Association between rs1799724 of TNF- α gene and early onset preeclampsia in Chinese: A pilot study.

Objective: To investigate the association between polymorphisms of TNF- α (rs1799724, rs1800629), VEGF (rs3025039) and VEGFR1 (rs 722503) and early onset preeclampsia (EOPE) in Chinese. Methods: A total of 132 EOPE patients from January 2016 to December 2018 at the Second Hospital of Tianjin Medical University were selected as the EOPE group, and 156 normal pregnant patients as the Control group. In both groups, 5 ml of peripheral venous blood was obtained after admission. The characteristics of genotype and allele distribution at the four SNPs in the study subjects were examined by matrix-assisted laser desorption ionization time-of-flight mass spectrometric genotyping. Results: The genotype frequency distribution and allele frequency distribution of rs1799724 were significantly different between the EOPE group and the Control group (P = 0.002，P = 0.003). The T allele was statistically associated with the development of EOPE under a dominant genetic model (P = 0.001). The genotype and allele frequency distributions of rs1800629, rs3025039, and rs 722503 did not differ significantly between the EOPE group and the Control group (P > 0.05). There was no linkage disequilibrium among rs1799724, rs1800629 and rs3025039 loci, the corresponding haploid cannot be formed. Conclusions: The rs1799724 of TNF- α gene is a genetic susceptibility locus for EOPE and may be a potential predictors of preeclampsia.

Calculation of carbon emissions in wastewater treatment and its neutralization measures: A review.

As the pursuit of "carbon neutrality" gains momentum, the emphasis on low-carbon solutions, emphasizing energy conservation and resource reuse, has introduced fresh challenges to conventional wastewater treatment approaches. Precisely evaluating carbon emissions in urban water supply and drainage systems, wastewater treatment plants, and establishing carbon-neutral operating models has become a pivotal concern in the future of wastewater treatment. Regrettably, limited research has been devoted to carbon accounting and the development of carbon-neutral strategies for wastewater treatment. In this review, to facilitate comprehensive carbon accounting, we initially recognizes direct and indirect carbon emission sources in the wastewater treatment process. We then provide an overview of several major carbon accounting methods and propose a carbon accounting framework. Furthermore, we advocate for a systemic perspective, highlighting that achieving carbon neutrality in wastewater treatment extends beyond the boundaries of wastewater treatment plants. We assess current technical measures both within and outside the plants that contribute to achieving carbon-neutral operations. Encouraging the application of intelligent algorithms for the multifaceted monitoring and control of wastewater treatment processes is paramount. Supporting resource and energy recycling is also essential, as is recognizing the benefits of synergistic wastewater treatment technologies. We advocate a systematic, multi-level planning approach that takes into account a wide range of factors. Our goal is to offer valuable insights and support for the practical implementation of water environment management within the framework of carbon neutrality, and to advance sustainable socio-economic development and contribute to a more environmentally responsible future.

The multi-modal fusion in visual question answering: a review of attention mechanisms.

Visual Question Answering (VQA) is a significant cross-disciplinary issue in the fields of computer vision and natural language processing that requires a computer to output a natural language answer based on pictures and questions posed based on the pictures. This requires simultaneous processing of multimodal fusion of text features and visual features, and the key task that can ensure its success is the attention mechanism. Bringing in attention mechanisms makes it better to integrate text features and image features into a compact multi-modal representation. Therefore, it is necessary to clarify the development status of attention mechanism, understand the most advanced attention mechanism methods, and look forward to its future development direction. In this article, we first conduct a bibliometric analysis of the correlation through CiteSpace, then we find and reasonably speculate that the attention mechanism has great development potential in cross-modal retrieval. Secondly, we discuss the classification and application of existing attention mechanisms in VQA tasks, analysis their shortcomings, and summarize current improvement methods. Finally, through the continuous exploration of attention mechanisms, we believe that VQA will evolve in a smarter and more human direction.

Adaptive control of time delay teleoperation system with uncertain dynamics.

A bilateral adaptive control method based on PEB control structure is designed for a class of time-delay force feedback teleoperation system without external interference and internal friction to study the uncertainty of dynamic parameters and time delay. The stability and tracking performances of the closed-loop constant time delay teleoperation system are analyzed by Lyapunov stability theory. Finally, the controller designed in this paper is successfully applied to the teleoperation system composed of a two-degree of freedom rotating manipulator as the master robot and the slave robot. The simulation is carried out in no operator and environment force or with operator and environment force. The adaptive bilateral control method's control performance is compared with that of the traditional time-delay teleoperation system. Finally, it is verified that the method has good control performance.

Sparse Angle CBCT Reconstruction Based on Guided Image Filtering.

Cone-beam Computerized Tomography (CBCT) has the advantages of high ray utilization and detection efficiency, short scan time, high spatial and isotropic resolution. However, the X-rays emitted by CBCT examination are harmful to the human body, so reducing the radiation dose without damaging the reconstruction quality is the key to the reconstruction of CBCT. In this paper, we propose a sparse angle CBCT reconstruction algorithm based on Guided Image FilteringGIF, which combines the classic Simultaneous Algebra Reconstruction Technique(SART) and the Total p-Variation (TpV) minimization. Due to the good edge-preserving ability of SART and noise suppression ability of TpV minimization, the proposed method can suppress noise and artifacts while preserving edge and texture information in reconstructed images. Experimental results based on simulated and real-measured CBCT datasets show the advantages of the proposed method.

Control of Time Delay Force Feedback Teleoperation System With Finite Time Convergence.

In order to make the teleoperation system more practical, it is necessary to effectively control the tracking error convergence time of the teleoperation system. By combining the terminal sliding mode control method with the neural network adaptive control method, a bilateral continuous finite time adaptive terminal sliding mode control method is designed for the combined teleoperation system. The Lyapunov theory is used to analyze the stability of the closed-loop system, and the position tracking error is able to effectively converge in time. Finally, the effectiveness of the proposed control scheme is verified by MATLAB Simulink numerical simulation, and the numerical analysis of the results shows that the method has better system performance. Compared with the traditional two-sided control method (TPDC) of PD time-delay teleoperation system, the control method in this paper has good performance, improves stability, and makes steady-state errors smaller and better tracking.

Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network.

The whole sentence representation reasoning process simultaneously comprises a sentence representation module and a semantic reasoning module. This paper combines the multi-layer semantic representation network with the deep fusion matching network to solve the limitations of only considering a sentence representation module or a reasoning model. It proposes a joint optimization method based on multi-layer semantics called the Semantic Fusion Deep Matching Network (SCF-DMN) to explore the influence of sentence representation and reasoning models on reasoning performance. Experiments on text entailment recognition tasks show that the joint optimization representation reasoning method performs better than the existing methods. The sentence representation optimization module and the improved optimization reasoning model can promote reasoning performance when used individually. However, the optimization of the reasoning model has a more significant impact on the final reasoning results. Furthermore, after comparing each module's performance, there is a mutual constraint between the sentence representation module and the reasoning model. This condition restricts overall performance, resulting in no linear superposition of reasoning performance. Overall, by comparing the proposed methods with other existed methods that are tested using the same database, the proposed method solves the lack of in-depth interactive information and interpretability in the model design which would be inspirational for future improving and studying of natural language reasoning.

Low-Dose CT Image Post-Processing Based on Learn-Type Sparse Transform.

As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease screening, detection, and disease tracking. This study exploits the ability of sparse representation to learn sparse transformations of information and combines it with image decomposition theory. The structural information of low-dose CT images is separated from noise and artifact information, and the sparse expression of sparse transformation is used to improve the imaging effect. In this paper, two different learned sparse transformations are used. The first covers more organizational information about the scanned object. The other can cover more noise artifacts. Both methods can improve the ability to learn sparse transformations to express various image information. Experimental results show that the algorithm is effective.

Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching.

Endoscopic imaging plays a very important role in the diagnosis and treatment of lesions. However, the imaging range of endoscopes is small, which may affect the doctors' judgment on the scope and details of lesions. Image mosaic technology can solve the problem well. In this paper, an improved feature-point pair purification algorithm based on SIFT (Scale invariant feature transform) is proposed. Firstly, the K-nearest neighbor-based feature point matching algorithm is used for rough matching. Then RANSAC (Random Sample Consensus) method is used for robustness tests to eliminate mismatched point pairs. The mismatching rate is greatly reduced by combining the two methods. Then, the image transformation matrix is estimated, and the image is determined. The seamless mosaic of endoscopic images is completed by matching the relationship. Finally, the proposed algorithm is verified by real endoscopic image and has a good effect.

Adaptive Neural Network Control of Time Delay Teleoperation System Based on Model Approximation.

A bilateral neural network adaptive controller is designed for a class of teleoperation systems with constant time delay, external disturbance and internal friction. The stability of the teleoperation force feedback system with constant communication channel delay and nonlinear, complex, and uncertain constant time delay is guaranteed, and its tracking performance is improved. In the controller design process, the neural network method is used to approximate the system model, and the unknown internal friction and external disturbance of the system are estimated by the adaptive method, so as to avoid the influence of nonlinear uncertainties on the system.

Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface.

At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.

Research on image classification method based on improved multi-scale relational network.

Small sample learning aims to learn information about object categories from a single or a few training samples. This learning style is crucial for deep learning methods based on large amounts of data. The deep learning method can solve small sample learning through the idea of meta-learning "how to learn by using previous experience." Therefore, this paper takes image classification as the research object to study how meta-learning quickly learns from a small number of sample images. The main contents are as follows: After considering the distribution difference of data sets on the generalization performance of measurement learning and the advantages of optimizing the initial characterization method, this paper adds the model-independent meta-learning algorithm and designs a multi-scale meta-relational network. First, the idea of META-SGD is adopted, and the inner learning rate is taken as the learning vector and model parameter to learn together. Secondly, in the meta-training process, the model-independent meta-learning algorithm is used to find the optimal parameters of the model. The inner gradient iteration is canceled in the process of meta-validation and meta-test. The experimental results show that the multi-scale meta-relational network makes the learned measurement have stronger generalization ability, which further improves the classification accuracy on the benchmark set and avoids the need for fine-tuning of the model-independent meta-learning algorithm.

Joint embedding VQA model based on dynamic word vector.

The existing joint embedding Visual Question Answering models use different combinations of image characterization, text characterization and feature fusion method, but all the existing models use static word vectors for text characterization. However, in the real language environment, the same word may represent different meanings in different contexts, and may also be used as different grammatical components. These differences cannot be effectively expressed by static word vectors, so there may be semantic and grammatical deviations. In order to solve this problem, our article constructs a joint embedding model based on dynamic word vector-none KB-Specific network (N-KBSN) model which is different from commonly used Visual Question Answering models based on static word vectors. The N-KBSN model consists of three main parts: question text and image feature extraction module, self attention and guided attention module, feature fusion and classifier module. Among them, the key parts of N-KBSN model are: image characterization based on Faster R-CNN, text characterization based on ELMo and feature enhancement based on multi-head attention mechanism. The experimental results show that the N-KBSN constructed in our experiment is better than the other 2017-winner (glove) model and 2019-winner (glove) model. The introduction of dynamic word vector improves the accuracy of the overall results.

Cellular and molecular mechanisms of xenobiotics-induced premature senescence.

Premature senescence, which share common features with replicative senescence such as morphology, senescence-associated galactosidase (SA-ß-gal) activity, cell cycle regulation, and gene expression, can be triggered by the exposure of various xenobiotics including environmental pollutant, peroxides, and anticancer drugs. The exact mechanisms underlying the senescence onset and stabilization are still obscure. In this review, we summarized the possible cellular and molecular mechanisms of xenobiotics-induced premature senescence, including induction of reactive oxygen species (ROS), tumor suppressors, and DNA damage; disequilibrium of calcium homeostasis; activation of transforming growth factor-ß (TGF-ß); and blockage of aryl hydrocarbon receptor (AHR) pathway. The deeper understanding of the molecular mechanisms underlying xenobiotics-induced senescence may shed light on new therapeutic strategies for age-related pathologies and extend healthy lifespan.

Construction of force haptic reappearance system based on Geomagic Touch haptic device.

Force haptic reappearance technology is considered to be one of the top ten technologies that can change human life in the future. It has broad application prospects and market demand. Most of the existing medical robots, especially the remote diagnosis and treatment robots, lack haptic feedback, or the calculation of feedback force is insufficient. Haptic reappearance technology is an effective method to solve the problem of haptic presence and improve the practicability of medical robot. The ultimate goal of the force haptic reappearance system is to let the operator feel the haptic feedback when interacting with the soft tissue model in the virtual environment in real time. Haptic device is the necessary condition to realize force haptic reappearance, and it is an essential part of the system. Its important role is to introduce the external force imposed by the operator into the virtual environment, and let the operator feel the force in the virtual environment, which effectively guarantees the operator's sense of reality and immersion when interacting with the virtual environment. Therefore, starting with the key technology of force and haptic reappearance system, this paper studies the construction of force and haptic reappearance system. Soft tissue surface model is drawn by OpenGL, and hand model is drawn by 3Ds Max. The haptic reappearance and visual feedback of soft tissue model of hand palpation are realized. The quality of feedback is evaluated. The haptic reappearance is stable and realistic, and the visual feedback is smooth. This indicates that the system has a certain application value and is worth to promote to the public.

Temporal evolution characteristics of PM2.5 concentration based on continuous wavelet transform.

Fine particulate matter (PM2.5) is an important haze index, and the researches on the evolutionary characteristics of the PM2.5 concentration will provide a fundamental and guiding prerequisite for the haze prediction. However, the past researchers were usually based on the overall time-domain evolution information of PM2.5. Since the temporal evolution of PM2.5 concentration is nonstationary, previous studies might neglect some important localization features that the evolution has various predominant periods at different scales. Therefore, we applied the wavelet transform to study the localized intermittent oscillations of PM2.5. First, we analyze the daily average PM2.5 concentration collected from the automatic monitoring stations. The result reveals that the predominant oscillation period does vary with time. There exist multiple oscillation periods on the scale of 14-32 d, 62-104 d, 105-178 d and 216-389 d and the 298d is the first dominant period in the entire evolutionary process. Moreover, we want to figure out whether the temporal characteristics of PM2.5 in the days with heavy haze also have localized intermittent periodicities. We select the hourly average PM2.5 concentration in 120 h when the haze pollution is serious. We find that the principal period has experienced two abrupt shifts and the energy at the 63-hour scale is the most powerful. The results in these two independent analyses come into the same conclusion that the multiscale features shown in the temporal evolution of PM2.5 cannot be ignored and may play an important role in the further haze prediction.

SMYD2 promotes cervical cancer growth by stimulating cell proliferation.

BACKGROUND: Cervical cancer is the most common gynecological malignancy with low terminal cure rate, and therefore new therapeutic targets are urgently needed to combat this disease. SMYD2, as an oncogene, is abnormal highly expressed in multiple types of tumors and further affects the occurrence and development, but the potential correlations between SMYD2 expression and cervical cancer progression is still unclear. METHODS: We first used the bioinformatics website to screen the data of cervical cancer in (The Cancer Genome Atlas) TCGA and survival analysis was used to find the different survival rates in the SMYD2 high expression group and low expression group. Through immunohistochemistry, the association between SMYD2 expression and clinical-pathological features of cervical cancer patients was further evaluated. Quantitative PCR and Immunoblot were applied to investigate the relative mRNA and protein expression levels, respectively. In vivo and in vitro experiments were performed to explore the function of SMYD2 in cancer progression. RESULTS: We first found a high expression of SMYD2 in cervical cancer, and survival analysis found that the poorer survival rate in the SMYD2 high expression group than that in the low expression group. Herein, our study demonstrated that the expression of SMYD2 in patients with cervical cancer was associated with FIGO stage, tumor size and further correlated with poor prognosis. Our data further showed that the inhibition of SMYD2 expression in cervical cancer cell line Caski and Siha could dramatically block the proliferation of cervical cancer cells. Additionally, SMYD2-shRNA lentivirus infected remarkably inhibited tumorigenesis in mice compared with the scramble group. CONCLUSIONS: Taken together, this study provides strong evidence of the involvement of SMYD2 in cervical cancer growth and indicates that it could have high potential as a therapeutic target of cervical cancer.

Clusterin increases mitochondrial respiratory chain complex I activity and protects against hexavalent chromium-induced cytotoxicity in L-02 hepatocytes.

Previous evidence revealed significant elevated liver cancer mortality in the areas where water was contaminated with hexavalent chromium [Cr(vi)], which highlighted that we should pay more attention to Cr(vi)-induced cytotoxicity in hepatocytes. We found that Clusterin (CLU) was up-regulated in Cr(vi)-exposed L-02 hepatocytes, but the role CLU played in Cr(vi)-induced cytotoxicity has never been explored. In the present study, we demonstrate Cr(vi) targeted mitochondrial respiratory chain complex I (MRCC I) activity and induced reactive oxygen species (ROS) accumulation, which caused mitochondrial damage that was characterized by the increase of permeability transition pore (PTP) open rate, the collapse of mitochondrial membrane potential (MMP), and the release of apoptosis-inducing factor (AIF) and Cytochrome C (Cyt C) from mitochondria to cytoplasm, which then induced cell viability loss and increased aspartate transaminase (AST)/alanine transaminase (ALT) leakage. We reveal that Cr(vi) may regulate CLU expression through the ROS-ataxia telangiectasia mutant (ATM)-insulin-like growth factor 1 (IGF-1) axis, and CLU expression was positively correlated to MRCC I activity. We further confirmed that CLU may regulate MRCC I activity via modulating its subunit nicotinamide adenine dinucleotide dehydrogenase (ubiquinone) Fe-S protein 3 (NDUFS3) expression. By the establishment of CLU over-expression cells, we found that over-expression of CLU alleviated Cr(vi)-induced MRCC I inhibition and further rescued cell viability loss and reduced AST and ALT leakage. Thus, we reached the conclusion that the CLU-induced increase of MRCC I activity protected against Cr(vi)-induced cytotoxicity. The present research will provide new experimental evidence for thoroughly clarifying the cytotoxicity and the carcinogenic mechanism of Cr(vi).

Pegylated liposomal­paclitaxel induces ovarian cancer cell apoptosis via TNF­induced ERK/AKT signaling pathway.

Ovarian cancer is one of the most common gynecological types of cancer and is characterized by a relatively high incidence and high mortality rate. Evidence has demonstrated that paclitaxel (PTX) is an effective therapeutic treatment for human ovarian cancer. In the present study, the inhibitory effects of pegylated liposomal (PL)­PTX on the growth of ovarian cancer cells were investigated in vitro; a CAOV­3­bearing mouse model was established to investigate the in vivo effects of PL­PTX on ovarian tumor growth. In the present study, the underlying mechanism of tumor necrosis factor (TNF)­induced inhibition of extracellular signal­regulated kinase (ERK)/protein kinase B (AKT) signaling pathway mediated by PL­PTX was analyzed within ovarian cancer cells. The results of the present study revealed that PL­PTX significantly inhibited the growth and aggressiveness of ovarian cancer cells in vitro and in vivo and apoptotic ability increased upon administration of PL­PTX. The expression levels of caspase­3/9 were significantly upregulated within PL­PTX­treated ovarian cancer cells. The expression and phosphorylation levels of ERK and AKT were markedly increased in response to PL­PTX treatment. In addition, the inhibitory effects of PL­PTX on ovarian cancer cells were eliminated by neutralizing antibodies against TNF. The observations of the present study revealed that PL­PTX induced ovarian cell apoptosis via the TNF­dependent pathway, which was significantly inhibited with the employment of antibodies against TNF. In vivo analysis demonstrated that PL­PTX treatment significantly inhibited ovarian tumor growth and prolonged the survival of tumor bearing mice. In conclusion, the findings of the present study have provided an insight into the potential mechanism of PL­PTX­induced apoptosis of ovarian cancer cells. As PL­PTX has been reported to induce ovarian tumor cell apoptosis via the TNF­induced ERK/AKT signaling pathway, PL­PTX may serve as an efficient anticancer drug for the treatment of ovarian cancer.

Effects of fermentation on the hemolytic activity and degradation of Camellia oleifera saponins by Lactobacillus crustorum and Bacillus subtilis.

The saponins, as components of tea seed meal, are undesirable hemolytic components and should be degraded for reducing their hemolytic activity in order to be used in animal feed. In this study, ß-glucuronidase was verified to be a potent hydrolase of tea seed saponins to reduce their hemolytic activity and a ß-glucuronidase-producing Lactobacillus crustorum strain was screened from raw bovine milk. Next, solid-state fermentation with the isolated L. crustorum and a Bacillus subtilis natto strain, which can produce cellulase and hence improve the fermentation performance of tea seed meal, was carried out for detoxification of tea seed meal. The 50% hemolytic dosage (HD50) value of tea seed saponins was increased from 6.69 to 27.43 µg mL-1. The results of LC-MS analysis showed that the percentage of saponin aglycones increased from 30.95 to 84.25% after the fermentation. According to the roles of sugar moieties in hemolytic activity, and the enzymatic hydrolysis characteristics of ß-glucuronidase, the degradation of tea seed saponins from glucosides to aglycones may contribute to the reduction of hemolytic activity. Therefore, tea seed meal may be used as animal feed after fermentation with the tested saponin-degrading microbial strains.