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Background: Young females are at a higher risk of developing unhealthy eating behaviors. This study investigated the relationship between appetitive traits and eating behaviors among female university students. Methods: The study participants were 520 female university students from a public university in Eastern China. Appetitive traits were assessed using the Chinese version of the Adult Eating Behavior Questionnaire (C-AEBQ). Data on eating behaviors, including food intake frequency, meal regularity, and dieting behavior, were collected using self-administered questionnaires. The body mass index (BMI) was calculated using self-reported data. Pearson's and Spearman's correlations were used to correlate appetitive traits with BMI and eating behaviors. Latent profile analysis (LPA) was used to identify different appetitive patterns, and logistic regression was used to analyze the relationship between different appetitive patterns and eating behaviors. Results: Two food-approach traits (food enjoyment and emotional over-eating) were positively correlated with BMI, while two food-avoidance traits (slowness in eating and satiety responsiveness) showed negative correlations. Food responsiveness was linked to a higher intake of delivered food, spicy food, and sugar-sweetened beverages, whereas satiety responsiveness was correlated with more frequent meal skipping. The LPA identified four appetitive patterns: food approachers, food approachers with emotional under-eating, food avoiders, and food avoiders with emotional over-eating. Food avoiders had significantly lower BMI than the other groups. Compared to food approachers, food avoiders skipped breakfast more frequently, and food avoiders with emotional over-eating skipped both breakfast and lunch more often. After adjusting for BMI, appetitive patterns showed no significant relationship with dieting behavior. Conclusion: Among female university students, appetitive patterns correlated with eating behaviors, and students with food-avoidance patterns had a higher risk of meal irregularity. These findings emphasize the importance of implementing trait- and pattern-specific approaches to promote healthy eating behaviors among female university students.

With the advancement of deep learning (DL) technology, DL-based intrusion detection models have emerged as a focal point of research within the domain of cybersecurity. This paper provides an overview of the datasets frequently utilized in the research. This article presents an overview of the widely utilized datasets in the research, establishing a basis for future investigation and analysis. The text subsequently summarizes the prevalent data preprocessing methods and feature engineering techniques utilized in intrusion detection. Following this, it provides a review of seven deep learning-based intrusion detection models, namely, deep autoencoders, deep belief networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers. Each model is examined from various dimensions, highlighting their unique architectures and applications within the context of cybersecurity. Furthermore, this paper broadens its scope to include intrusion detection techniques facilitated by the following two large-scale predictive models: the BERT series and the GPT series. These models, leveraging the power of transformers and attention mechanisms, have demonstrated remarkable capabilities in understanding and processing sequential data. In light of these findings, this paper concludes with a prospective outlook on future research directions. Four key areas have been identified for further research. By addressing these issues and advancing research in the aforementioned areas, this paper envisions a future in which DL-based intrusion detection systems are not only more accurate and efficient but also better aligned with the dynamic and evolving landscape of cybersecurity threats.

The development of the recommendation system (RS) has been focused on improving the accuracy of algorithms. As the number of new users grows, RS suffers from the cold start problem, data sparsity and long-tail problem. In this paper, we proposed a many objective commercial recommendation algorithm that extracts the core node through the pay-off function of game theory and makes it guide the evolutionary computation. Firstly, the core aim of commercial recommendations is to maximize total profit. Profits can vary wildly with the same accuracy. Thus, the profit value as one of the goals is considered in attention to accuracy, coverage, and novelty. Besides, the current user state value is updated according to user features and rating scores matrix with the attention mechanism at different time stamps. The nodes are divided into different communities, forming a dynamic community, and recommendations are made within each community. Meanwhile, to reduce the computational complexity and runtime, the pay-off function of game theory is used to extract the core user in each community. Each contribution value and user interaction as criteria to guide the current community of users. Experiments on real datasets show that selecting core nodes can improve the performance of business RS. Compared with other existing algorithms in many experiments, our algorithm performs better.

Both effortful and effortless training have been shown to be effective in enhancing individuals' executive functions. Effortful training improves domain-specific EFs, while effortless training improves domain-general EFs. Furthermore, effortful training has significantly higher training effects on EFs than effortless training. The neural mechanism underlying these different effects remained unclear. The present study conducted meta-analysis on neuroimaging studies to explore the changes of brain activations induced by effortful and effortless training. The results showed that effortful training induced greater activation in superior frontal gyrus, while effortless training induced greater activation in middle frontal gyrus, precuneus and cuneus. The brain regions of MD system enhanced by effortful training were more associated with core cognitive functions underlying EFs, while those enhanced by effortless training were more correlated with language functions. In addition, the significant clusters induced by effortful training had more overlaps with the MD system than effortless training. These results provided us with possibility to discuss the different behavioral results brought by effortful and effortless training.

Abstract Background A prospective interventional study comparing outcomes in critically ill patients receiving intermittent infusion (II) or continuous infusion (CI) of vancomycin during continuous venovenous hemofiltration (CVVH) is lacking. The objective of this study was to compare the pharmacokinetic/pharmacodynamics (PK/PD) target attainment, therapeutic efficacy and safety among critically ill patients who received CI or II of vancomycin in a prospective interventional trial and to explore the correlations of effluent flow rate (EFR) with PK/PD indices. Methods This prospective interventional study was conducted in two independent intensive care units (ICUs) from February 2021 to January 2022. Patients in one ICU were assigned to receive CI (intervention group) of vancomycin, whereas patients in the other ICU were assigned to receive II regimen (control group). The primary outcome was to compare the PK/PD target attainment, including target concentration and target area under the curve over 24 h to minimum inhibitory concentration (AUC24/MIC). Results Overall target attainment of PK/PD indices was higher with CI compared with II, irrespective of target concentration (78.7% vs. 40.5%; P

Abstract Background The chlorophyll content is a vital indicator for reflecting the photosynthesis ability of plants and it plays a significant role in monitoring the general health of plants. Since the chlorophyll content and the soil–plant analysis development (SPAD) value are positively correlated, it is feasible to predict the SPAD value by calculating the vegetation indices (VIs) through hyperspectral images, thereby evaluating the severity of plant diseases. However, current indices simply adopt few wavelengths of the hyperspectral information, which may decrease the prediction accuracy. Besides, few researches explored the applicability of VIs over rice under the bacterial blight disease stress. Methods In this study, the SPAD value was predicted by calculating the spectral fractal dimension index (SFDI) from a hyperspectral curve (420 to 950 nm). The correlation between the SPAD value and hyperspectral information was further analyzed for determining the sensitive bands that correspond to different disease levels. In addition, a SPAD prediction model was built upon the combination of selected indices and four machine learning methods. Results The results suggested that the SPAD value of rice leaves under different disease levels are sensitive to different wavelengths. Compared with current VIs, a stronger positive correlation was detected between the SPAD value and the SFDI, reaching an average correlation coefficient of 0.8263. For the prediction model, the one built with support vector regression and SFDI achieved the best performance, reaching R2, RMSE, and RE at 0.8752, 3.7715, and 7.8614%, respectively. Conclusions This work provides an in-depth insight for accurately and robustly predicting the SPAD value of rice leaves under the bacterial blight disease stress, and the SFDI is of great significance for monitoring the chlorophyll content in large-scale fields non-destructively.

Strawberry anthracnose, caused by Colletotrichum spp., is a major disease that causes tremendous damage to cultivated strawberry plants (Fragaria × ananassa Duch.). Examining and distinguishing plants potentially carrying the pathogen is one of the most effective ways to prevent and control strawberry anthracnose disease. Herein, we used this method on Colletotrichum gloeosporioides at the crown site on indoor strawberry plants and established a classification and distinguishing model based on measurement of the spectral and textural characteristics of the disease-free zone near the disease center. The results, based on the successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and interval random frog (IRF), extracted 5, 14, and 11 characteristic wavelengths, respectively. The SPA extracted fewer effective characteristic wavelengths, while IRF covered more information. A total of 12 dimensional texture features (TFs) were extracted from the first three minimum noise fraction (MNF) images using a grayscale co-occurrence matrix (GLCM). The combined dataset modeling of spectral and TFs performed better than single-feature modeling. The accuracy rates of the IRF + TF + BP model test set for healthy, asymptomatic, and symptomatic samples were 99.1%, 93.5%, and 94.5%, the recall rates were 100%, 94%, and 93%, and the F1 scores were 0.9955, 0.9375, and 0.9374, respectively. The total modeling time was 10.9 s, meaning that this model demonstrated the best comprehensive performance of all the constructed models. The model lays a technical foundation for the early, non-destructive detection of strawberry anthracnose.

Objective: To evaluate through meta-analysis the therapeutic efficacy, as well as changes in blood glucose and lipid metabolism in women with gestational diabetes mellitus (GDM) treated with Shenqi Jiangtang granule + Western medicine (SJG+WM). Methods: Web of Science, PubMed, and three Chinese databases, namely China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Platform (Wanfang), and Chinese Scientific Journal Database (VIP Database), were searched to identify clinical randomized controlled trials (RCTs) that investigated SJG+WM treatment effects on women with GDM. Risk assessment criteria were used to evaluate the included studies, as recommended by the Cochrane systematic evaluation guide. Meta-analysis was performed using STATA 12.0 and RevMan5.3. Results: A total of 2,743 participants were enrolled in 28 RCTs, 1372 (50.08%) in the WM treatment group and 1371 (49.92%) in the SJG+WM treatment group. The duration of treatment ranged from 2 to 14 weeks. Compared to WM treatment alone, SJG+WM could improve the therapeutic efficacy and the levels of fasting blood glucose (FBG) [mean difference (MD) = -0.95, 95% confidence interval (CI): -1.16 to -0.75, P < 0.00001], postprandial 2 h blood glucose (PBG) [MD = -1.46, 95% CI: -1.74 to -1.18), P < 0.00001], glycated hemoglobin (HbA1c) [MD = -0.91, 95% CI: -1.14 to 0.68), P < 0.00001], total cholesterol (TC) [MD = -0.64, 95% CI: -0.81 to -0.48, P < 0.00001], and triglycerides (TG) [MD = -0.56, 95% CI: -0.74 to -0.38, P < 0.00001]. The funnel plot and Egger's test results showed that only four indicators, including the total efficiency rate, FBG, PBG, and HbA1c, had significant publication bias, indicating that the conclusions were reliable. Conclusion: SJG+WM can significantly improve therapeutic efficacy and blood glucose and lipid metabolism in patients with GDM.

Foraminiferal environmental DNA (eDNA) is an efficient and reliable indicator of environmental changes. Here, we investigated the foraminiferal diversity of 25 stations (33–36°N) in the Yellow Sea based on eDNA and presented the first assessment on the correlations between foraminifera diversity and environmental conditions in the region. A total of 71 sediment samples were collected and local environmental parameters were measured. The foraminiferal eDNA was extracted from the samples and part of the small subunit (SSU) rRNA gene was amplified from the samples. After high–throughput sequencing, 1,701,899 amplified reads were detected and clustered into 426 operational taxonomic units (OTUs). The benthic foraminiferal eDNA community diversity and group diversity showed significant positive correlations with water depth and total inorganic carbon (TIC) in sediments, and significant negative correlations with temperature and total organic carbon (TOC) in sediments. However, in terms of the correlation between relative abundance and environmental factors, there were wide variations from species to species. In this work, planktic foraminiferal eDNA was detected in the sediments, and its OTUs number could reflect the changes in ocean currents and upper layer water masses to a certain extent. This is the first study on relationship of foraminiferal eDNA and environmental factors in the Yellow Sea shelf region of the western Pacific Ocean, and it demonstrates that community parameters in foraminiferal eDNA could be a valuable proxy for environmental changes in the shelf sea.

Crocin has a protective effect on neurodegenerative disease, but whether crocin can prevent atherosclerosis remains unclear. The present study aimed to explore the possible anti-atherosclerotic mechanism of crocin in LDLR-/- mice. Mice were fed an atherogenic high-fat/cholesterol diet and concomitantly treated by intragastric administration with normal saline (Model) or 20 or 40 mg/(kg.bw.day) crocin for 8 weeks. Pathological results showed that Model group exhibited a marked atherosclerotic plaque, while crocin significantly reduced atherosclerotic plaque, accompanied by a small amount of denatured collagen fiber and foam cells. Mechanistically, crocin, especially high-dose suppressed the formation of atherosclerotic plaques via enhancing hepatic antioxidant capacity. Besides, crocin effectively inhibited NLRP3 inflammasome and blocked TLR4 signal, through reducing oxidative stress based on Nrf2/Keap1 pathway, and increasing intestinal tight junction proteins and improving gut microbiota composition. These results suggest that crocin can ameliorate inflammation and attenuate atherosclerotic progression by oxidative stress reduction and intestinal barrier improvement.

Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky–Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention.

Abstract Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

Abstract Background TMPRSS2: ETS gene fusion occurs recurrently in a high proportion of prostate cancer (PCa) patients in Western countries. However, for Chinese PCa patients, no solid conclusion could be drawn from the present studies, as the results varied considerably between the limited reports. Results In this study, we evaluated the prevalence of such gene rearrangements in a small number of Chinese PCa patients and discovered that 6 out of 27 (22.2%) were found to harbor the TMPRSS2: ERG fusion, the ratio was much lower than that in Western countries. Furthermore, we first identified TMPRSS2: EGR1 gene fusion, suggesting other chromosome rearrangements besides ETS gene family harbor in prostate cancer. The hybrid transcript was predicted to encode a truncated EGR1 protein by ORF finder, which might play a key role in prostate cancer. Conclusions We reported that the total occurrence rate of TMPRSS2: ERG fusion gene in this small group of Chinese patients was lower than the reported frequencies in European descent patients but comparable to other reported frequencies in Asian populations. The occurrence of TMPRSS2: EGR1 gene fusion suggested other chromosome rearrangements in prostate cancer.

Computer-based training has attracted increasing attention from researchers in recent years. Several studies have found that computer-based training resulted in improved executive functions (EFs) in adults. However, it remains controversial whether children can benefit from computer-based training and what moderator could influence the training effects. The focus of the present meta-analysis was to examine the effects of computer-based training on EFs in children: working memory, cognitive flexibility, and inhibitory control. A thorough search of published work yielded a sample of 36 studies with 216 effect sizes. The results indicated that computer-based training showed moderate training effects on improving EFs in children (g = 0.35, k = 36, p < 0.001), while training effects of working memory were significantly higher. Furthermore, we found near-transfer effects were marginally significantly higher than far-transfer effects. The standard training method was significantly more effective than training with game elements. In computer-based training, typically developing children had significantly better training effects than atypically developing children. Some additional factors, such as the number of training sessions and age, also modulated the training effects. In conclusion, the present study investigated the effects and moderators of computer-based training for children's EFs. The results provided evidence that computer-based training (especially standard training) may serve as an efficient way to improve EFs in children (especially typically developing individuals). We also discussed some directions for future computer-based training studies.

Previously, we have shown that paraspeckle protein 1 (PSPC1), a protein component of paraspeckles that was involved in cisplatin-induced DNA damage response (DDR), probably functions at the G1/S checkpoint. In the current study, we further examined the role of PSPC1 in another DNA-damaging agent, methyl methanesulfonate (MMS)-induced DDR, in particular, focusing on MMS-induced apoptosis in HeLa cells. First, it was found that MMS treatment induced the expression of PSPC1. While MMS treatment alone can induce apoptosis, depletion of PSPC1 expression using siRNA significantly increased the level of apoptosis following MMS exposure. In contrast, overexpressing PSPC1 decreased the number of apoptotic cells. Interestingly, morphological observation revealed that many of the MMS-treated PSPC1-knockdown cells contained two or more nuclei, indicating the occurrence of mitotic catastrophe. Cell cycle analysis further showed that depletion of PSPC1 caused more cells entering the G2/M phase, a prerequisite of mitosis catastrophe. On the other hand, over-expressing PSPC1 led to more cells accumulating in the G1/S phase. Taken together, these observations suggest an important role for PSPC1 in MMS-induced DDR, and in particular, depletion of PSPC1 can enhance MMS-induced apoptosis through mitotic catastrophe.

The main conditions affecting electricity generation performance of an enhanced geothermal system (EGS) include reservoir porosity, reservoir permeability, rock heat conductivity, water production rate and injection temperature. Presently there is lack of systematic research the relative importance of the five aforementioned conditions. The orthogonal test method is a statistical approach to analyze multi-factor and multi-level influence on system performance. In this work, based on the geological data at Yangbajing geothermal field, we analyzed the five conditions affecting the electricity generation performance of EGS, and ranked the relative importance of the five factors. The results show that the order of the relative importance of the conditions on electric power is water production rate > injection temperature > reservoir porosity > rock heat conductivity > reservoir permeability; the order of the relative importance of the conditions on reservoir impedance is reservoir permeability > injection temperature > water production rate > reservoir porosity > rock heat conductivity; the order of the relative importance of the conditions on pump power is water production rate > reservoir permeability > injection temperature > reservoir porosity > rock heat conductivity, and; the order of the relative importance of the conditions on energy efficiency is water production rate > reservoir permeability > reservoir porosity > injection temperature > rock heat conductivity. The construction of an EGS reservoir should be located at a formation with higher reservoir porosity or rock heat conductivity, while the determination of reservoir permeability, water production rate and injection temperature should be based on the comprehensive target.

Kaposi's sarcoma (KS)-associated herpesvirus (KSHV) is the etiologic agent of KS, the most common AIDS-related malignancy. The majority of KS tumor cells harbor latent KSHV virus but only a small percentage undergoes spontaneous lytic replication. Viral reactivation from latency is crucial for the pathogenesis and development of KS, but the cellular mechanisms underlying the switch between viral latency and replication are not well understood.The level of let-7 miRNAs and MAP4K4 in KSHV infected 293T cells were quantified by real-time PCRs. Let-7 expression was silenced by the miRNA sponge technique. In let-7a transfected 293T cells, the expression of MAP4K4 was measured by real-time PCR and western blot. Luciferease expression was employed to examine the effect of let-7a on the 3'-untranslated region (UTR) of the MAP4K4 gene in 293T cells. Real-time PCR was used to quantify the KSHV copy numbers in BC-3 cells in which the expression of let-7a and/or MAP4K4 were altered. Finally, ERK, JNK and p38 protein production and their phosphorylation status were detected by western blots in let-7a or MAP4K4 transfected BCBL-1 cells.The expression of microRNA let-7 was dramatically decreased in KSHV infected 293T cells, but that of MAP4K4 was increased significantly. Let-7a is physically associated with and targets the MAP4K4 3'UTR, and inhibits MAP4K4 expression at both mRNA and protein levels. MAP4K4 stimulates KSHV reactivation from latency, whereas let-7a inhibits the function of MAP4K4 by reversing the function of MAP4K4 on JNK, phospho-JNK and phospho-ERK1/2 levels.Our results establish that let-7a specifically suppresses MAP4K4 expression, and further inhibits KSHV reactivation by interfering with the function of MAP4K4 on the MAPK pathway, highlighting let-7a as a potential treatment for KS.

In the process of remote sensing data processing, cloud data will have a considerable negative impact on data processing. Therefore, a matrix optimization cloud detection algorithm based on multiple receptive fields is proposed in this paper. First, the algorithm adopts a block reshaping model to improve the efficiency of the algorithm, while reducing the need for hardware configuration. Second, the cloud region adaptive sparse attention matrix is used to improve the characteristics of cloud regions with different concentrations. Finally, the multi-receptive field scaling module is used to improve the ability to segment cloud regions at different scales. The experimental results show that the accuracy of the proposed algorithm is 85.5%, and the recall rate is 86.4%. The proposed algorithm can basically accurately screen the location of a cloud region and provide technical support for the subsequent image application processing. [ABSTRACT FROM AUTHOR]

With the acceleration of urban development, the annual production of urban construction waste has been increasing yearly, which brings considerable challenges for urban supervision and management, and how to quickly and accurately identify construction waste is of great practical significance. In this paper, we propose a remote sensing image dust cover net construction waste recognition algorithm based on the improved U-network model to realize construction waste target recognition. The algorithm first prepares a dust cover net construction waste identification dataset using Google high-resolution remote sensing imagery as the database. Second, VGG16 is adopted as the backbone network of the U-Net model to improve the feature expression ability of the model. Finally, the Convolution Block Attention Module (CBAM) is embedded into the U-Net network to construct the CBAM-U-Net model to enhance the information extraction accuracy of high-resolution remote sensing images. With the remote sensing image encompassing Daxing District in Beijing as an example, the results show that the proposed algorithm can automatically and efficiently recognize the dust cover net construction waste with 95.51% recognition accuracy and 95.08% Mlou, which puts forward a new idea for the supervision of construction waste. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) has become an important data source in the field of object recognition owing to its high resolution and all-weather characteristics. The traditional data expansion method has difficulty increasing the diversity of samples, which limits the promotion and application of SAR data. Therefore, in view of the shortcomings of traditional SAR data augmentation methods, such as insufficient diversity and poor practicability, we proposed a new idea that can generate samples from different angles. First, Lee filtering and edge direction gradient algorithms are combined to construct a multiscale recursive template matching model, which can identify the target azimuth accurately. Second, we constructed an Angle-Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (Angle-InfoGAN) model for data generation and extended the original datasets with different new angles. Finally, we applied this method successfully to Moving and Stationary Target Acquisition and Recognition (MSTAR) datasets, and the Fréchet inception distance (FID) was used to compare other data enhancement models to validate the performance of the Angle-InfoGAN model. The samples generated by the Angle-InfoGAN model effectively improve the scale and diversity of SAR image datasets and lay a solid data foundation for deep-learning-based SAR object detection. [ABSTRACT FROM AUTHOR]

Vehicle object tracking is a research hotspot in computer vision. To solve the problem of single object extraction caused by the shadow effect and occlusion between vehicles, this paper presents a vehicle object tracking algorithm suitable for both dynamic and stationary states. First, the improved Canny algorithm is used to obtain the information in a video sequence, and the dynamic region of the object is extracted using the difference between the mean of the video sequence and the object frame. Secondly, the Gaussian mixture model is used for video object segmentation to obtain the foreground image and the background image, and the static object is identified through the intersection operation of the object dynamic region and the foreground image combined with the edge information. Then, chroma information is introduced into a statistical nonparametric model to eliminate the shadow of the foreground image, and the meanshift tracking algorithm is used for dynamic object tracking of the foreground image after eliminating the shadow. The experimental results show that the proposed tracking algorithm can identify and track vehicles effectively and quickly, providing new ideas for the future development of the sensor field. [ABSTRACT FROM AUTHOR]

Background and Objectives: Hearing loss is a sensory impairment caused by genetic and environmental factors. Previous epidemiological studies of magnesium intake and hearing loss have yielded conflicting results.Methods and Study Design: We investigated the association between serum magnesium concentrations and hearing loss in a population from the Zhejiang region of China. A cross-sectional study of 3,267 participants aged 18 years and older from five hospitals was conducted from October 2016 to May 2018. An audiometric examination was conducted, and hearing thresholds were computed as pure-tone averages (PTAs) at speech (0.5, 1, 2, and 4 kHz) and high frequencies (3, 4, and 6 kHz). Magnesium concentrations were measured using an inductively coupled plasma mass spectrometer.Results: A linear regression analysis revealed a negative association between magnesium levels and hearing losses from lower to high PTAs. After the adjustment of potential confounders, participants in the highest magnesium quartile had a lower PTA (quartile 4: -1.89%; 95% confidence interval (CI: -3.07 to -0.701); p=0.022) and high PTA (quartile 4: -3.05%; 95% CI: -4.64 to -1.46; p=0.005) than those in the lowest quartile. A logistic regression analysis showed a dose-dependent reduction in the odds of high frequency hearing loss across magnesium quartiles. In model 3, after adjusting for all potential confounders, participants with the highest magnesium quartiles had a 54.0% (OR: 0.460; 95% CI: 0.339-0.587) reduction in the odds of high-frequency hearing loss.Conclusions: Higher whole blood levels of magnesium in this population were associated with lower hearing thresholds and risk of hearing loss. [ABSTRACT FROM AUTHOR]

F-box proteins play important roles in plant growth/development and responses to environmental stimuli through targeting substrates into degradation machinery. A rice defense-related F-box protein gene, OsDRF1, was cloned and identified during a course of study aimed at elucidating the molecular basis of induced immunity in rice. OsDRF1 encodes a protein of 328 amino acids, containing a highly conserved F-box domain. Expression of OsDRF1 was induced upon treatment with benzothiadiazole (BTH), a chemical inducer of defense responses in rice. Moreover, in BTH-treated rice seedlings, expression of OsDRF1 was further induced by infection with Magnaporthe grisea, the rice blast fungus, compared with those in water-treated seedlings. OsDRF1 was also upregulated in rice seedlings after treatment with ABA. Overexpression of OsDRF1 in transgenic tobacco resulted in enhanced disease resistance against tomato mosaic virus (ToMV) and Pseudomonas syringae pv. tabaci and strengthened expression of defense-related genes after salicylic acid treatment or ToMV infection. Root elongation of the OsDRF1-overexpressing transgenic seedlings was significantly inhibited by ABA, indicating that overexpression of OsDRF1 resulted in increased ABA sensitivity. The results suggest that OsDRF1 plays a role in disease resistance via upregulating defense-related gene expression and that OsDRF1 may also be involved in the response to ABA. [ABSTRACT FROM AUTHOR]