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Diclofenac (DCF), one of the most current and widely used nonsteroidal anti-inflammatory drugs (NSAIDs), has been frequently detected in aquatic environments worldwide. However, the ecotoxicological effects of DCF on freshwater invertebrates remain largely unknown. In the present study, Corbicula fluminea were exposed to environmentally relevant concentrations of DCF (0, 2, 20 and 200 µg/L) for 28 days, and the potential adverse effects of DCF on siphoning behavior, antioxidant responses and apoptosis were investigated. Our results showed that the siphon efficiencies of clams were significantly suppressed by DCF exposure. DCF exerted neurotoxicity via reducing the activity of acetylcholinesterase (AChE) in gills and digestive gland of C. fluminea. Exposure to DCF induced antioxidant stress and led to increased malondialdehyde (MDA) levels in both tissues of C. fluminea. Transcriptional alterations of apoptosis-related genes indicated that DCF might induce apoptosis by triggering mitochondrial apoptotic pathway. Our findings will help enhance our awareness of the negative effects of DCF on freshwater bivalves and highlight the ecological risks of DCF in freshwater ecosystems.

Bacteria and fungi are dominant members of environmental microbiomes. Various bacterial-fungal interactions (BFIs) and their mutual regulation are important factors for ecosystem functioning and health. Such interactions can be highly dynamic, and often require spatiotemporally resolved assessments to understand the interplay which ranges from antagonism to mutualism. Many of these interactions are still poorly understood, especially in terms of the underlying chemical and molecular interplay, which is crucial for inter-kingdom communication and interference. BFIs are highly relevant under agricultural settings; they can be determinative for crop health. Advancing our knowledge related to mechanisms underpinning the interactions between bacteria and fungi will provide an extended basis for biological control of pests and pathogens in agriculture. Moreover, it will facilitate a better understanding of complex microbial community networks that commonly occur in nature. This will allow us to determine factors that are crucial for community assembly under different environmental conditions and pave the way for constructing synthetic communities for various biotechnological applications. Here, we summarize the current advances in the field of BFIs with an emphasis on agriculture.

An efficient catalytic asymmetric cascade cycloaddition reaction of arylalkynols with dioxopyrrolidines was developed. This reaction was achieved using Au(I) and (R)-BINOL-Ti(IV) bimetallic catalysts and exclusively delivered a series of chiral oxo-bridged bicyclic benzooxacine compounds in up to 86% yield with 96% ee as well as >33:1 dr. Meanwhile, three new σ bonds and three new stereogenic centers were formed in a one-pot process.

Computer tomography (CT) has been routinely used for decades in clinical neuroimaging. Compared to Magnetic resonance imaging (MR), CT is more readily available and more cost-effective, but the soft tissue contrast is much lower. In brain CT images, the unclear soft-tissue boundaries and the high noise level hamper accurate segmentation of the gray and white matter, leaving obstacles for the subsequent geometrical quantification of brain structures. To address this challenge, this paper specifically acquires same-patient CT and MR image pairs and proposes a multi-task learning model for simultaneous tissue segmentation and modality transfer. We aim to use the modality transfer task to learn corresponding MR and CT features which assists the segmentation task to achieve more accurate results than single modality learning. Moreover, we add a Shannon entropy loss function to the training loss to further combat the high noise influence and reduce the fragmentation problem of the segmentation results. Experimental results proved that our multi-task framework achieves more accurate segmentation than the single segmentation task, and the Shannon entropy loss results in much fewer broken brain regions than the state-of-the-art (SOTA) U-net method. Our study provides a useful tool for clinical neural CT image analysis.

Reconstructing personalized models of the human body is a key step for digital twin modeling of sports motion. There have been many studies reconstructing the body surface models based on video sequences, but these algorithms lack the modeling of internal structures (bones, muscles, blood vessels, etc.). Some researchers map internal organ structures into the 3D point cloud scanning of the body surface, but such mapping does not guarantee realistic tissue deformation and is not developed for video processing. This paper focuses on the video-based reconstruction of a personalized full-anatomy digital model in sports motion. Our method creates a four-dimensional (4D) model of the moving process composed of the 3D models of each time moment. We first use a deep network to regress the body surface of each time point and then estimate the internal structures by registering a previously constructed deformable human anatomy atlas to the body surfaces. To mimic realistic internal structure deformation, we applied articulated linear transform to the bony structures to avoid bone shape distortion and applied smooth nonlinear transform to the soft tissue to mimic the motion deformation. We also developed an intersection removal method to prevent the possible intersection between bones and soft tissues during the deformation. Our method is tested with athletes' sports videos and automatically generates full-anatomy motion models which cannot be achieved by previous methods. Our method also surpasses the existing video-based body surface modeling method by providing additional internal structures. The 4D human motion model constructed by our method is useful in sports modeling, biomechanics simulation, wearable device design, etc.

This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large‐scale multiomics analysis and real‐world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis‐related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI’s GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P

In population-oriented ergonomics product design and musculoskeletal kinetics analysis, digital spine models of different shape, pose and material property are in great demand. The purpose of this study was to construct a parameterized finite element spine model with adjustable spine shape and material property. We used statistical shape model approach to learn inter-subject shape variations from 65 CT images of training subjects. Second order polynomial regression was used to model the age-dependent changes in vertebral material property derived from spatially aligned CT images. Finally, a parametric spine generator was developed to create finite element instances of different shapes and material properties. For quantitative analysis, the generalization ability to emulate spine shapes of different people was evaluated by fitting into 17 test CT images. The median fitting accuracy was 0.8 for Dice coefficient and 0.43 mm for average surface distance. The age-dependent bone density regression curve was also proved to well agree with large population statistics data. Finite element simulation was performed to compare how shape parameters influenced the biomechanics distribution of spine. The proposed parametric finite element whole spine model will assist the design process of new devices and biomechanical simulation towards a wide range of population. peerReviewed

Background End-stage renal disease (ESRD) is the final stage during the development of renal failure. Depression is the most common psychiatric disorder in patients with ESRD, which in turn aggravates the progression of renal failure, however, its underlying mechanism remains unclear. This study aimed to reveal the pathogenesis and to discover novel peripheral biomarkers for ESRD patients with depression through metabolomic analysis. Methods Ultra-high-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) was used to explore changes of serum metabolites among healthy controls, ESRD patients with or without depression. The differential metabolites between groups were subjected to clustering analysis, pathway analysis, receiver operating characteristic (ROC) curve analysis. Results A total of 57 significant serum differential metabolites were identified between ESRD patients with or without depression, which were involved in 19 metabolic pathways, such as energy metabolism, glycerolipid metabolism, and glutamate-centered metabolism. Moreover, the area under the ROC curve of gentisic acid, uric acid, 5-hydroxytryptamine, 2-phosphoglyceric acid, leucyl-phenylalanine, propenyl carnitine, naloxone, pregnenolone, 6-thioxanthene 5'-monophosphate, hydroxyl ansoprazole, zileuton O-glucuronide, cabergoline, PA(34:2), PG(36:1), probucol and their combination was greater than 0.90. Conclusions Inflammation, oxidative stress and energy metabolism abnormalities, glycerolipid metabolism, and glutamate-centered metabolism are associated with the pathogenesis of ESRD with depression, which may be promising targets for therapy. Furthermore, the identified differential metabolites may serve as biomarkers for the diagnosis of ESRD patients with depression.

With the advancement of science and technology, digital technology and Internet of Things network technology have been developed rapidly, and multimedia technology has also been widely used. Multimedia formats such as digital TV and elevator posters are shaking up traditional media. At the same time, many media operation models and multimedia technologies are combined to plan operational strategies, determine operational goals, and change the traditional media structure to achieve commercial profits and society benefit. However, due to limitations in the existing operating model or unreasonable technical solutions, it is not easy to maximize the value of multimedia technology. The XML-based database has been submitted, and it will carry out the business requirements of the transaction network and the business platform of the transaction network. Integrated management mechanism is analyzed and applied. The framework design includes parallel quota processing module, update processing module, result processing module, and storage library and database connection management module. The department runs multiple parts of the system together and completes the database. The development of cloud database is based on cloud computing. It can effectively fill the shortcomings and gaps of traditional database storage and processing, and it can also provide high-reciprocity databases to provide storage and management services. It has high reliability. Cloud servers use fair weighted rounding algorithms to achieve load balancing and use the in-memory database Redis to realize terminal data caching. After a comprehensive test of the system, the system can perform all functions normally, and it has good performance and stable operation.

Sulfamethoxazole (SMX), a broad-spectrum antibiotic, has been widely used in the treatment and prevention of infection caused by bacteria in recent years. The present study was aimed to evaluate the response mechanisms to SMX stress in gills and digestive gland of Corbicula fluminea (O. F. Müller, 1774). To this end, clams were exposed to environmentally relevant concentrations of SMX (0, 1, 10 and 100 μg/L) for 7 and 28 days, and siphon behavior, tissue-specific enzymatic and transcriptional changes were assayed. Our results showed that exposure to SMX significantly suppressed filtration rate and acetylcholinesterase (AChE) activity, activated antioxidant defense system and elevated transcription of several genes related to cell apoptosis in gills and digestive gland of clams. In general, SMX at environmentally relevant concentrations exhibited a negative impact on siphon behavior and induced neurotoxicology, oxidative stress and cell apoptosis in C. fluminea. The current study will help broaden our understanding of the ecotoxicity of SMX on freshwater bivalves.

The blade unit of a tridimensional rotational flow sieve tray (TRST) is the origin for the rotational–perforated flow. The flow field of that was investigated using the Multi-fluid VOF method. The water fractions, and the gas perforation velocity on the blade unit were investigated. The liquid phase distribution in the jet and mixed flow is more dispersed than that in film and jet flow. The gas–liquid two-phase velocity fluctuated with the radial distance. The rotational and perforated flows on the blade unit were analyzed separately by the local rotational flow ratio . The variation range of the ratio for the gas-phase was 0.6 – 0.85, and that for the liquid-phase is 0.2 – 0.8. Based on the Q criterion, the vortices were mainly concentrated near the sieve holes. The simulation provides an important theoretical basis for the mass transfer investigation and structural optimization of the blade unit.

Ras GTPases act as molecular switches to control various cellular processes by coupling integrated signals in eukaryotes. Activities of Ras GTPases are triggered by Ras GTPase guanine nucleotide exchange factors (RasGEFs) in general, whereas the role of RasGEF in plant pathogenic fungi is largely unknown. In this study, we characterized the only RasGEF protein in Fusarium graminearum, FgCdc25, by combining genetic, cytological and phenotypic strategies. FgCdc25 directly interacted with RasGTPase FgRas2, but not FgRas1, to regulate growth and sexual reproduction. Mutation of the FgCDC25 gene resulted in decreased toxisome formation and deoxynivalenol (DON) production, which was largely depended on cAMP signalling. In addition, FgCdc25 indirectly interacted with FgSte11 in FgSte11-Ste7-Gpmk1 cascade, and the ΔFgcdc25 strain totally abolished the formation of infection structures and was nonpathogenic in planta, which was partially recovered by addition of exogenous cAMP. In contrast, FgCdc25 directly interplayed with FgBck1 in FgBck1-MKK1-Mgv1 cascade to negatively control cell wall integrity. Collectively, these results suggest that FgCdc25 modulates cAMP and MAPK signalling pathways and further regulates fungal development, DON production and plant infection in F. graminearum.

Clear cell renal cell carcinoma (ccRCC) is the most common and highly malignant pathological type of kidney cancer. We sought to establish a metabolic signature to improve post‐operative risk stratification and identify novel targets in the prediction models for ccRCC patients. A total of 58 metabolic differential expressed genes (MDEGs) were identified with significant prognostic value. LASSO regression analysis constructed 20‐mRNA signatures models, metabolic prediction models (MPMs), in ccRCC patients from two cohorts. Risk score of MPMs significantly predicts prognosis for ccRCC patients in TCGA (P

In this paper, we show by counterexamples that the six propositions about the fuzzy β-core in the fuzzy β-covering approximation space in Yang and Hu (2017) [3] are incorrect, and interpret the cause of errors.

The interval data from different surveyed persons for one linguistic word can reflect the intra- and inter-uncertainties of the word. This study shows how to construct shadowed set models for linguistic words based on the surveyed interval data. Firstly, corresponding to the popularly used fuzzy sets for linguistic words, four kinds of shadowed sets are introduced according to their shapes and named as the normal, the left-shoulder, the right-shoulder, and the non-cored shadowed sets. A data-driven approach that utilizes different statistics to determine the shapes and parameters of the shadowed set models is then proposed. The proposed data-driven approach includes two methods; the first is the tolerance limit method depending on the mean and standard deviation of the remaining interval data after pre-processing, whilst the other is the percentile method relying on the percentiles of the remaining interval data. Additionally, to evaluate the modelling performance, three novel indices are presented to measure the uncertainty-capture capability and accuracy of the constructed shadowed set models. Finally, the proposed approach is applied to two real-world problems . One is the modelling of 32 words in a codebook, and the other is the modelling of the thermal feeling words. The proposed methods are compared with other interval data driven methods, e.g. the enhanced interval approach and the fuzzy statistic method. Our results show that the proposed percentile method performs better in both applications. The proposed approach can also be applied to some other linguistic word modelling applications when it is reasonable to adopt shadowed sets as the words’ models.

A cocatalyst-controlled divergent cascade cycloaddition reaction was developed for the synthesis of two different complex oxygen-containing heterocyclic compounds from arylalkynols and dioxopyrrolidienes in the presence of Au(I) catalyst. Using two different cocatalysts, Ni(OTf)2 and (rac)-phosphoric acid, two completely different products, spiroketals and oxa-bridged eight-membered cyclic ethers, were precisely constructed with high yields and good stereoselectivities, respectively. These methods feature simple and high efficiency, good chemoselectivity and mild conditions in one-pot.

In the context of multicultural information, to explore and analyze the use effect of voice interactive educational robot in the classroom of adolescent students, and the physical and mental impact of movie characters on adolescent students, and to lay the foundation for studying the positive development of adolescents, under the guidance of positive psychology theory, the relationship between positive psychology and adolescent mental health is analyzed, the application of adolescent educational robot is discussed, and the relationship between adolescent educational robot combined with movies characters and positive development of adolescent is analyzed. The questionnaire is used to collect data, including the questionnaire on the influence of movie characters on the positive development of adolescents, the questionnaire of pre- and postpopularization of artificial intelligence in primary and secondary schools, and the satisfaction questionnaire of voice interactive educational robot. The reliability and validity of the questionnaire are analyzed. The results show that students who watch more than 20 movies are 20% in grade one and that is only 10% in grade three. 79% of the students think that the movie characters have an impact on themselves. The distribution of the number of the grade two students exposed to the movie is relatively uniform, 40% of them watch 10-20 movies, and 82% of them think that the movie characters have an impact on themselves; the Cronbach coefficient of classroom satisfaction questionnaire is 0.929 > 0.9, the average value of the correlation of total correction items of the corresponding item is 0.612 > 0.5, the Kaiser–Meyer–Olkin measurement value is 0.812 > 0.6, and the Sig value is 0.000

The development of medical image analysis algorithm is a complex process including the multiple sub-steps of model training, data visualization, human–computer interaction and graphical user interface (GUI) construction. To accelerate the development process, algorithm developers need a software tool to assist with all the sub-steps so that they can focus on the core function implementation. Especially, for the development of deep learning (DL) algorithms, a software tool supporting training data annotation and GUI construction is highly desired. In this work, we constructed AnatomySketch, an extensible open-source software platform with a friendly GUI and a flexible plugin interface for integrating user-developed algorithm modules. Through the plugin interface, algorithm developers can quickly create a GUI-based software prototype for clinical validation. AnatomySketch supports image annotation using the stylus and multi-touch screen. It also provides efficient tools to facilitate the collaboration between human experts and artificial intelligent (AI) algorithms. We demonstrate four exemplar applications including customized MRI image diagnosis, interactive lung lobe segmentation, human-AI collaborated spine disc segmentation and Annotation-by-iterative-Deep-Learning (AID) for DL model training. Using AnatomySketch, the gap between laboratory prototyping and clinical testing is bridged and the development of MIA algorithms is accelerated. The software is opened at https://github.com/DlutMedimgGroup/AnatomySketch-Software.

A new type of composite tridimensional rotational flow sieve tray is proposed. The flow pattern and operation domain of the tray were defined and divided by the image shooting method, combined with the standard deviation of the pressure difference sequence. There are membrane–drip column and foam–embolic flow in the TRST area of the internal packing–type tray, while bubbly and milk froth-ribbon flow are present in the packing area of the external packing-type tray. This study focused on the influence of the tray structure parameters on the pressure drop. Under the experimental operating conditions, the dry pressure drop was within 160 Pa, while the wet pressure drop was within 900 Pa. Under the same structural parameters, the internal packing–type pressure drop of the tray was higher than that of the external packing-type tray. A mathematical model of the pressure drop between dry and wet trays was established.

Diclofenac (DCF), one of typical non-steroidal anti-inflammatory drugs (NSAIDs), has been frequently detected in various environmental media. Nevertheless，the potential endocrine disrupting effects of DCF on fish were poorly understood. In the present study, zebrafish embryos/larvae were used as a model to evaluate the adverse effects of DCF on development and thyroid system. The results demonstrated that DCF only significantly decreased the heart rate at 72 h post-fertilization (hpf), exhibiting limited influence on the embryonic development of zebrafish. Treatment with DCF significantly reduced whole-body thyroxine (T4) levels, and changed transcriptional levels of several genes related to the hypothalamic-pituitary-thyroid (HPT) axis. These findings provide important information regarding to the mechanisms of DCF-induced developmental toxicity and thyroid disruption in fish.

An asymmetric allylic C-H functionalization has been developed by making use of transient chiral nucleophiles, as well as bimetallic synergistic catalysis with an achiral Pd

To alleviate the ill-posedness of bioluminescence tomography (BLT) reconstruction, anatomical information from computed tomography (CT) or magnetic resonance imaging (MRI) is usually adopted to improve the reconstruction quality. With the anatomical information, different organs could be segmented and assigned with appropriate optical parameters, and the reconstruction could be confined into certain organs. However, image segmentation is a time-consuming and challenging work, especially for the low-contrast organs. In this paper, we present a BLT reconstruction method in conjunction with an organ probability map to effectively incorporate the anatomical information. Instead of using a segmentation with a fixed organ map, an organ probability map is established by registering the CT image of the mouse to the statistical mouse atlas with the constraints of the mouse surface and high-contrast organs (bone and lung). Then the organ probability map of the low-contrast organs, such as the liver and kidney, is determined automatically. After discretization of the mouse torso, a heterogeneous model is established as the input for reconstruction, in which the optical parameter of each node is calculated according to the organ probability map. To take the advantage of the sparse Bayesian Learning (SBL) method in recovering block sparse signals in inverse problems, which is common in BLT applications where the target distribution has the characteristic of sparsity and block structure, a two-step method in conjunction with the organ probability map is presented. In the first step, a fast sparse algorithm, L1-LS, is used to reveal the source distribution on the organ level. In the second step, the bioluminescent source is reconstructed on the pixel level based on the SBL method. Both simulation and in vivo experiments are conducted, and the results demonstrate that the organ probability map in conjunction with the proposed two-step BLT reconstruction method is feasible to accurately reconstruct the localization of the bioluminescent light source.

Peripheral nerve interfaces are frequently used in experimental neuroscience and regenerative medicine for a wide variety of applications. Such interfaces can be sensors, actuators, or both. Traditional methods of peripheral nerve interfacing must either tether to an external system or rely on battery power that limits the time frame for operation. With recent developments of wireless, battery-free, and fully implantable peripheral nerve interfaces, a new class of devices can offer capabilities that match or exceed those of their wired or battery-powered precursors. This paper describes methods to (i) surgically implant and (ii) wirelessly power and control this system in adult rats. The sciatic and phrenic nerve models were selected as examples to highlight the versatility of this approach. The paper shows how the peripheral nerve interface can evoke compound muscle action potentials (CMAPs), deliver a therapeutic electrical stimulation protocol, and incorporate a conduit for the repair of peripheral nerve injury. Such devices offer expanded treatment options for single-dose or repeated dose therapeutic stimulation and can be adapted to a variety of nerve locations.

Statistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism features are helpful. However, most currently available brain PET templates were constructed using the Caucasian data. To enrich the family of publicly available brain PET templates, we created Chinese-specific template images based on 116 [18F]-fluorodeoxyglucose ([18F]-FDG) PET images of normal participants. These images were warped into a common averaged space, in which the mean and standard deviation templates were both computed. We also developed the SPM analysis programmes to facilitate easy use of the templates. Our templates were validated through the SPM analysis of Alzheimer’s and Parkinson’s patient images. The resultant SPM t-maps accurately depicted the disease-related brain regions with abnormal [18F]-FDG uptake, proving the templates’ effectiveness in brain function impairment analysis., Measurement(s)brain metabolism measurementTechnology Type(s)FDG-Positron Emission TomographyFactor Type(s)age • sexSample Characteristic - OrganismHomo sapiensSample Characteristic - LocationChina Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16382418

Positron emission tomography (PET)/computed tomography (CT) with [For image segmentation, a head anatomy template was registered to the subject image by integrating dual-modality image registration and landmark-constraint. The localizations of abnormalities were examined by quantitative comparison using the collected database. The PET/CT images of 20 reference patients and 11 patients with epilepsy were used to compare results between the proposed manual method and statistical parameter mapping (SPM). A dice coefficient analysis was performed on the six central brain regions to assess the segmentation effectiveness, and the diagnostic results of the epileptic regions were examined using pathological results as a reference.The dice results of the proposed method were generally higher than those of SPM, with the averaged dice values for the proposed method and SPM being 0.78 and 0.55, respectively, in the reference group (P0.001), and 0.73 and 0.48, respectively, in the epileptic group (P0.001). Our proposed method detected all the pathologically reported epileptic defects; however, using the visual assessment method, epileptic defects were missed in three patients. Both the proposed and visual assessment methods incorrectly identified non-epileptic areas as epileptic areas.The results provide strong evidence of the feasibility of using our proposed method for accurate brain region segmentation in the diagnosis of epilepsy. Our atlas-based approach has promise for clinical application in the image processing and diagnosis of patients with epilepsy.

PurposeThe computed tomography fat attenuation index (FAI) is an ideal quantifiable imaging factor to identify the inflammation degree of peri-tumor adipose tissue. We aimed to verify whether FAI could reflect peri-tumor adipose inflammation, predict the survival outcome of renal cell carcinoma (RCC), and discover transcriptomic features of tumor tissues and adjacent adipocytes.Materials and MethodsTwo clinical cohorts (Fudan University Shanghai Cancer Center [FUSCC] cohort [n=129] and TCGA cohort [n=218]) were used to explore the association between FAI and clinical outcome. A prospective cohort (n = 19) was used to discover the molecular phenotyping of peri-tumor adipose tissue and tumor tissue according to their FAI value. A clinical cohort (n = 32) in which patients received cyto-reductive surgery was used to reveal the dynamic change of FAI.ResultsA high peri-tumor FAI was significantly associated with a worse outcome in both the FUSCC (HR = 2.28, p = 0.01) and the TCGA cohort (HR = 2.24, p ConclusionIn summary, FAI could be used in RCC to reflect the biological characteristics and tumor immune micro-environment of both the tumor and the peri-tumor adipose. High peri-tumor FAI had the potential to predict a worse survival outcome in various cohorts. This study demonstrates that the crosstalk exists between a tumor and its micro-environment and could be reflected easily by imaging procedures, which could facilitate clinical decision making.

In recent years, network security has been frequently threatened by complex network attacks. Intrusion detection system(IDS) is an active network security protection, which can effectively detect the network system when it is attacked. However, most intrusion detection system only consider the contribution of each feature to the result. In this paper, we proposes an intrusion detection system based on Global-feature Contribution Net-work(GCN). The Global-feature Contribution Network consists of two parts: Factorization Machine(FM) part and Global-Local network part. Factorization machine carry out second-order combination of features to consider the correlation between different features in attack samples and reduce the impact of sparse sample features on detection results. Global-Local network continuously integrates the global features of samples into local features, so that the network can learn the local features of samples under the grasp of global information. We evaluated our model on KDDCup99 dataset to show the effectiveness of our method. Compared with traditional methods, the model improves the accuracy and recall rate, and makes an accurate judgment on the perception of network security situation.

INTRODUCTION AND OBJECTIVE:Oligo-metastatic prostate cancer is recognized as an intermediate stage between localized disease and widespread metastases. The role of radical local therapy (RLT) to th...

Traditionally, clear cell papillary renal cell carcinoma (ccpRCC) was considered to share similar molecular and histological characteristics with clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC). Here we aimed to identify somatic and germline variants of ccpRCC. For this purpose, we conducted whole-exome sequencing to detect somatic variants in the tissues of 18 patients with pathologically confirmed ccpRCC, who underwent surgical treatment at Fudan University Shanghai Cancer Center. Targeted sequencing was conducted to detect germline variants in paired tumor or normal tissues or blood. Somatic and germline variants of ccRCC and Renal cell carcinoma included in The Cancer Genome Atlas data and other published data were analyzed as well. The molecular profiles of ccpRCC, ccRCC and pRCC were compared. Among the 387 somatic variants identified, TCEB1 (3/18) and VHL (3/18) variants occurred at the highest frequencies. Germline mutation detection showed that nine variants associated with Fanconi anemia (VAFAs) pathway (FANCA, 6/18; FANCI, 3/18) were identified in 18 ccpRCC patients. Among ccpRCC patients with VAFAs, five out of eight patients had second primary malignancy or family history of cancer. Somatic variants characteristics may distinguish ccpRCC from ccRCC or pRCC and germline VAFAs may be a molecular characterization of ccpRCC. Compared with ccRCC or pRCC, ccpRCC patients may be significantly correlated with higher risk of developing second primary malignancy.

Under the power master-slave chain architecture, due to the autonomous operation and non-tampering characteristics of the blockchain, there is currently a lack of credible and efficient reliable supervision and governance mechanisms for the slave chain business. This article proposes a solution to this problem. The cross-chain supervision mechanism of the master and slave chains with the concept of “Governing the chain by chain ”, realizes the precise conversion of blockchain supervision and governance from system, theory to technology, and effectively prevents the operating risks of related power blockchain businesses.

This paper proposes a persistent data structure based on Trie Tree that is implemented on the blockchain. This data structure is mainly used to manage the management of digital identity data in the blockchain. When the existing blockchain account model does not meet actual needs, such as private key changes, a mechanism is needed to record the changed version while changing. For the current existing possible solutions, smart contracts can generally be used to implement them, but due to performance limitations, the contracts cannot support the high concurrency performance of large-scale users. This article implements a persistent Trie Tree with the features supported by the blockchain. It can provide credible data queries, and can support high-concurrency queries. Under specific needs, it can be used for large-scale digital identities in the block. The application of the chain provides a possible choice.

In the context of advocating the reduction of greenhouse gases, green energy trade has become the focus of attention. At this stage, green energy is relatively dispersed (such as wind power, hydropower, solar power, etc.), and energy generation is real-time. At present, green energy trading based on blockchain has become a better solution. In the transaction itself, the blockchain has better security and traceability, but in terms of statistical information and interval query, because the basic data structure of the blockchain is a key-value data structure, performance problems are prone to occur. A transaction-based static indexing method is proposed, which can effectively improve the efficiency of statistical information and interval query on the chain.

The influence law of a tracked vehicle grouser shape on the soil slide sinkage was investigated in this study via the numerical simulations and physical tests. A finite element model was built using the plastic incremental theory and generalized Hokker’s law, while constitutive soil parameters were obtained via triaxial test. The numerical simulation model was verified by physical test results. Based on the verified numerical simulation model, the influence law of the tracked vehicle grouser shape on the soil slide sinkage was determined. The results show that (1) shear displacement in the lateral direction can increase the soil sinkage in the vertical direction, which is referred to as the “slide sinkage”; (2) there is a linear relationship between the slide sinkage and the shear displacement; (3) the grouser width and height have a positive influence on the amount of slide sinkage. Under the same load, an increase in grouser width and height will cause an increase in the soil slide sinkage. Grouser thickness and pitch have a negative influence on the slide sinkage, and under the same load, increased grouser width and height cause a reduction in the soil slide sinkage. Grouser angle characteristics have no significant effect on the soil sinkage. The application of the slide sinkage in tracked vehicles traveling on soft roads was investigated in multibody system dynamics analysis software Recurdyn to confirm these observations. The results presented in this paper may provide a workable reference for the analysis of tracked vehicles in multibody system dynamics scenarios.

In this paper, we introduce the notion of quasi-pseudo-BL algebras as the generalization of pseudo-BL algebras and quasi-pseudo-MV algebras. First, we investigate the properties of quasi-pseudo-BL algebras and show the subdirect product composition of any quasi-pseudo-BL algebra. Especially, some properties of good quasi-pseudo-BL algebras are presented. Second, we discuss the filters of quasi-pseudo-BL algebras and prove that there exists a bijective correspondence between normal filters and filter congruences on a quasi-pseudo-BL algebra. The properties of some special filters are also discussed. Finally, we study the ideals of quasi-pseudo-BL algebras and investigate some connections between ideals and filters of a quasi-pseudo-BL algebra.

Background: Urea, the end product of protein metabolism, has been considered to have negligible toxicity for a long time. Our previous study showed a depression phenotype in urea transporter (UT) B knockout mice, which suggests that abnormal urea metabolism may cause depression. The purpose of this study was to determine if urea accumulation in brain is a key factor causing depression using clinical data and animal models. Methods: A meta-analysis was used to identify the relationship between depression and chronic diseases. Functional Magnetic Resonance Imaging (fMRI) brain scans and common biochemical indexes were compared between the patients and healthy controls. We used behavioural tests, electrophysiology, and molecular profiling techniques to investigate the functional role and molecular basis in mouse models. Findings: After performing a meta-analysis, we targeted the relevance between chronic kidney disease (CKD) and depression. In a CKD mouse model and a patient cohort, depression was induced by impairing the medial prefrontal cortex. The enlarged cohort suggested that urea was responsible for depression. In mice, urea was sufficient to induce depression, interrupt long-term potentiation (LTP) and cause loss of synapses in several models. The mTORC1-S6K pathway inhibition was necessary for the effect of urea. Lastly, we identified that the hydrolysate of urea, cyanate, was also involved in this pathophysiology. Interpretation: These data indicate that urea accumulation in brain is an independent factor causing depression, bypassing the psychosocial stress. Urea or cyanate carbamylates mTOR to inhibit the mTORC1-S6K dependent dendritic protein synthesis, inducing impairment of synaptic plasticity in mPFC and depression-like behaviour. CKD patients may be able to attenuate depression only by strict management of blood urea. Keywords: Urea, Depression, LTP, Carbamylation, mTOR

Background Atopic dermatitis (AD) is the most common skin disorder in infancy. However, the diagnosis and definite significance of infantile AD remains a debated issue. Objective To analyse the phenotypes of AD in infancy, to establish diagnostic criteria and to estimate the prevalence of this condition in China. Methods This is a multicentric study, in which 12 locations were chosen from different metropolitan areas of China. Following careful and complete history-taking and skin examination, the definite diagnosis of AD was made and the severity based on the SCORAD index was determined by local experienced dermatologists. Based on the detailed phenotyping, the major and representative clinical features of infantile AD were selected to establish the diagnostic criteria and evaluate their diagnostic efficacy. Results A total of 5967 infants were included in this study. The overall point prevalence of AD was 30.48%. The infantile AD developed as early as at the second month of life, and its incidence peaked in the third month of life at 40.81%. The proportion of mild, moderate and severe AD was 67.40%, 30.57% and 2.03%, respectively. The most commonly seen manifestations in the infantile AD were facial dermatitis (72.07%), xerosis (42.72%) and scalp dermatitis (27.93%). We established the novel diagnostic criteria of infants, which included: (i) onset after 2 weeks of birth; (ii) pruritus and/or irritability and sleeplessness comparable with lesions; and (iii) all two items above with one of the following items can reach a diagnosis of AD: (i) eczematous lesions distributed on cheeks and/or scalp and/or extensor limbs, and (ii) eczematous lesions on any other parts of body accompanied by xerosis. Conclusions In China, the prevalence of AD in infancy is 30.48% according to clinical diagnosis of dermatologists. The novel Chinese diagnostic criteria for AD in infants show a higher sensitivity and comparable specificity.

Objective: Growing evidence has proved obesity one of the confirmed important etiologic indicators for renal cell carcinoma (RCC). CD36 is underpinned to be involved in adipose absorption, but its role in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to investigate the mRNA expression of CD36 in anthropometric measures of adipose tissue and defining its value in predicting prognosis in ccRCC patients. Methods: Real-Time qPCR gene expression analysis was detected from 367 paired ccRCC and adjacent normal tissues. Distributions of categorical clinical-pathological data together with levels of CD36 expression were compared with χ2-test in a contingency table. Subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) were measured by magnetic resonance imaging (MRI) and identified at the level of the umbilicus. Pearson's correlation coefficient was utilized to quantify relations between body mass index (BMI), VAT%, SAT and CD36 expression respectively. Partial likelihood test from univariate and multivariate Cox regression analysis were developed to address the influence of independent factors on progression-free survival (PFS) and overall survival (OS). The Kaplan-Meier method and log-rank test were performed to assess the survival benefits between discrete levels. Results: In the current study, CD36 mRNA was demonstrated highly expressed in ccRCC compared with normal tissues. In addition, CD36 mRNA expression was significantly increased in patients with advanced TNM stage (p=0.003, p

BACKGROUND: An inherited susceptibility to renal cancers is associated with multiple predisposing genes, but most screening tests are limited to patients with a family history. Next-generation sequencing (NGS)–based multigene panels provide an efficient and adaptable tool for investigating pathogenic germline mutations on a larger scale. This study investigated the frequency of pathogenic germline mutations in renal cancer predisposition genes in patients with sporadic, early-onset disease. METHODS: An NGS-based panel of 23 known and potential renal cancer predisposition genes was used to analyze germline mutations in 190 unrelated Chinese patients under the age of 45 years who presented with renal tumors. The detected variants were filtered for pathogenicity, and then their frequencies were calculated and correlated with clinical features. Germline variants of the fumarate hydratase (FH) and BRCA1-associated protein 1 (BAP1) genes were comprehensively analyzed because of their aggressive potential. RESULTS: In total, 18 patients (9.5%) had germline mutations in 10 genes. Twelve of these 18 patients had alterations in renal cancer predisposition genes (6.3%), and 6 patients had mutations in potential predisposition genes such as BRCA1/2. Notably, pathogenic mutation carriers had a significant family history in second-degree relatives in comparison with those without pathogenic mutations (P < .001). Variants of unknown clinical significance in FH and BAP1 demonstrated evidence of additional somatic loss in tumors CONCLUSIONS: In patients with early-onset disease, a multigene panel identified a high pathogenic germline mutation rate in renal cancer predisposition genes. This study emphasizes the importance of screening patients with early-onset disease for mutations in cancer predisposition genes. Germline screening should be encouraged in early-onset patients to provide personalized medicine and improve patient outcomes.

In order to enable a certain instrument operator to receive as much training as possible during the training process, at the same time, reducing the use loss of equipment, it is planned to develop a set of simulation training software to achieve the above purpose. The software has three functions: one is to complete normal exercise functions, the second is the ability to test and score, the third is to be able to conduct operation guidance. Based on the above ideas, carry out the overall plan design of the software in the early stage. By designing the overall development plan of the software, standardize the specific implementation of the software, clarify the functional requirements that the software should meet, and lay the foundation for subsequent software development.

Urea transporter B (UT-B) is a membrane channel protein widely distributed in mammals, and plays a significant physiological role by regulating urea and water transportation in different tissues. More and more studies have found that UT-B is related to neurological diseases, including myelinopathy and depression. When urea accumulates in the brains of UT-B knockout mice, the synaptic plasticity of neurons is reduced, and the morphology and function of glial cells are also changed. However, the distribution and expression change of UT-B remain unclear. The purpose of this study is to determine the expression characteristics of UT-B in the brain. Through single-cell RNA sequencing, UT-B was found to express universally and substantially throughout the various cells in the central nervous system except for endothelial and smooth muscle cells. UT-B was detected in the third cerebral ventricular wall, granule cell layer of the dentate gyrus, and other parts of the hippocampal, cerebral cortex, substantia nigra, habenular, and lateral hypothalamic nucleus by immunohistochemistry. Compared with the membrane expression of UT-B in glial cells, the subcellular localization of UT-B is in the Golgi apparatus of neurons. Further, the expression of UT-B was regulated by osmotic pressure in vitro. In the experimental traumatic brain injury model (TBI), the number of UT-B positive neurons near the ipsilateral cerebral cortex increased first and then decreased over time, peaking at the 24 h. We inferred that change in UT-B expression after the TBI was an adaptation to changed urea levels. The experimental data suggest that the UT-B may be a potential target for the treatment of TBI and white matter edema.

An efficient catalytic asymmetric cascade cycloaddition reaction of arylalkynols with dioxopyrrolidines was developed. This reaction was achieved using Au(I) and (

To construct a robust genomic classifier for high-risk sporadic type 2 papillary renal cell carcinoma (sPRCC2) and identify potential therapeutic targets. A cohort from The Cancer Genome Atlas and two datasets from Gene Expression Omnibus were examined. Common differentially expressed genes were screened, and the ConsensusClusterPlus package was used to identify potential high-risk molecular subtypes of sPRCC2. Targeting protein for Xklp2 (TPX2) expression was used to simulate the classifier according to the logistic model. Ninety-two samples from Fudan University Shanghai Cancer Center (FUSCC) were obtained, and TPX2 immunostaining was performed to validate the predictive ability of the genomic classifier. Retrospective analysis was used to compare drug efficacy between the groups. High-risk sPRCC2 had worse overall (hazard ratio [HR] = 7.804) and disease-free survival (HR = 8.777) than low-risk sPRCC2, and the tumor and lymph node stages were significantly higher in high-risk sPRCC2. Gene set enrichment analysis revealed significant enrichment of genes in the mammalian target of rapamycin (mTOR) complex 1 signaling pathway in the high-risk group. In the FUSCC cohort, high-risk sPRCC2 (high TPX2 expression) was significantly correlated with worse overall and progression-free survival. Retrospective analysis indicated that mTOR inhibitor (everolimus) had greater efficacy in the high-risk group than in the low-risk group (overall response rate: 28.6% vs. 16.7%) and that everolimus had greater efficacy than sunitinib in the high-risk group (overall response rate: 28.6% vs. 20%). This study successfully constructed a genomic classifier for identifying high-risk sPRCC2. mTOR inhibitors may have good efficacy in patients with high-risk sPRCC2.