status in clinical stage IA lung adenocarcinoma patients. Methods: This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets (699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital) between January 2005 and December 2019. The Cancer Hospital dataset was randomly split into a training cohort (559 patients) and a validation cohort (140 patients) to train and tune a deep learning model based on a deep residual network (ResNet). The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model. Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography (HRCT) features for the model. The predictive performance was assessed by area under the curves (AUCs), accuracy, precision, recall, and F1 score. Subgroup analysis was performed to evaluate the potential bias of the study population. Results: A total of 1,009 patients were included in this study; 409 (40.5%) were male and 600 (59.5%) were female. The median age was 57.0 years (inter-quartile range, IQR: 50.0–64.0). The deep learning model achieved AUCs of 0.906 (95% CI: 0.873–0.938) and 0.893 (95% CI: 0.857–0.930) for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule (non-pGGN) testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.622). The precisions of this model for predicting pN0 disease were 0.979 (95% CI: 0.963–0.995) and 0.983 (95% CI: 0.967–0.998) in the testing cohort and the non-pGGN testing cohort, respectively. The deep learning model achieved AUCs of 0.848 (95% CI: 0.798–0.898) and 0.831 (95% CI: 0.776–0.887) for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.657). The recalls of this model for predicting pN2 disease were 0.903 (95% CI: 0.870–0.936) and 0.931 (95% CI: 0.901–0.961) in the testing cohort and the non-pGGN testing cohort, respectively. Conclusions: The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.

Abstract Smartphone-based biometric authentication has been widely used in various applications. Among several biometric characteristics, fingerphoto biometrics captured from smartphones are gaining popularity owing to their usability, scalability across different smartphones, and reliable verification. However, fingerphoto verification systems are vulnerable to both direct and indirect attacks. In this work, we propose a novel method to generate morphing attacks on fingerphoto biometrics captured using smartphones. We introduce three different image-level fingerphoto morphing attack generation algorithms that can generate high-quality fingerphoto morphing images with minimum distortions. Extensive experiments were conducted on two datasets captured using different smartphones under various environmental conditions. The results demonstrate that the proposed morphing algorithms are highly vulnerable to commercial off-the-shelf and block-directional fingerprint verification systems. To effectively detect morphing attacks on fingerphoto biometrics, we propose the use of fingerphoto morphing attack detection algorithms that utilize both handcrafted and deep features. However, our detection results showed a high error rate in accurately detecting these types of attacks.

Abstract Residential homes and light commercial buildings usually require substantial heat and electricity simultaneously. A combined heat and power system enables more efficient and environmentally friendly energy usage than that achieved when heat and electricity are produced in separate processes. However, due to financial and space constraints, residential and light commercial buildings often limit the use of traditional large-scale industrial equipment. Here we develop a micro–combined heat and power system powered by an opposed-piston engine to simultaneously generate electricity and provide heat to residential homes or light commercial buildings. The developed prototype attains the maximum AC electrical efficiency of 35.2%. The electrical efficiency breaks the typical upper boundary of 30% for micro–combined heat and power systems using small internal combustion engines (i.e.,

Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology. Modern medical care and imaging technology are becoming increasingly inseparable. However, the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (from signal to image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we develop an AI-based signal-to-knowledge diagnostic scheme for lung nodule classification directly from the computed tomography (CT) raw data (the signal). We find that the raw data achieves almost comparable performance with CT, indicating that it is possible to diagnose diseases without reconstructing images. Moreover, the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts (with a gain ranging from 0.01 to 0.12), demonstrating that raw data contains diagnostic information that CT does not possess. Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

Abstract Background Dietary and gastrointestinal (GI) problems have been frequently reported in autism spectrum disorder (ASD). However, the relative contributions of autism-linked traits to dietary and GI problems in children with ASD are poorly understood. This study firstly compared the dietary intake and GI symptoms between children with ASD and typically developing children (TDC), and then quantified the relative contributions of autism-linked traits to dietary intake, and relative contributions of autism-linked traits and dietary intake to GI symptoms within the ASD group. Methods A sample of 121 children with ASD and 121 age-matched TDC were eligible for this study. The dietary intake indicators included food groups intakes, food variety, and diet quality. The autism-linked traits included ASD symptom severity, restricted repetitive behaviors (RRBs), sensory profiles, mealtime behaviors, and their subtypes. Linear mixed-effects models and mixed-effects logistic regression models were used to estimate the relative contributions. Results Children with ASD had poorer diets with fewer vegetables/fruits, less variety of food, a higher degree of inadequate/unbalanced dietary intake, and more severe constipation/total GI symptoms than age-matched TDC. Within the ASD group, compulsive behavior (a subtype of RRBs) and taste/smell sensitivity were the only traits associated with lower vegetables and fruit consumption, respectively. Self-injurious behavior (a subtype of RRBs) was the only contributing trait to less variety of food. Limited variety (a subtype of mealtime behavior problems) and ASD symptom severity were the primary and secondary contributors to inadequate dietary intake, respectively. ASD symptom severity and limited variety were the primary and secondary contributors to unbalanced dietary intake, respectively. Notably, unbalanced dietary intake was a significant independent factor associated with constipation/total GI symptoms, and autism-linked traits manifested no contributions. Conclusions ASD symptom severity and unbalanced diets were the most important contributors to unbalanced dietary intake and GI symptoms, respectively. Our findings highlight that ASD symptom severity and unbalanced diets could provide the largest benefits for the dietary and GI problems of ASD if they were targeted for early detection and optimal treatment.

In recent years, the increasing global demand for mushrooms has made the enhancement of mushroom yield a focal point of research. Currently, the primary methods for developing high-yield mushroom varieties include mutation- and hybridization-based breeding. However, due to the long breeding cycles and low predictability associated with these approaches, they no longer meet the demands for high-yield and high-quality varieties in the expansive mushroom market. Modern molecular biology technologies such as RNA interference (RNAi) and gene editing, including via CRISPR-Cas9, can be used to precisely modify target genes, providing a new solution for mushroom breeding. The high-yield genes of mushrooms can be divided into four categories based on existing research results: the genes controlling mycelial growth are very suitable for genetic modification; the genes controlling primordium formation are directly or indirectly regulated by the genes controlling mycelial growth; the genes controlling button germination are more difficult to modify; and the genes controlling fruiting body development can be regulated during the mycelial stage. This article reviews the current research status for the four major categories of high-yield-related genes across the different stages of mushroom growth stages, providing a foundation and scientific basis for using molecular biology to improve mushroom yield and promote the economic development of the global edible-mushroom industry.

Traditional crank-based engines face limitations due to mechanical, thermal, and combustion inefficiencies. In contrast, the opposed piston design of a linear engine generator mitigates frictional losses by eliminating rotational motion and crankshaft linkages. Instead, electrical power is generated through the oscillation of a translator within a linear stator. However, the lack of geometric constraints in the free piston design leads to uncertainties regarding dead center positions, resulting in challenges such as misfire, stall, over-fueling, and rapid load changes.This study aims to fill this gap by reviewing existing modeling literature and advancing the fundamental understanding of opposed piston linear engines (OPLE) with springs through the development of an idealized, nondimensional model. A combination of analytical and numerical models was developed to better understand the behavior of the OPLE. The model introduces a streamlined symmetric analytical solution, succeeded by a broader general analytical resolution, with careful attention to the significant influence of thermodynamic effects at each phase, offering insights into the engine's dynamic performance. Limitations inherent to the analytical solutions are addressed by a numerical scheme utilizing the Runge-Kutta technique. This approach guarantees swift and dependable computational outcomes, which captures cyclic variation. The alternator model amalgamates both linear and sinusoidal work profiles, adjusting the magnetic work output in response to fluctuations in the compression ratio relative to the Top Dead Center (TDC) clearance distance. This adjustment dictates the generator load for each translator.The proposed model integrates the dynamics of a damped spring-mass system with thermodynamic expressions representing in-cylinder processes. Simplifications are made assuming perfect springs, ideal gases, instantaneous heat transfer, and an average friction force dependent on stroke length for work output. Selected cases are analyzed to elucidate the system's fundamental behavior, both within and outside the context of the Otto cycle, showcasing natural and forced stability over multiple operation cycles. Notably, enhancing the baseline cylinder pressure, or input heat relative to spring stiffness can elevate the work-to-heat ratio values. The attained work-to-heat ratio within compression ratio window of 16 is approximately 28 %. While this percentage may fluctuate in the context of general FPLE designs, the model developed for OPLE in this work plays a crucial role in maintaining the engine within this narrowly defined efficiency range, without compromising the potential performance demonstrated in this study. Differences in compression ratios provided intriguing insights into the dynamics of the translator and the in-cylinder thermodynamics of the interconnected system. Lower TDC clearance resulted in high translator displacement, velocity, and enhanced thermal efficiency.

Abstract Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (v e ), volume fraction of plasma space (v p ), volume transfer constant (K trans ), and reverse reflux rate constant (k ep ) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%–52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

Abstract In the past ten years, new progress has been made in the study of Chinese overseas, which provides important scientific basis for the work of Chinese overseas and the formulation of relevant policies. Through data mining and analysis of overseas Chinese literature from 2008 to 2021, this work studied the co-occurrence relationships among keywords, authors, research institutions, publication sources of target articles, references and citations. The results suggest a few things. In terms of keyword analysis, there is a high probability that keywords coexist among overseas Chinese, Chinese society and Chinese history. In addition, the degree of cooperation between authors and research institutions in overseas research in China is not high. In terms of target journals, references and citations, resources such as Journal of Overseas Chinese Historical Studies, Overseas Chinese Publishing Company and People’s Publishing House are the main publications published by most overseas Chinese research institutions. In addition, academic articles cite fewer of the journal’s journals. Based on the above problems, this study puts forward relevant recommendations for decision-making, in order to further promote the exchange, sharing and development of China’s overseas research results.

Abstract Background Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology. Methods The subjects' demographic data, baseline examination results, and annual follow‐up low‐dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model. Results A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%–0.46%, which was lower than the baseline level except the follow‐up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re‐examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67–0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively. Conclusions It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow‐up.

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic in China is ongoing. Some studies have shown that the incidence of respiratory and intestinal infectious diseases in 2020 decreased significantly compared with previous years. Interrupted time series (ITS) is a time series analysis method that evaluates the impact of intervention measures on outcomes and can control the original regression trend of outcomes before and after the intervention. This study aimed to analyse the impact of COVID-19 on the incidence rate of notifiable communicable diseases using ITS in China. Methods National data on the incidence rate of communicable diseases in 2009–2021 were obtained from the National Health Commission website. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models was used to analyse the changes in the incidence rate of infectious diseases before and after the COVID-19 epidemic. Results There was a significant short-term decline in the incidence rates of respiratory infectious diseases and enteric infectious diseases (step values of -29.828 and − 8.237, respectively), which remained at a low level for a long time after the decline. There was a short-term decline in the incidence rates of blood-borne and sexually transmitted infectious diseases (step = -3.638), which tended to recover to previous levels in the long term (ramp = 0.172). There was no significant change in the incidence rate of natural focus diseases or arboviral diseases before and after the epidemic. Conclusion The COVID-19 epidemic had strong short-term and long-term effects on respiratory and intestinal infectious diseases and short-term control effects on blood-borne and sexually transmitted infectious diseases. Our methods for the prevention and control of COVID-19 can be used for the prevention and control of other notifiable communicable diseases, especially respiratory and intestinal infectious diseases.

Abstract Background Plants accomplish multiple functions by the interrelationships between functional traits. Clarifying the complex relationships between plant traits would enable us to better understand how plants employ different strategies to adapt to the environment. Although increasing attention is being paid to plant traits, few studies focused on the adaptation to aridity through the relationship among multiple traits. We established plant trait networks (PTNs) to explore the interdependence of sixteen plant traits across drylands. Results Our results revealed significant differences in PTNs among different plant life-forms and different levels of aridity. Trait relationships for woody plants were weaker, but were more modularized than for herbs. Woody plants were more connected in economic traits, whereas herbs were more connected in structural traits to reduce damage caused by drought. Furthermore, the correlations between traits were tighter with higher edge density in semi-arid than in arid regions, suggesting that resource sharing and trait coordination are more advantageous under low drought conditions. Importantly, our results demonstrated that stem phosphorus concentration (SPC) was a hub trait correlated with other traits across drylands. Conclusions The results demonstrate that plants exhibited adaptations to the arid environment by adjusting trait modules through alternative strategies. PTNs provide a new insight into understanding the adaptation strategies of plants to drought stress based on the interdependence among plant functional traits.

Decarbonizing fossil-fuel usage is crucial in mitigating the impacts of climate change. The burning of fossil fuels in boilers during industrial process heating is one of the major sources of CO2 in the industry. Electrification is a promising solution for decarbonizing these boilers, as it enables renewable energy sources to generate electricity, which can then be used to power the electric boilers. This research develops a user-driven simulation model with realistic data and potential temperature data for a location to estimate boilers’ current energy and fuel usage and determine the equivalent electrical boiler capacity and energy usage. A simulation model is developed using the Visual Basic Application (VBA)® and takes factors such as current boiler capacity, steam temperature and pressure, condensate, makeup water, blowdown, surface area, and flue gas information as input. Random numbers generate the hourly temperature variation for a year for discrete-event Monte Carlo Simulation. The simulation generates the hourly firing factor, energy usage, fuel usage, and CO2 emissions of boilers for a whole year, and the result compares fossil-fuel and electrical boilers. The simulated data are validated using real system data, and sensitivity analysis of the model is performed by varying the input data.

Promoting rice straw in situ return is an important strategy for improving soil quality. From 2018 to 2021, we investigated the effects of rice straw return with microbial agents and film covering technology on soil physical and chemical properties at different layer depths, as well as the soil microbial community structure, in Hunan, southern China. This study was designed to evaluate the effects of microbial agents (T1), film mulch covering (T2), and the application of microbial agents combined with film mulch (T3) on the soil physicochemical properties and microbial community after rice straw in situ return. The results show that, after three years of continuous treatment, T3 significantly increased the soil temperature by 17.76–22.97%, T2 significantly increased the water content by 34.27–46.23%, and T1 and T3 significantly increased the soil pH. The addition of microbial agents combined with film mulch resulted in a notable increase in both the number of OTUs and the Chao1 index of soil microorganisms. Additionally, the model of promoting rice straw in situ return (the application of a microbial agent combined with film mulch) was shown to promote the growth of beneficial soil microorganisms. RDA was used for the investigation, and the findings showed that soil microorganisms were significantly influenced by the TOC content, pH, and water content. These findings provide evidence of an effective method for accelerating the decomposition of late rice straw and guiding soil improvement in tobacco–rice rotation regions.

Straw incorporation into soil contributes significantly to the sustainable development of agriculture. To investigate the impact of tobacco straw returns on a tobacco–rice replanting system, we designed an experiment with two straw return levels and a control group: T1 (full return), T2 (root return), and CK (no straw return). Over a three-year field experiment in rice fields in South China, we assessed the effects of tobacco straw return on soil quality, microbial diversity, dry matter accumulation, and yield composition of rice. The results demonstrated that returning tobacco straw to the field effectively enhanced rice yield by positively influencing various soil physical, chemical, and biological properties. Compared to those in the CK treatment, as the soil porosity increased from 9.0% to 12.4%, the mean weight diameter of the soil aggregates substantially increased, ranging from 28.7% to 45.2%. There were significant increases in soil organic matter, total nitrogen, and alkaline dissolved nitrogen. Soil sucrase activity increased between 29.8% and 44.9%, and urease activity increased between 4.3% and 62.2% over the three consecutive years of straw return. The diversity index of soil fungi significantly increased. Additionally, rice yield increased markedly, ranging from 1.8% to 5.1%. Overall, the enhancement effect of T1 surpassed that of T2. According to our comprehensive analysis, the incorporation of tobacco straw into the field was found to enhance the physical properties of the soil, elevate soil enzyme activity, and increase the abundance of soil microorganisms. Consequently, this practice led to improved rice yield and a reduction in agricultural waste output. Overall, the return of tobacco straw to the field represents a clean and dependable approach in rice-cultivated tobacco areas to improve soil health and rice productivity.

ABSTRACTOur previous work revealed that unbalanced dietary intake was an important independent factor associated with constipation and gastrointestinal (GI) symptoms in children with autism spectrum disorder (ASD). Growing evidence has shown the alterations in the gut microbiota and gut microbiota-derived metabolites in ASD. However, how the altered microbiota might affect the associations between unbalanced diets and GI symptoms in ASD remains unknown. We analyzed microbiome and metabolomics data in 90 ASD and 90 typically developing (TD) children based on 16S rRNA and untargeted metabolomics, together with dietary intake and GI symptoms assessment. We found that there existed 11 altered gut microbiota (FDR-corrected P-value

Background: Salmonella, a widespread pathogen, poses a significant threat to global food safety, leading to foodborne diseases and substantial economic losses. The timely and accurate detection of foodborne pathogens is pivotal for averting food contamination and outbreaks across the food production chain. This study assesses the cost-effectiveness of traditional culture-based methods versus risk-based approaches, incorporating polymerase chain reaction (PCR) for Salmonella detection. Methods: We employed a stochastic scenario tree model to simulate scenarios based on the sampling inspection plan for raw aquatic products conducted by the Guangzhou Center for Disease Control and Prevention from 2018 to 2020. Various detection methods (culture or PCR) were applied to these aquatic products based on their categorized risk level. Sensitivity values were derived from published data, and incremental cost-effectiveness ratios were used to compare the different scenarios against the traditional culture method. Results: A total of 360 samples were collected for analysis. The cost of culture-based detection alone amounted to 125,423.20 Chinese Yuan (CNY) and yielded nine instances of positive Salmonella detections. The risk-based detection strategy, which combined the more sensitive PCR method with high-risk sample characteristics, while employing the culture method for the remaining combinations, imposed a total cost of 128,775.83 CNY and yielded ten positive detections. This approach cost approximately 3391.74 CNY per additional positive sample detected compared to the culture method alone. Meanwhile, PCR-only detection imposed a total cost of 62,960.03 CNY. Conclusions: In comparison to traditional culture-based methods, both the risk-based detection strategy and the PCR-only approach demonstrated superior capabilities with respect to detecting contaminated aquatic products. Implementing risk-based detection strategies can enhance cost-effectiveness, not only ensuring food safety but also reducing the incidence and economic burden of foodborne diseases.

The subarray-level multibeam structure is advantageous for minimizing system complexity and enhancing multi-target detection and estimation performance. Furthermore, the subarray configuration plays a major role in determining the multibeam antenna pattern parameters. Therefore, we propose a subarray partition technique based on the tournament algorithm to enable spatial multibeam synthesis. In this paper, subarray configuration and excitation are alternately optimized through multiple rounds of tournament iterations. Each iteration calculates subarray excitations using convex optimization methods to fulfill requirements for the main radiation direction and low sidelobes, among other performance criteria. At the same time, excitations for each unassigned array element are calculated within each subarray separately. Simulation results for linear arrays demonstrate the proposed algorithm is not only suitable for linear arrays but also applicable to other array configurations, such as circular ring arrays and cylindrical arrays. And the proposed algorithm is effective in subarray periodicity, suppressing sidelobe levels, and allocating resources based on priority.

BackgroundIron deficiency (ID) and iron deficiency anemia (IDA) during pregnancy are highly prevalent worldwide. Hepcidin is considered an important biomarker of iron status. Currently, few longitudinal cohort studies have assessed the potential causal relationship between hepcidin and ID/IDA. Therefore, we aimed to investigate the association of first-trimester maternal serum hepcidin with third-trimester ID/IDA risk in a prospective cohort.MethodsTotal of 353 non-ID/IDA pregnant women at 11–13 weeks’ gestation were enrolled in Southern China and followed up to 38 weeks of gestation. Data on demography and anthropometry were obtained from a structured questionnaire at enrollment. Iron biomarkers including hepcidin were measured at enrollment and follow-up. Regression models were used to evaluate the association of first-trimester hepcidin with third-trimester ID/IDA risk.ResultsSerum hepcidin levels substantially decreased from 19.39 ng/mL in the first trimester to 1.32 ng/mL in the third trimester. Incidences of third-trimester ID and IDA were 46.2 and 11.4%, respectively. Moreover, moderate and high levels of first-trimester hepcidin were positively related to third-trimester hepcidin (log-transformed β = 0.51; 95% CI = 0.01, 1.00 and log-transformed β = 0.66; 95% CI = 0.15, 1.17). Importantly, elevated first-trimester hepcidin was significantly associated with reduced risk of third-trimester IDA (OR = 0.38; 95% CI = 0.15, 0.99), but not with ID after adjustment with potential confounders.ConclusionFirst-trimester hepcidin was negatively associated with IDA risk in late pregnancy, indicating higher first-trimester hepcidin level may predict reduced risk for developing IDA. Nonetheless, given the limited sample size, larger studies are still needed.

Pseudomonas aeruginosa is known to generate bacterial biofilms that increase antibiotic resistance. With the increase of multi-drug resistance in recent years, the formulation of a new therapeutic strategy has seemed urgent. Preliminary findings show that Prodigiosin (PG), derived from chromium-resistant Serratia marcescens, exhibited efficient anti-biofilm activity against Staphylococcus aureus. However, its anti-biofilm activity against P. aeruginosa remains largely unexplored. The anti-biofilm activity of PG against three clinical single drug-resistant P. aeruginosa was evaluated using crystal violet staining, and the viability of biofilms and planktonic cells were also assessed. A model of chronic lung infection was constructed to test the in vivo antibiofilm activity of PG. The results showed that PG inhibited biofilm formation and effectively inhibited the production of pyocyanin and extracellular polysaccharides in vitro, as well as moderated the expression of interleukins (IL-1β, IL-6, IL-10) and tumor necrosis factor (TNF-α) in vivo, which might be attributed to the downregulation of biofilm-related genes such as algA, pelA, and pslM. These findings suggest that PG could be a potential treatment for drug-resistant P aeruginosa and chronic biofilm infections.

Based on authorized patents of China’s artificial intelligence industry from 2013 to 2022, this paper constructs an Industry–University–Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm’s innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers.

Compared with the traditional antenna array, the time-modulated array can increase the flexibility of the array by adding time switches. However, due to the introduction of time switches, the time-modulated array antenna is more sensitive to random errors than the traditional array. Besides random errors caused by unavoidable factors such as array deformation and defects of processing and assembly in the traditional array, time-modulation parameter errors will be introduced by time switches. As errors in the traditional array can degrade the performance of the array, time-modulation parameter errors can lower the performance of the array pattern as well, which will lift the side-lobe and increase the dynamic range of the side-lobe. Aiming at the time-modulation parameter errors in the time-modulated array, an anti-error robust pattern synthesis algorithm (AERPS) based on the convex optimization (CVX) is proposed in this paper. In the algorithm, the optimization model for the pattern synthesis of the time-modulated array is established and analyzed. Then the model is divided into two sub-models of the center frequency and the first sideband, and the convex optimization solution is first performed on the center frequency model. After the center frequency model is solved, the convex optimization solution is used to solve the first-order sideband model to obtain the final results. The simulation results of the time-modulated array in this paper show that the algorithm can reduce the average value and the dynamic range of the normalized side-lobe level, thus verifying the effectiveness and the robustness of the algorithm.

BackgroundPinus yunnanensis is a major silvicultural species in Southwest China. Currently, large areas of twisted-trunk Pinus yunnanensis stands severely restrict its productivity. Different categories of rhizosphere microbes evolve alongside plants and environments and play an important role in the growth and ecological fitness of their host plant. However, the diversity and structure of the rhizosphere microbial communities between P. yunnanensis with two different trunk types—straight and twisted—remain unclear.MethodsWe collected the rhizosphere soil of 5 trees with the straight and 5 trees with the twisted trunk type in each of three sites in Yunnan province. We assessed and compared the diversity and structure of the rhizosphere microbial communities between P. yunnanensis with two different trunk types by Illumina sequencing of 16S rRNA genes and internal transcribed spacer (ITS) regions.ResultsThe available phosphorus in soil differed significantly between P. yunnanensis with straight and twisted trunks. Available potassium had a significant effect on fungi. Chloroflexi dominated the rhizosphere soils of the straight trunk type, while Proteobacteria was predominant in the rhizosphere soils of the twisted trunk type. Trunk types significantly explained 6.79% of the variance in bacterial communities.ConclusionThis study revealed the composition and diversity of bacterial and fungal groups in the rhizosphere soil of P. yunnanensis with straight and twisted trunk types, providing proper microbial information for different plant phenotypes.

IntroductionThe risk of fragility fractures is increased in patients with type 2 diabetes mellitus (T2DM). Many reports indicate that inflammatory and immune responses are associated with osteoporosis and osteopenia. The monocyte-to-lymphocyte ratio (MLR) is a novel potential marker of inflammatory and immune responses. The present study evaluated the associations between the MLR and osteoporosis in postmenopausal females with T2DM.MethodsData were obtained from 281 T2MD postmenopausal females, and divided into three groups: Osteoporosis group, osteopenia group and normal BMD group. ResultData analyses revealed that the MLR was significantly lower in T2MD postmenopausal females with osteoporosis than in those with osteopenia and normal BMD. Logistic regression showed that the MLR was an independent protective factor for osteoporosis in postmenopausal females with T2DM (odds ratio [OR]: 0.015, 95% confidence interval [CI]: 0.000–0.772). Based on the receiver operating characteristic (ROC) curve, the MLR for diagnosing osteoporosis in postmenopausal females with T2DM was projected to be 0.1019, an area under the curve of 0.761 (95% CI: 0.685–0.838), a sensitivity of 74.8% and a specificity of 25.9%. ConclusionsThe MLR have a high efficacy in diagnosis for osteoporosis in postmenopausal females with T2DM. MLR have the potential to be used as diagnosis marker for osteoporosis in postmenopausal females with T2DM.

Abstract A numerical modeling method is proposed for the melting process of Titanium metals of Titanium alloys powder preparation used for 3D printing. The melting process simulation, which involves the tight coupling between electromagnetic field, thermal field and fluid flow as well as deformation associated during the melting process, is conducted by adopting the finite element method. A two-way coupling strategy is used to include the interactions between these fields by incorporating the material properties dependent on temperature and the coupling terms. In addition, heat radiation and phase change are also considered in this paper. The arbitrary Lagrangian–Eulerian formulation is exploited to model the deformation of Titanium metal during the melting process. The distribution of electromagnetic flux density, eddy current density, temperature, and fluid flow velocity at different time can be determined by utilizing this numerical method. In a word, the method proposed in this paper provides a general way to predict the melting process of electrode induction melting gas atomization (EIGA) dynamically, and it also could be used as a reference for the design and optimization of EIGA.

IntroductionHow enterprises should practice digitalization transformation to effectively improve green innovation performance is related to the sustainable development of enterprises and the economy, which is an important issue that needs to be clarified.MethodsThis research uses the perspective of production and operation to deconstruct the digitalization of industrial listed enterprises from 2016 to 2020 into six features. A variety of machine learning methods are used, including DBSCAN, CART and other algorithms, to specifically explore the complex impact of enterprise digitalization feature configuration on green innovation performance.Conclusions(1) The more advanced digitalization transformation the enterprises have, the more possibly the high green innovation performance can be achieved. (2) Digitalization innovation is the digitalization element with the strongest influence ability on green innovation performance. (3) As the advancement of digitalization transformation, enterprises should also focus on digitalization innovation input and digitalization operation output, otherwise they should pay attention to digitalization management and digitalization operation output.DiscussionThe conclusions of this research will help enterprises understand their digitalization competitiveness and how to practice digitalization transformation to enhance green innovation performance, and also help the government to formulate policies to promote the development of green innovation in the digital economy era.

BackgroundThe evidence for associations of emotional/behavioral status with sedentary behavior (SB), physical activity (PA) and step counts is scarce in children with autism spectrum disorder (ASD). Also, ASD-related deficiencies may affect actual levels of PA. We aimed to describe accelerometer-measured SB, PA and step counts in children with ASD, and to examine the associations of emotional/behavioral problems with SB, PA and step counts after assessing associations between accelerometer-measured SB, PA and step counts and ASD-related deficiencies.MethodsA total of 93 ASD children, aged 6–9 years, were recruited from the Center for Child and Adolescent Psychology and Behavioral Development of Sun Yat-sen University in Guangzhou, China. Participants wore an accelerometer for seven consecutive days. Of the original 93, 78 participants' accelerometer-measured valid PA were obtained, and the data were shown as time spent in SB, light, moderate, moderate-to-vigorous and vigorous PA, and step counts. Participants' emotional/behavioral problems were assessed via the Strengths and Difficulties Questionnaire (SDQ), and anxiety symptoms were evaluated by the Screen for Child Anxiety Related Emotional Disorders (SCARED). ASD-associated deficiencies include restricted repetitive behaviors (Repetitive Behavior Scale-Revised), poor social competence (Social Responsiveness Scale Second Edition) and motor development restrictions (Developmental Coordination Disorder Questionnaire).ResultsOf the 78 participants, daily vigorous PA (VPA) and moderate-to-vigorous PA (MVPA) averaged 15.62 and 51.95 min, respectively. After adjustment for covariates, SDQ emotional symptoms (β = −0.060, p = 0.028) were inversely associated with the average daily minutes in VPA. Meanwhile, SDQ emotional symptoms (β = −0.033, p = 0.016) were inversely associated with the average daily MVPA minutes in the crude model. After adjustment for covariates, SCARED somatic/panic (β = −0.007, p = 0.040) and generalized anxiety (β = −0.025, p = 0.014) were negatively associated with the average daily VPA minutes; SCARED total anxiety (β = −0.006, p = 0.029) was conversely associated with daily MVPA duration. After adjustment for covariates, no significant associations between accelerometer-measured SB, PA and step counts and ASD-related deficiencies were found (p > 0.05).ConclusionsAccelerometer-measured SB, PA and step counts showed no associations with ASD-related deficiencies. On this basis, we further found that the emotional symptoms were inversely associated with VPA and MVPA. These results emphasize the importance of VPA and MVPA in children with ASD. The longitudinally investigations on the directionality of these associations between emotional symptoms with VPA and MVPA are needed in the future.

China's increasingly aging population is resulting in an imbalance between supply and demand for elderly care resources. The theory of “combined medical and elderly care” (CMEC) has introduced a new perspective in the conception of China's elderly care problems. This study employed the service blueprint, fuzzy failure mode and effects analysis (Fuzzy-FMEA), and the theory of inventive problem solving (TIPS or the Russian acronym TRIZ) for the process optimization of CMEC services in three phases. In the first phase (service process analysis), potential service failure points in the service process were analyzed using the service blueprint technique. In the second phase (service failure diagnosis), Fuzzy-FMEA was applied to diagnose the service failure modes and explore the possible causes and effects. The service failure modes were then prioritized based on fuzzy numbers and the cumulative fuzzy risk priority number (Fuzzy-RPN). Finally, in the third phase (generation of service optimization solutions), the TRIZ parameters, inventive principles, and contradiction matrix were first employed to select TRIZ inventive principles. The selected TRIZ inventive principles were then used to inspire inventive solutions for new service processes. Finally, a case study was conducted on the service processes of elderly care institutions to demonstrate the applicability of the optimization solutions.

BackgroundEmotional and behavioral problems are common in children with autism spectrum disorder (ASD). It's still unclear whether children with ASD have abnormal sugar-sweetened beverages (SSBs) and milk intake and whether this abnormality will affect their emotions and behavior remains unclear. The current study aims to investigate the association of SSBs and milk intake with emotional and behavioral problems in children with autism spectrum disorder (ASD).Methods107 children with ASD and 207 typical developing (TD) children aged 6-12 years old were recruited for the study. The frequency of SSBs and milk intake was assessed by a self-designed questionnaire. Emotional and behavioral problems were assessed by Strength and Difficulties Questionnaire (SDQ). Then, the linear regression model was produced to evaluate the association of SSBs and milk intake with emotional and behavioral problems.ResultsIn the current study, there was no difference in frequency of SSBs intake between children with ASD and TD children (p > 0.05), and children with ASD consumed less milk compared to TD children (p < 0.05). After adjusting sex, age, maternal and paternal education, and monthly family income, we found a significant difference in each subscale score of SDQ in the two groups (p < 0.05). In children with ASD, higher frequent SSBs intake was positively associated with the scores of the emotional problem (p for trend 0.05).ConclusionIn children with ASD, frequency of SSBs and milk intake was associated with the emotional problem and prosocial behavior, respectively. Children with ASD should increase the frequency of milk intake and decrease the frequency of SSBs intake.

Dual innovation, which includes exploratory innovation and exploitative innovation, is crucial for firms to obtain a sustainable competitive advantage. The knowledge base of firms greatly influences or even determines the scope, direction, and path of their dual-innovation activities, which drive their innovation process and produce different innovation performances. This study uses data source patents obtained by 285 focal firms in the Chinese new-energy vehicle industry in the period 2015–2020. Five knowledge-base features are selected by analyzing the correlation and multicollinearity, and four different firm clusters are found by using the k-means clustering algorithm. Based on the classification and regression tree (CART) algorithm, we mine the potential decision rules governing the dual-innovation performance of firms. The results show that the exploratory innovation performance of firms in different clusters is mainly affected by two different knowledge-base features. Knowledge-base scale is a key factor affecting the exploitative innovation performance of firms. Firms in different clusters can improve their dual-innovation performance by rationally tuning the combination of knowledge-base features.

Background:Between January and April 2020, China implemented differentiated prevention and control strategies across the country, based on the severity of the COVID-19 epidemic/pandemic in different regions. These strategies included lockdowns, social distancing, and the closure of public places. These measures may have affected dietary intake to varying degrees. This study aimed to assess variations in food intake and diet quality among pregnant women according to regional severity and related control measures during the most severe period of COVID-19 restrictions in 2020.MethodsA total of 3,678 pregnant women from 19 provinces/municipalities in mainland China were analyzed in this nationwide, multi-center study. Food intake data were obtained and assessed using a validated food frequency questionnaire (FFQ). Diet quality was quantified using the Diet Balance Index for Pregnancy (DBI-P), which included high bound score (HBS, excessive dietary intake), low bound score (LBS, insufficient dietary intake), and diet quality distance (DQD, dietary imbalance). Linear trend tests and multivariable regression analyses were performed to examine the association between food intake, DBI-P and the severity of pandemic.ResultsThe median daily intake of vegetables, fruit, livestock/poultry meat, dairy, and nuts decreased (p < 0.05) according to low, moderate, and high severity of the pandemic, while no significant differences in cereals/potatoes, eggs, and fish/shrimp intake. The median daily intake of cereals/potatoes exceeded the recommended ranges, and the daily intake of eggs and fish/shrimp was below recommended ranges regardless of the pandemic severity (p < 0.05). Regarding diet quality, HBS decreased (lower excessive consumption) (p = 0.047) and LBS increased (greater insufficient consumption) (p = 0.046) with increased severity of the pandemic. On multivariable analyses, moderate and high pandemic severity were related to lower HBS risk (OR = 0.687, OR = 0.537) and higher LBS risk (β = 1.517, β = 3.020) when compared to low pandemic severity.ConclusionsUnder more severe COVID-19 pandemic conditions, pregnant women consumed less quality food, characterized by reduced consumption of vegetables, fruit, livestock/poultry meat, dairy and nuts, while the quality of the foods that pregnant women consumed in excess tended to improve, but the overconsumption of cereals/potatoes was a problem.

Abstract Background Sodium oligomannate (GV-971), a marine-derived oligosaccharide, is a novel agent that may improve cognition in AD patients. Methods The 24-week multicenter, randomized, double-blind, placebo parallel controlled clinical trial was conducted in AD in China between 24 October 2011 and 10 July 2013. The study included a 4-week screening/washout period, followed by a 24-week treatment period. Patients were randomized in a 1:1:1 ratio to receive GV-971 900 mg, 600 mg, or placebo capsule in treatment period, respectively. The primary outcome was cognitive improvement as assessed by changes in Alzheimer’s Disease Assessment Scale-cognitive subscale 12-item (ADAS-cog12) scores from baseline to week 24. The secondary efficacy outcomes included CIBIC-Plus, ADCS-ADL, and NPI at 24 weeks after treatment compared with baseline. A subgroup study was assessment of the change in cerebral glucose metabolism by fluorodeoxyglucose positron emission tomography measurements. Results Comparing with the placebo group (n = 83, change − 1.45), the ADAS-cog12 score change in the GV-971 600-mg group (n = 76) was − 1.39 (p = 0.89) and the GV-971 900-mg group (n = 83) was − 2.58 (p = 0.30). The treatment responders according to CIBIC-Plus assessment were significantly higher in the GV-971 900-mg group than the placebo group (92.77% vs. 79.52%, p

In order to realize real-time marriage legal consultation automatically, a marriage legal dialogue system based on the parallel C4.5 decision tree was designed in this paper. Firstly, the legal consultation problem is transformed into a classification task. Secondly, a legal consultation classification prediction model based on the parallel C4.5 decision tree algorithm was trained with MapReduce by collected data. Finally, a model based on the SVM algorithm, which has a strategy that was designed to provide automatic interaction for users, was designed to extract attribute value from the user's input. When a new user comes to consult, an automatic legal dialogue is launched to respond to user intelligently. The proposed system works well in some real applications, such as the divorce problem, and the experimental results show that it is outperforming the SVM and NB algorithm and more applicable than the other two algorithms. Moreover, the system can return consultation results with fewer questions asked to the user than some automatic legal consultation websites, which improves the efficiency of consultation.

In modern electronic warfare, the intelligence of the jammer greatly worsens the anti-jamming performance of traditional passive suppression methods. How to actively design anti-jamming strategies to deal with intelligent jammers is crucial to the radar system. In the existing research on radar anti-jamming strategies’ design, the assumption of jammers is too ideal. To establish a model that is closer to real electronic warfare, this paper explores the intelligent game between a subpulse-level frequency-agile (FA) radar and a transmit/receive time-sharing jammer under jamming power dynamic allocation. Firstly, the discrete allocation model of jamming power is established, and the multiple-round sequential interaction between the radar and the jammer is described based on an extensive-form game. A detection probability calculation method based on the signal-to-interference-pulse-noise ratio (SINR) accumulation gain criterion (SAGC) is proposed to evaluate the game results. Secondly, considering that the competition between the radar and the jammer has the feature of imperfect information, we utilized neural fictitious self-play (NFSP), an end-to-end deep reinforcement learning (DRL) algorithm, to find the Nash equilibrium (NE) of the game. Finally, the simulation results showed that the game between the radar and the jammer can converge to an approximate NE under the established model. The approximate NE strategies are better than the elementary strategies from the perspective of detection probability. In addition, comparing NFSP and the deep Q-network (DQN) illustrates the effectiveness of NFSP in solving the NE of imperfect information games.

Target azimuth information can help further improve the accuracy of magnetic orientation, but the current periodic magnetic field generated by the magnetic beacon is multivalued, so it is not suitable for azimuth measurement. According to the distribution of a rotating magnetic field and the phase angle measuring principle, we put forward a new magnetic source structure design of a multiple rotating permanent magnet array by adjusting the spacing d, the rotating speed ω and the initial rotation angle φ, and then verified the mathematical model using COMSOL simulation software. A triple structure was obtained by comparison (d3=3d1=3d2=43 m, d3=3d1=3d2=43 m, φ1=0, φ2=4π5 rad. φ3=π rad), which can produce a strong characteristic magnetic signal similar to a heart-shaped field pattern. Finally, a signal transceiver system was set up for the experiment. The experimental result shows that the waveform of the magnetic signal generated by the real beacon meets the requirement of having a unique maximum value and good directivity within a period, which proves the practical application effect of the structure.

The traditional method of related commodity discovery mainly focuses on direct co-occurrence association of commodities and ignores their indirect connection. Link prediction can estimate the likelihood of links between nodes and predict the existent yet unknown future links. This paper proposes a potentially related commodities discovery method based on link prediction (PRCD) to predict the undiscovered association. The method first builds a network with the discovered binary association rules among items and uses link prediction approaches to assess possible future links in the network. The experimental results show that the accuracy of the proposed method is better than traditional methods. In addition, it outperforms the link prediction based on graph neural network in some datasets.

Abstract Background Tumor-infiltrating immune cells (TIICs) are highly relevant to clinical outcome of glioma. However, previous studies cannot account for the diverse functions that make up the immune response in malignant transformation (MT) from low-grade glioma (LGG) to high-grade glioma (HGG). Methods Transcriptome level, genomic profiles and its relationship with clinical practice were obtained from TCGA and CGGA database. The “Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT)” algorithm was used to estimate the fraction of 22 immune cell types. We divided the TCGA and CGGA set into an experiment set (n = 174) and a validation set (n = 74) by random number table method. Univariate and multivariate analyses were performed to evaluate the 22 TIICs’ value for MT in LGG. ROC curve was plotted to calculate area under curve (AUC) and cut-off value. Results Heterogeneity between TIICs exists in both intra- and inter-groups. Several TIICs are notably associated with tumor grade, molecular subtypes and survival. T follicular helper (TFH) cells, activated NK Cells and M0 macrophages were screened out to be independent predictors for MT in LGG and formed an immune risk score (IRS) (AUC = 0.732, p

PURPOSE: To build radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict malignant potential and mitotic count of gastrointestinal stromal tumors (GISTs). PATIENTS AND METHODS: A total of 333 GISTs patients were retrospectively included in our study. Radiomic features were extracted from the preoperative CE-CT images. According to postoperative pathology, patients were categorized by malignant potential and mitotic count, respectively. The most valuable radiomic features were chosen to build a logistic regression model to predict the malignant potential and a random forest classifier model to predict the mitotic count. The performance of radiomic models was assessed with the receiver operating characteristics curve. Our study further developed a radiomic nomogram to preoperatively predict malignant potential in a personalized way for patients with GISTs. RESULTS: The predictive model was built to discriminate high– from low–malignant potential GISTs with an area under the curve (AUC) of 0.882 (95% CI 0.823-0.942) in the training set and 0.920 (95% CI 0.870-0.971) in the validation set. Moreover, the other radiomic model was built to differentiate high– from low–mitotic count GISTs with an AUC of 0.820 (95% CI 0.753-0.887) in the training set and 0.769 (95% CI 0.654-0.883) in the validation set. CONCLUSION: The radiomic models using CE-CT showed a good predictive performance for preoperative risk stratification of GISTs and hold great potential for personalized clinical decision making.

Background: We aimed to identify a magnetic resonance imaging (MRI)-based model for assessment of the risk of individual distant metastasis (DM) before initial treatment of nasopharyngeal carcinoma (NPC). Methods: This retrospective cohort analysis included 176 patients with NPC. Using the PyRadiomics platform, we extracted the imaging features of primary tumors in all patients who did not exhibit DM before treatment. Subsequently, we used minimum redundancy-maximum relevance and least absolute shrinkage and selection operator algorithms to select the strongest features and build a logistic model for DM prediction. The independent statistical significance of multiple clinical variables was tested using multivariate logistic regression analysis. Findings: In total, 2780 radiomic features were extracted. A DM MRI-based model (DMMM) comprising seven features was constructed for the classification of patients into high- and low-risk groups in a training cohort and validated in an independent cohort. Overall survival was significantly shorter in the high-risk group than in the low-risk group (P

A similarity measure is one of the most important tasks in the fields of time series data mining. Its quality often affects the efficiency and effectiveness of the related algorithms that need to measure the similarity between two time series in advance. Dynamic time warping is one of the most robust methods to compare one time series with another based onwarping alignments. In this paper, the design of an incremental warping window is used to improve the performance of dynamic time warping. The incremental warping window is changeable for various time series with different lengths. Moreover, the improved dynamic time warping based on the novel window considers the recent alignments as much as possible, which indicates that the proposed method concentrates on more information of the recent data points than that of the previous data points. In addition, it is suitable for online similarity measure between data stream. The experimental evaluation shows that the proposed method is effective and efficient for time series mining.

The high dimension of multivariate time series (MTS) is one of the major factors that impact on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time-based dimensionality, and variable-based dimensionality. They often cause most of the algorithms and techniques applied to the field of MTS data mining to be a failure. In view of the importance of the correlation between any two variables in an MTS, the covariances between any two variables are applied to analyze the extraction of the features for every MTS. In this way, a covariance sequence can be constructed to represent the characteristic of the MTS. Furthermore, an excellent method of dimensionality reduction, principal component analysis (PCA), is used to extract the features of the covariance sequences that derived from an MTS dataset. Thus Euclidean distance is suitable to measure the similarity between the features fast. The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining.

Similarity search is a foundational task in time series data mining. Although there are many ways to measure the similarity of time series, a lot of evidence indicates that dynamic time warping (DTW) has the best robustness in many applications. Unfortunately, the expensive computational cost limits its application in large-scale databases. To speed up similarity search under DTW, we design a framework of two-stage similarity search for time series. In the first stage, we propose an improved lower bounding distance, which can be used to discard plenty of dissimilar series to get a set of candidate sequences. In the second stage, to efficiently get the retrieval result from the set of candidate sequences, we explore early abandoning strategy to avoid the full calculation of DTW. Extensive experiments are conducted on real-world data sets. The experimental results indicate that the proposed method can improve the retrieval efficiency of similarity search under DTW and guarantee no false dismissals.

With the rapid development of computer and communication technology, anticipatory computing has been identified as one of the most important factors affecting human behavioral change. The future of anticipatory computing will not be bright if it fails to provide useful help to human life and work. Anticipatory computing applied to behavioral change intervention (BCI) is full of challenges and is a research topic of increasing interest and importance. This paper provides an overview of the concept of anticipatory computing, BCI, as well as anticipatory computing for the BCI and offers a multistage literature analysis. Also, a systematic analytical framework articulated from the existing literature is presented to reveal the progress and details of anticipatory computing for the BCI. This framework is divided into four dimensions: 1) sensing and context inferring; 2) context prediction; 3) behavioral guidance and intervention, and; 4) application. Based on our literature analysis, 11 elements of anticipatory computing for BCI are identified and discussed in terms of principles, enablers, and activities. Afterward, contributions and possible future directions for research are summarized at the end of this paper.

Traditional topic research divides similar topics into the same cluster according to clustering or classification from the perspective of users, which ignores the deep relationship within and between topics. In this paper, topic analysis is achieved from the perspective of the topic network. Based on the initial core topics obtained by the keyword importance and affinity propagation clustering, co-occurrence time series between topics are constructed according to time sequence and topic frequency. Subsequence segments of each topic co-occurrence time series are divided by sliding windows, and the similarity between subsequence segments is calculated. Based on the topic similarity matrix, the topic network is constructed. The topic network is divided according to the community detection algorithm, which realizes the topic re-clustering and reveals the deep relationship between topics in fine-grained. The results show there is no relationship between topic center representation and keyword popularity, and topics with a wide range of concepts are more likely to become topic network centers. The proposed approach takes into account the influence of time factors on topic analysis, which not only expands the analysis in the field of topic research but also improves the quality of topic research.

Based on the time series of articles obtained from the literature, we propose three analysis methods to deeply examine the characteristics of these articles. This method can be used to analyze the construction and development of various disciplines in institutions, and to explore the features of the publications in important periodicals in the disciplines. By defining the concepts and methods relevant to research and discipline innovation, we propose three methods for analyzing the characteristics of agency publications: numerical distribution, trend, and correlation network analyses. The time series of the issuance of articles in 30 important journals in the field of management sciences were taken, and the new analysis methods were used to discover some valuable results. The results showed that by using the proposed methods to analyze the characteristics of institution publications, not only did we find similar levels of discipline development or similar trends in institutions, achieving a more reasonable division of the academic levels, but we also determined the preferences of the journals selected by the institutions, which provides a reference for subject construction and development.

BackgroundMAPK-RAP1A signaling, which is involved in cancer progression, remains to be defined. Upregulation of MAPK-RAP1A signaling accounts for most cancers that harbor high incident rate, such as non-small cell lung cancer (NSCLC) and pancreatic cancer, especially in hepatocellular carcinoma (HCC). MAPK-RAP1A signaling plays an important function as clinical diagnosis and prognostic value in cancers, and the role of MAPK-RAP1A signaling related with immune infiltration for HCC should be elucidated.MethodsMicroarray data and patient cohort information from The Cancer Genome Atlas (TCGA; n = 425) and International Cancer Genome Consortium (ICGC; n = 405) were selected for validation. The Cox regression and least absolute shrinkage and selection operator (LASSO) were used to construct a clinical prognostic model in this analysis and validation study. We also tested the area under the curve (AUC) of the risk signature that could reflect the status of predictive power by determining model. MAPK-RAP1A signaling is also associated with tumor-infiltrating immune cells (TICs) as well as clinical parameters in HCC. The GSEA and CIBERSORT were used to calculate the proportion of TICs, which should be beneficial for the clinical characteristics (clinical stage, distant metastasis) and positively correlated with the survival of HCC patients.ResultsHCC patients with enrichment of MAPK-RAP1A signaling were associated with clinical characteristics and favorable T cell gamma delta (Vδ T cells), and STMN1, RAP1A, FLT3, HSPA8, ANGPT2, and PGF were used as candidate biomarkers for risk scores of HCC. To determine the molecular mechanism of this signature gene association, Gene Set Enrichment Analysis (GSEA) was proposed. Cytokine–cytokine receptor interaction, TGF-β signaling pathway, and Intestinal immune network for IgA production gene sets were closely related in MAPK-RAP1A gene sets. Thus, we established a novel prognostic prediction of HCC to deepen learning of MAPK-RAP1A signaling pathways.ConclusionOur findings demonstrated that HCC patients with enrichment of MAPK-RAP1A signaling were associated with clinical characteristics and favorable T cell gamma delta (Vδ T cells), which may be a novel prognostic prediction of HCC.

An integrated optimization of sum and difference beam for time-modulated linear antenna array is studied in this paper. The goal of sum and difference beam synthesis is to generate sum beam in the main band and difference beams in the first-order sideband with low side-lobe level through timing switches. The turn-on times of antenna array are achieved by solving a quadratic constraint linear programming; meanwhile, the opening times are optimized by particle swarm optimization algorithm. The results of linear array show that the sum and difference beam can be scanned within ±40 degrees, with lower peak side-lobe level.