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Abstract Background Non-communicable diseases (NCDs) pose a major challenge to health economic cost and residents’ health status. Community health workers (CHWs) are the gatekeeper of primary health care. Objective This study aimed to conduct a situational analysis of current human resource and requirements of NCDs-related training among CHWs in Chengdu with regard to address to understand the suggestions for improvement of challenges and barriers. Methods A descriptive online cross-sectional survey was conducted among CHWs (doctors and nurses) from 23 districts and counties in Chengdu. Sociodemographic and NCDs-related variables were collected. Univariate analysis and multiple response analysis were used to describe the characteristics of these variables. Results 711 doctors and 637 nurses completely responded. There were significant differences among gender, age, educational levels, professional title, working year, type of institution, urban circle and registration in general practice between doctors and nurses (P

BackgroundTreatment of heart failure post myocardial infarction (post-MI HF) with mesenchymal stem/stromal cells (MSCs) holds great promise. Nevertheless, 2-dimensional (2D) GMP-grade MSCs from different labs and donor sources have different therapeutic efficacy and still in a low yield. Therefore, it is crucial to increase the production and find novel ways to assess the therapeutic efficacy of MSCs.Materials and methodshUC-MSCs were cultured in 3-dimensional (3D) expansion system for obtaining enough cells for clinical use, named as 3D MSCs. A post-MI HF mouse model was employed to conduct in vivo and in vitro experiments. Single-cell and bulk RNA-seq analyses were performed on 3D MSCs. A total of 125 combination algorithms were leveraged to screen for core ligand genes. Shinyapp and shinycell workflows were used for deploying web-server.Result3D GMP-grade MSCs can significantly and stably reduce the extent of post-MI HF. To understand the stable potential cardioprotective mechanism, scRNA-seq revealed the heterogeneity and division-of-labor mode of 3D MSCs at the cellular level. Specifically, scissor phenotypic analysis identified a reported wound-healing CD142+ MSCs subpopulation that is also associated with cardiac protection ability and CD142- MSCs that is in proliferative state, contributing to the cardioprotective function and self-renewal, respectively. Differential expression analysis was conducted on CD142+ MSCs and CD142- MSCs and the differentially expressed ligand-related model was achieved by employing 125 combination algorithms. The present study developed a machine learning predictive model based on 13 ligands. Further analysis using CellChat demonstrated that CD142+ MSCs have a stronger secretion capacity compared to CD142- MSCs and Flow cytometry sorting of the CD142+ MSCs and qRT-PCR validation confirmed the significant upregulation of these 13 ligand factors in CD142+ MSCs.ConclusionClinical GMP-grade 3D MSCs could serve as a stable cardioprotective cell product. Using scissor analysis on scRNA-seq data, we have clarified the potential functional and proliferative subpopulation, which cooperatively contributed to self-renewal and functional maintenance for 3D MSCs, named as “division of labor” mode of MSCs. Moreover, a ligand model was robustly developed for predicting the secretory efficacy of MSCs. A user-friendly web-server and a predictive model were constructed and available (https://wangxc.shinyapps.io/3D_MSCs/).

BackgroundDepressive symptoms are frequently observed in patients with Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD), a prevalent metabolic disorder that affects many individuals. It is not yet clear whether there is an association between serum chromium levels and depression.ObjectiveThe purpose of this research was to explore the association between serum chromium level and the manifestation of depression among patients with MAFLD.MethodsThe selection of 1837 patients diagnosed with MAFLD was based on data obtained from the 2017-2018 National Health and Nutrition Examination Survey (NHANES) database in this research. The Patient Health Questionnaire-9 (PHQ-9) was employed to evaluate the severity of depression. The researchers utilized logistic regression models that were weighted for multiple variables to investigate the association between depression and serum chromium levels.ResultsIn our study, we found that 8.98% of US adults with MAFLD were suffering from depression at the time of evaluation. In the logistic regression model, serum chromium levels showed an inverse association with depression (OR=0.82, 95%CI: 0.69-0.96; p=0.016), this relationship remained after adjusting for fully confounding factors (OR=0.83, 95%CI: 0.71-0.97; p=0.021), subgroup analyses showed that the association between serum chromium levels and depression existed in relatively high-prevalence of depression groups.ConclusionPatients diagnosed with MAFLD have a greater likelihood of experiencing depression, whereas individuals with higher levels of serum chromium are less likely to suffer from depression, and this association persists even after adjusting for other factors. These findings indicate supplementing chromium may be a viable treatment for their depressive symptoms.

Abstract The evolution of the Internet of Things technology (IoT) has boosted the drastic increase in network traffic demand. Caching and multicasting in the multi-clouds scenario are effective approaches to alleviate the backhaul burden of networks and reduce service latency. However, existing works do not jointly exploit the advantages of these two approaches. In this paper, we propose COCAM, a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning to minimize the transmission number in the multi-clouds scenario with limited storage capacity in each edge cloud. Specifically, by integrating a cooperative transmission model with the caching model, we provide a concrete formulation of the joint problem. Then, we cast this decision-making problem as a multi-agent extension of the Markov decision process and propose a multi-agent actor-critic algorithm in which each agent learns a local caching strategy and further encompasses the observations of neighboring agents as constituents of the overall state. Finally, to validate the COCAM algorithm, we conduct extensive experiments on a real-world dataset. The results show that our proposed algorithm outperforms other baseline algorithms in terms of the number of video transmissions. Graphical Abstract

In order to explore the effects of salting conditions on the morphology and composition of cooked salted duck egg yolk (SDEY), and reduce the content of hard core in the cooked SDEY, the physicochemical properties of different parts of salted egg yolk and the microstructure of raw and cooked salted egg yolk were analyzed through weighing, atomic flame absorption spectrometry, Kjeldahl method, differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM). The results revealed an appearance of the hard core of SDEY in the second week of the pickling process, which gradually increased with time. Specifically, the yolk hard core of the well-pickled salted duck egg accounted for 33.64%~44.80% of the total mass of the yolk and 21.47%~23.49% of the moisture content. The Na+ content of the hard core was 10.66~11.47 mg/g. Notably, the hard core of SDEY had lower free fat content, which was about 17.71%~27.90% compared with 34.79%~36.34% of the outer layer, while the free fat content of the hard core was positively correlated with the salt concentration of the pickling liquid. In addition, the protein content of the hard core of egg yolk increased to more than 30% after salted, which was higher than the outside. Moreover, it was found that the particle size of the yolk particles in the hard core was smaller after heating, which presented a continuous structure with smaller gaps. Thus, from the results, the order of salt penetration led to the differences in the structure and composition of different parts of salted egg yolk, which led to the differences in the hard core and the outside of the yolk after heating. Higher pickling time and salt concentration significantly aggravated the formation of the hard core. Therefore, using a concentration of 15% and pickling time for 4 week could make less hard core and better food quality. Hence, this study provides a guide to improving the quality of SDEY.

Accurate and dependable weed detection technology is a prerequisite for weed control robots to do autonomous weeding. Due to the complexity of the farmland environment and the resemblance between crops and weeds, detecting weeds in the field under natural settings is a difficult task. Existing deep learning-based weed detection approaches often suffer from issues such as monotonous detection scene, lack of picture samples and location information for detected items, low detection accuracy, etc. as compared to conventional weed detection methods. To address these issues, WeedNet-R, a vision-based network for weed identification and localization in sugar beet fields, is proposed. WeedNet-R adds numerous context modules to RetinaNet’s neck in order to combine context information from many feature maps and so expand the effective receptive fields of the entire network. During model training, meantime, a learning rate adjustment method combining an untuned exponential warmup schedule and cosine annealing technique is implemented. As a result, the suggested method for weed detection is more accurate without requiring a considerable increase in model parameters. The WeedNet-R was trained and assessed using the OD-SugarBeets dataset, which is enhanced by manually adding the bounding box labels based on the publicly available agricultural dataset, i.e. SugarBeet2016. Compared to the original RetinaNet, the mAP of the proposed WeedNet-R increased in the weed detection job in sugar beet fields by 4.65% to 92.30%. WeedNet-R’s average precision for weed and sugar beet is 85.70% and 98.89%, respectively. WeedNet-R outperforms other sophisticated object detection algorithms in terms of detection accuracy while matching other single-stage detectors in terms of detection speed.

Farmers must accurately and promptly identify sugarcane leaf diseases with identical symptoms. RGB images have a beneficial function in disease identification. Nevertheless, complex backgrounds and identical symptoms can significantly reduce the recognition accuracy and robustness. To overcome these challenges, the SLViT hybrid network is presented, in which the transformer encoder is converted to a flexible plug-in (LViT) that is subsequently integrated into several locations of a lightweight CNN architecture (SHDC). SLViT is initially trained on the publicly available disease dataset Plant Village before being moved to the self-created sugarcane leaf disease dataset SLD10k, which consists of seven classes and 10,309 images. The ablation experiments demonstrate that all the adjustments to SLViT have contributed positively to its overall performance. SLViT outperforms six SOTA models and three custom-designed leaf-disease recognition models on Plant Village in terms of speed (1,832 FPS), weight (2 MB), consumption (50 M), and precision (98.84 %). SLViT also outperformed MobileNetV3_small on the SLD10k dataset with an accuracy bonus of 1.87 % and a size reduction of 66.3 %. The experiment also reveals that SLViT has absorbed the advantages of both the lightweight CNN and the noise-resistant transformer. This study demonstrates the applicability of SLViT for sugarcane leaf diagnosis in the field.

Postoperative cognitive dysfunction (POCD) is one of the most serious postoperative complications in the elderly population. Perioperative central neuroinflammation is considered to be an important pathological mechanism of POCD, with the activation of astrocytes playing a key role in central neuroinflammation. Maresin1 (MaR1) is a specific pro-resolving mediator synthesized by macrophages in the resolution stage of inflammation, and provides unique anti-inflammatory and pro-resolution effects by limiting excessive neuroinflammation and promoting postoperative recovery. However, the question remains whether MaR1 can have a positive effect on POCD. The objective of this study was to investigate the protective effect of MaR1 on POCD cognitive function in aged rats after splenectomy. Morris water maze test and IntelliCage test showed that splenectomy could cause transient cognitive dysfunction in aged rats; however, the cognitive impairment of rats was significantly mitigated when MaR1 pretreatment was administered. MaR1 significantly alleviated the fluorescence intensity and protein expression of glial fibrillary acidic protein and central nervous system specific protein in the cornu ammonis 1 region of the hippocampus. Simultaneously, the morphology of astrocytes was also severely altered. Further experiments showed that MaR1 inhibited the mRNA and protein expression of several key proinflammatory cytokines–interleukin-1β, interleukin-6, and tumor necrosis factor-α in the hippocampus of aged rats following splenectomy. The molecular mechanism underlying this process was explored by evaluating expression of components of the nuclear factor κB (NF-κB) signaling pathway. MaR1 substantially inhibited the mRNA and protein expression of NF-κB p65 and κB inhibitor kinase β. Collectively, these results suggest that MaR1 ameliorated splenectomy-induced transient cognitive impairment in elderly rats, and this neuroprotective mechanism may occur through regulating the NF-κB pathway to inhibit astrocyte activation.

Dragon fruit (Hylocereus undatus) is a tropical and subtropical fruit that undergoes multiple ripening cycles throughout the year. Accurate monitoring of the flower and fruit quantities at various stages is crucial for growers to estimate yields, plan orders, and implement effective management strategies. However, traditional manual counting methods are labor-intensive and inefficient. Deep learning techniques have proven effective for object recognition tasks but limited research has been conducted on dragon fruit due to its unique stem morphology and the coexistence of flowers and fruits. Additionally, the challenge lies in developing a lightweight recognition and tracking model that can be seamlessly integrated into mobile platforms, enabling on-site quantity counting. In this study, a video stream inspection method was proposed to classify and count dragon fruit flowers, immature fruits (green fruits), and mature fruits (red fruits) in a dragon fruit plantation. The approach involves three key steps: (1) utilizing the YOLOv5 network for the identification of different dragon fruit categories, (2) employing the improved ByteTrack object tracking algorithm to assign unique IDs to each target and track their movement, and (3) defining a region of interest area for precise classification and counting of dragon fruit across categories. Experimental results demonstrate recognition accuracies of 94.1%, 94.8%, and 96.1% for dragon fruit flowers, green fruits, and red fruits, respectively, with an overall average recognition accuracy of 95.0%. Furthermore, the counting accuracy for each category is measured at 97.68%, 93.97%, and 91.89%, respectively. The proposed method achieves a counting speed of 56 frames per second on a 1080ti GPU. The findings establish the efficacy and practicality of this method for accurate counting of dragon fruit or other fruit varieties.

To optimize the formula of high addition tartary buckwheat noodles and study its antioxidant activity, single factor experiments and response surface method were used to determine the optimal formula of high-added tartary buckwheat noodles. Secondly, the thermal mechanical properties of dough and the cooking characteristics of noodle and its antioxidant activity were determined. The results showed that the best formula of high addition tartary buckwheat noodles were as follows: 62% tartary buckwheat, 38% flour, 0.4% sucrose ester, 0.2% edible alkali, 0.4% konjac gum and 2% salt. The dough cooking stability (C4/C3) of this formula was better than that of the pure wheat noodles and the comprehensive score of the high addition tartary buckwheat noodles prepared with this formula was the highest. The total phenol content and in vitro antioxidant activity results showed that the total phenol content and antioxidant capacity of the improved group were significantly higher than the pure wheat noodles (P

Abstract Background The number of banana plants is closely related to banana yield. The diameter and height of the pseudo-stem are important morphological parameters of banana plants, which can reflect the growth status and vitality. To address the problems of high labor intensity and subjectivity in traditional measurement methods, a fast measurement method for banana plant count, pseudo-stem diameter, and height based on terrestrial laser scanning (TLS) was proposed. Results First, during the nutritional growth period of banana, three-dimensional (3D) point cloud data of two measured fields were obtained by TLS. Second, the point cloud data was preprocessed. And the single plant segmentation of the canopy closed banana plant point cloud was realized furtherly. Finally, the number of banana plants was obtained by counting the number of pseudo-stems, and the diameter of pseudo-stems was measured using a cylindrical segmentation algorithm. A sliding window recognition method was proposed to determine the junction position between leaves and pseudo-stems, and the height of the pseudo-stems was measured. Compared with the measured value of artificial point cloud, when counting the number of banana plants, the precision,recall and percentage error of field 1 were 93.51%, 94.02%, and 0.54% respectively; the precision,recall and percentage error of field 2 were 96.34%, 92.00%, and 4.5% respectively; In the measurement of pseudo-stem diameter and height of banana, the root mean square error (RMSE) of pseudo-stem diameter and height of banana plant in field 1 were 0.38 cm and 0.2014 m respectively, and the mean absolute percentage error (MAPE) were 1.30% and 5.11% respectively; the RMSE of pseudo-stem diameter and height of banana plant in field 2 were 0.39 cm and 0.2788 m respectively, and the MAPE were 1.04% and 9.40% respectively. Conclusion The results show that the method proposed in this paper is suitable for the field measurement of banana count, pseudo-stem diameter, and height and can provide a fast field measurement method for banana plantation management.

Rapid industrial development and extensive use of chemicals have resulted in elevated concentrations of emerging contaminants worldwide, posing a substantial threat to the ecological environment and human health. Per- and polyfluoroalkyl substances (PFASs) have been recognized as emerging pollutants that are widely distributed and accumulated in the environment and they have drawn the attention of scholars for several decades. The variety, long-term use, and long-distance transmission of PFASs have resulted in the ubiquitous contamination of global ecosystems, especially in aquatic environments. Ever since perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) were added to the Stockholm Convention on Persistent Organic Pollutants (POPs), they have become the most typical, eye-catching, and frequently investigated PFASs. Owing to the high stability and bioaccumulation of PFASs, as well as the adverse impact on the endocrine, immune, and nervous systems, investigating their contamination levels, risk of transfer along the food chain, and ecotoxicity should be prioritized. In addition to the important evolutionary significance as primitive vertebrates and the main consumers of aquatic environment, fishes generally exist in various aquatic food chains from the bottom to the top and occupy a critical position in terms of aquatic ecology protection; while amphibians, as the key link from aquatic to terrestrial organisms, are highly sensitive to different environmental pollutants. This review is a comprehensive summary of the toxic effects and toxicity-related factors of PFASs on aquatic vertebrates, mainly Pisces and Amphilla organisms, the characteristics of different aquatic vertebrates in toxicity investigations, and the evaluation of the feasibility of PFASs substitute applications.

Abstract Microvascular invasion (MVI) is presently evaluated as a high-risk factor to be directly relative to postoperative prognosis of hepatocellular carcinoma (HCC). Up to now, diagnosis of MVI mainly depends on the postoperative pathological analyses with H&E staining assay, based on numbers and distribution characteristics of MVI to classify the risk levels of MVI. However, such pathological analyses lack the specificity to discriminate MVI in HCC specimens, especially in complicated pathological tissues. In addition, the efficiency to precisely define stages of MVI is not satisfied. Thus, any biomarker for both conforming diagnosis of MVI and staging its levels will efficiently and effectively promote the prediction of early postoperative recurrence and metastasis for HCC. Through bioinformatics analysis and clinical sample verification, we discovered that Stathmin 1 (STMN1) gene was significantly up-regulated at the locations of MVI. Combining STMN1 immunostaining with classic H&E staining assays, we established a new protocol for MVI pathological diagnosis. Next, we found that the degrees of MVI risk could be graded according to expression levels of STMN1 for prognosis prediction on recurrence rates and overall survival in early HCC patients. STMN1 affected epithelial-mesenchymal transformation (EMT) of HCC cells by regulating the dynamic balance of microtubules through signaling of “STMN1-Microtubule-EMT” axis. Inhibition of STMN1 expression in HCC cells reduced their lung metastatic ability in recipients of mouse model, suggesting that STMN1 also could be a potential therapeutic target for inhibiting HCC metastasis. Therefore, we conclude that STMN1 has potentials for clinical applications as a biomarker for both pathological diagnosis and prognostic prediction, as well as a therapeutic target for HCC.

Abstract Background Circulating tumor DNA (ctDNA) has potential as a specific, noninvasive, and cost‐effective new biomarker for patients with lung cancer. This study aimed to determine whether plasma ctDNA can be used to predict treatment outcomes in patients with lung cancer. Methods Pre‐ and in‐treatment blood samples were collected from 14 patients with lung cancer receiving chemotherapy. Based on next‐generation sequencing technology, we constructed a unique molecular identifier (UMI) library and performed targeted deep sequencing of 72 genes (15 000×). We used dVAF to evaluate the change level and trend of variant allele frequency (VAF). Results We identified MUC16, KMT2D, AMER1, and NTRK1 as the most‐frequently mutated genes in ctDNA associated with lung cancer. Furthermore, we showed that the change trend of dVAF in patients with lung cancer undergoing chemotherapy was closely related to the changes in both tumor volume and tumor biomarkers, including CEA, CA125, NSE, and CK (Cytokeratin). Moreover, the ctDNA analysis revealed disease progression of SCLC patients earlier than did computed tomography. Conclusions The dynamic detection of plasma ctDNA VAF has the potential value as a biomarker for evaluating the efficacy of chemotherapy in patients with SCLC and advanced NSCLC, and may predict the progression of lung cancer patients earlier than radiography.

The state of angle cocks determines the air connectivity of freight trains, and detecting their state is helpful to improve the safety of the running trains. Although the current research for fault detection of angle cocks has achieved high accuracy, it only focuses on the detection of the closed state and non-closed state and treats them as normal and abnormal states, respectively. Since the non-closed state includes the fully open state and the misalignment state, while the latter may lead to brake abnormally, it is very necessary to further detect the misalignment state from the non-closed state. In this paper, we propose a coarse-to-fine localization method to achieve this goal. Firstly, the localization result of an angle cock is obtained by using the YOLOv4 model. Following that, the SVM model combined with the HOG feature of the localization result of an angle cock is used to further obtain its handle localization result. After that, the HOG feature of the sub-image only containing the handle localization result continues to be used in the SVM model to detect whether the angle cock is in the non-closed state or not. When the angle cock is in the non-closed state, its handle curve is fitted by binarization and window search, and the tilt angle of the handle is calculated by the minimum bounding rectangle. Finally, the misalignment state is detected when the tilt angle of the handle is less than the threshold. The effectiveness and robustness of the proposed method are verified by extensive experiments, and the accuracy of misalignment state detection for angle cocks reaches 96.49%.

Per- and polyfluoroalkyl substances (PFASs) are a class of compounds that persist in the environment globally. Besides being transported to the soil and sediments, which act as their sinks, PFASs can be transferred to several species of higher organisms directly or via bacteria, eliciting a wide range of adverse effects. Caenorhabditis elegans has been widely used in toxicological studies and life science research owing to its numerous advantages over traditional vertebrate models; notably, C. elegans has 65% conserved human-disease-associated genes and does not require ethical approvals for experimental use. This review covers a range of topics, from reported accumulation characteristics and lethal concentrations of PFAS in C. elegans to the mechanisms underlying the toxicity of PFAS at different levels, including reproductive, developmental, cellular, neurologic, oxidative, metabolic, immune, and endocrine toxicities. Additionally, the toxicity levels of some PFAS substitutes are summarized. Lastly, we discuss the toxicological mechanisms of these PFAS substitutes and the importance and promising potential of nematodes as in vivo models for life science research, epidemiological studies (obesity, aging, and Alzheimer’s disease research), and toxicological investigations of PFASs and other emerging pollutants compared with other soil animals or model organisms.

Abstract Background Lung cancer has the highest mortality rate among malignant tumors, with non‐small cell lung cancer (NSCLC) being the most common type. As the main component of the human microflora, the intestinal flora interacts with the human body to affect immunity, metabolism, and the formation of diseases. Methods Forty‐five patients with advanced NSCLC who received platinum‐containing dual‐drug chemotherapy were enrolled in a prospective exploratory cohort study. The intestinal flora was dynamically collected at baseline and after two chemotherapy cycles. Next‐generation sequencing and metagenomics were then used to analyze the species and function of the intestinal flora at all levels. Results Significant differences in the intestinal flora of patients with NSCLC were found according to sex and age. At the family level, the abundances of Streptococcaceae, Lactobacillaceae, and Leuconostocaceae after platinum‐containing dual‐drug chemotherapy were significantly higher compared to those before chemotherapy. At the family level, patients with chemotherapy‐induced gastrointestinal reactions had a significantly higher abundance of Leuconostocaceae than those without gastrointestinal responses. Meanwhile, patients with gastrointestinal reactions had higher metabolism, human diseases, cellular processes, and environmental information processing than those who did not. At the genus level, responders had higher abundances of Bacteroides compared to nonresponders. Moreover, nonresponders had higher levels of the six major metabolic pathways compared to responders. Conclusions The intestinal flora of Chinese patients with advanced NSCLC differed according to sex and age. Moreover, significant differences in the intestinal flora were noted after chemotherapy, which could be associated with chemotherapy‐induced gastrointestinal reactions and the efficacy of chemotherapy.

The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robots, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting, and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon- or heuristic-search-algorithm-based CCPP methods, when coping with the field with irregular boundaries, obstacles, and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency, and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, the proposed method innovatively extends the traditional genetic algorithm’s chromosomes and single-point mutation into chromosome pairs and multi-point mutation, and proposed a multi-objective equilibrium fitness function. The simulation and experimental results on simple regular fields showed that the proposed improved-genetic-algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, the proposed CCPP method reduces 38.54% of repeated operation area and 35.00% of number of U-turns, and can save 7.82% of energy consumption on average. This proved that the proposed CCPP method has a strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.

Abstract To explore the clinical application value of chest CT quantitative pulmonary inflammation index (PII) in the evaluation of the course and treatment outcome of COVID-19 pneumonia. One hundred and eighteen patients with COVID-19 pneumonia diagnosed by RT-PCR were analyzed retrospectively. The correlation between chest CT PII, clinical symptoms and laboratory examinations during the entire hospitalization period was compared. The average age of the patients was 46.0 ± 15 (range: 1–74) years. Of the 118 patients, 62 are male (52.5%) and 56 are female (47.5%). Among them, 116 patients recovered and were discharged, 2 patients died, and the median length of hospital stay was 22 (range: 9–41) days. On admission, 76.3% of the patients presented with fever, and the laboratory studies showed a decrease in lymphocyte (LYM) count and an increase in lactate dehydrogenase (LDH) levels, C-reactive protein (CRP) levels, and erythrocyte sedimentation rate (ESR). Within the studies’ chest CTs, the median number of involved lung lobes was 4 (range: 0–5) and the median number of involved lung segments was 9 (range 0–20). The left lower lobe and the right lower lobe were the most likely areas to be involved (89.0% and 83.9%), and 84.7% of the patients had inflammatory changes in both lungs. The main manifestations on chest CT were ground glass opacities (31.4%), ground glass opacities and consolidation (20.3%), ground glass opacities and reticular patterns (32.2%), mixed type (13.6%), and white lungs (1.7%); common accompanying signs included linear opacities (55.9%), air bronchograms (46.6%), thick small vessel shadows (36.4%), and pleural hypertrophy (13.6%). The chest CT at discharge showed complete absorption of lesions in 19 cases (16.1%), but not in the remaining 99 cases. Lesions remained in a median of 3 lung lobes (range: 0–5). Residual lesions remained in a median of 5 lung segments (range: 0–20). The residual lesions mainly presented as ground glass opacities (61.0%), and the main accompanying sign was linear opacities (59.3%). Based on chest CT, the median maximum PII of lungs was 30.0% (range: 0–97.5%), and the median PII after discharge in the patients excluding the two deaths was 12.5% (range: 0–53.0%). PII was significantly negatively correlated with the LYM count and significantly positively correlated with body temperature, LDH, CRP, and ESR. There was no significant correlation between the PII and the white blood cell count, but the grade of PII correlated well with the clinical classification. PII can be used to monitor the severity and the treatment outcome of COVID-19 pneumonia, provide help for clinical classification, assist in treatment plan adjustments and aid assessments for discharge.

Under the explosive growth of information available on the Web, recommender systems have been used as an effective technology to filter useless information and attempt to recommend the most useful items. The proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices has gradually driven many novel emerging services which are latency-sensitive and computation-intensive with a higher quality-of-service. Under such circumstances, the data sources contain four key characteristics (i.e., sparsity, heterogeneity, mobility, volatility). The conventional recommender systems based on cloud computing are incapable of digging the information of user demands. Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service. This paper comprehensively reviews the state of the art literature on the convergence of recommender systems and edge computing, and identify the future directions along this dimension. This paper can provide an array of new perspectives on the convergence for researchers, practitioners, and tap into the richness of this interdisciplinary research area.

The poor quality of optical imaging caused by the complex and varying underwater environment is a significant challenge to underwater target recognition. Moreover, the insufficiency of relevant datasets may lead to the overfitting problem in target recognition models based on deep learning. Taking the instance segmentation of three underwater creatures (echinus, holothurian, and starfish) as an example, we propose a new method for recognition of underwater creatures. It combines the MSRCR (multi-scale Retinex with color restoration) image enhancement algorithm and the Mask R-CNN (region-based convolutional neural work) framework, and achieves a mAP (mean average accuracy) value higher than 90% on a small sample dataset. This method consists of three major steps. First, the dataset with 84 images is augmented (flip, adding noise, and GAN (generative adversarial networks)) to 430 images, and all images are enhanced with MSRCR to improve their qualities; Second, the model is pre-trained on the COCO (Microsoft common objects in context) dataset to shorten the training time and overcome overfitting; Finally, the pre-trained model is transferred to the underwater dataset, and the whole training process is completed. We achieve 97.46% precision and 94.52% recall, and the mAP (intersection over union (IOU) = 50) is 94.84%. The effectiveness of the proposed method is verified by comparing it with several popular target recognition models, including SSD (Single Shot Detector), YOLOv3 (You only look once), original Mask R-CNN, and a SIFT-based (Scale-invariant feature transform) model.

Protein sources are the second most important component in poultry diets. Due to the fluctuation in price of soybean meal (SBM) and persistent increase in feed prices, nutritionists have been exploring alternative protein sources. Replacement of SBM with alternative protein sources in poultry diets could reduce human-livestock competition for soybean and support the production of more animal protein. However, the use of alternative protein sources is limited to low inclusion due to the presence of anti-nutritional factors (ANF) such as glucosinolates (rapeseed meal), gossypol (cottonseed meal), non-starch polysaccharides (NSP) in lupin flour, high fibre (palm kernel cake), total phenolic contents and phytic acid (canola meal) known to impair animal performance, nutrient digestibility and feed utilization. As a processing technique, solid-state fermentation (SSF) has been researched for a long time in the food industry. An important objective of SSF is the production of enzymes, organic acids and other metabolites of economic importance. In recent times, SSF has been employed to enhance nutrient bioavailability, inhibit gut pathogenic bacteria and reduce ANF in plant protein sources resulting in improved nutrient digestibility, thereby improving performance and gut health of broiler chickens. Unlike pigs, there is still a dearth of information on feeding solid-state fermented feed ingredients to broiler chickens. This review aims to describe the nutritional value of the solid-state fermented products of rapeseed meal, canola meal, cottonseed meal, palm kernel cake and lupin flour on performance and intestinal health of broiler chickens. Keywords: Anti-nutritional factor, Broiler chicken, Nutritional value, Protein source, Solid-state fermentation

Abstract Background Radiation therapy can cause cerebral arteriopahty, resulting in ischemic stroke. We document late-delayed cerebral arteriopathy by high-resolution magnetic resonance imaging (HR-MRI) in a middle aged man who had cranial irradiation 19 years earlier. Case presentation A 45-year-old man was diagnosed with frontal lobe glioma 19 years ago and was treated with radiation after surgical resection. He was admitted to our hospital with an acute cerebral infarction in November 8, 2017. Traditional MRI examination and HR-MRI (sagittal, reconstruction of coronal and axial) were performed at admission. He was treated with prednisone (30 mg/day) and clinical symptoms disappeared after 3 months by telephone follow-up. Our patient complained of dizziness and blurred vision and traditional MRI examination indicated acute ischemic stroke in temporal lobe and occipital lobe and microbleeds. In order to define the exact mechanism of stroke, blood tests, auto-immune screening and thrombophilia were performed and results were normal. Electrocardiography and echocardiography were negative and cardiogenic cerebral embolism was excluded. In cerebrospinal fluid (CSF) examination, level of albumin and IgG were elevated. HR-MRI showed vessel wall thickening in T1-weighted imaging, narrow lumen in proton density imaging and vessel wall concentric enhancement in contrast-enhanced T1- weighted imaging. Combined with radiotherapy history, the patient was diagnosed with radioactive vasculitis. Conclusion Radiation-induced cerebrovascular damages could be a lasting progress, which we cannot ignore. HR-MRI can provide sensitive and accurate diagnostic assessment of radiation-induced arteritis and may be a useful tool for the screening of causes of cryptogenic stroke.

Sugarcane seedling emergence is important for sugar production. Manual counting is time-consuming and hardly practicable for large-scale field planting. Unmanned aerial vehicles (UAVs) with fast acquisition speed and wide coverage are becoming increasingly popular in precision agriculture. We provide a method based on improved Faster RCNN for automatically detecting and counting sugarcane seedlings using aerial photography. The Sugarcane-Detector (SGN-D) uses ResNet 50 for feature extraction to produce high-resolution feature expressions and provides an attention method (SN-block) to focus the network on learning seedling feature channels. FPN aggregates multi-level features to tackle multi-scale problems, while optimizing anchor boxes for sugarcane size and quantity. To evaluate the efficacy and viability of the proposed technology, 238 images of sugarcane seedlings were taken from the air with an unmanned aerial vehicle. Outcoming with an average accuracy of 93.67%, our proposed method outperforms other commonly used detection models, including the original Faster R-CNN, SSD, and YOLO. In order to eliminate the error caused by repeated counting, we further propose a seedlings de-duplication algorithm. The highest counting accuracy reached 96.83%, whilst the mean absolute error (MAE) reached 4.6 when intersection of union (IoU) was 0.15. In addition, a software system was developed for the automatic identification and counting of cane seedlings. This work can provide accurate seedling data, thus can support farmers making proper cultivation management decision.

The influence of microbiota on host health and disease has attracted adequate attention, and gut microbiota components and microbiota-derived metabolites affect host immune homeostasis locally and systematically. Some studies have found that gut dysbiosis, disturbance of the structure and function of the gut microbiome, disrupts pulmonary immune homeostasis, thus leading to increased disease susceptibility; the gut-lung axis is the primary cross-talk for this communication. Gut dysbiosis is involved in carcinogenesis and the progression of lung cancer through genotoxicity, systemic inflammation, and defective immunosurveillance. In addition, the gut microbiome harbors the potential to be a novel biomarker for predicting sensitivity and adverse reactions to immunotherapy in patients with lung cancer. Probiotics and fecal microbiota transplantation (FMT) can enhance the efficacy and depress the toxicity of immune checkpoint inhibitors by regulating the gut microbiota. Although current studies have found that gut microbiota closely participates in the development and immunotherapy of lung cancer, the mechanisms require further investigation. Therefore, this review aims to discuss the underlying mechanisms of gut microbiota influencing carcinogenesis and immunotherapy in lung cancer and to provide new strategies for governing gut microbiota to enhance the prevention and treatment of lung cancer.

Abstract Introduction Targeted therapies are based on specific gene alterations. Various specimen types have been used to determine gene alterations, however, no systemic comparisons have yet been made. Herein, we assessed alterations in selected cancer‐associated genes across varying sample sites in lung cancer patients. Materials and Methods Targeted deep sequencing for 48 tumor‐related genes was applied to 153 samples from 55 lung cancer patients obtained from six sources: Formalin‐fixed paraffin‐embedded (FFPE) tumor tissues, pleural effusion supernatant (PES) and pleural effusion cell sediments (PEC), white blood cells (WBCs), oral epithelial cells (OECs), and plasma. Results Mutations were detected in 96% (53/55) of the patients and in 83% (40/48) of the selected genes. Each sample type exhibited a characteristic mutational pattern. As anticipated, TP53 was the most affected sequence (54.5% patients), however this was followed by NOTCH1 (36%, across all sample types). EGFR was altered in patient samples at a frequency of 32.7% and KRAS 10.9%. This high EGFR/ low KRAS frequency is in accordance with other TCGA cohorts of Asian origin but differs from the Caucasian population where KRAS is the more dominant mutation. Additionally, 66% (31/47) of PEC samples had copy number variants (CNVs) in at least one gene. Unlike the concurrent loss and gain in most genes, herein NOTCH1 loss was identified in 21% patients, with no gain observed. Based on the relative prevalence of mutations and CNVs, we divided lung cancer patients into SNV‐dominated, CNV‐dominated, and codominated groups. Conclusions Our results confirm previous reports that EGFR mutations are more prevalent than KRAS in Chinese lung cancer patients. NOTCH1 gene alterations are more common than previously reported and reveals a role of NOTCH1 modifications in tumor metastasis. Furthermore, genetic material from malignant pleural effusion cell sediments may be a noninvasive manner to identify CNV and participate in treatment decisions.

Introduction: The disruption of the balance between antioxidants and oxidants plays a vital role in the pathogenesis of acute lung injury (ALI). Evidence has shown that Lycium barbarum polysaccharide (LBP) has antioxidant feature. We examined the efficacy and mechanisms of LBP on hyperoxia-induced acute lung injury (ALI) in the present study. Materials and methods: C57BL/6 wild-type (WT) mice and nuclear factor erythroid 2-related factor 2 (Nrf2)-deficient (Nrf2–/–) mice were used in the present study. LBP was fed by gavages once daily for 1 week. Then, the mice were exposed to hyperoxia or room air for 72 h. Additional dosage of LBP was given per 24 h. Results: Reactive oxygen species production was increased in WT mice exposed to hyperoxia. Inflammatory cytokines including interleukin (IL)-1β as well as IL-6, and inflammatory cells were increased infiltration in the lung after 3 days hyperoxia exposure. Hyperoxia exposure also induced pulmonary edema and histopathological changes. These hyperoxia-induced changes were improved in LBP treated group. Moreover, elevated activities of heme oxygenase-1 and glutathione peroxidase and enhanced activation of Nrf2 were observed in mice treated with LBP. However, the benefit of LBP on hyperoxic ALI was abolished in Nrf2–/– mice. Moreover, our cell study showed that the LBP-induced activation of Nrf2 was dampened in pulmonary microvascular endothelial cells when the AMPK signal was inhibited by siRNA. Conclusions: LBP improves hyperoxic ALI via Nrf2-dependent manner. The LBP-induced activation of Nrf2 is mediated, at least in part, by AMPK pathway.

Platelets are traditionally considered to be essential components of primary hemostasis. Recent investigations have revealed that platelets can be activated in patients with sepsis and are implicated in the development of sepsis and sepsis-induced-acute kidney injury (SAKI). In the present study, ticagrelor was used to induce a mouse model of SAKI by cecal ligation and puncture. It was found that ticagrelor could inhibit platelet activity, decrease the levels of interleukin-1β and serum creatinine, reduce infiltration of neutrophils in renal tissue, and attenuate cell apoptosis in the kidney. The results suggested that ticagrelor could protect renal function by inhibiting inflammation, recruitment of neutrophils into the kidney, and cell apoptosis in renal tissue. Thus, the findings might provide new strategies for preventing SAKI.

Video streaming is a dominant application over today's Internet. The current mainstream video streaming solution is to utilize the services of a Content Delivery Network (CDN) provider. By replicating video content closer to the network edge, caching provides an effective mechanism for alleviating the demand for massive bandwidth for the Internet backbone. It reduces the network traffic and capital expense for streaming the video content, and in the meantime, enhance Internet's Quality of Service (QoS). In this paper, we propose a neural adaptive caching approach, named NA-Caching, for helping cache learn to make caching decisions from its own experiences rather than a specific mathematical model, in a way similar to how a human being learns a new skill (e.g. cycling, swimming). NA-Caching leverages the benefits of the Recurrent Neural Network (RNN) as well as the Deep Reinforcement Learning (DRL) to maximize the cache efficiency by jointly learning request features, caching space dynamics and making decisions. Specifically, we utilize Gated Recurrent Unit (GRU) to characterize the evolving features of the dynamic requests and caching space. Moreover, the above GRU-based representation network is integrated into a Deep Q-Network (DQN) framework for making adaptive caching decisions online. To evaluate the performance of the proposed approach, we conduct extensive experiments on anonymized real-world traces from a video provider. The results demonstrate that our algorithm significantly outperform several candidate methods.

With the popularity of smart mobile devices, information exchange between users has become more and more frequent, and Mobile Social Networks (MSNs) have attracted significant attention in many research areas. Nowadays, discovering social relationships among people, as well as detecting the evolution of community have become hotly discussed topics in MSNs. One of the major features of MSNs is that the network topology changes over time. Therefore, it is not accurate to depict the social relationships of people based on a static network. In this paper, we present a survey of this emerging field from a temporal perspective. The state-of-the-art research of MSNs is reviewed with focus on four aspects: social property, time-varying graph, temporal social property, and temporal social properties-based applications. Some important open issues with respect to MSNs are discussed.

Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (Rv = 0.79, RMSEv = 0.11). Support vector machine (SVM) and BPNN were then used to classify the irrigation levels based on five spectral features which had high correlations with irrigation levels. SVM reached a higher accuracy of 80.6%. The results of this study demonstrated that high resolution multispectral images could provide effective information for CNC prediction and water irrigation level recognition for sugarcane crop.

Traditional centralized token exchange (CEX) has been suffering from hacking due to the centralized management of users’ tokens. In contrast, decentralized token exchange (DEX) maintains users’ assets by smart contracts in a decentralized manner, but introduces additional overhead in terms of gas fee and transaction confirmation latency. Hybrid decentralized token exchange (HEX) has been proposed to combine the benefits of CEX and DEX. However, existing HEX is criticized for two issues. First, trading transactions are time-consuming and expensive for frequent token traders. Second, excessive simultaneous transactions might cause the pending transaction congestion in the Ethereum network. In this paper, we propose a payment channel based HEX, which extends existing solutions by adding a new payment channel layer to benefit frequent traders and alleviate the pending transaction congestion. Besides, we propose the very first gas-price vs. transaction-confirmation-latency function to guide Ethereum transaction issuers to choose an optimal gas price that minimizes the overall cost. Extensive simulations are conducted to compare the cost in the proposed HEX with that in the conventional HEX. The results demonstrate the effectiveness of our proposed mechanism in terms of reducing gas fees and transaction confirmation latency for frequent traders as well as the pending transaction congestion in Ethereum.

Banana Fusarium wilt (BFW) is a devastating disease with no effective cure methods. Timely and effective detection of the disease and evaluation of its spreading trend will help farmers in making right decisions on plantation management. The main purpose of this study was to find the spectral features of the BFW-infected canopy and build the optimal BFW classification models for different stages of infection. A RedEdge-MX camera mounted on an unmanned aerial vehicle (UAV) was used to collect multispectral images of a banana plantation infected with BFW in July and August 2020. Three types of spectral features were used as the inputs of classification models, including three-visible-band images, five-multispectral-band images, and vegetation indices (VIs). Four supervised methods including Support Vector Machine (SVM), Random Forest (RF), Back Propagation Neural Networks (BPNN) and Logistic Regression (LR), and two unsupervised methods including Hotspot Analysis (HA) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) were adopted to detect the BFW-infected canopies. Comparing to the healthy canopies, the BFW-infected canopies had higher reflectance in the visible region, but lower reflectance in the NIR region. The classification results showed that most of the supervised and unsupervised methods reached excellent accuracies. Among all the supervised methods, RF based on the five-multispectral-band was considered as the optimal model, with higher overall accuracy (OA) of 97.28% and faster running time of 22 min. For the unsupervised methods, HA reached high and balanced OAs of more than 95% based on the selected VIs derived from the red and NIR band, especially for WDRVI, NDVI, and TDVI. By comprehensively evaluating the classification results of different metrics, the unsupervised method HA was recommended for BFW recognition, especially in the late stage of infection; the supervised method RF was recommended in the early stage of infection to reach a slightly higher accuracy. The results found in this study could give advice for banana plantation management and provide approaches for plant disease detection.

Lithium-rich manganese-based layered cathode materials are considered to be one of the best options for next-generation lithium-ion batteries, owing to their ultra-high specific capacity (>250 mAh·g−1) and platform voltage. However, their poor cycling stability, caused by the release of lattice oxygen as well as the electrode/electrolyte side reactions accompanying complex phase transformation, makes it difficult to use this material in practical applications. In this work, we suggest a molybdenum surface modification strategy to improve the electrochemical performance of Li1.2Mn0.54Ni0.13Co0.13O2. The Mo-modified Li1.2Mn0.54Ni0.13Co0.13O2 material exhibits an enhanced discharge specific capacity of up to 290.5 mAh·g−1 (20 mA·g−1) and a capacity retention rate of 82% (300 cycles at 200 mA·g−1), compared with 261.2 mAh·g−1 and a 70% retention rate for the material without Mo modification. The significantly enhanced performance of the modified material can be ascribed to the formation of a Mo-compound-involved nanolayer on the surface of the materials, which effectively lessens the electrolyte corrosion of the cathode, as well as the activation of Mo6+ towards Ni2+/Ni4+ redox couples and the pre-activation of a Mo compound. This study offers a facile and effective strategy to address the poor cyclability of lithium-rich manganese-based layered cathode materials.

We aimed to determine whether the average mean arterial pressure (aMAP) in the first 24 hours of hospital admission is useful in predicting short-term outcomes of patients with intermediate- and high-risk pulmonary embolism (PE). We conducted a single-center retrospective study. From May 2012 to April 2019, 122 patients with intermediate- and high-risk PE were included. The primary outcome was in-hospital mortality. The secondary outcome was adverse events. Receiver operating characteristic (ROC) curves and cutoff values for aMAP predicting in-hospital death were computed. According to cutoff values, we categorized 5 groups defined as follows: group 1: aMAP < 70 mm Hg; group 2: 70 mm Hg ≤ aMAP < 80 mm Hg; group 3: 80 mm Hg ≤ aMAP < 90 mm Hg; group 4: 90 mm Hg ≤ aMAP

Abstract The objective of this study was to identify molecular responses in Ditylenchus destructor to cold and desiccation by means of transcriptomes analyses. A total of 102,517 unigenes were obtained, with an average length of 1,076 bp, in which 58,453 (57%) had a functional annotation. A total of 1154 simple sequence repeats (SSRs) distributed over 1078 unigenes were detected. Gene expression profiles in response to cold and desiccation stress and the expression of specific stress-related genes were compared. Gene ontology analysis and pathway-based analysis were used to further investigate the functions of the differentially expressed genes. The reliability of the sequencing data was verified through quantitative real-time PCR analysis of 19 stress-related genes. RNA interference used to further assess the functions of the cold-related unigenes 15628 and 15596 showed that the knockdown of each of these genes led to decreased cold tolerance of D. destructor. Hence, this study revealed molecular processes and pathways active in cold- or dessication-treated nematodes. The transcriptome profiles presented in this study provide insight into the transcriptome complexity and will contribute to further understand stress tolerance in D. destructor.

Recently, due to the increasing popularity of enjoying various multimedia services on mobile devices (e.g., smartphones, ipads, and electronic tablets), the generated mobile data traffic has been explosively growing and has become a serve burden on mobile network operators. To address such a serious challenge in mobile networks, an effective approach is to manage data traffic by using complementary technologies (e.g., small cell network, WiFi network, and so on) to achieve mobile data offloading. In this paper, we discuss the recent advances in the techniques of mobile data offloading. Particularly, based on the initiator diversity of data offloading, we classify the existing mobile data offloading technologies into four categories, i.e., data offloading through small cell networks, data offloading through WiFi networks, data offloading through opportunistic mobile networks, and data offloading through heterogeneous networks. Besides, we show a detailed taxonomy of the related mobile data offloading technologies by discussing the pros and cons for various offloading technologies for different problems in mobile networks. Finally, we outline some opening research issues and challenges, which can provide guidelines for future research work.

Recently, for satisfying users' various mobile Internet service requests for data exchange anytime and anywhere even in their moving vehicles, the generated mobile data traffic has been rapidly increasing and has become a serious burden on current cellular networks. To partially address such a serve challenge, vehicular ad hoc networks (VANETs) have emerged as an effective approach for enhancing vehicular services and applications by equipping vehicles with wireless and processing capabilities. In this paper, we survey the recent advances in the data offloading techniques through VANETs. Particularly, based on the communication patterns among vehicles and infrastructures, we classify these techniques into three categories, i.e., data offloading through vehicle-to-vehicle communications, vehicle-to-infrastructure communications, and vehicle-to-everything communications. Besides, we present a detailed taxonomy of the related techniques by discussing the pros and cons for various offloading techniques for different problems in VANETs. Finally, some opening research issues and challenges are outlined to provide guidelines for future research work.

Flaxseed oil is a rich source of n-3 fatty acid alpha-linolenic acid which has been identified as having significant cardioprotective effects. The objective of this study was to investigate the effect of partial replacement of lard with flaxseed oil on atherosclerosis and investigate the underlying mechanisms. Apolipoprotein-E knockout mice were given a normal chow diet, a lard based western-type high-fat diet (WTD), or a WTD with partial replacement of lard with 10% flaxseed oil (w/w) for 16 weeks, respectively. Results demonstrated that partial replacing of lard with flaxseed oil significantly ameliorated atherosclerosis, as well as improved lipid abnormalities, oxidative stress, and inflammation. These data were associated with modification effects on expression of genes involved in lipid metabolism (SREBP-2, HMGCR, SREBP-1c, and ACC), oxidative stress (NADPH oxidase), and inflammation (TNF-α, IL-6, MCP-1, and VCAM-1). Our data providing evidence that flaxseed oil could be a promising functional food in cardiovascular health promotion.

Feeding cereal grain to cattle is common practice for optimal beef and milk production. High concentrations of starch and other soluble carbohydrates may cause acidosis. Information on the effect of processing on starch and protein degradability in the rumen are scarce. This study was to determine the ruminal degradation patterns of common grains and the effect of steam pelleting on starch and crude protein (CP) degradability in the rumen. The ruminal degradation pattern of dry matter (DM), starch, and CP of ground and pelleted sorghum, barley, wheat, and samples along with ground oats and triticale were determined using the in situ nylon bags method. Cereals were incubated for 0, 2, 4, 6, 8, 16, 32, and 60 h, and the fast and slowly degradable fraction, the effective degradation rate, and effective degradability (ED) of DM, starch, and CP were calculated. The starch ED of ground and pelleted sorghum, barley, and two wheat samples were 57.3, 93.6, 95.2, and 97.2%; and 61.5, 93.8, 93.8, and 95.6%, and their crude protein ED was 54.8, 82.3, 83.3, 82.6% and 51.9, 79.2, 81.8, and 78.1% respectively. The starch ED of ground oat and triticale were 98.3 and 94.7%, and that of CP were 93.7 and 75.2%, respectively. The degradability of sorghum was significantly lower than that of the other grains. Pelleting increased the fast-degradable DM and starch faction of sorghum and tended to improve its DM degradability (p = 0.081). Pelleting significantly reduced the fast-degradable fraction of DM and starch of wheat samples and numerically reduced its degradability.

In recent years, demands for rich multimedia services over mobile networks have been soaring at a tremendous pace. Traditional dedicated networking equipment may not be able to efficiently support the phenomenal growth of the traffic load and user demand dynamics while consuming an unnecessarily large amount of energy resources. Recently, mobile content caching, whereby popular contents are cached inside the mobile front-haul and back-haul networks so that demands for these contents from users in proximity can be easily accommodated without redundant transmissions from the remote sources, has emerged as an efficient technique for multimedia content delivery. Mobile content caching is particularly suitable for fifth generation (5G) mobile systems that are being designed to incorporate advanced cloud computing technologies and network function virtualization techniques. Therefore, in this paper, we first propose the concept of “Caching-as-a-Service” (CaaS) based on cloud-based radio access networks, and virtualized evolved packet core, which provides the capability to cache anything at anytime, anywhere in the cloud-based 5G mobile systems to satisfy user demands from any service location with high elasticity and adaptivity, and to empower third-party service providers with flexible controllability and programmability. Then, we study the potential techniques related to the virtualization of caching, and discuss the technical details of virtualization and optimization of CaaS in 5G mobile networks. Some novel schemes for CaaS are proposed to target different mobile applications and services. We also explore new opportunities and challenges for further research.

Camelina meal (CAM) was fermented in solid-state using food grade Aspergillus fungi (A. sojae, A. ficuum and their co-cultures), and the physicochemical composition, microbiological and functional properties were investigated. SSF increased the starch contents but reduced (p < 0.05) the contents of soluble carbohydrate. The microbiological counts of the fermented meals were higher (p < 0.05) than that of the unfermented CAM. Phytic acid content reduced (p < 0.05) in the fermented meals. SSF reduced the protein molecular weight and colour attributes of CAM. The fermented camelina meals had increased (p < 0.05) bulk density and swelling capacity but reduced (p < 0.05) water absorption capacity. Thus, the study indicated that SSF with A. sojae, A. ficuum and their co-cultures influenced the physicochemical, microbiological and functional properties of CAM. There is potential for the development of value-added novel food and feed products from solid-state fermented camelina meal.

Camelina (Camelina sativa) also known as false flax or gold of pleasure is an oilseed crop of the Brassica (Cruciferae) family. Camelina is not a food crop, however, the by-product (meal or cake) obtained from the oil extraction of camelina seeds is useful as animal feed because of its moderate crude protein content. The dietary use of camelina meal in broiler diets is limited to low inclusion due to the presence of anti-nutritional factors such as fibre, phytic acid, glucosinolates and tannins which have negative effects on broiler performance. Solid-state fermentation (SSF) is a suitable processing method for enriching agroindustrial by-products since it offers several cost-effective and practical advantages. In the present study, the effect of SSF on the nutrient composition, phytic acid and total phenolic contents of expeller-extracted camelina meal was evaluated. Aspergillus ficuum (ATCC 66876) was used for SSF under aerobic conditions at 30oC for 7 days. Unfermented and fermented camelina meals were analyzed for dry matter, crude protein, crude fat, crude fibre, total sugar (sucrose) and starch as well as for pH, phytic acid and total phenolic contents. Crude protein was improved by 6.79% while total sugar and starch were reduced by 90.99% and 75.78%, respectively in the solid-state fermented camelina meal. Phytic acid and total phenolic contents were also decreased by 39.17% and 56.11%, respectively. This study revealed that SSF could be used to improve the nutritional quality of camelina meal for improved use in poultry feed formulation.

Mobile crowd-sensing is a prospective paradigm especially for intelligent mobile terminals, which collects ubiquitous data efficiently in metropolis. The existing crowd-sensing schemes based on intelligent terminals mainly consider the current trajectory of the participants, and the quality highly depends on the spatial-temporal coverage which is easily weakened by the mobility of participants. Nowadays, public transports are widely used and affordable in many cities around the globe. Public transports embedded with substantial sensors act as participants in crowd-sensing, but different from the intelligent terminals, the trajectory of public transports is schedulable and predictable, which sheds an opportunity to achieve high-quality crowd-sensing. Therefore, based on the predictable trajectory of public transports, we design a novel system model and formulate the selection of public transports as an optimization problem to maximize the spatial–temporal coverage. After proving the public transport selection is non-deterministic polynomial-time hardness, an approximation algorithm is proposed and the coverage is close to 1. We evaluate the proposed algorithm with samples of real T-Drive trajectory data set. The results show that our algorithm achieves a near optimal coverage and outperforms existing algorithms.

ObjectiveTo assess the correlation between the incidence of non-erosive reflux disease (NERD) and mental and psychological factors, deepen the understanding of the pathogenesis of NERD and explore effective treatments.MethodsNERD patients with mood disorders who met the inclusion criteria were randomly divided into a drug treatment group, a psychotherapy group, and a psychotherapy combined with drug treatment group. Before and after treatment, the patients were retrospectively analyzed using the gastroesophageal reflux disease Questionnaire, Hamilton Depression Scale, Hamilton Anxiety Scale, and SF-36 Quality of Life Scale.ResultsAll three treatments were found to relieve patients’ symptoms and improve their quality of life to some extent. The psychotherapy combined with drug treatment group showed the best overall curative effect. The Hamilton Depression and Anxiety Scale scores were significantly lower in the psychotherapy-alone group and psychotherapy combined with drug treatment group than in the drug treatment alone group at 4, 8, and 12 weeks (P

Recently, mobile networking systems have been designed with more complexity of infrastructure and higher diversity of associated devices and resources, as well as more dynamical formations of networks, due to the fast development of current Internet and mobile communication industry. In such emerging mobile heterogeneous networks (HetNets), there are a large number of technical challenges focusing on the efficient organization, management, maintenance, and optimization, over the complicated system resources. In particular, HetNets have attracted great interest from academia and industry in deploying more effective solutions based on artificial intelligence (AI) techniques, e.g., machine learning, bio-inspired algorithms, fuzzy neural network, and so on, because AI techniques can naturally handle the problems of large-scale complex systems, such as HetNets towards more intelligent and automatic-evolving ones. In this paper, we discuss the state-of-the-art AI-based techniques for evolving the smarter HetNets infrastructure and systems, focusing on the research issues of self-configuration, self-healing, and self-optimization, respectively. A detailed taxonomy of the related AI-based techniques of HetNets is also shown by discussing the pros and cons for various AI-based techniques for different problems in HetNets. Opening research issues and pending challenges are concluded as well, which can provide guidelines for future research work.