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Abstract The abilities of an organism to cope with extrinsic stresses and activate cellular stress responses decline during aging. The signals that modulate stress responses in aged animals remain to be elucidated. Here, we discover that feeding Caenorhabditis elegans (C. elegans) embryo lysates to adult worms enabled the animals to activate the mitochondrial unfolded protein response (UPRmt) upon mitochondrial perturbations. This discovery led to subsequent investigations that unveil a hedgehog-like signal that is transmitted from the germline to the soma in adults to inhibit UPRmt in somatic tissues. Additionally, we find that the levels of germline-expressed piRNAs in adult animals markedly decreased. This reduction in piRNA levels coincides with the production and secretion of a hedgehog-like signal and suppression of the UPRmt in somatic cells. Building upon existing research, our study further elucidates the intricate mechanisms of germline-to-soma signaling and its role in modulating the trade-offs between reproduction and somatic maintenance within the context of organismal aging.

To evaluate the plan quality and robustness of volumetric modulated arc therapy (VMAT) and intensity modulated radiation therapy (IMRT) for breast cancer, 50 patients, including 25 patients who received radiotherapy after breast-conserving surgery (BCR) and 25 patients who received postmastectomy radiotherapy (PRT), were selected for this study. Nominal VMAT and IMRT plans were generated for each patient on Eclipse treatment planning system (version 15.6). The dosimetric metrics, dose distribution, gamma passing rate, and delivery time were compared. In addition, 12 uncertainty plans with plan isocenter uncertainty and CT density uncertainty were recalculated based on the nominal plans for each patient. The dose volume histogram (DVH) band width (DVHBW) was adopted to quantify the plan robustness of the nominal plans for the perturbed scenarios in this study. For BCR, the dosimetric metrics except planning target volume (PTV) conformal index (CI) and ipsilateral lung V 5 were not statistically different for IMRT and VMAT plans. PTV CI of VMAT plans was better than that of IMRT plans (VMAT: 0.923 ± 0.024, IMRT: 0.855 ± 0.032, p = 0.003). The ipsilateral lung V 5 of VMAT plan was higher than that of IMRT plan (VMAT: 42.4% ± 2.8%, IMRT: 40.5% ± 4.0%, p = 0.045). The VMAT plans save more than 1.20 min compared to the IMRT plans (VMAT: 0.87 min, IMRT: 2.08 min, p < 0.001). The gamma passing rates of VMAT plans were better than those of IMRT plans (3 mm/3%, VMAT: 99.7% ± 0.2%, IMRT: 99.4% ± 0.4%, p < 0.001; 2 mm/2%, VMAT: 97.2% ± 1.0%, IMRT: 96.9% ± 0.6%, p = 0.108). For PRT, the dosimetric metrics of VMAT plans, including PTV D mean, homogeneity index (HI), CI, and D max of spinal cord, were significantly better than those of IMRT plans. The VMAT plans save more than 45% time compared with IMRT plans (VMAT: 1.54 min, IMRT: 2.81 min, p < 0.001). The difference in gamma passing rates between VMAT plans and IMRT plans was not statistically significant. For the plan robustness, the DVHBW of VMAT plans and IMRT plans for BCR were 2.09% ± 0.23% and 2.98% ± 0.40%, respectively (p < 0.05). For PRT, the DVHBW of VMAT plans was significantly better than those of IMRT plans (VMAT: 3.05% ± 0.26%, IMRT: 3.57% ± 0.27%, p < 0.05). The results show that the dosimetric metrics of VMAT plans were comparable to those of IMRT plans. More importantly, the VMAT plans had excited dose distribution and fast execution efficiency. The plan robustness of VMAT plans were superior.

Among the existing prediction methods for strata behaviors law in working faces, the methods based on numerical simulation and statistical regression cannot achieve real-time and precise prediction of strata behaviors law in working faces. Deep learning methods have problems such as a large number of hyperparameters that are difficult to set and slow model training speed. In order to solve the above problems, based on the time-series data of internal stress changes in the coal body monitored during the mining process of the 402102 working face in Hujiahe Coal Mine, the particle swarm optimization based gate recurrent unit (PSO-GRU) is applied to predict the strata behaviors law in the working face. The PSO algorithm is used to optimize GRU. The PSO-GRU model is constructed to achieve automatic optimization of hyperparameters, thereby improving the training speed and prediction precision of GRU. Based on the prediction results, the analytic hierarchy process (AHP) is used to establish the evaluation index system of the main control factors of the strata behaviors of the 402102 mining face. The roof conditions, mining technology, coal seam occurrence, and geological structure are identified as the first level indicators affecting the strata behaviors of the working face. The 14 representative second level indicators are further subdivided. The test results show the following points. ① Compared with the unoptimized GRU model, the mean square error (MSE) of the PSO-GRU model is reduced by 83.9%, the root mean square error (RMSE) is reduced by 59.8%, the mean absolute error (MAE) is reduced by 59.0%, and the coefficient of determination R2 is increased by 28.9%. ② The PSO-GRU model has a fitting degree of over 0.980 for predicting strata behaviors data, demonstrating good nonlinear fitting and generalization capabilities. ③ The occurrence factors of coal seams in geological conditions have the greatest impact on the strata behaviors of the mining face, with a weight of 0.47. Among the factors that can be intervened by humans, the impact of advancing speed on the strata behaviors of the working face is the greatest, with a weight of 0.13.

Objective: Hepatic insulin resistance, which leads to increased hepatic gluconeogenesis, is a major contributor to fasting hyperglycemia in type 2 diabetes mellitus (T2DM). However, the mechanism of impaired insulin-dependent suppression of hepatic gluconeogenesis remains elusive. Delta/Notch-like epidermal growth factor (EGF)-related receptor (DNER), firstly described as a neuron-specific Notch ligand, has been recently identified as a susceptibility gene for T2DM through genome-wide association studies. We herein investigated whether DNER regulates hepatic gluconeogenesis and whether this is mediated by enhanced insulin signaling. Methods: The association between DNER, tribbles homolog 3 (TRB3) and Akt signaling was evaluated in C57BL/6J, ob/ob and db/db mice by western blot analysis. DNER loss-of-function and gain-of-function in hepatic gluconeogenesis were analyzed by western blot analysis, quantitative real-time PCR, glucose uptake and output assay in AML-12 cells and partially validated in primary mouse hepatocytes. Hepatic DNER knockdown mice were generated by tail vein injection of adenovirus to confirm the effects of DNER in vivo. The interaction between DNER and TRB3 was investigated by rescue experiments, cycloheximide chase analysis, co-immunoprecipitation and immunofluorescence. The potential insulin-stimulated phosphorylation sites of DNER were determined by co-immunoprecipitation, LC-MS/MS analysis and site-specific mutagenesis. Results: Here we show that DNER enhanced hepatic insulin signaling in gluconeogenesis by inhibiting TRB3, an endogenous Akt inhibitor, through the ubiquitin-proteasome degradation pathway. In AML-12 hepatocytes, insulin-stimulated activation of Akt and suppression of gluconeogenesis are attenuated by DNER knockdown, but potentiated by DNER over-expression. In C57BL/6J mice, hepatic DNER knockdown is accompanied by impaired glucose and pyruvate tolerance. Furthermore, the in vitro effects of DNER knockdown or over-expression on both Akt activity and hepatic gluconeogenesis can be rescued by TRB3 knockdown or over-expression, respectively. In response to insulin stimulation, DNER interacted directly with insulin receptor and was phosphorylated at Tyr677. This site-specific phosphorylation is essential for DNER to upregulate Akt activity and then downregulate G6Pase and PEPCK expression, by interacting with TRB3 directly and inducing TRB3 proteasome-dependent degradation. Conclusions: Taken together, the crosstalk between insulin-Akt and DNER-TRB3 pathways represents a previously unrecognized mechanism by which insulin regulates hepatic gluconeogenesis.

Overheating fault caused by poor contacts of plum blossom contact is one of the typical defects of switchgear and gas insulation combined appliances. The study of current-carrying friction characteristics and remaining life prediction of plum blossom contacts in different gas medium is of great theoretical and applied significance for the accurate evaluation of the health status of this type of equipment. The test samples are processed according to the real plum blossom contact materials and process. The current-carrying tests in air, N2 and SF6 gas medium are carried out by using a self-built current-carrying table, and the contact resistance degradation curves are obtained. The analysis of the surface morphology of the contact shows that the sample surface shows obvious adhesive wear under the effect of large current and mechanical displacement, the gas medium affects the degradation process of the contact by changing the morphology and element composition of the wear region. The contact resistance failure threshold is determined according to the V-T relationship of the contact. The Savitzky-Golay filter is used to smooth the original curves of the contact resistance. The life of plum blossom contacts in three gas medium is predicted by using Wiener degradation model updated by Bayesian. The results show that the model is more accurate than the conventional Wiener model. The remaining life of the plum blossom contact in the air is 9 500 cycles. The life of the plum blossom contact in N2 is 61 300 cycles. The life of the plum blossom contact in SF6 is 32 200 cycles.

With literature and empirical evidence, this article argues against the existing claim that what if-questions are idioms and proposes that they are the result of the syntactic and semantic combination of what and if-conditionals. With further evidence, it is argued that what can be a propositional anaphor which occurs in different contexts, rather than being restricted to what if-questions. A formal characterization of the syntactic and semantic properties of the propositional what is formulated in the framework of Dynamic Syntax, demonstrating how this word makes semantic and syntactic contributions to the interpretation of utterances in English as a mechanism of interaction between interlocutors.

In recent years, the sense of social responsibility among college students has garnered significant scholarly interest due to its impact on individual development and its implications for national modernization and strength. This study introduces the Information-Motivation-Behavioral Skills (IMB) model as an innovative approach to fostering social responsibility in students at private colleges in Anhui Province. By employing a questionnaire, we assessed the current state of social responsibility cultivation among these students. Subsequently, an intervention study was conducted to evaluate the effectiveness of the IMB model. The intervention group received a social responsibility intervention based on the IMB model, while the control group continued with traditional methods. The findings indicate that the intervention group outperformed the control group across various dimensions—information, motivation, behavioral skills, and social responsibility behaviors. Notably, the overall sense of social responsibility in the intervention group increased by 13.13% post-intervention, which is 14.87% higher than that observed in the control group. These results suggest that the IMB model is a potent strategy for enhancing social responsibility among college students.

Fire evacuation path planning involves multiple data sources. In order to develop a dynamic planning, a comprehensive knowledge of the environment involving building information and fire development is required. This article presents a semantic approach that integrates Building Information Modeling (BIM) and Internet of Things (IoT) information to provide a data foundation for dynamic path planning. First, a fire evacuation (FE) ontology is introduced to fuse both knowledge and information relevant to dynamic path planning. Next, a dynamic knowledge graph that evolves according to the development of fire situation is instantiated based on the relevant FE ontology. Finally, to validate the feasibility of the semantic approach based on the ontology and knowledge graph, an example of application is conducted using a specific building as an example. This study provides a data foundation for more intelligent and precise decision-making in fire evacuation scenarios and offers a new approach for safety design and management in the field of construction.

Abstract Methionine restriction (MR) provides metabolic benefits in many organisms. However, mechanisms underlying the MR-induced effect remain incompletely understood. Here, we show in the budding yeast S. cerevisiae that MR relays a signal of S-adenosylmethionine (SAM) deprivation to adapt bioenergetic mitochondria to nitrogenic anabolism. In particular, decreases in cellular SAM constrain lipoate metabolism and protein lipoylation required for the operation of the tricarboxylic acid (TCA) cycle in the mitochondria, leading to incomplete glucose oxidation with an exit of acetyl-CoA and α-ketoglutarate from the TCA cycle to the syntheses of amino acids, such as arginine and leucine. This mitochondrial response achieves a trade-off between energy metabolism and nitrogenic anabolism, which serves as an effector mechanism promoting cell survival under MR.

Although often used as diagnostic tools in discourse studies, irregular wh-questions in English, which take the form of what/how about … ? and what/how if … ?, have not received sufficient attention in terms of their semantic and syntactic properties. This article investigates irregular wh-questions from semantic and syntactic perspectives. Semantically, there is a topic-comment relationship between the wh-word and the rest of the sentence. Syntactically, an irregular wh-question neither displays an explicit matrix subject-predicate structure nor involves a fronted auxiliary. This article pursues three objectives. First, a descriptive overview of irregular wh-questions is provided, which shows that the complement of about in irregular wh-questions can belong to various syntactic categories. Second, the topic-comment structure of irregular wh-questions is compared with that of the topic-comment structure in declarative sentences, which reveals some semantic properties of irregular wh-questions. Third, a formal account of irregular wh-questions is formulated in the framework of Dynamic Syntax, which captures the interaction of the semantic and syntactic properties of the components of an irregular wh-question. Additionally, the present dynamic account of irregular wh-questions is contrasted with an existing dynamic account of the hanging topic construction in English, which highlights some merits of the current account.

Target recognition and temperature extraction of the typical component of gas insulated switchgear (GIS) are the key to realizing the infrared intelligent detection of equipment heating state. In this paper, an improved YOLOv4 algorithm based on convolutional block attention module (CBAM) is proposed to achieve rapid target detection and hot spot temperature extraction of GIS bus, disconnector and other components. Firstly, the original infrared images are acquired at a substation site, and an infrared dataset containing typical GIS components is constructed by sharpening the images and marking components. Then, the deep separable convolutional network is used to reduce the amount of model parameters, and the CBAM is integrated to optimize the recognition ability of the model, on the basis of which a GIS infrared component target rapid detection algorithm with improved YOLOv4 is constructed. Finally, the gray-scale difference method is used to extract the temperature value of the hot area for the detected typical target components of GIS. The results show that the proposed algorithm can achieve a recognition speed of 31.5 frame per second and an recognition accuracy of 82.3% on the GIS infrared feature dataset, which is significantly better than other target algorithms. The error between the calculated value and the measured value of temperature rise of GIS components is within ±1℃. The algorithm proposed in this paper can be deployed in edge intelligent terminals such as unmanned aerial vehicles and inspection trolleys to achieve refined identification and rapid diagnosis of the temperature rise status of on-site GIS equipment, thus improving the digitalization and intelligence level of health management of GIS.

The communication industry is crucial for promoting a country's network strength, leading scientific and technological innovation, and driving economic and social transformation. Novel network architectures are the core of future network and communication industry and require urgent research. This study expounds on the implication of novel network architecture and presents several representative architectures, including information center network, expressive Internet architecture, low-Earth-orbit satellite network, service customization network, full-dimensional definable network, and multimodal network. Moreover, the development trends of novel network architectures, including policies and regulations, industry development, and challenges are summarized. Key technologies such as deterministic networking, segmented routing, cloud computing, and cloud-network integration are explored. Meanwhile, measures to tackle critical problems are proposed, such as launching special science and technology projects and promoting application demonstration. Furthermore, we suggest that China should enhance the coordinated implementation of policies, laws, and regulations, cultivate a benign development ecology for the novel network architecture industry, promote in-depth cooperation among industry, universities, and research institutes, and build comprehensive and innovative research teams.

Abstract Background The mortality of colorectal cancer is high, the malignant degree of poorly differentiated colorectal cancer is high, and the prognosis is poor. Objective To screen the characteristic intestinal microbiota of poorly differentiated intestinal cancer. Methods Fecal samples were collected from 124 patients with moderately differentiated CRC and 123 patients with poorly differentiated CRC, and the bacterial 16S rRNA V1-V4 region of the fecal samples was sequenced. Alpha diversity analysis was performed on fecal samples to assess the diversity and abundance of flora. The RDP classifier Bayesian algorithm was used to analyze the community structure. Linear discriminant analysis and Student's t test were used to screen the differences in flora. The PICRUSt1 method was used to predict the bacterial function, and six machine learning models, including logistic regression, random forest, neural network, support vector machine, CatBoost and gradient boosting decision tree, were used to construct a prediction model for the poor differentiation of colorectal cancer. Results There was no significant difference in fecal flora alpha diversity between moderately and poorly differentiated colorectal cancer (P > 0.05). The bacteria that accounted for a large proportion of patients with poorly differentiated and moderately differentiated colorectal cancer were Blautia, Escherichia-Shigella, Streptococcus, Lactobacillus, and Bacteroides. At the genus level, there were nine bacteria with high abundance in the poorly differentiated group, including Bifidobacterium , norank_f__Oscillospiraceae , Eisenbergiella, etc. There were six bacteria with high abundance in the moderately differentiated group, including Megamonas , Erysipelotrichaceae_UCG-003 , Actinomyces, etc. The RF model had the highest prediction accuracy (100.00% correct). The bacteria that had the greatest variable importance in the model were Pseudoramibacter, Megamonas and Bifidobacterium. Conclusion The degree of pathological differentiation of colorectal cancer was related to gut flora, and poorly differentiated colorectal cancer had some different bacterial flora, and intestinal bacteria can be used as biomarkers for predicting poorly differentiated CRC.

Discovering maximal frequent itemsets is an important issue and key technique in many data mining problems such as association rule mining. In the literature, generating maximal frequent itemsets proves either to be NP-hard or to have O(l34l(m+n))O\left({l}^{3}{4}^{l}\left(m+n)) complexity in the worst case from the perspective of generating maximal complete bipartite graphs of a bipartite graph, where mm, nn are the item number and the transaction number, respectively, and ll denotes the maximum of ∣C∣∣Ψ(C)∣/(∣C∣+∣Ψ(C)∣−1)| C| | \Psi \left(C)| \hspace{0.1em}\text{/}\hspace{0.1em}\left(| C| +| \Psi \left(C)| -1), with the maximum taken over all maximal frequent itemsets CC. In this article, we put forward a method for discovering maximal frequent itemsets, whose complexity is O(3mn2β+4βn)O\left(3mn{2}^{\beta }+{4}^{\beta }n), lower than the known complexity both in the worst case, from the perspective of semigroup algebra, where β\beta is the number of items whose support is more than the minimum support threshold. Experiments also show that an algorithm based on the algebraic method performs better than the other three well-known algorithms. Meanwhile, we explore some algebraic properties with respect to items and transactions, prove that the maximal frequent itemsets are exactly the simplified generators of frequent itemsets, give a necessary and sufficient condition for a maximal i+1i+1-frequent itemset being a subset of a closed ii-frequent itemset, and provide a recurrence formula of maximal frequent itemsets.

AIM. To study the clinical effect of body composition-based lifestyle intervention in patients with polycystic ovary syndrome (PCOS). MATERIAL AND METHODS. In the period of November, 2020 – July, 2021, 91 patients diagnosed with PCOS in the First People's Hospital of Urumqi were prospectively selected and divided into a control group (46 cases) and an experimental group (45 cases) according to random number method. The patients in the control group were treated with Drospirenone and Ethinylestradiol tablets (II), while the patients in the experimental group were treated with Drospirenone and Ethinylestradiol tablets (II) and lifestyle adjustment based on body composition. The changes of the indices before and after 3 months of treatment were compared between the two groups. RESULTS. The indices of PCOS patients in the two groups after treatment (except 1 basal metabolic rate and muscle weight, 2. FSH and 3. HDL) were decreased, and the decrease in the experimental group was more significant (P < 0.05). Patients in the experimental group had significantly increased spontaneous ovulation and pregnancy rate / Conducive to ovulation and pregnancy. CONCLUSION. In PCOS patients, the combination of Drospirenone and Ethinylestradiol tablets (II) and lifestyle intervention based on human body components can improve reproductive hormone, glucose metabolism and insulin resistance more effectively, which is beneficial to spontaneous ovulation and pregnancy, and reduce the weight of patients.

Abstract Background The extent of surgery in nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs) has not well established, partly owing to the dilemma of precise prediction of lymph node metastasis (LNM) preoperatively. This study proposed to develop and validate the value of MRI features for predicting LNM in NF-PNETs. Methods A total of 187 patients with NF-PNETs who underwent MR scan and subsequent lymphadenectomy from 4 hospitals were included and divided into training group (n = 66, 1 center) and validation group (n = 121, 3 centers). The clinical characteristics and qualitative MRI features were collected. Multivariate logistic regression model for predicting LNM in NF-PNETs was constructed using the training group and further tested using validation group. Results Nodal metastases were reported in 41 patients (21.9%). Multivariate analysis showed that regular shape of primary tumor (odds ratio [OR], 4.722; p = .038) and the short axis of the largest lymph node in the regional area (OR, 1.488; p = .002) were independent predictors for LNM in the training group. The area under the receiver operating characteristic curve in the training group and validation group were 0.890 and 0.849, respectively. Disease-free survival was significantly different between model-defined LNM and non-LNM group. Conclusions The novel MRI-based model considering regular shape of primary tumor and short axis of largest lymph node in the regional area can accurately predict lymph node metastases preoperatively in NF-PNETs patients, which might facilitate the surgeons’ decision on risk stratification.

The determination of Initial Track Tension (ITT) is an important issue for the moving performance of tracked vehicle under soft ground. At the present time, however, the selection of ITT only according to a moving performance or an operating condition under hard ground. Focus on this, a novel approach for determination of ITT without changing the basic vehicle parameters is proposed in this paper, which can improve the moving performance under soft ground comprehensively. Analytical models for predicting ground pressure under tracked vehicle on soft ground including ITT were used based on the track-soil coupling relationship, movement characteristics and turning characteristics of an articulated track vehicle, which applied to the straight driving condition, steering-driving condition and climbing condition. The intrinsic mechanisms between the ITT and the Mean Maximum Pressure (MMP) under roadwheels were revealed. The stresses produced by the action of track and sprocket, roadwheels, idle wheel, soil respectively were studied in consideration of the vehicle-soil mechanics theory and strength theory. The stress state and changes for an element of the track were investigated. What’s more, the relationship between the ITT and fatigue life of the track was explained in detail based on the Miner’s rule. On this basis, an optimal study for the ITT was performed taking advantage of NSGAII, which used the MMP under roadwheels and fatigue life of track as optimization objectives. And then, the determination principles of the ITT were presented, and it was identified by an articulated track vehicle. The results are significant to the prediction and control of the moving performance of track vehicle under soft ground.

In this paper, according to the power output characteristics of distributed photovoltaic users, the SSA-ELM (Sparrow Search Algorithm - Extreme Learning Machine) model based on weather type division is proposed for photovoltaic power day ahead prediction. Because the solar panel power generation sequence of photovoltaic users contains high frequency fluctuations, in this paper we use the power sequence convergence effect to make cluster prediction on all photovoltaic panels to reduce the randomness of distributed photovoltaic. The prediction accuracy is further improved by dividing weather types. The historical data of distributed PV users in a region of Gansu province is used for modeling verification, and the results show that the prediction error of the proposed method is lower. In bad weather, the root mean square error is at least 0.02 less than the comparison model, and the average annual accuracy rate is 93.2%, which proves the applicability of the proposed method in different output types.

Abstract We report the case of a 41‐year‐old human with third‐degree atrioventricular block caused due to intoxication with water concoction prepared from Rhododendron leaves. Such poisoning is rare. It is prone to arrhythmia with hemodynamic instability and is confused with various diseases. For these reasons, the correct diagnosis and treatment of this poisoning are particularly important. We confirmed it by analyzing the remaining liquid carried by the family members. After symptomatic and supportive treatment, the patient was discharged uneventfully.

Grid generation is an important step of computational fluid dynamics.In the process of large-scale numerical simulation,the time consumption of grid generation increases with the number of grids which often increases with the simulation accuracy.Based on the grid generation algorithm in an open-source software called OpenFoam,this paper proposes a hybrid parallel me-thod of OpenMP and MPI for polyhedral grid generation.By theoretical analysis,we show that when the hybrid parallel method is used to generate the same quality grids,increasing the number of threads and grid cells will reduce the time consumption of grid generation.Three numerical simulations using different solvers show that the grids generated by the hybrid parallel method and the original method have close qualifications,and the simulation results are almost indistinguishable from those of the original method.Furthermore,the time consumption of this method to generate the same quality and quantity grids can be reduced to less than a quarter of the time consumption without using OpenMP parallel method.

Objective: To assess the value of microbubble contrast agent SonoVue in the thorough preoperative lymphatic mapping of patients with thyroid carcinoma, including the lymphatic drainage region, the detection of sentinel lymph node (SLN), and the diagnosis of lymph node metastasis (LNM).Materials and methods: 55 patients with 62 thyroid malignancies proved by surgical pathology (59 papillary thyroid carcinomas and three medullary thyroid carcinomas) who underwent preoperative lymphatic contrast-enhanced ultrasound (LCEUS) with microbubble contrast agent SonoVue were enrolled. All LNM were confirmed by pathology. The location of thyroid lesions, ultrasonic features of lymph nodes, lymphatic drainage region, and detection of SLN were assessed. The diagnostic performance (sensitivity, specificity, positive predictive value, negative predictive value and accuracy) of different parameters for the LNM diagnosis was calculated.Results: SonoVue effectively demonstrated the lymphatic drainage region for all enrolled thyroid carcinomas. The most common lymphatic drainage region for thyroid carcinomas was region VI (93.55%), followed by region III (62.90%), region IV (48.39%) and region II (4.84%). When divided by the lesion location, the most common lymphatic drainage regions for the nodule in isthmus, superior lobe and inferior lobe of the thyroid were region VI, region III, and region VI respectively. SLN was detected in 96.77% (60/62) of cases. The two cases without SLN demonstration had pathologically proven LNM. The most common sonographic sign of LNM was perfusion defect (54.17%). The diagnostic accuracy of SonoVue in central and lateral compartment LNM was 86.67% and 91.67%, respectively.Conclusion: Microbubble contrast agent SonoVue is a valuable imaging contrast medium for thorough preoperative lymphatic mapping in patients with thyroid carcinoma, including the lymphatic drainage region, the detection of SLN, and the diagnosis of LNM. LCEUS with SonoVue alone has limitations of false negatives when there is lymphatic vessel obstruction and may need to be combined with other ultrasound modalities.

The present study aims at investigating the effect of organic-modified nickel phyllosilicate (NiPS) particles on the structure–property relationships of epoxy (EP) composites. The organic-modified NiPS particles were synthesized using a facile sol–gel method and subsequently mixed into EP matrix by physical blending to improve its tribological, mechanical and thermal properties. Based on a series of tests and analyses, it was found that organic-modified NiPS particles playing a great role in improving the tribological properties under dry sliding. The steady-state friction coefficient was just decreased moderately, whereas the wear rate was dropped significantly to 0.36 × 10−5 mm3/N·m, showing a reduction up to ca. 95% in comparison with pure EP. According to the analysis of worn surfaces, the main mechanism of wear resistance was discussed. The elastic modulus for the EP composites was increased remarkably while the elongation at break decreased. However, the added organic-modified NiPS particles show a negligible influence on the tensile strength. Moreover, the incorporated NiPS particles could enhance the thermal stability of EP composites, showing the notably suppressed mass loss rate and added residual chars. This study provided a promising way for preparing the high-performance EP composites with organic-modified lamellar particles.

Artificial intelligence (AI) video image analysis is an important part of intelligent coal preparation plant. It can realize the intelligent monitoring of important parameters of the equipment, environment, personnel and the whole process of coal preparation. The basic structure of the intelligent coal preparation plant is proposed. It is pointed out that the existing research mostly uses AI video image analysis technology to construct the safety monitoring system of coal preparation plant for personnel, machine, environment and management. The construction process of the intelligent video image monitoring system is proposed. In view of the two goals of safe and environment-friendly production and improving product quality in the intelligent construction of coal preparation plant, the application status of AI video image analysis technology in the intelligent coal preparation plant is introduced from six aspects. The aspects include foreign object detection, intelligent separation, equipment running state monitoring, coal particle size detection, personnel behavior monitoring and environmental safety detection. The intelligent application of AI video image analysis in coal preparation plant is proposed. It is pointed out that it is necessary to build a multi-level video monitoring system based on 5G communication, the Internet of Things, AI, intelligent control theory and coal preparation industry technology from the macro architecture. It is also necessary to optimize existing general intelligent video monitoring methods or algorithms from a micro perspective, and develop intelligent video image analysis technology suitable for the coal preparation plant environment. Machine vision and computer vision should be highly integrated with deep learning. The different advantages of machine vision and computer vision should be reasonably applied in different working conditions. It is suggested to establish a multi-level integrated monitoring system framework, and deploy and optimize the algorithm model within the framework. It is suggested to establish a diversified video image database, make full use of data characteristics of different image types, and develop targeted analysis algorithms. It is suggested to deeply study the distributed data stream and real-time AI video image analysis, build a real-time AI distributed system, reasonably schedule the video image analysis model, and improve the calculation efficiency and accuracy of the real-time model.

This study evaluates the efficacy and safety of bevacizumab (BEV) in the treatment of non-small cell lung cancer (NSCLC) patients with brain metastases (BM) by performing meta-analyses of response and survival indices. Seventeen studies were included. BEV treatment was associated with a lower new BM incidence (hazard ratio: 0.30 [95% confidence interval (CI): 0.14, 0.46]) during follow-up. Disease control rate (DCR) of BEV-treated patients with BM was 91% [95% CI: 85, 95]. However, intracranial DCR was relatively higher (94% [95% CI: 87, 98]) than extracranial DCR (86% [95% CI: 74, 96]). DCR of NSCLC patients with BM was significantly better with BEV than with control therapies (odds ratio: 2.71 [95% CI: 1.26, 5.86], P = 0.01). Progression-free survival (PFS) of BEV-treated patients with and without BM was 7.1 months [95% CI: 6.2, 8.0] and 7.4 months [95% CI: 6.3, 8.4], respectively. Intracranial PFS of BEV-treated patients with BM was 8.0 months [95% CI: 6.0, 10.0]. Overall survival of BEV-treated NSCLC patients with and without BM was 13.5 months [95% CI: 11.4, 15.6] and 12.5 months [95% CI: 10.2, 14.8], respectively. The incidence of bleeding/hemorrhage in the central nervous system was 1% with BEV treatment.

Abstract Background The aim of this study was to explore the correlation between serum vitamin D levels in couples undergoing in-vitro fertilisation (IVF) and normal fertilisation process. Methods Between March 2016 and March 2017, we performed a prospective cohort study at an academic reproductive medicine centre to measure serum 25-hydroxyvitaminD (25-OHD) levels of 1232 couples before controlled ovarian stimulation. Generalized linear regression and binary multivariate logistic regression were employed to assess whether 25-OHD levels in men and women correlated with normal fertilisation rates and low fertilisation rate (LFR). Results Serum 25-OHD levels in women were classified into three groups: Group A, less than 10%; Group B, between 10 and 90%; and Group C, greater than 90%. Using generalized linear regression, we observed that female 25-OHD levels were related to normal fertilisation rates. Adjusted normal fertilisation rates from Group A to Group C in women were 59.50, 62.72, and 66.13%, respectively (P = 0.007). After binary logistic regression analysis, for women, compared with Group C, the ORs for LFR were 4.814 in Group A (95% CI, 1.266–18.309, P = 0.021) and were 3.204 in Group B (95% CI, 0.949–10.812, P = 0.061). Male 25-OHD levels were not related to the probability of low fertilisation rate (P > 0.05). Conclusions Circulating 25-OHD concentrations in women appear to be associated with normal fertilisation rates and low fertilisation rates in IVF cycles, but not in men. A further randomized controlled trial with vitamin D supplementation is needed to demonstrate whether female vitamin D levels exert an effect on the normal fertilisation process. Trial registration https://clinicaltrials.gov/; NCT03305510 ; Registered 08 October 2017 - Retrospectively registered.

Knowledge representations of building codes are essential and critical resources for the organization, retrieval, sharing, and reuse of implicit knowledge in the AEC industry. Against this background, traditional code compliance checking is time-consuming and error-prone. This research aimed to utilize various knowledge representation techniques to establish a knowledge model of building codes to facilitate the automated code compliance checking. The proposed knowledge model consists of three levels to achieve conceptual, logical, and correlational representations of building codes. The concept-level model provides the basic knowledge elements. The clause-level model was developed based on a unified top schema and provides the conceptual graph, mapping logics, and checking logics of each clause. The code-level model is constructed based on the explicit cross-references and semantic connections between clauses. The investigations on the model applications indicate two aspects. On the one hand, the proposed knowledge model shows high potential for semantic searching and knowledge recommendation. On the other hand, the automated code-compliance-checking processes based on the proposed multiscale knowledge model can achieve three main advantages: guiding designers to create a building model with completely necessary information, mitigating the differences between building information and regulatory information, and making the checking procedures more friendly and relatively transparent to users.

Objective To detect the expression of FBXL20 in gastric cancer cells, and determine its effect on the proliferation, migration and invasion of gastric cancer cells. Methods The expression levels of FBXL20 in gastric cancer cell lines BGC-823, AGS, MKN-45 and normal gastric epithelial cell line GES-1 were determined by Western blot. FBXL20 expression in AGS cells was inhibited by targeting FBXL20 shRNA lentiviral vectors. CCK-8 assay and Transwell assay were used to evaluate the proliferation, migration and invasion of AGS cells. The expression of Wnt pathway-related proteins were determined by Western blot. Results FBXL20 expression was significantly up-regulated in gastric cancer cell lines, compared with the normal gastric epithelial cell line (P < 0.05). Compared with the control group, AGS cells with FBXL20 knockdown had lower proliferation, migration and invasion abilities (P < 0.05). Inhibition of FBXL20 reduced ubiquitination degradation of GSK-3β, which increased the expression levels of GSK-3β, p-GSK-3βTyr216, and decreased the expression levels of β-catenin, p-β-catenin, Cyclin D1 and E-cadherin. Conclusion FBXL20 may promote the proliferation, migration and invasion of gastric cancer cells by regulating Wnt pathway.

A distributed multi-target localization system based on optical Wavelength Division Multiplexing (WDM) network is demonstrated. The wideband orthogonal waveforms are generated by introducing the chaotic OptoElectronic Oscillator (OEO). The optical WDM network is introduced to transmit the wideband signals from multiple distributed transmitting and receiving units to the central station for processing, and the accurate localization of multiple targets is achieved based on the time of arrival localization method. The multiple optical carriers are generated at the central station, the complex processing to achieve the highprecision of the target localization is supported by the resources at the central station, and the remote transmitting and receiving units are simplified. Moreover, a proof of concept of the distributed multi-target localization system based on optical WDM network is obtained. The localization system comprising two transmitters and two receivers is experimentally established. The orthogonal chaotic waveforms with the frequency range of 3.1～10.6 GHz are successfully generated from the chaotic OEOs. The two-dimensional localization of two targets is realized via the maximum positioning error of 7.09 cm. Additionally, the reconfiguration of the system is experimentally verified.

Background: Thallium (Tl) exposure remains a public health problem with potential impacts on humans. Studies have suggested that prenatal exposure to thallium may be associated with fetal growth, but no studies are known have explored its association with early childhood anthropometry. Objective: To investigate the effects of prenatal Tl exposures on early child growth and development aged 0–2 years in a prospective birth cohort study. Methods: 3080 pregnant women and their children participated in the study, which were recruited from a birth cohort in China. Serum samples collected in the first and second trimester of pregnant subjects and umbilical cord blood of infants were analyzed for Tl exposure assessment. Infant length or standing height and weight were obtained from medical records and 2 years planned visits. We used length/height and weight to calculate z-scores for weight-for-age (WAZ), height-for-age (HAZ), weight-for-height (WHZ), and body mass index-for-age (zBMI) based on World Health Organization standards. Linear mixed model was used to investigate the association between serum concentrations of Tl and the children's anthropometric characteristics (WAZ, HAZ, WHZ, and zBMI), and stratification analysis by sex was also examined. Results: The median (P25-P75) of Tl levels in the first trimester, second trimester and umbilical cord serum were 61.7 (50.7–77.0), 60.1 (50.9–74.8) and 38.4 (33.6–43.9) ng/L, respectively. Paired Mann-Whitney tests found Tl concentrations in umbilical cord serum were significantly less than that in maternal serum during the first and second trimesters (all p

Deep learning-based methods for predicting spatial-temporal data such as crowd flows need to consider both spatial dependency and temporal dependency. Previous research mainly focused on modeling spatial dependency, whereas studies on temporal dependency are few. Existing finite deep learning-based methods for temporal dependency modeling can be divided into RNN-based methods and domain knowledge-based methods. However, RNN-based methods are hard to learn very long-term temporal dependency, and domain knowledge-based methods cannot model temporal dependency automatically, depending on data pre-processing based on prior knowledge. In view of the problem, crowd flows prediction in regular gridded regions are studied and a model called Pre-trained Bidirectional Temporal Representation (PBTR) based on Transformer encoder is proposed capable of modeling very long-term temporal dependency automatically. PBTR is simple, scalable, and can be combined with any other spatial component. Furthermore, we introduce Crowd Flows Prediction based on PBTR (CPPBTR) to form a Transformer based encoder-decoder framework. There are two decode stages in the proposed model. At decoder-stage 1, `draft' sequence is generated. At decoder-stage 2, each timestep of the `draft' sequence is masked and fed into PBTR to predict the refined flow for each masked position. Experiment results demonstrate that our method outperforms RNN-based methods and domain knowledge-based methods.

Data quality management, especially data cleansing, has been extensively studied for many years in the areas of data management and visual analytics. In the paper, we first review and explore the relevant work from the research areas of data management, visual analytics and human-computer interaction. Then for different types of data such as multimedia data, textual data, trajectory data, and graph data, we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis stages. Based on a thorough analysis, we propose a general visual analytics framework for interactively cleansing data. Finally, the challenges and opportunities are analyzed and discussed in the context of data and humans. MSC: 00-01, 99-00, Keywords: Data quality management, Visual analytics, Data cleansing