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Detection and counting of abalones is one of key technologies of abalones breeding density estimation. The abalones in the breeding stage are small in size, densely distributed, and occluded between individuals, so the existing object detection algorithms have low precision for detecting the abalones in the breeding stage. To solve this problem, a detection and counting method of juvenile abalones based on improved SSD network is proposed in this research. The innovation points of this method are: Firstly, the multi-layer feature dynamic fusion method is proposed to obtain more color and texture information and improve detection precision of juvenile abalones with small size; secondly, the multi-scale attention feature extraction method is proposed to highlight shape and edge feature information of juvenile abalones and increase detection precision of juvenile abalones with dense distribution and individual coverage; finally, the loss feedback training method is used to increase the diversity of data and the pixels of juvenile abalones in the images to get the even higher detection precision of juvenile abalones with small size. The experimental results show that the AP@0.5 value, AP@0.7 value and AP@0.75 value of the detection results of the proposed method are 91.14%, 89.90% and 80.14%, respectively. The precision and recall rates of the counting results are 99.59% and 97.74%, respectively, which are superior to the counting results of SSD, FSSD, MutualGuide, EfficientDet and VarifocalNet models. The proposed method can provide support for real-time monitoring of aquaculture density for juvenile abalones.

Abstract Background The traditional surgical procedures for upper lumbar disc herniation (ULDH) usually lead to frequent complications. We aim to investigate the clinical efficacy of the unilateral biportal endoscopy (UBE) technique in treating upper lumbar disc herniation (ULDH). Methods From January 2020 to December 2021, the clinical data of 28 patients with ULDH treated with the UBE technique were collected and analyzed for surgery time under UBE, postsurgical drainage, postsurgical hospital stay, and complications. The clinical efficacy was evaluated according to the modified MacNab score, Oswestry disability index (ODI), and visual analogue scale (VAS) of low back pain and lower limb pain before the surgery; one week, one month, and three months after the surgery; and at the last follow-up. Results All patients underwent the UBE surgery successfully. The surgery time under UBE for non-fusion cases was 47.50 ± 11.84 min (monosegment) and 75.00 ± 20.66 min (two segments), while that for fusion cases was 77.50 ± 21.02 min. The postsurgical drainage for non-fusion cases was 25.00 ± 13.94 mL (monosegment) and 38.00 ± 11.83 mL (two segments), while that for fusion cases was 71.25 ± 31.72 mL. The postsurgical hospital stay was 8.28 ± 4.22 days. The follow-up time was 15.82 ± 4.54 months. The VAS score for each time period after the surgery was significantly lower (P

Abstract Ultrashort pulses, characterized by their short pulse duration, diverse spectral content, and high peak power, are widely used in fields including laser processing, optical storage, biomedical sciences, and laser imaging. The complex, highly-nonlinear process of ultrashort pulse evolution within fiber lasers is influenced by numerous aspects such as dispersion, loss, gain, and nonlinear effects. Traditionally, the split-step Fourier transforms method is employed for simulating ultrashort pulses in fiber lasers, which involves traversing multiple parameters within the fiber to attain the pulse’s optimal state. The simulation is a significantly time-consuming process. Here, we use a neural network model to fit and predict the impact of multiple parameters on the pulse characteristics within fiber lasers, enabling parameter optimization through genetic algorithms to determine the optimal pulse duration, pulse energy, and peak power. Integrating artificial intelligence algorithms simplifies the acquisition of optimal pulse parameters and enhances our understanding of multiple parameters’ impact on the pulse characteristics. The investigation of ultrashort pulse optimization based on artificial intelligence holds immense potential for laser design.

Abstract Wound infections pose a major healthcare issue, affecting the well‐being of millions of patients worldwide. Effective intervention and on‐site detection are important in wound management. However, current approaches are hindered by time‐consuming analysis and a lack of technology for real‐time monitoring and prompt therapy delivery. In this study, a smart wound patch system (SWPS) designed for wireless closed‐loop and in‐situ wound management is presented. The SWPS integrates a microfluidic structure, an organic electrochemical transistor (OECT) based sensor, an electrical stimulation module, and a miniaturized flexible printed circuit board (FPCB). The OECT incorporates a bacteria‐responsive DNA hydrogel‐coated gate for continuous monitoring of bacterial virulence at wound sites. Real‐time detection of OECT readings and on‐demand delivery of electrical cues to accelerate wound healing is facilitated by a mobile phone application linked with an FPCB containing low‐power electronics equipped with parallel sensing and stimulation circuitry. In this proof‐of‐concept study, the functionality of the SWPS is validated and its application both in vitro and in vivo is demonstrated. This proposed system expands the arsenal of tools available for effective wound management and enables personalized treatment.

Domain adaptation has been demonstrated to be an important technique to reduce the expensive annotation costs for remote sensing segmentation. However, for remote sensing images (RSIs) acquired from different imaging modalities with significant differences, a model trained on one modality can hardly be utilized for images of other modalities. This leads to a greater challenge in domain adaptation, called heterogeneous domain adaptation (HDA). To address this issue, we propose a novel method called transfer diffusion (Trans-Diff), which is the first work to explore the diffusion model for HDA remote sensing segmentation. The proposed Trans-Diff constructs cross-domain unified prompts for the diffusion model. This approach enables the generation of images from different modalities with specific semantics, leading to efficient HDA segmentation. Specifically, we first propose an interrelated semantic modeling method to establish semantic interrelation between heterogeneous RSIs and annotations in a high-dimensional feature space and extract the unified features as the cross-domain prompts. Then, we construct a semantic guidance diffusion model to further improve the semantic guidance of images generated with the cross-domain prompts, which effectively facilitates the semantic transfer of RSIs from source modality to target modality. In addition, we design an adaptive sampling strategy to dynamically regulate the generated images' stylistic consistency and semantic consistency. This can effectively reduce the cross-domain discrepancies between different modalities of RSIs, ultimately significantly improving the HDA remote sensing segmentation performance. Experimental results demonstrate the superior performance of Trans-Diff over advanced methods on several heterogeneous RSI datasets.

Abstract To evaluate the safety and efficacy of low-intensity focused ultrasound (LIFU) therapy in facilitating fundus descent and relieving postpartum breast pain compared with sham treatment. A multicentre, randomised, sham-controlled, blinded trial was conducted. A cohort of 176 eligible participants, who had normal prenatal check-ups and met the inclusion and exclusion criteria, were recruited from three medical centres and subsequently randomized into either the LIFU or sham group. All participants received three treatment sessions, wherein LIFU signal was applied to the uterus and breast sites using coupling gel, with the absence of ultrasound signal output in the sham group. Fundal height measurement and breast pain score were performed after each treatment. The primary outcome, uterine involution, was presented by measuring the fundal height of the uterus. The visual analogue scale (VAS) score, as a secondary outcome, was used to assess breast pain and determine the correlation between breast pain and fundal height as the outcome simultaneously. All participants were randomly assigned to either the LIFU group (n = 88) or sham group (n = 88), with seven individuals not completing the treatment. Overall, a statistically significant difference was noted in the rate and index of fundus descent after each treatment. The rate and index of fundus descent showed greater significance following the second treatment (rate: 1.5 (1.0, 2.0) cm/d; index: 0.15 (0.1, 0.18), P

Recent research has demonstrated that global warming significantly enhances peak vegetation growth on the Tibetan Plateau (TP), underscoring the influence of climatic factors on vegetation dynamics. Nevertheless, the effects of different drought types on peak vegetation growth remain underexplored. This study utilized satellite-derived gross primary productivity (GPP) and the normalized difference vegetation index (NDVI) to assess the impacts of soil moisture (SM) and vapor pressure deficit (VPD) on peak vegetation growth (GPPmax and NDVImax) across the TP from 2001 to 2022. Our findings indicate that NDVImax and GPPmax exhibited increasing trends in most regions, displaying similar spatial patterns, with 65.28% of pixels showing an increase in NDVImax and 72.98% in GPPmax. In contrast, the trend for SM primarily showed a decrease (80.86%), while VPD showed an increasing trend (74.75%). Through partial correlation analysis and ridge regression, we found that peak vegetation growth was significantly affected by SM or VPD in nearly 20% of the study areas, although the magnitude of these effects varied considerably. Furthermore, we revealed that hydrothermal conditions modulated the responses of peak vegetation growth to SM and VPD. In regions with annual precipitation less than 650 mm and an annual mean temperature below 10 °C, decreased SM and increased VPD generally inhibited peak vegetation growth. Conversely, in warm and humid areas, lower SM and higher VPD promoted peak vegetation growth. These findings are crucial for deepening our understanding of vegetation phenology and its future responses to climate change.

Introduction: It is known that patients with immune-abnormal co-pregnancies are at a higher risk of adverse pregnancy outcomes. Traditional pregnancy risk management systems have poor prediction abilities for adverse pregnancy outcomes in such patients, with many limitations in clinical application. In this study, we will use machine learning to screen high-risk factors for miscarriage and develop a miscarriage risk prediction model for patients with immune-abnormal pregnancies. This model aims to provide an adjunctive tool for the clinical identification of patients at high risk of miscarriage and to allow for active intervention to reduce adverse pregnancy outcomes.Methods: Patients with immune-abnormal pregnancies attending Sichuan Provincial People’s Hospital were collected through electronic medical records (EMR). The data were divided into a training set and a test set in an 8:2 ratio. Comparisons were made to evaluate the performance of traditional pregnancy risk assessment tools for clinical applications. This analysis involved assessing the cost-benefit of clinical treatment, evaluating the model's performance, and determining its economic value. Data sampling methods, feature screening, and machine learning algorithms were utilized to develop predictive models. These models were internally validated using 10-fold cross-validation for the training set and externally validated using bootstrapping for the test set. Model performance was assessed by the area under the characteristic curve (AUC). Based on the best parameters, a predictive model for miscarriage risk was developed, and the SHapley additive expansion (SHAP) method was used to assess the best model feature contribution.Results: A total of 565 patients were included in this study on machine learning-based models for predicting the risk of miscarriage in patients with immune-abnormal pregnancies. Twenty-eight risk warning models were developed, and the predictive model constructed using XGBoost demonstrated the best performance with an AUC of 0.9209. The SHAP analysis of the best model highlighted the total number of medications, as well as the use of aspirin and low molecular weight heparin, as significant influencing factors. The implementation of the pregnancy risk scoring rules resulted in accuracy, precision, and F1 scores of 0.3009, 0.1663, and 0.2852, respectively. The economic evaluation showed a saving of ¥7,485,865.7 due to the model.Conclusion: The predictive model developed in this study performed well in estimating the risk of miscarriage in patients with immune-abnormal pregnancies. The findings of the model interpretation identified the total number of medications and the use of other medications during pregnancy as key factors in the early warning model for miscarriage risk. This provides an important basis for early risk assessment and intervention in immune-abnormal pregnancies. The predictive model developed in this study demonstrated better risk prediction performance than the Pregnancy Risk Management System (PRMS) and also demonstrated economic value. Therefore, miscarriage risk prediction in patients with immune-abnormal pregnancies may be the most cost-effective management method.

BackgroundShort-term poor uterine involution manifests as uterine contraction weakness. This is one of the important causes of postpartum hemorrhage, posing a serious threat to the mother's life and safety. The study aims to investigate whether low-intensity focused ultrasound (LIFUS) can effectively shorten lochia duration, alleviate postpartum complications, and accelerate uterine involution compared with the sham treatment.MethodsA multicenter, concealed, randomized, blinded, and sham-controlled clinical trial was conducted across three medical centers involving 176 subjects, utilizing a parallel group design. Enrollment occurred between October 2019 and September 2020, with a 42-day follow-up period. Participants meeting the inclusion and exclusion criteria based on normal prenatal examinations were randomly divided into the LIFUS group or the sham operation group via computer-generated randomization. Patients in the LIFUS group received usual care with the LIFUS protocol, wherein a LIFUS signal was transmitted to the uterine site through coupling gel, or sham treatment, where no low-intensity ultrasound signal output was emitted. The primary outcome, lochia duration, was assessed via weekly telephonic follow-ups post-discharge. The involution of the uterus, measured by uterine fundus height, served as the secondary outcome.ResultsAmong the 256 subjects screened for eligibility, 176 subjects were enrolled and randomly assigned to either the LIFUS group (n = 88) or the Sham group (n = 88). Data on the height of the uterine fundus were obtained from all the patients, with 696 out of 704 measurements (99%) successfully recorded. Overall, a statistically significant difference was noted in time to lochia termination (hazard ratio: 2.65; 95% confidence interval [CI]: 1.82-3.85; P < 0.001). The decline in fundal height exhibited notable discrepancies between the two groups following the second treatment session (mean difference: -1.74; 95% CI: -1.23 to -2.25; P < 0.001) and the third treatment session (mean difference: -3.26; 95% CI: -2.74 to -3.78; P < 0.001) after delivery. None of the subjects had any adverse reactions, such as skin damage or allergies during the treatment.ConclusionsThis study found that LIFUS treatment can promote uterine involution and abbreviate the duration of postpartum lochia. Ultrasound emerges as a safe and effective intervention, poised to address further clinical inquiries in the domain of postpartum rehabilitation.

The high similarity of shellfish images and unbalanced samples are key factors affecting the accuracy of shellfish recognition. This study proposes a new shellfish recognition method FL_Net based on a Convolutional Neural Network (CNN). We first establish the shellfish image (SI) dataset with 68 species and 93 574 images, and then propose a filter pruning and repairing model driven by an output entropy and orthogonality measurement for the recognition of shellfish with high similarity features to improve the feature expression ability of valid information. For the shellfish recognition with unbalanced samples, a hybrid loss function, including regularization term and focus loss term, is employed to reduce the weight of easily classified samples by controlling the shared weight of each sample species to the total loss. The experimental results show that the accuracy of shellfish recognition of the proposed method is 93.95%, 13.68% higher than the benchmark network (VGG16), and the accuracy of shellfish recognition is improved by 0.46%, 17.41%, 17.36%, 4.46%, 1.67%, and 1.03% respectively compared with AlexNet, GoogLeNet, ResNet50, SN_Net, MutualNet, and ResNeSt, which are used to verify the efficiency of the proposed method.

As the most common histological subtype of primary lung cancer, lung adenocarcinoma (LUAD) causes enormous cancer deaths worldwide. Radiotherapy has been frequently used in LUAD cases, and radiosensitivity is vital for LUAD therapy. This research sought to explore the genetic factors affecting radiosensitivity in LUAD and inner mechanisms. LINC00511, miR-497-5p, and SMAD3 expression in LUAD cells were detected via qRT-PCR and western blot. CCK-8 assays, colony formation, and flow cytometry assays were employed to explore the cell viability, apoptosis, and radiosensitivity in PC-9 and A549 cells. The targeting relationship between LINC00511, miR-497-5p, and SMAD3 was verified by dual luciferase reporter assay. Furthermore, xenograft experiments were performed for the in vivo verification. In conclusion, LINC00511 was overexpressed in LUAD cells, which downregulated downstream miR-497-5p expression and mediately led to SMAD3 activation. LINC00511 downregulation suppressed cell viability while enhanced apoptosis rate in LUAD cells. Also, LINC00511 and SMAD3 were overexpressed, while miR-497-5p was downregulated in LUAD cells exposed to 4Gy irradiation treatment. Moreover, LINC00511 inhibition could block SMAD3 expression and promoted the radiosensitivity both in vitro and in vivo. These findings uncover LINC00511 knockdown promoted miR-497-5p expression and subsequently led to lower SMAD3 level, which enhanced radiosensitivity in LUAD cells. LINC00511/miR-497-5p/SMAD3 axis could be of considerable potential to enhance radiosensitivity in LUAD.

BackgroundPreeclampsia is a pregnancy-specific disorder that always causes maternal and fetal serious adverse outcome. Disturbances in maternal immune tolerance to embryo at the maternal-fetal interface (MFI) may be associated with preeclampsia onset. Recent studies have revealed the reduced expression pattern of HLA-F at the MFI in preeclampsia, while the mechanism of it mediating maternal fetal immune tolerance has not been revealed.MethodsSingle-cell RNA sequencing on placental decidua was performed to reveal the immune disturbances landscape at the MFI in preeclampsia. Human Jar cells and NK-92MI cells were employed to study the role of HLA-F in trophoblasts and lymphocyte.ResultsA total of 101,250 cells were classified into 22 cell clusters. Disease-related IGFBP1+SPP1+ extracellular villus trophoblast (EVT) was identified in the preeclamptic placental decidua, accompanied by newly discovered immune cellular dysfunction such as reduced ribosomal functions of NK populations and abnormal expression of antigen-presenting molecules in most cell clusters. Certain genes that are characteristic of the intermediate stage of myeloid or EVT cell differentiation were found to have unexplored but important functions in the pathogenesis of preeclampsia; specifically, we detected enhanced cell cross-talk between IGFBP1+SPP1+ EVT2 or SPP1+M1 cells and their receptor cell populations at the MFI of PE patients compared to controls. With respect to HLA-F, mIF staining confirmed its reduced expression in PE samples compared to controls. Over-expression of HLA-F in Jar cells promoted cell proliferation, invasion, and migration while under-expression had the opposite effect. In NK-92MI cells, over-expression of HLA-F increased the secretion of immunoregulation cytokines such as CSF1 and CCL22, and promoted adaptive NKG2C+NK cell transformation.ConclusionsWe revealed the immune disturbance landscape at the MFI in preeclampsia. Our findings regarding cellular heterogeneity and immune cellular dysfunction, as revealed by scRNA-seq, and the function of HLA-F in cells provide new perspectives for further investigation of their roles in the pathogenesis of preeclampsia, and then provide potential new therapeutic target.

A vertex set $ S $ of a graph $ G $ is called a double total dominating set if every vertex in $ G $ has at least two adjacent vertices in $ S $. The double total domination number $ \gamma_{\times 2, t}(G) $ of $ G $ is the minimum cardinality over all the double total dominating sets in $ G $. Let $ G \square H $ denote the Cartesian product of graphs $ G $ and $ H $. In this paper, the double total domination number of Cartesian product of paths is discussed. We determine the values of $ \gamma_{\times 2, t}(P_i\square P_n) $ for $ i = 2, 3 $, and give lower and upper bounds of $ \gamma_{\times 2, t}(P_i\square P_n) $ for $ i \geq 4 $.

Abstract Recently, matrix factorization-based hashing has gained wide attention because of its strong subspace learning ability and high search efficiency. However, some problems need to be further addressed. First, uniform hash codes can be generated by collective matrix factorization, but they often cause serious loss, degrading the quality of hash codes. Second, most of them preserve the absolute similarity simply in hash codes, failing to capture the inherent semantic affinity among training data. To overcome these obstacles, we propose a Discrete Multi-similarity Consistent Matrix Factorization Hashing (DMCMFH). Specifically, an individual subspace is first learned by matrix factorization and multi-similarity consistency for each modality. Then, the subspaces are aligned by a shared semantic space to generate homogenous hash codes. Finally, an iterative-based discrete optimization scheme is presented to reduce the quantization loss. We conduct quantitative experiments on three datasets, MSCOCO, Mirflickr25K and NUS-WIDE. Compared with supervised baseline methods, DMCMFH achieves increases of $$0.22\%$$ 0.22 % , $$3.00\%$$ 3.00 % and $$0.79\%$$ 0.79 % on the image-query-text tasks for three datasets respectively, and achieves increases of $$0.21\%$$ 0.21 % , $$1.62\%$$ 1.62 % and $$0.50\%$$ 0.50 % on the text-query-image tasks for three datasets respectively.

To identify the abnormal characteristics of the oplegnathus punctatus is great importance to the detection of iridovirus disease in the breeding environment. In this paper, an advanced neural network model to identify the characteristics of the oplegnathus punctatus and predict its different periods of suffering from iridovirus disease is proposed based on the establishment of a data set. First of all, a standard format data set of oplegnathus punctatus and an abnormal format date set are established in order to verify the effectiveness of the method in this paper. And then, the feature extraction fusion method is used for preprocessing in terms of the abnormal format data set, which combines the edge features extracted by the improved multi-template Sobel operator and the color features extracted by the HSV model. Finally, an improved VGG-GoogleNet network recognition model comes into being through the fusion and improvement of the VGG and GoogleNet neural network structure. The experiments results show that the prediction accuracy rate for oplegnathus punctatus suffering from iridovirus disease in the the abnormal format data set and the standard format data set are improved, which reach 98.55% and 69.18%.

Abstract Background Brucellosis poses a serious threat to human and animal health, particularly in developing countries such as China. The Inner Mongolia Autonomous Region is one of the most severely brucellosis-endemic provinces in China. Currently, the host immune responses functioning to control Brucella infection and development remain poorly understood. The aim of this study is to further clarify the key immunity characteristics of diverse stages of brucellosis in Inner Mongolia. Methods We collected a total of 733 blood samples from acute (n = 137), chronic (n = 316), inapparent (n = 35), recovery (n = 99), and healthy (n = 146) groups from the rural community of Inner Mongolia between 2014 and 2015. The proportions of CD4+, CD8+, Th1, Th2, and Th17 T cells in peripheral blood and the expression of TLR2 and TLR4 in lymphocytes, monocytes and granulocytes were examined using flow cytometry analysis. The differences among the five groups were compared using one-way ANOVA and the Kruskal–Wallis method, respectively. Results Our results revealed that the proportions of CD4+ and CD8+ T cells were significantly different among the acute, chronic, recovery, and healthy control groups (P 0.05). The proportion of Th2 lymphocytes was significantly higher in the acute and healthy groups than in the inapparent group (P 0.5), except for the inapparent group between lymphocytes and granulocytes (r = 0.34). Conclusions Two key factors (CD8+ T cells and TLR4) in human immune profiles may closely correlate with the progression of brucellosis. The detailed function of TLR4 in the context of a greater number of cell types or tissues in human or animal brucellosis and in larger samples should be further explored in the future. Graphical Abstract

The design and fabrication of nanomaterials with controllable morphology and size is of critical importance to achieve excellent catalytic performance in heterogeneous catalysis. In this work, cobalt oxide (Co3O4) nanostructures with different morphologies (nanoplates, microflowers, nanorods and nanocubes) were successfully constructed in order to establish the morphology–property–performance relationship of the catalysts. The morphology and structure of the nanostructured Co3O4 were characterized by various techniques, and the catalytic performance of the as-prepared nanostructures was studied by monitoring the reduction of p-nitrophenol to p-aminophenol in the presence of excess NaBH4. The catalytic performance was found to be strongly dependent on their morphologies. The experimental results show that the pseudo-first-order reaction rate constants for Co3O4 nanostructures with various shapes are, respectively, 1.49 min−1 (nanoplates), 1.40 min−1 (microflowers), 0.78 min−1 (nanorods) and 0.23 min−1 (nanocubes). The Co3O4 nanoplates exhibited the highest catalytic activity among the four nanostructures, due to their largest specific surface area, relatively high total pore volume, best redox properties and abundance of defect sites. The established correlation between morphology, property and catalytic performance in this work will offer valuable insight into the design and application of nanostructured Co3O4 as a potential non-noble metal catalyst for p-nitrophenol reduction.

Therapeutic oligonucleotides (TOs) represent one of the most promising drug candidates in the targeted cancer treatment due to their high specificity and capability of modulating cellular pathways that are not readily druggable. However, efficiently delivering of TOs to cancer cellular targets is still the biggest challenge in promoting their clinical translations. Emerging as a significant drug delivery vector, nanoparticles (NPs) can not only protect TOs from nuclease degradation and enhance their tumor accumulation, but also can improve the cell uptake efficiency of TOs as well as the following endosomal escape to increase the therapeutic index. Furthermore, targeted and on-demand drug release of TOs can also be approached to minimize the risk of toxicity towards normal tissues using stimuli-responsive NPs. In the past decades, remarkable progresses have been made on the TOs delivery based on various NPs with specific purposes. In this review, we will first give a brief introduction on the basis of TOs as well as the action mechanisms of several typical TOs, and then describe the obstacles that prevent the clinical translation of TOs, followed by a comprehensive overview of the recent progresses on TOs delivery based on several various types of nanocarriers containing lipid-based nanoparticles, polymeric nanoparticles, gold nanoparticles, porous nanoparticles, DNA/RNA nanoassembly, extracellular vesicles, and imaging-guided drug delivery nanoparticles.

This investigation is aiming at the development of a method for in-situ 3D imaging and reconstructions of objects in the rain. The proposed method is based on the use of monochromatic sinusoidal fringe pattern generated by the designed optical system, the polarization technique, and the Fourier-transform-based algorithm of reconstruction. Based on the theoretical analyses and experimental results, it shows that the generated laser-beam-based signal keeps being coherent until reaching the observed object. The coherence of the projected sinusoidal signal is the key feature of the proposed method and ensures the accuracy of measurement and reconstruction of objects under rainy conditions. Moreover, the effects resulted from the spectral absorption and multiple scattering on the propagation of the projected sinusoidal fringe pattern in the rain can be removed using the polarization technique. The developed method is capable of obtaining accurate 3D reconstructions of the objects under rainy conditions together with background illumination, the multiply-scattered light, the vibration from environmental influences including the wind, and the inhomogeneous medium of rain. The application of this method does not suffer the limitation of focusing, precise synchronization, and the speed of measurement that might be a problem for the time-of-flight technique and digital-light-processing-based profilometry.

Mycoplasma ovipneumoniae (MO) is a principle causative agent of chronic respiratory disease in ruminants, including sheep, goats, and deer, posing a great threat to the ruminant industry worldwide. However, the pathogenesis of MO infection still remains not well understood and needs further clarification. Here we report a time-dependent apoptosis in cultured murine alveolar macrophage (MH-S) cell lines in response to MO infection in vitro. Mechanistically, MO infection activated apoptosis in MH-S cells through caspase-8-dependent extrinsic pathway and through tumor protein 53 (p53)- and reactive oxygen species (ROS)-dependent intrinsic mitochondrial pathways. Moreover, MO infection promoted both transcription and translation of proinflammatory cytokine genes including interleukin-1β (IL-1β), IL-18, and tumor necrosis factor-α (TNF-α), in a caspase-8-, p53-, and ROS-dependent manner, implying a potential link between MO-induced inflammation and apoptotic cell death. Collectively, our results suggest that MO infection induces the activation of extrinsic and intrinsic apoptotic pathways in cultured MH-S cells, which is related to upregulated expression of proinflammatory cytokines. Our findings will contribute to the elucidation of pathogenesis in MO infection and provide valuable reference for the development of new strategies for controlling MO infection.

ABSTRACT Recently, the incidence of diseases caused by nontuberculous mycobacteria (NTM) has been increasing worldwide, especially in immunocompromised patients and those with potential chronic lung disease. Vitek MS v3.0 matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid and reliable method for identifying mycobacteria in clinical laboratories. This study aimed to evaluate the performance of Vitek MS v3.0 by isolating NTM directly from automated liquid medium systems using patient samples. A total of 855 Mycobacterium growth indicator tube (MGIT)-positive liquid cultures were investigated. Among them, 658 (77.0%) liquid cultures were correctly identified to the species, group, or complex level, 192 (23.0%) resulted in no identification, and 5 (0.6%) were misidentified at the species level. DNA sequencing identified 855 NTM isolates from liquid cultures, comprising 316 isolates of rapidly growing mycobacteria (RGM) and 539 isolates of slow-growing mycobacteria (SGM). Using the Vitek MS system, the RGM integral identification rate (276/316 [87.34%]) was higher than the SGM rate (381/539 [70.69%]) (P

It is of great significance to use underwater video and image processing technology to detect and analyze fish behaviors. In this paper, an Oplegnathus image dataset for fish behaviors study by deep learning algorithm is constructed, and the data is captured from two cameras (one above water and another below water); and then an improved Neural Network model based on multi-scale features is proposed for fish behaviors learning automatically. To overcome the occlusion and blur problems of the images, the lightweight neural network MobileNet-SSD is improved by adding a dilate convolution, and SE blocks are added to the feature maps at different scales to establish a self-attention mechanism; the Focal Loss function is used to calculate the classification loss and to balance the proportion of background and target samples. The results of the experiments show that the average behaviors detection accuracy of our method reach 90.94% and 88.36% in both overwater and underwater datasets.

Selective labeling of distinct bacteria and biofilm is poised for the fundamental understanding of bacterial activities, interactions, and coupled phenomena occurring at the microscale. However, a simple and effective way to achieve selective bacterial labeling is still lacking. Herein, we report a fluorescence probe with core-shell nanostructure that has polydopamine (PDA) coating on the surface of fluorescent silicon quantum dots (SiQDs@PDA). The surface of the SiQDs@PDA can be functionalized by various molecules (2-mercaptoethylamine hydrochloride, PEG, d-alanine, glucose amide) through different strategies (Michael addition, π-π interaction, and ion–ion interaction). Importantly, the d-alanine (D-Ala)- and gluconamide (Glc)-functionalized SiQDs@PDA fluorescence probes are capable of selectively labeling gram-positive and gram-negative bacteria, as well as their biofilms. The excellent performance in universal functionalization and selective labeling and imaging of bacteria and their biofilms demonstrate that SiQDs@PDA are a promising fluorescence tool in microbe research.

Let $G=(V,E)$ be a connected graph of order $n$. $S \subseteq V$ is an edge metric generator of $G$ if any pair of edges in $E$ can be distinguished by some element of $S$. The edge metric dimension $edim(G)$ of a graph $G$ is the least size of an edge metric generator of $G$. In this paper, we give the characterization of all connected bipartite graphs with $edim=n-2$, which partially answers an open problem of Zubrilina (2018). Furthermore, we also give a sufficient and necessary condition for $edim(G)=n-2$, where $G$ is a graph with maximum degree $n-1$. In addition, the relationship between the edge metric dimension and the clique number of a graph $G$ is investigated by construction.

Let $m$ be a positive integer and $C = \{1,2,\dots,m\}$ be a set of $m$ colors. A polychromatic $m$-coloring of a hypergraph is a coloring of its vertices in such a way that every hyperedge contains at least one vertex of each color in $C$. This problem is a generalization of $2$-colorings of hypergraphs and has close relations with the longest lifetime problem for a wireless sensor network, cover decompositions problem of hypergraphs and vertex cover problem of hypergraphs. In this paper, a main work is to find the maximum $m$ that a hypergraph $H$, with $n$ hyperedges, admits a polychromatic $m$-coloring such that each color appears at least $k$ times on each hyperedge. A $2\ln n$ approximation to the number is obtained when $k$ is a fixed positive integer. For the case that $k=O(n\ln n)$, there exists an $O(\ln n)$ approximation algorithm; for the case that $k=\omega (n\ln n)$, there exists a $(2+\sqrt{3})k$ approximation algorithm.

This article proposes a global prototypical network (GPN) to solve the problem of hyperspectral image classification using limited supervised samples (i.e., few-shot problem). In the proposed method, a strategy of global representation learning is adopted to train a network (fθ) to transfer the samples from the original data space to an embedding-feature space. In the new feature space, a vector called global prototypical representation for each class is learned. In terms of the network (fθ), we designed an architecture of a deep network consisting of a dense convolutional network and the spectral-spatial attention network. For the classification, the similarities between the unclassified samples and the global prototypical representation of each class are evaluated and the classification is finished by nearest neighbor classifier. Several public hyperspectral images were utilized to verify the proposed GPN. The results showed that the proposed GPN obtained the better overall accuracy compared with existing methods. In addition, the time expenditure of the proposed GPN was similar with several existing popular methods. In conclusion, the proposed GPN in this article is state-of-the-art for solving the problem of hyperspectral image classification using limited supervised samples.

Colloidal suspensions of nanoparticles (e.g., metals and oxides) have been considered as a promising working fluid in microreactors for achieving significant process intensification. Existing examples include their uses in microflow as catalysts for enhancing the reaction efficiency, or as additives to mix with the base fluid (i.e., to form the so-called nanofluids) for heat/mass transfer intensification. Thus, hydrodynamic characterization of such suspension flow in microreactors is of high importance for a rational design and operation of the system. In this work, experiments have been conducted to investigate the flow pattern and pressure drop characteristics under slug flow between N2 gas and colloidal suspensions in the presence of TiO2 or Al2O3 nanoparticles through polytetrafluoroethylene (PTFE) capillary microreactors. The base fluid consisted of water or its mixture with ethylene glycol. The slug flow pattern with nanoparticle addition was characterized by the presence of a lubricating liquid film around N2 bubbles, in contrast to the absence of liquid film in the case of N2-water slug flow. This shows that the addition of nanoparticles has changed the wall wetting property to be more hydrophilic. Furthermore, the measured pressure drop under N2-nanoparticle suspension slug flow is well described by the model of Kreutzer et al. (AIChE J 51(9):2428–2440, 2005) at the mixture Reynolds numbers ca. above 100 and is better predicted by the model of Warnier et al. (Microfluidics and Nanofluidics 8(1):33–45, 2010) at lower Reynolds numbers given a better consideration of the effect of film thickness and bubble velocity under such conditions in the latter model. Therefore, the employed nanoparticle suspension can be considered as a stable and pseudo single phase with proper fluid properties (e.g., viscosity and density) when it comes to the pressure drop estimation.

One of the key challenges in using fossil fuels is the environmental impacts of these energy sources, and to reduce these destructive effects, the use of renewable energy sources (biofuels) is necessary. One of the important biofuels is biodiesel, which can be produced from cottonseed. To properly manage the chain dealing with biodiesel production from the cottonseed chain (from farm to manufactory), environmental hotspots must be pinpointed. In the present study, it was attempted to examine the environmental impacts of the biodiesel production cycle from cottonseed (agronomic stages, ginning, oiling, and biodiesel production). The data obtained in all three stages were analyzed by the Impact 2002+ method in the SimaPro software. The highest contribution to creating environmental indicators at the agricultural stage was related to the use of nitrogen fertilizers, direct emission from the farm and fossil fuels, the ginning and oiling stage involving the use of diesel fuel and sulfuric acid, and the production of biodiesel in the manufactory involving the use of methanol and electricity. The potential environmental impacts of a functional unit of 1 kg of biodiesel include: human health, 9.05–10−6 (DAYLY); ecosystem quality, 1.369 (PDF*m2*year); climate changes, (kg CO2 eq.) 17.247; and resources (MJ primary), 89.116. Results showed that agriculture has more significant participation in the environmental impact than other sections (ginning and oiling and biodiesel production), especially due to the application of fertilizers and fuel. Surveying the environmental indicators of the results showed that at the agricultural stage, the human health indicator is 10.43, 1.21, and 5.32 times higher than the ecosystem quality, climate change, and resource indicators, respectively; at the ginning and oiling stages, it is 2.35, 31.68, and 2.09 times higher, respectively; and at the stage of biodiesel production in the manufactory, it is 16.41, 1.96, and 0.99 times higher, respectively, in terms of the destructive effects. The overall results showed that the hotspot points in the present study can be largely modified by reducing the consumption of nitrogen fertilizers, using new equipment and machinery, ginning and oiling, and using fewer methanol ratios than oil.

In this study, convolutional neural network (CNN) was used to classify and recognize the strawberry diseases and pests which cause tremendous economic losses for farmers. Based on AlexNet, the goal of this paper is to improve the recognition algorithm of strawberry diseases and pests by using the following methods: First, transfer learning is used to accelerate the time of model training and improve the recognition accuracy. Second, the model is fine-tuned to reduce the amount of training parameters and training time. The operator of combing inner product and ℓ∞ -norm was proposed and applied in the last fully connected layers to further improve the accuracy of recognition for strawberry diseases and pests. Compared with other start-of-the-art models, the experimental results show that our model achieves lower computational complexity and higher top-1 accuracy of the recognition for the strawberry diseases and pests on the test set.

For realizing the fine-grained access control with non-interactive approach, and effectively guaranteeing the comprehensive security for information under the post-quantum environment, this paper proposes an attribute-based signcryption (ABSC) scheme based on the intractability of lattices. The proposed ABSC scheme is proved indistinguishable against the inner adaptive-chosen ciphertext attacks (IND-CCA2) and existentially unforgeable against inner chosen-message attacks (EUF-CMA), in the standard model. The theoretical analysis presents that the public key size and the computational cost of the signcryption operation are both reduced obviously, compared with the signature and then encryption mechanism. An efficient variant is also presented that significantly decreases the computational complexity of unsigncryption operation at the expense of an increase in the ciphertext size.

Besides the target in synthetic aperture radar (SAR) image, the structural information remained in speckle and shadow is also very important when we extract the invariant feature of SAR image. However, the output of the classical pulse coupled neural network (PCNN) is a binary value. All the information remained in speckle and shadow is lost, when the classical PCNN is used to process SAR image directly. To overcome this problem, a multi-gray level simplified PCNN (MSPCNN) is proposed in this paper. All the useful information in speckle, shadow, and target can be considered when we use MSPCNN to compute the invariant feature of the SAR target. In order to suppress the negative influence of speckle noise and keep the useful structured information, the improved speckle reducing algorithm (SRAD) is used first. By an adaptive threshold delt0, we can keep the remained speckle in different SAR images to an ideal same level. The negative influence of the remained speckle to different targets' signature becomes basically the same and can be ignored. Combining with SRAD and MSPCNN, the SAR image invariant feature extraction scheme AD-MSPCNN is put forward. After analyzing the performance of different signature computing methods, we splice the time signature and the entropy signature together and use the new spliced vector as the invariant feature of the SAR target. The validity and robustness of AD-MSPCNN are proved by the experimental results on MSTAR database.

Topological invariant features take priority over other vision features in early visual perception stage, which is the core idea of topological perception theory. In order to improve the robustness and distinguishability of the invariant features extracted by pulse coupled neural network (PCNN), the topological properties are integrated into PCNN. The improved PCNN model is called as topological property motivated PCNN (TPCNN), which adopts the saliency map calculated by the spectral residual approach as the important topological properties (the connectivity, and the number of holes in target). In TPCNN, firstly, the normalized saliency map is used as a linking coefficient to enhance the importance of saliency object when we calculating the invariant features. Secondly, the entropy signature of the saliency map is treated as an additional new feature and merged into original features calculated by PCNN, then the final invariant feature is obtained. The proposed TPCNN is used to calculate the invariant feature of different kinds of fish in the paper. Experimental results show that TPCNN outperforms the state-of-art models on invariant features extraction.

Abstract Background Host genetic factors affect the immune response to Mycobacterium tuberculosis (Mtb) infection as well as the progression of the disease. Epiregulin (EREG) belongs to the epidermal growth factor (EGF) family, which binds to the epidermal growth factor receptor (EGFR) to regulate the immune response of the host during infections. Our study aimed to compare EREG levels in tuberculosis (TB) patients and healthy controls and assess whether polymorphisms in EREG increase the risk of TB. Methods We used ELISA to determine the plasma EREG level from 30 healthy controls and 50 tuberculosis patients. By evaluating the EREG gene from 624 TB patients and 600 healthy controls, we determined the allelic and genotypic frequencies for association with susceptibility to TB infections in this group. Results This paper shows that the pulmonary tuberculosis (PTB) and extrapulmonary tuberculosis (EPTB) groups showed a significantly higher plasma EREG level (1014 ± 733.9 pg/ml, 700.2 ± 676.6 pg/ml, respectively) than the healthy controls (277 ± 105.4 pg/ml). The rs2367707 polymorphism was associated with a higher risk of PTB and EPTB (P = 0.00051, P = 0.0012). Analyses of haplotype frequencies found that people with the haplotype CACAT had a higher risk of PTB and EPTB (P = 0.00031, OR = 1.43; P = 0.000053, OR = 1.65). Moreover, the rs6446993 polymorphism of the EREG gene was found to be associated with EPTB (P = 0.00087, OR = 1.54; 95% CI = 1.23–1.94). Conclusions Compared to that of healthy controls, the level of EREG in the plasma of TB patients increased significantly. Based on these data, we demonstrated that EREG polymorphisms are genetic factors for susceptibility to TB and various forms of TB.

Cross-modal hashing has been studied extensively in the past decades for its significant advantage in computation and storage cost. For heterogeneous data points, the cross-modal hashing aims at learning a sharing Hamming space in where one query from one modality can retrieve relevant items of another modality. Although the cross-modal hashing method has achieved significant progress, there are some limitations that need to be further solved. First, to leverage the semantic information in hash codes, most of them learn hash codes from a similarity matrix, which is constructed by class labels directly, ignoring the fact that the class labels may contain noises in the real world. Second, most of them relax the discrete constraint on hash codes, which may cause large quantization error and inevitably results in suboptimal performance. To address the above issues, we propose a discrete robust supervised hashing (DRSH) algorithm in this paper. Specifically, both the class labels and features from different modalities are first fused to learn a robust similarity matrix through low-rank constraint that can reveal its structure and capture the noises in it. And then, hash codes are generated by preserving the robust similarity matrix-based similarities in the sharing Hamming space. The optimization is challenging due to the discrete constraint on hash codes. Finally, a discrete optimal algorithm is proposed to address this issue. We evaluate the DRSH on three real-world datasets, and the results demonstrate the superiority of DRSH over several existing hashing methods.

The nature of biosensing is a biochemical reaction. DNA‐based chemical reaction networks (DNA‐CRNs) as a powerful programming language for describing behaviors of chemical reactions have shown great potential in designs and applications of biosensors. Due to their programmability, modularity, and versatility of the DNA strand, the performance of different detection strategies can be improved mainly by the rational design of DNA‐CRNs. Herein, an overview of the fundamental theory and biosensing processes of DNA‐CRNs is provided. Various detection strategies of DNA‐CRNs are introduced, either in a simple low‐order reaction model or in a complicated high‐order reaction type, in combination with some typical cases for the different detection purposes. In addition, an overview of the recent development of DNA‐CRNs for monitoring the cell microenvironment is presented, which is of significance to uncover some specific cell behaviors and functions. Finally, the roles of DNA‐CRNs in the rational design of high‐performance biosensors are summarized by pointing out the remaining challenges that impede the precise biosensing using DNA‐CRNs in complicated biological environments.

To improve the accuracy of automatic recognition and classification of vegetables, this paper presents a method of recognition and classification of vegetable image based on deep learning, using the open source deep learning framework of Caffe, the improved VGG network model was used to train the vegetable image data set. We propose to combine the output feature of the first two fully connected layers (VGG-M). The Batch Normalization layers are added to the VGG-M network to improve the convergence speed and accuracy of the network (VGG-M-BN). The experimental verification, this paper method in the test data set on the classification of recognition accuracy rate as high as 96.5%, compared with VGG network (92.1%) and AlexNet network (86.3%), the accuracy rate has been greatly improved. At the same time, increasing the Batch Normalization layers make the network convergence speed nearly tripled. Improve the generalization ability of the model by expanding the scale of the training data set. Using VGG-M-BN network to train different number of vegetable image data sets, the experimental results show that the recognition accuracy decreases as the number of data sets decreases. By contrasting the activation functions, it is verified that the Rectified Linear Unit (ReLU) activation function is better than the traditional Sigmoid and Tanh functions in VGG-M-BN networks. The paper also verifies that the classification accuracy of VGG-M-BN network is improved due to the increase of batch_size.

Objective This study was performed to describe a new minimally invasive surgical technique and to explore its effects and practical use in the clinical setting. Methods In total, 22 patients with single-segment thoracolumbar burst fractures underwent treatment with an intelligently inflatable reduction device before common percutaneous pedicle screw fixation. Complications were recorded and short-term effectiveness was evaluated using the visual analogue scale (VAS) score for pain, Oswestry Disability Index (ODI), kyphotic Cobb angle, and anterior edge height of the fractured vertebra preoperatively and postoperatively. Results The patients were followed up from 2 to 5 years. The differences in the VAS score and ODI reached statistical significance at different time points. Similar significant differences were observed in the kyphotic Cobb angle and the vertebral body anterior height except between the two postoperative measurements. Conclusions The current study indicated that use of the intelligently inflatable reduction device with conventional percutaneous pedicle screw fixation can improve the reduction and healing of single-segment thoracolumbar burst fractures in adult patients. This technique induces minimal trauma, provides reliable fixation, and has few complications.

Few-shot object recognition, which exploits a set of well-labeled data to build a classifier for new classes that have only several samples per class, has received extensive attention from the machine learning community. In this paper, we investigate the problem of designing an optimal loss function for few-shot object recognition and propose a novel few-shot object recognition system that includes the following three steps: (1) generate a loss function architecture using a recurrent neural network (generator); (2) train a base embedding network with the generated loss function on a training set; (3) fine-tune the base embedding network using the few-shot instances from a validation set to obtain the accuracy and use it as a reward signal to update the generator. This procedure is repeated and implemented in the reinforcement learning framework for finding the best loss architecture such that the embedding network yields the highest validation accuracy. Our key insight is to create a search space of the loss function architectures and evaluate the quality of a particular loss function on the dataset of interest. We conduct experiments on three popular datasets for few-shot learning. The results show that the proposed approach achieves better performance than state-of-the-art methods.

To increase prediction accuracy of dissolved oxygen (DO) in aquaculture, a hybrid model based on multi-scale features using ensemble empirical mode decomposition (EEMD) is proposed. Firstly, original DO datasets are decomposed by EEMD and we get several components. Secondly, these components are used to reconstruct four terms including high frequency term, intermediate frequency term, low frequency term and trend term. Thirdly, according to the characteristics of high and intermediate frequency terms, which fluctuate violently, the least squares support vector machine (LSSVR) is used to predict the two terms. The fluctuation of low frequency term is gentle and periodic, so it can be modeled by BP neural network with an optimal mind evolutionary computation (MEC-BP). Then, the trend term is predicted using grey model (GM) because it is nearly linear. Finally, the prediction values of DO datasets are calculated by the sum of the forecasting values of all terms. The experimental results demonstrate that our hybrid model outperforms EEMD-ELM (extreme learning machine based on EEMD), EEMD-BP and MEC-BP models based on the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE) and root mean square error (RMSE). Our hybrid model is proven to be an effective approach to predict aquaculture DO.

This study aimed to identify novel immunogenic epitopes from Mycobacterium tuberculosis (MTB) that could be used in tuberculosis (TB) diagnostics. To determine the diagnostic potential of mycobacterial antigens in serodiagnosis of TB, 256 patients were enrolled in a study and divided into two groups: 126 smear-positive pulmonary TB patients (SPPT) and 130 smear-negative pulmonary TB patients (SNPT); 152 bacillus Calmette-Guerin (BCG)-vaccinated healthy people were used as a control. Murine results showed that antigens Rv0310c-E from RD 8 and Rv1255c-E from RD 10 were strongly immunogenic to Th1 cells and induced a great humoral response. Receiver operating characteristic analysis indicated that Rv0310c-E (area under the curve (AUC): 0.800) and Rv1255c-E (AUC: 0.808) performed better than ESAT-6 (AUC: 0.665) and CFP-10 (AUC: 0.623) proteins but were comparable with Rv3425 (AUC: 0.788) protein in a human serum IgG analysis. Rv0310c-E demonstrated the highest diagnostic ability for the SPPT group (Youden index: 0.5602, sensitivity: 69.84%, specificity: 86.18%), while Rv1255c-E demonstrated the highest diagnostic ability for the SNPT group (Youden index: 0.5674, sensitivity: 73.84%, specificity: 82.89%). In addition, combination analysis found that antigen Rv0310c-E, coupled with the Rv3425 protein (Youden index: 0.6098, sensitivity: 87.30%, specificity: 73.68%) had the strongest performance for TB diagnostics of the SPPT group, and the single antigen Rv1255c-E was strongest for the SNPT group. These results suggest that antigens Rv0310c-E and Rv1255c-E are potential antigens for TB serodiagnostic tests, which may facilitate detection of MTB in smear-negative and smear-positive patients.Emerging Microbes & Infections (2017) 6, e64; doi:10.1038/emi.2017.54; published online 12 July 2017

This work presents a new approach of surface measurement of human face via the combination of the projection of monochromatic structured light, the optical filtering technique, the polarization technique and the Fourier-transform-based image-processing algorithm. The theoretical analyses and experimental results carried out in this study showed that the monochromatic feature of projected fringe pattern generated using our designed laser-beam-based optical system ensures the use of optical filtering technique for removing the effect of background illumination; the linearly-polarized characteristic makes it possible to employ a polarizer for eliminating the noised signal contributed by multiply-scattered photons; and the high-contrast sinusoidal fringes of the projected structured light provide the condition for accurate reconstruction using one-shot measurement based on Fourier transform profilometry. The proposed method with the portable and stable optical setup may have potential applications of indoor medical scan of human face and outdoor facial recognition without strict requirements of a dark environment and a stable object being observed.

This study analyzed changes in nighttime light during the 2019 Spring Festival using Luojia 1-01 nighttime images in six western cities of China (Chengdu, Panzhihua, Kunming, Yuxi, Lhasa, and Jinchang). First, the radiance of the nighttime images was calculated. Second, the light area (LA) and average light intensity (ALI) were estimated for both Spring Festival and non-festival dates. Third, the differences in LA and ALI between the Spring Festival and non-festival were analyzed for all six cities. Migration population data from Baidu Inc. were used to examine the relationship between the changes of nighttime light and the population migration. The results show that, during the non-festival to Spring Festival period, the decrease in LA values coincided with negative net immigration. During the Spring Festival to non-festival period, the LA values increased, which coincided with positive net immigration. The F-test shows that the positive linear relationship between the normalized change in LA and the normalized net immigration is significant at the 0.05 level. This strongly indicates that population migration causes changes in LA. Moreover, while the population is considerably less in these cities during the Spring Festival, the ALI is noticeably higher, which suggests that urban activities are intensified during this period. This study demonstrates the applicability of using Luojia 1-01 nighttime images to detect the nighttime light changes for the Spring Festival in western cities, China, which can then be used to evaluate population migration and urban activities in the Spring Festival. Considering the higher spatial resolution of Luojia 1-01 than NPP (National Polar-orbiting Partnership) / VIIRS (Visible infrared Imaging Radiometer), this study may inspire more applications of Luojia 1-01 to track the activities in a variety of festival-cultures and cities.

This study proposes a deep quadruplet network (DQN) for hyperspectral image classification given the limitation of having a small number of samples. A quadruplet network is designed, which makes use of a new quadruplet loss function in order to learn a feature space where the distances between samples from the same class are shortened, while those from a different class are enlarged. A deep 3-D convolutional neural network (CNN) with characteristics of both dense convolution and dilated convolution is then employed and embedded in the quadruplet network to extract spatial-spectral features. Finally, the nearest neighbor (NN) classifier is used to accomplish the classification in the learned feature space. The results show that the proposed network can learn a feature space and is able to undertake hyperspectral image classification using only a limited number of samples. The main highlights of the study include: (1) The proposed approach was found to have high overall accuracy and can be classified as state-of-the-art; (2) Results of the ablation study suggest that all the modules of the proposed approach are effective in improving accuracy and that the proposed quadruplet loss contributes the most; (3) Time-analysis shows the proposed methodology has a similar level of time consumption as compared with existing methods.

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

A sudden rainstorm that occurred in the northeast Sichuan Basin of China in early May 2017 was associated with a southwest low-level jet (SWLJ) and a mountainous low-level jet (MLLJ). This study investigates the impact of the double low-level jets (LLJs) on rainfall diurnal variation by using the data from ERA5 reanalysis, and explores the characteristics of water vapor transport, including the main paths and sources of moisture, by using the HYSPLIT-driven data of the ERA—interim, GDAS (Global Data Assimilation System), and NCEP/NCAR reanalysis data. The analysis shows that the sudden rainstorm in the mountain terrain was located at the left side of the large-scale SWLJ at 700 hPa, and at the exit region of the meso-scale MLLJ at 850 hPa. The double LLJs provide favorable moisture conditions, and the enhancement (weakening) of the LLJs is ahead of the start (end) of the rainstorm. The capacity of the LLJ at 850 hPa with respect to moisture convergence is superior to that at 700 hPa, especially when the MLLJ and the southerly LLJ at 850 hPa appear at the same time. The HYSPLIT backward trajectory model based on Lagrangian methods has favorable applicability in the event of sudden rainstorms in mountainous terrain, and there is no special path of moisture transport in this precipitation event. The main moisture sources of this process are the East China Sea–South China Sea, the Arabian Sea–Indian Peninsula, the Bay of Bengal, and the Middle East, accounting for 38%, 34%, 17% and 11% of the total moisture transport, respectively. Among them, the moisture transport in the Bay of Bengal and the South China Sea–East China Sea is mainly located in the lower troposphere, which is below 900 hPa, while the moisture transport in the Arabian Sea–Indian Peninsula and the Middle East is mainly in the middle and upper layers of the troposphere. The moisture changes of the transport trajectories are affected by the topography, especially the high mountains around the Sichuan Basin.

To extract original pulse signals in measurement while drilling (MWD), different low-pass filtering methods were designed based on intrinsic mode functions through the ensemble empirical mode decomposition (EEMD). After shaping square waves, a filtering and shaping algorithm on pulse signals was designed. A judgment criterion of filtering algorithms was established according to the approximation index and relevance index of the algorithm. To simulate pulse signals in measurement while drilling, unit impulse signal, periodic noise signal and Gaussian white noise signal were combined. The denoising effect on the simulated signals was analyzed. The optimum denoising algorithm is composed of the intrinsic mode functions (without the front 4 intrinsic mode functions) and the remainders in EEMD. The approximation index of denoising algorithm is 0.771 9 and relevance index is as high as 0.892 9. The real-time mud signals of MWD was analyzed and discussed with the help of the algorithm, the obtained results were reasonable and effective. Key words: pulse signal, Ensemble Empirical Mode Decomposition (EEMD), low-pass filtering, optimum denoising algorithm

A novel clustering algorithm for wireless sensor networks was proposed to resolve the hot spot problem and the single failure problem.The algorithm divided the network into unequal grids,and the nodes in the same grid form a cluster.The number of cluster heads in each grid was determined according to the failure probability of nodes,and the data collection of nodes in a grid was implemented by multiple cluster heads of the grid cooperatively.By adjusting the number of nodes that could participate in cluster head rotation of each grid,the algorithm was able to balance the energy dissipation of nodes on a long view.The algorithm reduced the reliance of cluster members to single cluster head by forming cluster with multiple cluster heads.Furthermore,the algorithm adopted some measures to diminish energy consumption.Simulation results show that the algorithm has higher balanced extent of energy dissipation and reliability of data collection,and prolongs the lifetime of network.

Researches on the problem that the existing data fusion algorithms couldn't satisfy the new application re-quirements,ie,frequent dynamic change of network and hi demand with reliability of data communication,a struc-ture-free and dynamic-adaptive data fusion algorithm was presented which divided the network into several grids,sensors perform data communication according to grids and adopt many-to-many data communication manner,there was no need for sensors to establish and maintain a structure.The results of simulation experiments show that this algorithm is able to adapt the dynamic change of network and offer high reliable data communication while performs the task of data fusion.

Mycobacterium tuberculosis infection is associated with a spectrum of clinical outcomes, from long-term latent infection to different manifestations of progressive disease. Pro-inflammatory pathways, such as those controlled by IL-1β, have the contrasting potential both to prevent disease by restricting bacterial replication, and to promote disease by inflicting tissue damage. Thus, the ultimate contribution of individual inflammatory pathways to the outcome of M. tuberculosis infection remains ambiguous. In this study, we identified a naturally-occurring polymorphism in the human IL1B promoter region, which alters the association of the C/EBPβ and PU.1 transcription factors and controls Mtb-induced IL-1β production. The high-IL-1β expressing genotype was associated with the development of active tuberculosis, the severity of pulmonary disease and poor treatment outcome in TB patients. Higher IL-1β expression did not suppress the activity of IFN-γ-producing T cells, but instead correlated with neutrophil accumulation in the lung. These observations support a specific role for IL-1β and granulocytic inflammation as a driver of TB disease progression in humans, and suggest novel strategies for the prevention and treatment of tuberculosis.

The Liver X receptors (LXRs), Liver X receptor A (LXRA) and Liver X receptor B (LXRB), regulate lipid metabolism and antimicrobial response. LXRs have a crucial role in the control of Mycobacterium tuberculosis (M.tb). Lacking LXRs mice is more susceptibility to infection M.tb, developing higher bacterial burdens and an increase in the size and number of granulomatous lesions. We aimed to assess the associations between single nucleotide polymorphisms (SNPs) in LXRs and risk of tuberculosis.We sequenced the LXRs genes to detect SNPs and to examine genotypic frequencies in 600 patients and 620 healthy controls to investigate for associations with tuberculosis (TB) in the Chinese Han population. DNA re-sequencing revealed eight common variants in the LXRs genes.The G allele of rs1449627 and the T allele of rs1405655 demonstrated an increased risk of developing TB (p