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Abstract Background Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease’s burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios. Method To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection. Results The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy. Conclusion The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

With the increasing amount of space debris in near-earth space, the surveillance of space has become more crucial to prevent potential space collisions. In an optical surveillance system, a multitude of debris with varying sizes, speeds, and intensities exists within images, which brings great difficulties for the extraction of targets. Current methodologies mainly emphasize local saliency enhancement and source extraction based on global statistical attributes, yet often overlook the distribution similarity of individuals and how to map them in a standardized space. To address this limitation, this article introduces an innovative transformation from the grayscale space to the correlation space. Initially, a fourfold-structure model is designed to measure the local correlation for each element in the grayscale space. Subsequently, the standardized correlation space is constructed by correlation measurement. Then, combining an adaptive threshold choosing method based on statistics and a threshold limitation strategy based on correlation, the individuals can be separated from the feature space. Finally, a deblending method is applied to disentangle merged individuals based on the local gradient correlation. Thoroughly assessed using ten subimage sequences featuring targets with typical traits against complex backgrounds, the results confirm its superior extraction capabilities and effectiveness when compared to ten common baseline methods, which achieves an average 90.9% true positive rate in just 2.8 ms. Furthermore, the extraction outcomes serve as a basis for detecting true target trajectories in continuous sequences. This integration holds significant implications for practical applications in space surveillance engineering.

BackgroundCervical cancer is one of the most common malignant tumors worldwide. Radical hysterectomy is the first choice for patients with early-stage cervical cancer. Studies have suggested that acupuncture may be a more effective therapy for the prevention and treatment of urinary retention after radical hysterectomy.ObjectiveTo systematically evaluate the clinical efficacy of acupuncture in the prevention and treatment of urinary retention after radical hysterectomy.MethodsWe searched the Cochrane library, Web of science, PubMed, Embase, Chinese Biomedical Literature Database, Wanfang database, Wipu database, China National Knowledge Infrastructure Database and ClinicalTrials.gov with the time from inception until December 2023, to collect randomized controlled studies on the clinical efficacy of acupuncture for prevention and treatment of urinary retention after radical hysterectomy. Literature meeting criteria was screened for data extraction. Quality evaluation was performed according to the Cochrane Handbook for Systematic Reviews of Interventions. And meta-analysis was performed using RevMan5.3 and stata14.0 software.Results22 Randomized controlled trials with 1,563 patients, 854 in treatment group and 709 in control group, were included totally. Meta-analysis results showed that: the total effective rate in acupuncture group was higher than that in control group, with a statistically significant difference [relative risk (RR)] = 1.43, 95% confidence interval (CI 1.22, 1.68), p

The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets.

Dissolved oxygen is an important biogenic element in marine ecosystem, and the variation of its content and distribution could affect marine life activities directly or indirectly. In this paper, the spatial and temporal variaton characteristics were systematically analyzed and the occurrence of hypoxia was preliminarily discussed based on the seawater field monitoring data in Zhejiang coastal area from 2014 to 2018. The results showed that the spatial and temporal variation characteristics of dissolved oxygen and its saturation were obvious in Zhejiang coastal area. The spatial span analysis showed that the horizontal distribution of dissolved oxygen and its saturation presented different characteristics in different seasons, and the vertical distribution showed that dissolved oxygen and its saturation in the surface layer was higher than that in the bottom layer, and the difference between the surface layer and the bottom layer was larger in spring and summer, but smaller in autumn and winter, which was related to surface photosynthesis and seasonal thermohalocline. The time span analysis showed that the highest dissolved oxygen content in surface layer appeared in winter and the lowest in summer, which were affected by water temperature and surface photosynthesis, and the highest dissolved oxygen in surface layer in summer appeared in 2018 and the lowest in 2015, which was mainly caused by sea surface temperature. In addition, hypoxia was also found in some areas outside the coastal area, and its potential risks are being further tracked and monitored.

The objectives of this study was to evaluate the effects of background color and restoration depth on color adjustment potential of a new single-shade resin composite versus multi-shade resin composites. Two multi-shade resin composites (Spectrum TPH3 and Clearfil AP-X) marked A2 shade and a new single-shade resin composite (Charisma Diamond One) were tested. Four base shades (A1, A2, A3, and A3.5) of the same resin composite (Filtek Z250) were selected as different background colors. Dual specimens with 1-, 2-, and 3-mm restoration depth and single specimens of all materials were fabricated. CIE color coordinates were measured using a spectrophotometer, then color differences (∆E00) and translucency parameter (TP00) were calculated using the CIEDE2000 formula. Independent observers performed visual scoring. CAP-I and CAP-V values were calculated according to ΔE00 and visual scoring. The results revealed that CAP-I and CAP-V were significantly affected by resin composite type, background color, and restoration depth. CAP-I and CAP-V decreased as restoration depth increased at the same background color for all materials. Charisma Diamond One had the highest CAP-I and CAP-V values at all background colors and restoration depths, with the highest TP00 value. These findings demonstrated that color adjustment potential was dependent on resin composite type, background color, and restoration depth, so shade selection is indispensable for multi-shade resin composites. Charisma Diamond One exhibited the highest color adjustment potential and the most pronounced color shifting, contributing to simplifying the process of shade selection and improving the efficiency of clinical work.

Blood pressure has a 24-hour repetitive and regular variation which shows circadian rhythm. Using the multivariate time series analysis method of vector autoregressive model, we could realize the simultaneous prediction for both systolic and diastolic blood pressures. We choose blood pressure from 6 AM to 10 AM in 3 weeks as an episode to construct a prediction model. Missing values were imputed by regression models. Subsequently, we defined segments as positive or negative segments according to blood pressure measurements. The predictions were accomplished by vector autoregressive model (VAR). Both positive and negative segments were randomly selected from each patient to summarize the effect of prediction models. In this study, the MAPE (Mean Absolute Percentage Error) of systolic blood pressure and diastolic blood pressure were both less than 10%, indicating that the VAR model was adaptable in predicting the blood pressure of hypertensive patients. Based on VAR, we could provide early warning to breakthrough of blood pressure thresholds. The sensitivity, specificity, and accuracy for patients in the training sets were 77.50%, 81.58 %, and 79.49% respectively, and the sensitivity, specificity, and accuracy for patients in the training sets were 76.92%, 80.00% and 78.43% respectively. This research took information of both systolic and diastolic blood pressures at the same time to establish the VAR models and enabled simultaneous prediction for systolic and diastolic blood pressure.

Deep Neural Network (DNN)-based point cloud semantic segmentation has presented significant breakthrough using large-scale labeled aerial laser point cloud datasets. However, annotating such large-scaled point clouds is time-consuming. Self-Supervised Learning (SSL) is a promising approach to this problem by pre-training a DNN model utilizing unlabeled samples followed by a fine-tuned downstream task involving very limited labels. The traditional contrastive learning for point clouds selects the hardest negative samples by solely relying on the distance between the embedded features derived from the learning process, potentially evolving some negative samples from the same classes to reduce the contrastive learning effectiveness. This work proposes a hard-negative sample-aware self-supervised contrastive learning algorithm to pre-train the model for semantic segmentation. We designed a k-means clustering-based Absolute Positive And Negative samples (AbsPAN) strategy to filter the possible false-negative samples. Experiments on two typical ALS benchmark datasets demonstrate that the proposed method is more appealing than supervised training schemes without pre-training. Especially when the labels are severely inadequate (10% of the ISPRS training set), the results obtained by the proposed HAVANA method still exceed 94% of the supervised paradigm performance with full training set.

Accurate data is the basis for all marine scientific research. Different from the traditional sampling monitoring process, it is fully automated for marine buoy monitoring from sample collection to data results, and the focus of data quality control is no longer focused on the "sample collection-analysis and testing" . In this study, the quality control system for marine buoy monitoring data through analysis of the buoy data in the past two years was designed, including six items: floating body stability, instrument equipment calibration, system operation and maintenance, data transmission and reception, contrast monitoring, abnormal data determination, and abnormal data determination and contrast monitoring were focused on. This research established a quality control method library, and proposed three levels of abnormal data determination rules for buoy data quality control: valid, credible, and relatively accurate, which was innovative. The first layer is the valid data set obtained after removing the obvious fault values, outliers, and other invalid data; the second layer is the credible data set obtained after screening by various quality control methods from the method library; the third layer is relatively accurate data set verified against field contrast monitoring results. Based on the marine buoy monitoring data in Zhejiang Province, the design ideas and methods of the quality control system for buoy monitoring data were proposed, which would help to improve the quality of the online monitoring data of buoys in the coastal waters of Zhejiang. This design would provide a reference for the national marine buoy monitoring data quality control.

Tooth extractions can lead to complications such as post-extraction bleeding and bone resorption, which may result in unfavorable outcomes for implant restoration afterwards. To strive for an optimal condition for further restoration procedures, appropriate strategies, such as hemostasis or bone regeneration, are encouraged to be employed. However, the existing products are failed to meet both needs. As a widely employed tissue engineering materials, hydroxyapatite and calcium alginate both have demonstrated excellent performance in osteogenesis. However, their inferior mechanical strength poses a major limitation to their use in supporting the contracted extraction socket, which can easily lead to alveolar crest atrophy and barely achieve satisfying results. Calcium alginate improves the mechanical strength of hydroxyapatite, enabling the formation of new bone tissue and degradable in vivo. In this study, we demonstrated the biocompatibility and haemostatic ability of nCA particles on a rat tooth extraction model. In addition, long-term observation has revealed minimal inflammation and bone tissue regeneration. Our findings suggest a promising insight for clinical applications in hemostasis and bone regeneration after tooth extraction.

The segmentation of airborne laser scanning (ALS) point clouds remains a challenge in remote sensing and photogrammetry. Deep learning methods, such as KPCONV, have proven effective on various datasets. However, the rigid convolutional kernel strategy of KPCONV limits its potential use for 3D object segmentation due to its uniform approach. To address this issue, we propose an Integrated Point Convolution (IPCONV) based on KPCONV, which utilizes two different convolution kernel point generation strategies, one cylindrical and one a spherical cone, for more efficient learning of point cloud data features. We propose a customizable Multi-Shape Neighborhood System (MSNS) to balance the relationship between these convolution kernel point generations. Experiments on the ISPRS benchmark dataset, LASDU dataset, and DFC2019 dataset demonstrate the validity of our method.

One of the major pathogenesis mechanisms of SARS-CoV-2 is its potent suppression of innate immunity, including blocking the production of type I interferons. However, it is unknown whether and how the virus interacts with different innate-like T cells, including NKT, MAIT and γδ T cells. Here we reported that upon SARS-CoV-2 infection, invariant NKT (iNKT) cells rapidly trafficked to infected lung tissues from the periphery. We discovered that the envelope (E) protein of SARS-CoV-2 efficiently down-regulated the cell surface expression of the antigen-presenting molecule, CD1d, to suppress the function of iNKT cells. E protein is a small membrane protein and a viroporin that plays important roles in virion packaging and envelopment during viral morphogenesis. We showed that the transmembrane domain of E protein was responsible for suppressing CD1d expression by specifically reducing the level of mature, post-ER forms of CD1d, suggesting that it suppressed the trafficking of CD1d proteins and led to their degradation. Point mutations demonstrated that the putative ion channel function was required for suppression of CD1d expression and inhibition of the ion channel function using small chemicals rescued the CD1d expression. Importantly, we discovered that among seven human coronaviruses, only E proteins from highly pathogenic coronaviruses including SARS-CoV-2, SARS-CoV and MERS suppressed CD1d expression, whereas the E proteins of human common cold coronaviruses, HCoV-OC43, HCoV-229E, HCoV-NL63 and HCoV-HKU1, did not. These results suggested that E protein-mediated evasion of NKT cell function was likely an important pathogenesis factor, enhancing the virulence of these highly pathogenic coronaviruses. Remarkably, activation of iNKT cells with their glycolipid ligands, both prophylactically and therapeutically, overcame the putative viral immune evasion, significantly mitigated viral pathogenesis and improved host survival in mice. Our results suggested a novel NKT cell-based anti-SARS-CoV-2 therapeutic approach.

ObjectiveTo improve the bioavailability of active substances and reduce the toxic and side effects on the human body, natural biological macromolecules are used to load active substances and control their release speed in different environments of the human body. In this study, mesoporous silica (MSN) was combined with konjac glucomannan (KGM) and sodium alginate (AC) to prepare pH-sensitive konjac glucomannan/sodium alginate–mesoporous silica loaded naringin gel spheres (KS/MSN). On this basis, the structure, morphology, and release properties of the composite gel spheres were characterized. The results showed that the cumulative release rates of both simulated gastric fluid (SGF) and Simulated colonic fluid (SCF) were lower than that of simulated small intestinal fluid (SIF), which indicated that the prepared composite gel spheres were pH-sensitive to SIF and obtained the best release rate of about 70% under SIF environment.MethodsThe pH-sensitive konjac glucomannan/sodium alginate composite gel spheres (KGM/SA) were prepared by combining inorganic nano-materials mesoporous silica (MSN) with natural macromolecular polysaccharides konjac glucomannan (KGM) and sodium alginate (SA) and characterized.ResultsThe results showed that there was a process of ionic crosslinking and entanglement between konjac glucomannan (KGM) and sodium alginate (SA). Naringin (NG) and mesoporous silica (MSN) were successfully compounded and had good compatibility. The gel microstructure diagram showed that the addition of MSN improved the gel properties of KGM, and KGM and SA gel spheres (KGM/SA) had good compatibility with mesoporous silica/naringenin nanoparticles (NG/MSN). The study of the simulated digestive environment of the gastrointestinal release medium showed that Konjac glucomannan/sodium alginate-mesoporous silica loaded naringin gel spheres (KS/NM) composite gel spheres had the best slow-release effect and the highest final-release completion degree in SIF. The release of NG from KS/NM composite gel spheres showed a slow upward trend. The results showed that KS/NM composite gel spheres were pH-sensitive.ConclusionThe KS/NM composite gel spheres showed obvious pH sensitivity to the release of NG, and the gel spheres had a good sustained release effect on NG.

Aiming at maritime infrared target detection with low contrast influenced by maritime clutter and illumination, this paper proposes a Modified Histogram Equalization with Edge Fusion (MHEEF) pre-processing algorithm in backlight maritime scenes and establishes Local-Contrast Saliency Models with Double Scale and Modes (LCMDSM) for detecting a target with the properties of positive and negative contrast. We propose a local-contrast saliency mathematical model with double modes in the extension of only one mode. Then, the big scale and small scale are combined into one Target Detection Unit (TDU), which can approach the “from bottom to up” mechanism of the Visual Attention Model (VAM) better and identify the target with a suitable size, approaching the target’s actual shape. In the experimental results and analysis, clutter, foggy, backlight, and dim maritime scenes are chosen to verify the effectiveness of the target detection algorithm. From the enhancement result, the LCMDSM algorithm can achieve a Detection Rate (DR) with a value of 98.26% under each maritime scene on the average level and can be used in real-time detection with low computational cost.

The S-wave DD⁎ scattering in the isospin I=0,1 channels is studied in Nf=2 lattice QCD at mπ≈350 MeV. It is observed that the DD⁎ interaction is repulsive in the I=1 channel when the DD⁎ energy is near the DD⁎ threshold. In contrast, the DD⁎ interaction in the I=0 channel is definitely attractive in a wide range of the DD⁎ energy. This is consistent with the isospin assignment I=0 for Tcc+(3875). By analyzing the components of the DD⁎ correlation functions, it turns out that the quark diagram responsible for the different properties of I=0,1 DD⁎ interactions can be understood as the charged ρ meson exchange effect. This observation provides direct information on the internal dynamics of Tcc+(3875).

Here, the authors present VESPER, a program for EM density map search and alignment. Using benchmark datasets, they demonstrate that VESPER performs accurate global and local alignments and comparisons of EM maps.

BackgroundTo explore the relationship between unexplained chest pain in children and head-up tilt test (HUTT).Materials and MethodsA total of 825 children with the main symptom of unexplained chest pain were admitted to the Specialist Outpatient Clinic of Children’s Cardiovascular Disease from October 2000 to November 2021 at The Second Xiangya Hospital, Central South University. Among them, 473 were male and 352 were female, with a mean age of 10.61 ± 2.21 years. The control group included 58 cases, comprising 35 males and 23 females, with a mean age of 10.26 ± 2.66 years. The detailed history, physical examinations, conventional 12-lead electrocardiogram, chest X-ray, echocardiography, myocardial enzymes, electroencephalogram, and blood series were all examined. Disorders of the chest wall, lung, heart, mediastinum, and esophageal reflux, as well as drug effects, were ruled out. All the children underwent HUTT. Demographic description, univariate analysis, and multivariate logistic regression analysis were used to explore the possible linear or non-linear relationships between the children’s unexplained chest pain and HUTT.ResultsAmong the 825 chest pain cases, 301 (36.48%) were HUTT positive and 524 (63.52%) were HUTT negative. HUTT-positive patients were older than HUTT-negative patients (11.04 ± 2.03 vs. 10.34 ± 2.31 years, P < 0.001). The logistic regression results showed that each year of age increased the probability of being HUTT positive by 17.90% (P < 0.000), and females were 91.30% more likely to be HUTT positive than males (P < 0.000).ConclusionClinically unexplained chest pain in children is mainly caused by unbalanced autonomic nervous function. HUTT can help clear the cause of unexplained chest pain.

Objective: Stroke is a leading cause of mortality and morbidity globally. This study aimed to analyze the burden and 30-year trends of ischemic stroke, intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH) in China.Methods: Data that include incidence and mortality of stroke in China from January 1, 1990 to December 31, 2019 were derived from the Global Burden of Disease (GBD) study 2019. The absolute numbers of incident cases and deaths over the time, and age-standardized rates per 100,000, such as age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR), were analyzed.Results: In 2019, there were 3.9 (95% uncertainty intervals (UI) 3.4–4.5) million incident cases and 2.1 (3.4–4.5) million deaths related to stroke in China. The ASIR and ASMR of stroke in China was 200 (176–230) and 127 (110–144). From 1990 to 2019, the ASIR of ischemic stroke had increased by 35.0% (29.0–40.0) while the ASIR of ICH and SAH had decreased by −53.0% (−56.0 to −50.0) and by −39.0% (−44.0 to −35.0), respectively. The ASMR of ischemic stroke had increased by 3.0% (−26.0 to 16.0) while the ASMR of ICH and SAH had decreased by −48.0% (−59.0 to −38.0) and by −84.0% (−89.0 to −69.0), respectively.Conclusion: Although the incidence and mortality rates of stroke in China were decreased from 1990 to 2019, the number of incident cases and deaths nearly doubled. A sharp increase in the incidence rate of ischemic stroke was observed. A higher incidence rate of ischemic stroke in the women was also observed.

Background: Early shunt obstruction (SO) remains the most common cause of lumboperitoneal shunt (LPS) failure. Although there is anecdotal evidence that the level of cerebrospinal fluid (CSF) parameters might affect shunt performance, its association with early LPS obstruction in adults with post-hemorrhagic hydrocephalus (PHH) is unclear.Methods: The retrospective study was performed by reviewing the adults with PHH treated by LPS from years 2014 to 2018. We included patients with CSF samples analyzed within 1 week prior to shunt insertion or at the time of shunt insertion. Baseline characteristics of each patient were collected. The primary outcomes were the incidence rate and associated factors of SO occurring within 3 months of shunt placement. The secondary outcomes included scores on the National Institute of Health Stroke Scale (NIHSS) and Evans Index at discharge.Results: A total of 76 eligible patients were analyzed, of whom 61 were obstruction-free and 15 were early SO. The overall rate of early SO was 15.6%. The RBCs count and nucleated cells count in preoperative CSF were actually higher in patients with early SO, compared to patients in the control group. Multivariate analysis identified RBC elevation (>0 × 106/L; OR: 10.629, 95% CI: 1.238–91.224, p = 0.031) as a dependent risk factor for early SO. NIHSS dramatically decreased at discharge while the alteration of ventricular size was not observed.Conclusions: This study suggested that the presence of RBCs in preoperative CSF was associated with early SO in patients with PHH treated by LPS.

When using digital images to obtain the displacement of a structure, noise is always a factor that greatly affects the accuracy of the displacement results. To overcome this problem, a phase-correlation improvement method, based on phase filtering by phase standard deviation (PSD), is proposed. We use PSD to remove the severe noise interference area in the cross-power spectrum matrix, then the singular value decomposition (SVD) is used to convert the two-dimensional phase to one-dimensional to reduce the difficulty of phase unwrapping. Finally, straight-line fitting is used to obtain the high-precision displacement results. Experiments were carried out using both simulated data and real data. For the simulated data, the absolute accuracy of the proposed method is around 0.3 pixels, while, for the real data, the absolute accuracy of the proposed method is 0.018 mm. The experimental results show that the method can effectively resist noise interference and still obtain accurate displacement estimation results in the case of severe noise.

The regeneration of periodontium represents important challenges to controlling infection and achieving functional regeneration. It has been recognized that tissue engineering plays a vital role in the treatment of periodontal defects, profiting from scaffolds that create the right microenvironment and deliver signaling molecules. Attributable to the excellent physicochemical and antibacterial properties, nanomaterials show great potential in stimulating tissue regeneration in tissue engineering. This article reviewed the up-to-date development of nanomaterials in scaffolds for periodontal tissue engineering. The paper also represented the merits and defects of different materials, among which the biocompatibility, antibacterial properties, and regeneration ability were discussed in detail. To optimize the project of choosing materials and furthermore lay the foundation for constructing a series of periodontal tissue engineering scaffolds, various nanomaterials and their applications in periodontal regeneration were introduced.

Abstract Background Epithelial ovarian cancer is one of the most severe public health threats in women. Since it is still challenging to screen in early stages, identification of core genes that play an essential role in epithelial ovarian cancer initiation and progression is of vital importance. Results Seven gene expression datasets (GSE6008, GSE18520, GSE26712, GSE27651, GSE29450, GSE36668, and GSE52037) containing 396 ovarian cancer samples and 54 healthy control samples were analyzed to identify the significant differentially expressed genes (DEGs). We identified 563 DEGs, including 245 upregulated and 318 downregulated genes. Enrichment analysis based on the gene ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways showed that the upregulated genes were significantly enriched in cell division, cell cycle, tight junction, and oocyte meiosis, while the downregulated genes were associated with response to endogenous stimuli, complement and coagulation cascades, the cGMP-PKG signaling pathway, and serotonergic synapse. Two significant modules were identified from a protein-protein interaction network by using the Molecular Complex Detection (MCODE) software. Moreover, 12 hub genes with degree centrality more than 29 were selected from the protein-protein interaction network, and module analysis illustrated that these 12 hub genes belong to module 1. Furthermore, Kaplan-Meier analysis for overall survival indicated that 9 of these hub genes was correlated with poor prognosis of epithelial ovarian cancer patients. Conclusion The present study systematically validates the results of previous studies and fills the gap regarding a large-scale meta-analysis in the field of epithelial ovarian cancer. Furthermore, hub genes that could be used as a novel biomarkers to facilitate early diagnosis and therapeutic approaches are evaluated, providing compelling evidence for future genomic-based individualized treatment of epithelial ovarian cancer.

Most of the current complex network studies about epilepsy used the electroencephalogram (EEG) to directly construct the static complex network for analysis and discarded the dynamic characteristics. This study constructed the dynamic complex network on EEG from pediatric epilepsy and pediatric control when they were asleep by the sliding window method. Dynamic features were extracted and incorporated into various machine learning classifiers to explore their classification performances. We compared these performances between the static and dynamic complex network. In the univariate analysis, the initially insignificant topological characteristics in the static complex network can be transformed to be significant in the dynamic complex network. Under most connectivity calculation methods between leads, the accuracy of using dynamic complex network features for discrimination was higher than that of static complex network features. Particularly in the imaginary part of the coherency function (iCOH) method under the full-frequency band, the discrimination accuracies of most machine learning classifiers were higher than 95%, and the discrimination accuracies in the higher-frequency band (beta-frequency band) and the full-frequency band were higher than that of the lower-frequency bands. Our proposed method and framework could efficiently summarize more time-varying features in the EEG and improve the accuracies of the discrimination of the machine learning classifiers more than using static complex network features.

As cancer mortality is high in most regions of the world, early screening of cancer has become increasingly important. Minimally invasive screening programs that use peripheral blood mononuclear cells (PBMCs) are a new and reliable strategy that can achieve early detection of tumors by identifying marker genes. From 797 datasets, four (GSE12771, GSE24536, GSE27562, and GSE42834) including 428 samples, 236 solid tumor cases, and 192 healthy controls were chosen according to the inclusion criteria. A total of 285 genes from among 440 reported genes were selected by meta-analysis. Among them, 4 of the top significantly differentially expressed genes (ANXA1, IFI44, IFI44L, and OAS1) were identified as marker genes of PBMCs. Pathway enrichment analysis identified, two significant pathways, the 'primary immunodeficiency' pathway and the 'cytokine-cytokine receptor interaction' pathway. Protein- protein interaction (PPI) network analysis revealed the top 27 hubs with a degree centrality greater than 23 to be hub genes. We also identified 3 modules in Molecular Complex Detection (MCODE) analysis: Cluster 1 (related to ANXA1), Cluster 2 (related to IFI44 and IFI44L) and Cluster 3 (related to OAS1). Among the 4 marker genes, IFI44, IFI44L, and OAS1 are potential diagnostic biomarkers, even though their results were not as remarkable as those for ANXA1 in our study. ANXA1 is involved in the immunosuppressive mechanism in tumor-bearing hosts and may be used in a new strategy involving the use of the host's own immunity to achieve tumor suppression.

Three-dimensional (3D) building façade model reconstruction is of great significance in urban applications and real-world visualization. This paper presents a newly developed method for automatically generating a 3D regular building façade model from the photogrammetric mesh model. To this end, the contour is tracked on irregular triangulation, and then the local contour tree method based on the topological relationship is employed to represent the topological structure of the photogrammetric mesh model. Subsequently, the segmented contour groups are found by analyzing the topological relationship of the contours, and the original mesh model is divided into various components from bottom to top through the iteration process. After that, each component is iteratively and robustly abstracted into cuboids. Finally, the parameters of each cuboid are adjusted to be close to the original mesh model, and a lightweight polygonal mesh model is taken from the adjusted cuboid. Typical buildings and a whole scene of photogrammetric mesh models are exploited to assess the proposed method quantitatively and qualitatively. The obtained results reveal that the proposed method can derive a regular façade model from a photogrammetric mesh model with a certain accuracy.

Estimating the potential achievable solar energy in urban buildings is significantly important for the long-term planning and development of environmental protection strategies. Nevertheless, conventional methods based on LiDAR data are often costly and require more than one platform to obtain complete building surface information. The development of oblique photogrammetry technology enables fast and accurate acquiring of the 3D information of the surface. In this paper, we propose an efficient method to estimate the potential achievable solar energy of building surfaces based on photogrammetric mesh models. In this method, we use photogrammetric mesh models as the input data and then propose a hierarchical algorithm for shadow analysis. Combined with the solar radiation model, we then obtain the potential achievable solar energy of the building surface. We further investigate the performance of the proposed method and it is shown that this method outperforms the multi-source LiDAR data.

NKT cells are CD1d-restricted innate-like T cells expressing both T cell receptor and NK cell markers. The major group of NKT cells in both human and mice is the invariant NKT (iNKT) cells and the best-known function of iNKT cells is their potent anti-tumor function in mice. Since its discovery 25 years ago, the prototype ligand of iNKT cells, α-galactosylceramide (α-GalCer) has been used in over 30 anti-tumor clinical trials with mostly suboptimal outcomes. To realize its therapeutic potential, numerous preclinical models have been developed to optimize the scheme and strategies for α-GalCer-based cancer immunotherapies. Nevertheless, since there is no standard protocol for α-GalCer delivery, we reviewed the preclinical studies with a focus on B16 melanoma model in the goal of identifying the best treatment schemes for α-GalCer treatment. We then reviewed the current progress in developing more clinically relevant mouse models for these preclinical studies, most notably the generation of new mouse models with a humanized CD1d/iNKT cell system. With ever-emerging novel iNKT cell ligands, invention of novel α-GalCer delivery strategies and significantly improved preclinical models for optimizing these new strategies, one can be hopeful that the full potential of anti-tumor potential for α-GalCer will be realized in the not too distant future.

Proteins are involved in almost all functions in a living cell, and functions of proteins are realized by their tertiary structures. Obtaining a global perspective of the variety and distribution of protein structures lays a foundation for our understanding of the building principle of protein structures. In light of the rapid accumulation of low-resolution structure data from electron tomography and cryo-electron microscopy, here we map and classify three-dimensional (3D) surface shapes of proteins into a similarity space. Surface shapes of proteins were represented with 3D Zernike descriptors, mathematical moment-based invariants, which have previously been demonstrated effective for biomolecular structure similarity search. In addition to single chains of proteins, we have also analyzed the shape space occupied by protein complexes. From the mapping, we have obtained various new insights into the relationship between shapes, main-chain folds, and complex formation. The unique view obtained from shape mapping opens up new ways to understand design principles, functions, and evolution of proteins.

The right temporoparietal junction (rTPJ) has been thought to be associated with the difference in self-other decision making. In the present study, using noninvasive transcranial direct current stimulation (tDCS), we examined whether stimulating the rTPJ could modulate the self-other decision-making difference. We found that after receiving anodal stimulation of the rTPJ, participants were more likely to choose a high-value item for others than for themselves in the situations where the win probability of the high-value item was equal to or greater than that of a low-value item, indicating that elevating the cortical excitability of the rTPJ might increase the self-other decision-making difference in certain decision contexts. Our results suggest that decision making for others depends on neural activity in the rTPJ and regulation of the excitability of the rTPJ can influence the self-other decision-making difference.

Detecting changes between the existing building basemaps and newly acquired high spatial resolution remotely sensed (HRS) images is a time-consuming task. This is mainly because of the data labeling and poor performance of hand-crafted features. In this paper, for efficient feature extraction, we propose a fully convolutional feature extractor that is reconstructed from the deep convolutional neural network (DCNN) and pre-trained on the Pascal VOC dataset. Our proposed method extract pixel-wise features, and choose salient features based on a random forest (RF) algorithm using the existing basemaps. A data cleaning method through cross-validation and label-uncertainty estimation is also proposed to select potential correct labels and use them for training an RF classifier to extract the building from new HRS images. The pixel-wise initial classification results are refined based on a superpixel-based graph cuts algorithm and compared to the existing building basemaps to obtain the change map. Experiments with two simulated and three real datasets confirm the effectiveness of our proposed method and indicate high accuracy and low false alarm rate.

This paper presents an automatic building extraction method which utilizes a photogrammetric digital surface model (DSM) and digital orthophoto map (DOM) with the help of historical digital line graphic (DLG) data. To reduce the need for manual labeling, the initial labels were automatically obtained from historical DLGs. Nonetheless, a proportion of these labels are incorrect due to changes (e.g., new constructions, demolished buildings). To select clean samples, an iterative method using random forest (RF) classifier was proposed in order to remove some possible incorrect labels. To get effective features, deep features extracted from normalized DSM (nDSM) and DOM using the pre-trained fully convolutional networks (FCN) were combined. To control the computation cost and alleviate the burden of redundancy, the principal component analysis (PCA) algorithm was applied to reduce the feature dimensions. Three data sets in two areas were employed with evaluation in two aspects. In these data sets, three DLGs with 15%, 65%, and 25% of noise were applied. The results demonstrate the proposed method could effectively select clean samples, and maintain acceptable quality of extracted results in both pixel-based and object-based evaluations.

Nitrile-butadiene rubber is the sealing material of isopropyl nitrate; however, leakage or vaporization phenomena frequently occur. In order to analyze the causes of leakage and volatilization and study on the various properties of nitrile-butadiene rubber after the swelling of isopropyl nitrate, the author prepared nitrile rubber which contained 20%, 30% and 40% of acrylonitrile respectively, the quality and volume change rate, surface topography, organization structure, mechanical properties of the swollen nitrile rubber were tested separately. The results showed that the mass and volume of nitrile-butadiene rubber increase greatly after swelling; The higher the acrylonitrile content is or the higher the temperature is, the more obvious the swelling effect will be; Small molecules like plasticizers in nitrile-butadiene rubber will be precipitated during swelling process; The mechanical properties of the nitrile rubber decrease severely after swelling. The main cause of swelling lies in the similarity of polarity between NBR and isopropyl nitrate, and it becomes hard for the nitrile rubber to meet the sealing requirements.

Understanding the fates of soil hydrological processes and nitrogen (N) is essential for optimizing the water and N in a dryland crop system with the goal of obtaining a maximum yield. Few investigations have addressed the dynamics of dryland N and its association with the soil hydrological process in a freeze-thawing agricultural area. With the daily monitoring of soil water content and acquisition rates at 15, 30, 60 and 90 cm depths, the soil hydrological process with the influence of rainfall was identified. The temporal-vertical soil water storage analysis indicated the local albic soil texture provided a stable soil water condition for maize growth with the rainfall as the only water source. Soil storage water averages at 0-20, 20-40 and 40-60 cm were observed to be 490.2, 593.8, and 358 m3 ha-1, respectively, during the growing season. The evapo-transpiration (ET), rainfall, and water loss analysis demonstrated that these factors increased in same temporal pattern and provided necessary water conditions for maize growth in a short period. The dry weight and N concentration of maize organs (root, leaf, stem, tassel, and grain) demonstrated the N accumulation increased to a peak in the maturity period and that grain had the most N. The maximum N accumulative rate reached about 500 mg m-2d-1 in leaves and grain. Over the entire growing season, the soil nitrate N decreased by amounts ranging from 48.9 kg N ha-1 to 65.3 kg N ha-1 over the 90 cm profile and the loss of ammonia-N ranged from 9.79 to 12.69 kg N ha-1. With soil water loss and N balance calculation, the N usage efficiency (NUE) over the 0-90 cm soil profile was 43%. The soil hydrological process due to special soil texture and the temporal features of rainfall determined the maize growth in the freeze-thawing agricultural area.

The coastal waters of Zhejiang Province in China experience a high incidence of harmful algal blooms (HABs). It is therefore important to carry out hazard zoning to prevent and mitigate these HAB disasters. According to the historical record data of HAB disasters from 1933 to 2021, the frequency, duration, area and algal toxicity of HAB occurrences can be used as evaluation indicators. A method for hazard zoning of HAB disasters and a method for verifying the rationality of the zoning results are proposed. The hazard zoning results of HAB disasters in the coastal waters of Zhejiang from 1933 to 2021 (H1933-2021) show that very-high hazard degree areas of coastal waters of Zhejiang Province were found in Shengshan waters, the eastern waters of Zhujiajian and Nanji Islands' waters. Due to the influence of marine environmental factors, the spatial distribution of hazard degree of HAB disasters is dynamic, so it is recommended that the hazard zoning of HAB disasters be carried out once every five years. The rationality of the zoning results from 2017 to 2021 is verified, showing that the hazard zoning method of HAB disasters proposed in this study has a good rationality and credibility. This study utilized a qualitative approach to visually identify the spatial distribution of hazard degree zoning of HAB disasters in the Zhejiang Province coastal waters over the last 88 years. This has provided useful information to help prevent and mitigate HAB disasters and guide mariculture and coastal tourism planning. [ABSTRACT FROM AUTHOR]