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Objectives To evaluate the extent to which marriage influences cancer-specific survival (CSS) by influencing the insurance status among patients with common solid cancers and the feasibility of reducing the survival gap caused by marriage by increasing private insurance coverage for unmarried patients.Setting A retrospective cohort study with patients retrieved from the Surveillance, Epidemiology and End Results programme.Participants Patients with nine common solid cancers diagnosed between 2007 and 2016 were included. Patients were excluded if their marital status, insurance status, socioeconomical status, stage or cause of death was unavailable, if survival time was less than 1 month, or if they were younger than 18 years at the time of diagnosis.Primary and secondary outcome measures The primary outcome was CSS, which was compared between married and unmarried individuals. Mediation analyses were conducted to determine the contribution of insurance status to the association between marriage and CSS.Results Married patients had better CSS than those unmarried (time ratio 1.778; 95% CI 1.758 to 1.797). Private health insurance was a key factor mediating the association between marital status and CSS (proportion mediated (PM), 17%; 95% CI 17% to 17.1%). The PM ranges from 10.7% in prostate cancer to 20% in kidney cancer. The contribution of private insurance to the association between marital status and CSS was greater among women than among men (PM 18.5% vs 16.7%). The mediating effect of private insurance was the greatest for the comparison between married and separated individuals (PM 25.6%; 95% CI 25.3% to 25.8%) and smallest for the comparison between married and widowed individuals (PM 11.0%; 95% CI 10.9% to 11.1%).Conclusions 17% of the marital disparities in CSS are mediated by private insurance coverage. Increasing private insurance coverage for unmarried patients may reduce the survival gap related to marital status and sex. However, it is unclear whether better publicly funded insurance would have the same effect.

EEG emotion recognition based on Granger causality (GC) brain networks mainly focus on the EEG signal from the same-frequency bands, however, there are still some causality relationships between EEG signals in the cross-frequency bands. Considering the functional asymmetric of the left and right hemispheres to emotional response, this paper proposes an EEG emotion recognition scheme based on cross-frequency GC feature extraction and fusion in the left and right hemispheres. Firstly, we calculate the GC relationship of EEG signals according to the frequencies and hemispheres, and mainly focus on the causality of the cross-frequency EEG signals in left and right hemispheres. Then, to remove the redundant connections of the GC brain network, an adaptive two-stage decorrelation feature extraction scheme is proposed under the condition of maintaining the best emotion recognition performance. Finally, a multi-GC feature fusion scheme is designed to balance the recognition accuracy and feature number of each GC feature, which comprehensively considers the influence of the recognition accuracy and computational complexity. Experimental results on the DEAP emotion dataset show that the proposed scheme can achieve an average accuracy of 84.91% for four classifications, which improved the classification accuracy by up to 8.43% compared with that of the traditional same-frequency band GC features.

Background/aimsPrimary sclerosing cholangitis (PSC) is a chronic inflammatory biliary disease for which the immunopathological basis remains an enigma. Natural killer (NK) cells are key components of innate immunity and seemingly play diversified roles in different autoimmune disorders (AIDs). The aim of this study was to determine the role of NK cells in the pathogenesis of PSC.MethodsThe frequency and phenotype of circulating NK cells in a large cohort of patients with PSC and healthy controls (HCs) were systematically examined. In addition, the functional capacity of NK cells including cytotoxicity and cytokine production was studied.ResultsThe frequency of CD3−CD56dimCD16+ (defined as CD56dim) NK cells in PSC patients was significantly lower in comparison to HCs. CD56dim NK cells from PSC displayed a more immature phenotype including high expression of the natural killing receptor NKp46 and downregulation of the highly differentiated NK cell marker CD57. Interestingly, the reduction of CD57 expression of NK cells was associated with the disease severity of PSC. In addition, PSC CD56dim NK cells exhibited increased CD107a degranulation and cytolytic activity toward target cells compared with HCs. Further analysis demonstrated that CD57−CD56dim NK cells from PSC had elevated expression of NKp46, NKp30, IL-2 receptor, and KLRG1 and higher cytotoxic capacity as compared to CD57+CD56dim NK cells.ConclusionsOur data demonstrate that the differentiation of PSC NK cells is dysregulated with enhanced cytotoxic activity. This change is likely to be functionally involved in pathogenesis and disease progression, deducing the potential of NK-directed immunotherapy for PSC.

The continuous development of deep learning improves target detection technology day by day. The current research focuses on improving the accuracy of target detection technology, resulting in the target detection model being too large. The number of parameters and detection speed of the target detection model are very important for the practical application of target detection technology in embedded systems. This article proposed a real-time target detection method based on a lightweight convolutional neural network to reduce the number of model parameters and improve the detection speed. In this article, the depthwise separable residual module is constructed by combining depthwise separable convolution and non–bottleneck-free residual module, and the depthwise separable residual module and depthwise separable convolution structure are used to replace the VGG backbone network in the SSD network for feature extraction of the target detection model to reduce parameter quantity and improve detection speed. At the same time, the convolution kernels of 1 × 3 and 3 × 1 are used to replace the standard convolution of 3 × 3 by adding the convolution kernels of 1 × 3 and 3 × 1, respectively, to obtain multiple detection feature graphs corresponding to SSD, and the real-time target detection model based on a lightweight convolutional neural network is established by integrating the information of multiple detection feature graphs. This article used the self-built target detection dataset in complex scenes for comparative experiments; the experimental results verify the effectiveness and superiority of the proposed method. The model is tested on video to verify the real-time performance of the model, and the model is deployed on the Android platform to verify the scalability of the model.

BackgroundAdverse childhood experiences (ACEs) tend to cluster together in daily life, and most studies focus on the level of depression at certain points, but the dynamic process of depression is often neglected. Thus, research is urgently needed to explore the relationship between ACEs pattern and trajectory of depressive symptom levels at multiple time points in order to provides early targeted interventions to those who are most at risk.ObjectiveWe aimed to explore patterns of ACEs, including types and timing, associated with depression trajectories in college students.MethodsA school-based health survey was used to collect data as part of a longitudinal study in two medical college in Anhui province, China. Questionnaires were issued to 3,662 participants aged 17–22 and recorded details of ACEs (types and timing) and depression. Latent class analysis (LCA) was used to identify “patterns” of ACEs type and timing. Depressive symptom trajectories employed latent class growth analysis (LCGA). Multiple logistic regressions were employed to evaluate the relationships between ACEs patterns and depressive symptom trajectories.ResultsWe identified five ACEs patterns: “High neglect/emotional abuse/community violence,” “High neglect/emotional abuse,” “High neglect/family dysfunction,” “High neglect,” “Low ACEs.” We traced three depression trajectories: “High depressive symptom” “Moderate depressive symptom,” “Low depressive symptom.” “High neglect/emotional abuse/community violence,” “High neglect/emotional abuse” and “High neglect/family dysfunction” demonstrated a high risk for “High depressive symptom” and “Moderate depressive symptom.” “High neglect” showed a high risk for “Moderate depressive symptom” but not for “High depressive symptom” (P < 0.05).ConclusionsThe findings address the need for a comprehensive consideration of exposure to childhood adversity associated with the risk of depression in young adults through identifying more problematic ACEs patterns amongst exposed children.

Aims: To develop, optimize, and validate a novel model using alanine aminotransferase (ALT) and total bilirubin (TB) dynamic evolution patterns in predicting acute liver failure (ALF) in drug-induced liver injury (DILI) patients.Methods: The demographics, clinical data, liver biopsy, and outcomes of DILI patients were collected from two hospitals. According to the dynamic evolution of ALT and TB after DILI onset, the enrolled patients were divided into ALT-mono-peak, TB-mono-peak, double-overlap-peak, and double-separate-peak (DSP) patterns and compared. Logistic regression was used to develop this predictive model in both discovery and validation cohorts.Results: The proportion of ALF was significantly higher in patients with the DSP pattern than in the ALT-mono-peak pattern and DOP pattern (10.0 vs. 0.0% vs. 1.8%,p < 0.05). The area under receiver operating characteristic curve (AUROC) of the DSP pattern model was 0.720 (95% CI: 0.682–0.756) in the discovery cohort and 0.828 (95% CI: 0.788–0.864) in the validation cohort in predicting ALF, being further improved by combining with international normalized ratio (INR) and alkaline phosphatase (ALP) (AUROC in the discovery cohort: 0.899; validation cohort: 0.958). Histopathologically, patients with the DSP pattern exhibited a predominantly cholestatic hepatitis pattern (75.0%, p < 0.05) with a higher degree of necrosis (29.2%, p = 0.084).Conclusion: DILI patients with the DSP pattern are more likely to progress to ALF. The predictive potency of the model for ALF can be improved by incorporating INR and ALP. This novel model allows for better identification of high-risk DILI patients, enabling timely measures to be instituted for better outcome.

Quercetin exerts anti-inflammatory, anti-oxidant and other protective effects. Previous studies have shown that senescent cells, such as fibroblasts and type II airway epithelial cells, are strongly implicated in the development of pulmonary fibrosis pathology. However, the role of senescent macrophages during silicosis remains unclear. We investigated the effects of quercetin on macrophage senescence and pulmonary fibrosis, and explored underlying mechanisms. Mice were randomized to six model groups. Vitro model was also established by culturing RAW264.7 macrophages with silica (SiO2). We examined the effects of quercetin on fibrosis, senescence-associated β-galactosidase (SA-β-Gal) activity, and senescence-specific genes (p16, p21, and p53). We showed that quercetin reduced pulmonary fibrosis and inhibited extracellular matrix (ECM) formation. Quercetin also attenuated macrophage senescence induced by SiO2 both in vitro and in vivo. In addition, quercetin significantly decreased the expressions of the senescence-associated secretory phenotype (SASP), including proinflammatory factors (interleukin-1α (Il-1α), Il-6, tumor necrosis factor-α (TNF-α), and transforming growth factor-β1 (TGF-β1)) and matrix metalloproteinases (MMP2, MMP9, and MMP12). In conclusion, quercetin mediated its anti-fibrotic effects by inhibiting macrophage senescence, possibly via SASP.

Intelligent vehicles were widely used in logistics handling, agriculture, medical service, industrial production, and other industries, but they were often not smooth enough in planning the path, and the number of turns was large, resulting in high energy consumption. Aiming at the unsmooth path planning problem of four-wheel intelligent vehicle path planning algorithm, this article proposed an improved genetic and ant colony hybrid algorithm, and the physical model of intelligent vehicle was established. This article first improved ant colony optimization algorithm about heuristic function with the adaptive change of evaporation factor. Then, it improved the genetic algorithm on fitness function, adaptive adjustment of crossover factor, and mutation factor. Last, this article proposed the improved hybrid algorithm with the addition of a deletion operator, adoption of an elite retention strategy, and addition of suboptimal solutions obtained from the improved ant colony algorithm to improved genetic algorithm to obtain optimized new populations. The simulation environment for this article is windows 10, the processor is Intel Core i5-5257U, the running memory is 4GB, the compilation environment is MATLAB2018b, the number of ant samples is 50, the maximum number of iterations is 100, the initial population size of the genetic algorithm is 200, and the maximum number of iterations is 50. Simulation and physical experiments show that the improved hybrid algorithm is effective. Compared with the traditional hybrid algorithm, the improved hybrid algorithm reduced by 46% in the average number of iterations and 75% in the average number of turns in a simple grid. The improved hybrid algorithm reduced by 47% in the average number of iterations and 21% in the average number of turns in a complex grid. The improved hybrid algorithm works better to reduce the number of turns in simple maps.

This study investigates the relationship between tax incentives, tax enforcement, and R&D investment in Chinese enterprises. Tax compliance is an important part of organizational behavioral psychology, which will impact organizational innovation activities. Therefore, the present study utilizes the panel data of Chinese A-share listed companies collected from the China Stock Market & Accounting Research (CSMAR) database from 2011 to 2020 to conduct empirical research. It tests the policy effect of tax incentives on enterprise R&D investment and examines the impact of tax enforcement on enterprise R&D behavior and its moderating effect on the relationship between tax incentives and enterprise R&D investment. The results show that China’s preferential tax policies positively affect enterprise innovation activities, and stable and continuous tax incentives can stimulate enterprises to increase R&D investment. Increasing the intensity of tax enforcement has a significant positive impact on enterprise R&D investment, which means that the promotion effect of “governance effect” and “incentive effect” caused by tax enforcement may exceed the negative impact brought by its “taxation effect” and “rent-seeking effect.” The study suggests that with the strengthening of tax enforcement, the promotion effect of tax incentives is weakened, which is only reflected in non-state-owned enterprises. Finally, we propose relevant policy recommendations based on the research results. This includes further optimizing the collocation of tax types and preferential methods, encouraging enterprises to face up to the role of tax enforcement as an external governance mechanism, and promoting the tax department to standardize the enforcement process and taxation services. The paper presents a range of theoretical and practical implications for both firm managers and policymakers.

Wastewater treatment plants (WWTPs) are energy-intensive facilities that fulfill stringent effluent quality norms. Energy consumption prediction in WWTPs is crucial for cost savings, process optimization, compliance with regulations, and reducing the carbon footprint. This paper evaluates and compares a set of 23 candidate machine-learning models to predict WWTP energy consumption using actual data from the Melbourne WWTP. To this end, Bayesian optimization has been applied to calibrate the investigated machine learning models. Random Forest and XGBoost (eXtreme Gradient Boosting) were applied to assess how the incorporated features influenced the energy consumption prediction. In addition, this study investigated the consideration of information from past data in improving prediction accuracy by incorporating time-lagged measurements. Results showed that the dynamic models using time-lagged data outperformed the static and reduced machine learning models. The study shows that including lagged measurements in the model improves prediction accuracy, and the results indicate that the dynamic K-nearest neighbors model dominates state-of-the-art methods by reaching promising energy consumption predictions.

The importance of the network defined by phosphatidylinositol-3-kinase (PI3K), AKT and mammalian target of rapamycin (mTOR) downstream of Receptor Tyrosine Kinase (RTK) has been recognized for many years. However, the central role of RICTOR (rapamycin-insensitive companion of mTOR) in this pathway has only recently come to light. The function of RICTOR in pan-cancer still needs to be systematically elucidated. In this study, we examined RICTOR’s molecular characteristics and clinical prognostic value by pan-cancer analysis. Our findings indicate that RICTOR was overexpressed in twelve cancer types, and a high RICTOR expression was linked to poor overall survival. Moreover, the CRISPR Achilles’ knockout analysis revealed that RICTOR was a critical gene for the survival of many tumor cells. Function analysis revealed that RICTOR-related genes were mainly involved in TOR signaling and cell growth. We further demonstrated that the RICTOR expression was significantly influenced by genetic alteration and DNA-methylation in multiple cancer types. Additionally, we found a positive relationship between RICTOR expression and the immune infiltration of macrophages and cancer-associated fibroblasts in Colon adenocarcinoma and Head and Neck squamous cell carcinoma. Finally, we validated the ability of RICTOR in sustaining tumor growth and invasion in the Hela cell line using cell-cycle analysis, the cell proliferation assay, and wound-healing assay. Our pan-cancer analysis highlights the critical role of RICTOR in tumor progression and its potential as a prognostic marker for various cancer types.

Rapid simulation of land subsidence can provide an effective means of facilitating its management and control. This paper innovatively introduced a back-propagation (BP) neural network subsidence simulation model which was optimized by the sparrow search algorithm (SSA), hereinafter referred to as the SSA-BP model, to simulate land subsidence in Tongzhou District, Beijing. First, based on the time series interferometric synthetic aperture radar (InSAR) monitoring, different technologies such as spatial analysis, Google Earth Engine (GEE), and machine learning were utilized together with a variety of multi-source spatial data, including groundwater level, compressible layer thickness, Visible Infrared Imager Radiometer Suite (VIIRS) nighttime light images, and the OpenStreetMap (OSM) road distribution. Furthermore, we determined the optimal grid scale for land subsidence research and worked out a multifactor-driven SSA-BP land subsidence simulation model for which sensitivity analysis was performed with great care. Main conclusions: (1) From November 2010 to January 2020, the average annual surface displacement rate in Beijing’s subcentre, Tongzhou, ranged from −133.9 to +3.9 mm/year. (2) Our SSA-BP land subsidence simulation model allows for a relatively high overall accuracy. The average root mean square error (RMSE) is 5.00 mm/year, the average mean absolute error (MAE) is 3.80 mm/year, and the average coefficient of determination (R2) is 0.83. (3) Sensitivity analysis shows that the SSA-BP model driven by multiple factors has a far better simulation performance than the model driven by any single weighting factor, and displays the highest sensitivity to the groundwater level factor among all the weighting factors. In terms of subdividing the study area, our SSA-BP land subsidence model runs with multifunctional zones displayed a higher accuracy than that without. This paper made use of a machine learning model to simulate land subsidence in Beijing’s Tongzhou District and explored the sensitivity of different factors to land subsidence, which is helpful for its scientific prevention.

The Space–Air–Ground Integrated Network (SAGIN) expands cyberspace greatly. Dynamic network architecture, complex communication links, limited resources, and diverse environments make SAGIN’s authentication and key distribution much more difficult. Public key cryptography is a better choice for terminals to access SAGIN dynamically, but it is time-consuming. The semiconductor superlattice (SSL) is a strong Physical Unclonable Function (PUF) to be the hardware root of security, and the matched SSL pairs can achieve full entropy key distribution through an insecure public channel. Thus, an access authentication and key distribution scheme is proposed. The inherent security of SSL makes the authentication and key distribution spontaneously achieved without a key management burden and solves the assumption that excellent performance is based on pre-shared symmetric keys. The proposed scheme achieves the intended authentication, confidentiality, integrity, and forward security, which can defend against masquerade attacks, replay attacks, and man-in-the-middle attacks. The formal security analysis substantiates the security goal. The performance evaluation results confirm that the proposed protocols have an obvious advantage over the elliptic curve or bilinear pairings-based protocols. Compared with the protocols based on the pre-distributed symmetric key, our scheme shows unconditional security and dynamic key management with the same level performance.

Metabolic disorders entail both health risks and economic burdens to our society. A considerable part of the cause of metabolic disorders is mediated by the gut microbiota. The gut microbial structure and function are susceptible to dietary patterns and host physiological activities. A sedentary lifestyle accompanied by unhealthy eating habits propels the release of harmful metabolites, which impair the intestinal barrier, thereby triggering a constant change in the immune system and biochemical signals. Noteworthy, healthy dietary interventions, such as intermittent fasting, coupled with regular physical exercise can improve several metabolic and inflammatory parameters, resulting in stronger beneficial actions for metabolic health. In this review, the current progress on how gut microbiota may link to the mechanistic basis of common metabolic disorders was discussed. We also highlight the independent and synergistic effects of fasting and exercise interventions on metabolic health and provide perspectives for preventing metabolic disorders.

This study focused on comprehensively analyzing the construction, mechanism, and design theory of the Thick Rubber Bearing–Disk Spring Bearing (TNRB-DSB) system, with the aim of evaluating its isolation effect. Mechanical tests were conducted to examine the dynamic characteristics of large-span isolated structures equipped with TNRB-DSBs, and laminated rubber bearings (LRBs), as well as the dynamic responses of non-isolated structures and large-span horizontal isolated structures equipped with natural rubber bearings (NRBs) and LRBs, under various seismic excitations. Finite element software was utilized to compare the behaviors of these structures. The study revealed that the large-span isolated structure equipped with TNRB-DSBs and LRBs had a vertical natural vibration period 1.23 times as long as that of the isolated structure with NRBs and LRBs, and 4.27 times as long as that of the non-isolated structure. The TNRB-DSB system demonstrated good vertical and horizontal isolation capabilities, which compensated for the isolation limitations of other rubber bearings to some extent.

As a major apurinic/apyrimidinic endonuclease and a redox signaling protein in human cells, APE1 plays a crucial role in cellular function and survival. The relationship between alterations of APE1 expression and subcellular localization and the initiation, development and treatment of various cancers has received extensive attention. However, comparing the in-vivo activity of APE1 in normal and cancerous breast live cells remains challenging due to the low efficiency of commonly used liposome transfection methods in delivering DNA substrate probes into human normal breast epithelial cells (MCF-10A). In this work, we develop a DNA/RNA hybrid-based small magnetic fluorescent nanoprobe (25 ± 3 nm) that can be taken up by various live cells under magnetic transfection. The D0/R-nanoprobe demonstrates an outstanding specificity toward APE1 and strong resistance to the cellular background interference. Using this nanoprobe, we are not only able to visualize the intracellular activity of APE1 in breast ductal carcinoma (MCF-7) live cells, but also demonstrate the APE1 activity in MCF-10A live cells for the first time. The method is then extended to observe the changes in APE1 levels in highly metabolically active neuroendocrine cells under normal conditions and severe attacks by reactive oxygen species in real-time. The fluorescent nanoprobe provides a useful tool for studying the dynamic changes of intracellular APE1 in normal or cancerous live cells. It also displays the potential for visible and controllable release of miRNA drugs within live cells for therapeutic purposes.

Global sea level rise is a major environmental concern for many countries and cities, particularly for low-lying coastal areas where urban development is threatened by the combined effects of sea level rise and land subsidence. This study employed an improved two-layer network Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technology to obtain high-precision land subsidence in Singapore from 2015 to 2019. Landsat images from 1973 to 2020 were also utilized to extract changes in Singapore’s coastline. Geological, topographical, and global sea level rise data were integrated to investigate the causes and impacts of land subsidence in Singapore. The results indicate that the areas with severe subsidence coincide with land reclamation areas, where subsidence is mainly due to soil consolidation. Based on WorldDEM, land subsidence, and sea level rise data, the maximum inundation depth in Singapore by 2050 is estimated to be 1.24 m, with the Marina Bay area in Singapore’s central business district being the most vulnerable to sea level rise. This study provides data support and a scientific basis for understanding the impact of land subsidence on Singapore’s coastal areas under the influence of multiple factors using advanced InSAR technology.

Pseudomonas aeruginosa (PsA) is an opportunistic bacterial pathogen that causes life-threatening infections in individuals with compromised immune systems and exacerbates health concerns for those with cystic fibrosis (CF). PsA rapidly develops antibiotic resistance; thus, novel therapeutics are urgently needed to effectively combat this pathogen. Previously, we have shown that a novel cationic Zinc (II) porphyrin (ZnPor) has potent bactericidal activity against planktonic and biofilm-associated PsA cells, and disassembles the biofilm matrix via interactions with eDNA In the present study, we report that ZnPor caused a significant decrease in PsA populations in mouse lungs within an in vivo model of PsA pulmonary infection. Additionally, when combined with an obligately lytic phage PEV2, ZnPor at its minimum inhibitory concentration (MIC) displayed synergy against PsA in an established in vitro lung model resulting in greater protection of H441 lung cells versus either treatment alone. Concentrations above the minimum bactericidal concentration (MBC) of ZnPor were not toxic to H441 cells; however, no synergy was observed. This dose-dependent response is likely due to ZnPor’s antiviral activity, reported herein. Together, these findings show the utility of ZnPor alone, and its synergy with PEV2, which could be a tunable combination used in the treatment of antibiotic-resistant infections.

The regulation of solid-state emission based on cocrystal engineering is an emerging strategy for developing next-generation luminescent materials. Here, three luminescent cocrystals (AA-DITFB, AA-TFTPA and AA-TCNB) of 9-anthraldehyde (9-AA) are reported, which exhibit a broad range of green-to-red emission. Tuning the photophysical properties of 9-AA via cocrystals is based on different mechanisms. Compared with single-component crystals, the difference in the emission properties of AA-DITFB and AA-TFTPA originates from the π–π interaction between chromophores. As for AA-TCNB, its prominent red-shifted emission is the result of the charge-transfer interaction, which is confirmed by infrared spectroscopy, “hole-electron” analysis and charge-transfer spectroscopy. This work not only reveals the relationship between molecular structure and fluorescent properties, but also proposes a strategy to develop multicolor luminescent systems with tunable efficiency and lifetime.

Thermosalient (TS) crystals have gained considerable attention due to their potential applications in various fields, including in actuators, sensors, energy harvesting, and artificial muscles. Herein, co-crystallization was employed to construct TS crystals by forming a twisted angle between aromatic rings. Two multicomponent trimethoprim (TMP) TS cocrystals, TMP-25HBA and TMP-OA, were obtained. Differential scanning calorimetry (DSC) and variable-temperature powder X-ray diffraction (VT-PXRD) revealed that TMP-OA undergoes a solid-to-solid phase transition, while TMP-25HBA does not exhibit any phase transition. To the best of our knowledge, TMP-25HBA is the first multicomponent TS crystal without phase transition. The TS effect of both crystals is a result of the unit cell’s anisotropic expansion.

This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method’s performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data.

The green foxtail, Setaria viridis (L.) P. Beauv. (Poales: Poaceae), is a troublesome and widespread grass weed in China. The acetolactate synthase (ALS)-inhibiting herbicide nicosulfuron has been intensively used to manage S. viridis, and this has substantially increased the selection pressure. Here we confirmed a 35.8-fold resistance to nicosulfuron in an S. viridis population (R376 population) from China and characterized the resistance mechanism. Molecular analyses revealed an Asp-376-Glu mutation of the ALS gene in the R376 population. The participation of metabolic resistance in the R376 population was proved by cytochrome P450 monooxygenases (P450) inhibitor pre-treatment and metabolism experiments. To further elucidate the mechanism of metabolic resistance, eighteen genes that could be related to the metabolism of nicosulfuron were obtained bythe RNA sequencing. The results of quantitative real-time PCR validation indicated that three ATP-binding cassette (ABC) transporters (ABE2, ABC15, and ABC15-2), four P450 (C76C2, CYOS, C78A5, and C81Q32), and two UDP-glucosyltransferase (UGT) (UGT13248 and UGT73C3), and one glutathione S-transferases (GST) (GST3) were the major candidates that contributed to metabolic nicosulfuron resistance in S. viridis. However, the specific role of these ten genes in metabolic resistance requires more research. Collectively, ALS gene mutations and enhanced metabolism may be responsible for the resistance of R376 to nicosulfuron.

Favipiravir (FP) and Ebselen (EB) belong to a broad range of antiviral drugs that have shown active potential as medications against many viruses. Employing molecular dynamics simulations and machine learning (ML) combined with van der Waals density functional theory, we have uncovered the binding characteristics of these two antiviral drugs on a phosphorene nanocarrier. Herein, by using four different machine learning models (i.e., Bagged Trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Regression Trees (RT)), the Hamiltonian and the interaction energy of antiviral molecules in a phosphorene monolayer are trained in an appropriate way. However, training efficient and accurate models for approximating the density functional theory (DFT) is the final step in using ML to aid in the design of new drugs. To improve the prediction accuracy, the Bayesian optimization approach has been employed to optimize the GPR, SVR, RT, and BT models. Results revealed that the GPR model obtained superior prediction performance with an R2 of 0.9649, indicating that it can explain 96.49% of the data’s variability. Then, by means of DFT calculations, we examine the interaction characteristics and thermodynamic properties in a vacuum and a continuum solvent interface. These results illustrate that the hybrid drug is an enabled, functionalized 2D complex with vigorous thermostability. The change in Gibbs free energy at different surface charges and temperatures implies that the FP and EB molecules are allowed to adsorb from the gas phase onto the 2D monolayer at different pH conditions and high temperatures. The results reveal a valuable antiviral drug therapy loaded by 2D biomaterials that may possibly open a new way of auto-treating different diseases, such as SARS-CoV, in primary terms.

Abstract Background Prenatal exposure to heavy metals is implicated in the etiology of birth defects. We investigated whether concentrations of cadmium (Cd) and lead (Pb) in umbilical cord tissue are associated with risk for neural tube defects (NTDs) and whether selected genetic variants of the fetus modify their associations. Methods This study included 166 cases of NTD fetuses/newborns and 166 newborns without congenital malformations. Umbilical cord tissue was collected at birth or elective pregnancy termination. Cd and Pb concentrations were assessed by inductively coupled plasma-mass spectrometry, and 20 single-nucleotide polymorphisms (SNPs) in 9 genes were genotyped. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to estimate the risk for NTDs in association with metal concentrations or genotype using logistic regression. Multiplicative-scale interactions between the metals and genotypes on NTD risk were assessed with logistic regression, and additive-scale interactions were estimated with a non-linear mixed effects model. Results Higher concentrations of Cd were observed in the NTD group than in the control group, but no difference was found for Pb. Concentrations of Cd above the median level showed a risk effect, while the association between Pb and NTD risk was not significant in univariate analyses. The association of Cd was attenuated after adjusting for periconceptional folic acid supplementation. Fetuses with the AG and GG genotypes of rs4880 in SOD2 (superoxide dismutase 2) tended to have a lower risk, but fetuses with the CT and TT genotypes of rs1801133 in MTHFR (5,10-methylenetetrahydrofolatereductase) have a higher risk for NTDs when compared to their respective wild-type. rs4880 and Cd exhibited a multiplicative-scale interaction on NTD risk: the association between higher Cd and the risk for NTDs was increased by over fourfold in fetuses carrying the G allele [OR 4.43 (1.30–15.07)] compared to fetuses with the wild-type genotype. rs1801133 and Cd exposure showed an additive interaction, with a significant relative excess risk of interaction [RERI 0.64 (0.02–1.25)]. Conclusions Prenatal exposure to Cd may be a risk factor for NTDs, and the risk effect may be enhanced in fetuses who carry the G allele of rs4880 in SOD2 and T allele of rs1801133 in MTHFR.

Abstract Glioblastoma is the most aggressive and common intracranial malignant tumor, and the prognosis is still poor after various treatments. Based on the poor prognosis of glioma, new drugs that suppress the rapid progression and aggressive growth of glioma are urgently needed. It has been reported that nitidine chloride (NC) can inhibit tumor growth and epithelial‐mesenchymal transition (EMT), and EMT is associated with cancer stem cell properties. The present study aimed to investigate the inhibitory effect of NC on the EMT process and stem cell‐like properties in glioma cells. The results showed that the migration and invasion abilities in U87 and LN18 glioma cells were significantly increased after the induction of EMT and these effects were inhibited by NC in a concentration‐dependent manner. NC treatment decreased the expression of EMT markers in glioma cells and self‐renewal capacity of glioma stem‐like cells. We demonstrated that these effects of NC were achieved via JAK2/STAT3 signaling. Taken together, these results indicate that NC inhibits the EMT process and glioma stem‐like properties via JAK2/STAT3 signaling pathway, suggesting that NC may be a potential anti‐glioma drug.

Gao et al. shows that testes-specific protease 50 (TSP50) binds to PKM2 and promotes the Warburg effect by increasing PKM2 K433 acetylation level and PKM2 acetylation site (K433R) mutation remarkably abrogated the TSP50-induced aerobic glycolysis, cell proliferation in vitro and tumor formation in vivo. Our study reveals a link between an oncogene and a key enzyme in HCC glycolysis, which provides new ideas for human HCCs treatment with TSP50 as the target.

In this study, the surface water balance method was used to calculate the interaction between surface water and groundwater (SGW) in the Taoer River alluvial fan in Jilin Province, China, from 1956 to 2014. The automatic linear model was used to determine the key and non-key influencing factors, and correlation analysis was performed to evaluate their relationship with one another. River runoff and groundwater level were the key factors affecting the SGW interaction, and sand–gravel exposure in the fan was more conducive to SGW interaction. There was a positive correlation between runoff and SGW interaction, and the relationship between the groundwater and surface water levels was correlated and affected by groundwater exploitation and groundwater runoff. Groundwater exploitation and evaporation and precipitation indirectly influenced the SGW interaction by affecting the groundwater level and river runoff key factors, respectively, and were considered non-key factors. HIGHLIGHTS The annual and monthly interactions of the Taoer River alluvial fan were calculated from 1956 to 2014.; They are influencing factors of the interaction between surface water and groundwater (SGW).; The influencing factors of SGW transformation are analyzed and classified.; The stability factor affecting SGW interaction is riverbed lithology.; The key factors affecting SGW interaction are river runoff and groundwater level.;

Abstract Background Overexpressed vascular endothelial growth factor A (VEGFA) and phosphorylated signal transducer and activator of transcription 3 (P-STAT3) cause unrestricted tumor growth and angiogenesis of breast cancer (BRCA), especially triple-negative breast cancer (TNBC). Hence, novel treatment strategy is urgently needed. Results We found sphingosine 1 phosphate receptor 1 (S1PR1) can regulate P-STAT3/VEGFA. Database showed S1PR1 is highly expressed in BRCA and causes the poor prognosis of patients. Interrupting the expression of S1PR1 could inhibit the growth of human breast cancer cells (MCF-7 and MDA-MB-231) and suppress the angiogenesis of human umbilical vein endothelial cells (HUVECs) via affecting S1PR1/P-STAT3/VEGFA axis. Siponimod (BAF312) is a selective antagonist of S1PR1, which inhibits tumor growth and angiogenesis in vitro by downregulating the S1PR1/P-STAT3/VEGFA axis. We prepared pH-sensitive and tumor-targeted shell-core structure nanoparticles, in which hydrophilic PEG2000 modified with the cyclic Arg-Gly-Asp (cRGD) formed the shell, hydrophobic DSPE formed the core, and CaP (calcium and phosphate ions) was adsorbed onto the shell; the nanoparticles were used to deliver BAF312 (BAF312@cRGD-CaP-NPs). The size and potential of the nanoparticles were 109.9 ± 1.002 nm and − 10.6 ± 0.056 mV. The incorporation efficacy for BAF312 was 81.4%. Results confirmed BAF312@cRGD-CaP-NP could dramatically inhibit tumor growth and angiogenesis in vitro and in MDA-MB-231 tumor-bearing mice via downregulating the S1PR1/P-STAT3/VEGFA axis. Conclusions Our data suggest a potent role for BAF312@cRGD-CaP-NPs in treating BRCA, especially TNBC by downregulating the S1PR1/P-STAT3/VEGFA axis. Graphic abstract

Abstract In this study, we developed a long noncoding RNA (lncRNA)‐based prognostic signature for stratification of patients with head a nd neck squamous cell carcinoma (HNSCC). In total, 493 HNSCC samples obtained from the Cancer Genome Atlas database were divided into training and testing cohorts (3:2 ratio). We identified 3913 immune‐related lncRNAs in the HNSCC training cohort by Pearson correlation analysis; only seven were independently associated with overall survival and were used to develop an immune‐related lncRNA prognostic signature (IRLPS) grouping of HNSCC patients into high‐ and low‐IRLPS subgroups. Univariate and multivariate Cox analyses revealed that low‐IRLPS patients had a better prognosis in all the cohorts, which was retained after stratification by sex, grade, and HPV status. Although the TNM stage was also an independent prognostic factor, the IRLPS had a better discriminability with higher AUC at the 3‐ and 5‐year follow‐ups in all cohorts. Low‐IRLPS samples had more immune cell infiltration and were enriched in immune‐related pathways, while high‐ IRLPS samples were enriched in metabolic pathways. A nomogram constructed including age, TNM stage, and IRLPS showed good calibration. Thus, IRLPS improves the prognostic prediction and also distinguishes different tumor microenvironment (TME) in HNSCC patients.

A straightforward and fast transient temperature rise calculation model is necessary for electrical engineers to manage power cable load. A self-heating lumped parameter model (LPM) without the requirement of monitoring cable surface temperature is developed to evaluate the real-time transient temperature rise of the power cable in ducts. A mutual-heating lumped parameter model to describe the mutual heat interaction between any two cables is proposed in this paper as well. The transient temperature rise of any power cable in duct is the sum of the self-heating temperature rise and the mutual-heating temperature rise from all the other power cables in ducts. All parameters of these two models are obtained by transient thermal circuit analysis, CYMCAP calculation results and generic algorithm. A case study has been conducted to verify the feasibility of the proposed LPMs and the self and mutual temperature rise for each cable in ducts have been obtained. These results agree with the accurate data obtained from CYMCAP well, indicating that LPM can be used as a fast and straightforward tool to calculate the transient temperature rise of power cables in duct.

Abstract Background Oral cavity (OC), oropharyngeal (OP), hypopharyngeal (HP), and laryngeal (LA) squamous cell carcinoma (SCC) have a high incidence of regional lymph node metastasis (LNM). Elective irradiation for clinically node‐negative neck is routinely administered to treat lymph nodes harboring occult metastasis. However, the optimal elective irradiation schemes are still inconclusive. In this study, we aimed to establish individualized elective irradiation schemes for the ipsilateral and contralateral node‐negative neck of these four types of cancer. Methods From July 2005 to December 2018, 793 patients with OC‐SCC, 464 with OP‐SCC, 413 with HP‐SCC, and 645 with LA‐SCC were recruited retrospectively. Based on the actual incidence of LNM and the tumor characteristics, risk factors for contralateral LNM, as well as node level coverage schemes for elective irradiation, were determined using logistic regression analysis. Additionally, we developed a publicly available online tool to facilitate the widespread clinical use of these schemes. Results For the ipsilateral node‐negative neck, elective irradiation at levels I‐III for OC‐SCC and levels II‐IVa for OP‐, HP‐ and LA‐SCC are generally recommended. In addition, level VIIa should be included in patients with OP‐SCC. Multivariate analyses revealed that posterior hypopharyngeal wall and post‐cricoid region involvement were independently associated with level VIIa metastasis in HP‐SCC (all P < 0.05). For the contralateral node‐negative neck, multivariate analyses revealed that ipsilateral N2b2‐N3, tumors with body midline involvement, and degree of tumor invasion were the independent factors for contralateral LNM (all P < 0.05). In patients who require contralateral neck irradiation, levels I‐II are recommended for OC‐SCC, and additional level III is recommended for patients with ipsilateral N3 disease. Levels II‐III are recommended for OP‐, HP‐, and LA‐SCC, and additional level IVa is recommended for patients with advanced T or ipsilateral N classifications. Furthermore, additional level VIIa is recommended only for OP‐SCC with T4 and ipsilateral N3 disease. Conclusion Based on our findings, we suggest that individualized and computer‐aided elective irradiation schemes could reduce irradiation volumes in OC‐, OP‐ and HP‐SCC patients, as compared to current guidelines, and could thus positively impact the patients' quality of life after radiotherapy.

Abstract The electrochemical production of green and low-cost ammonia requests the development of high-performance electrocatalysts. In this work, the ampoule method was applied to modulate the surface of the zinc electrode by implanting defects and low-valent active sites. The N-doped ZnS electrocatalyst was thus generated by sulfurization with thiourea and applied for electrocatalytic nitrogen reduction reaction (ENRR). Given the rich sulfur vacancies and abundant Zn-N active sites on the surface, excellent catalytic activity and selectivity were obtained, with an NH3 yield rate of 2.42 × 10–10 mol s−1 cm−2 and a Faradaic efficiency of 7.92% at − 0.6 V vs. RHE in 0.1 M KOH solution. Moreover, the as-synthesized zinc electrode exhibits high stability after five recycling tests and a 24 h potentiostatic test. The comparison with Zn foil, non-doping ZnS/Zn and recent metal sulfide electrocatalysts further demonstrated advanced catalytic performance of N@ZnS/Zn for ENRR. By simple synthesis, S vacancies, and N-doping defects, this promising electrocatalyst would represent a good addition to the arena of transition-metal-based catalysts with superior performance in ENRR. Graphic abstract

Objective: This study aimed to describe the implementation of the surgical safety check policy and the surgical safety checklist for invasive procedures outside the operating room (OR) and evaluate its effectiveness. Methods: In 2017, to improve the safety of patients who underwent invasive procedures outside of the OR, the hospital quality and safety committee established the surgery safety check committee responsible for developing a new working plan, revise the surgery safety check policy, surgery safety check form, and provide training to the related staff, evaluated their competency, and implemented the updated surgical safety check policy and checklist. The study compared the data of pre-implementation (Apr to Sep 2017) and two post-implementation phases (Apr to Sep 2018, Apr to Sep 2019). It also evaluated the number of completed surgery safety checklist, correct signature, and correct timing of signature. Results: The results showed an increase in the completion rate of the safety checklist after the program implementation from 41.7% (521/1,249) to 90.4% (3,572/3,950), the correct rates of signature from 41.9% (218/521) to 99.0% (4,423/4,465), and the correct timing rates of signature from 34.4% (179/521) to 98.5% (4,401/4,465), with statistical significance (P

Abstract Transient simulation model of doubly‐fed induction generator‐based wind turbine has a complex model structure and considerable control parameters. An accurate simulation model is especially important for studying the static and transient characteristics of doubly‐fed induction generator‐based wind turbine and the grid‐connected system. Based on the measured data of low voltage ride‐through testing, the integrated parameter identification method is proposed here. Combined with the time‐domain simulation analysis, curve fitting is used to determine the control strategy of doubly‐fed induction generator‐based wind turbine. The improved genetic algorithm is used to identify the generator parameters, power control parameters, and resistance of Crowbar of doubly‐fed induction generator‐based wind turbine. Combined with the electrical parameters provided by the wind turbine manufacturer, electromagnetic transient model on full operation condition and rapid starting period, corresponding to a measured doubly‐fed induction generator‐based wind turbine in West Inner Mongolia Power Grid, is then established, which can operate in the full wind speed range and quickly enter the stable state within 1 s after start‐up. Comparison of simulation results and measured data shows that the established electromagnetic transient simulation model using parameters identified by the proposed method can accurately reflect the key characteristics of doubly‐fed induction generator‐based wind turbine.

Agronomic traits such as plant height (PH), leaf number (LN), leaf length (LL), and leaf width (LW), which are closely related to yield and quality, are important in tobacco (Nicotiana tabacum L.). To identify quantitative trait loci (QTLs) associated with agronomic traits in tobacco, 209 recombinant inbred lines (RILs) and 537 multiparent advanced generation intercross (MAGIC) lines were developed. The biparental RIL and MAGIC lines were genotyped using a 430 K single-nucleotide polymorphism (SNP) chip assay, and their agronomic traits were repeatedly evaluated under different conditions. A total of 43 QTLs associated with agronomic traits were identified through a combination of linkage mapping (LM) and association mapping (AM) methods. Among these 43 QTLs, three major QTLs, namely qPH13-3, qPH17-1, and qLW20-1, were repeatedly identified by the use of various genetically diverse populations across different environments. The candidate genes for these major QTLs were subsequently predicted. Validation and utilization of the major QTL qLW20-1 for the improvement of LW in tobacco were investigated. These results could be applied to molecular marker-assisted selection (MAS) for breeding important agronomic traits in tobacco.

Genetic algorithm is widely used in multi-objective mechanical structure optimization. In this paper, a genetic algorithm-based optimization method for ladle refractory lining structure is proposed. First, the parametric finite element model of the new ladle refractory lining is established by using ANSYS Workbench software. The refractory lining is mainly composed of insulating layer, permanent layer and working layer. Secondly, a mathematical model for multi-objective optimization is established to reveal the functional relationship between the maximum equivalent force on the ladle lining, the maximum temperature on the ladle shell, the total mass of the ladle and the structural parameters of the ladle refractory lining. Genetic algorithm translates the optimization process of ladle refractory lining into natural evolution and selection. The optimization results show that, compared with the unoptimized ladle refractory lining structure (insulation layer thickness of 0 mm, permanent layer thickness of 81 mm, and working layer thickness of 152 mm), the refractory lining with insulation layer thickness of 8.02 mm, permanent layer thickness of 76.20 mm, and working layer thickness of 148.61 mm has the best thermal insulation performance and longer service life within the variation of ladle refractory lining structure parameters. Finally, the results of the optimization are verified and analyzed in this paper. The study found that by optimizing the design of the ladle refractory lining, the maximum equivalent force on the ladle lining, the maximum temperature on the ladle shell and the ladle mass were reduced. The thermal insulation performance and the lightweight performance of the ladle are improved, which is very important for improving the service life of the ladle.

As a key technology for the non-invasive human-machine interface that has received much attention in the industry and academia, surface EMG (sEMG) signals display great potential and advantages in the field of human-machine collaboration. Currently, gesture recognition based on sEMG signals suffers from inadequate feature extraction, difficulty in distinguishing similar gestures, and low accuracy of multi-gesture recognition. To solve these problems a new sEMG gesture recognition network called Multi-stream Convolutional Block Attention Module-Gate Recurrent Unit (MCBAM-GRU) is proposed, which is based on sEMG signals. The network is a multi-stream attention network formed by embedding a GRU module based on CBAM. Fusing sEMG and ACC signals further improves the accuracy of gesture action recognition. The experimental results show that the proposed method obtains excellent performance on dataset collected in this paper with the recognition accuracies of 94.1%, achieving advanced performance with accuracy of 89.7% on the Ninapro DB1 dataset. The system has high accuracy in classifying 52 kinds of different gestures, and the delay is less than 300 ms, showing excellent performance in terms of real-time human-computer interaction and flexibility of manipulator control.

Background and AimsAptamers are artificial ligands that bind to biological targets with high specificity and affinity. We previously selected a group of aptamers against the serum of primary hepatic carcinoma (PHC) via systematic evolution of ligands by exponential and enrichment (SELEX) method, and some of the aptamers were valuable for PHC diagnosis in polyacrylamide gel electrophoresis (PAGE) analysis. Here, we used aptamers to develop a novel method suitable for the clinical diagnosis of PHC.MethodsThe intensities of serum autofluorescence, cell-free DNA (cfDNA)-related fluorescence and aptamer-related fluorescence, named the aptamer-based triple serum fluorescence intensity (ATSFI), were sequentially measured at 8 °C and 37 °C in one tube by using a real-time polymerase chain reaction (PCR) system as a fluorimeter in patients with PHC (n=346) or liver cirrhosis (n=321). The diagnostic performances of ATSFI indicators alone and in combination were evaluated by area under the receiver operator characteristic curve (AUROC), and the underlying clinical mechanisms were analyzed by bivariate correlation.ResultsThe measurement of ATSFI was high throughput, rapid, convenient, and low cost. The aptamer-related fluorescence indicator SEA-SE37 was the most valuable for PHC diagnosis among all fluorescence indicators and superior to alpha-fetoprotein (AFP) (AUROC 0.879 vs. 0.836). The logistic model of ATSFI indicators exhibited excellent diagnostic performance for PHC, including AFP-negative, early and small PHCs, with AUROCs of 0.935-0.950 and accuracies of 86.8-88.3%. The diagnostic performance was further improved when ATSFI indicators were combined with AFP, with AUROCs of approximately 0.95 and accuracies of approximately 90%, suggesting ATSFI was independent of but complementary to AFP in PHC diagnosis. ATSFI models were highly valuable in clinical decision-making. The aptamer-related fluorescence intensity was generally independent of the clinicopathological characteristics of PHC but correlated with laboratory characteristics of PHC serum.ConclusionsThe ATSFI assay is a novel, robust and feasible method for the clinical diagnosis of PHC.

Ginkgo (Ginkgo biloba L.) is a traditional economic tree species in China. Ginkgo biloba extract (GBE) is widely used in combination to treat hypertension and complications in clinical practice. However, the antihypertensive effect of GBE alone is weak and it is also difficult to study the mechanism because of its complex composition. This study was to prepare a new component group of Ginkgo biloba leaves (GBLCG) with clear chemical structures, and to investigate its effect on reducing blood pressure and improving myocardial hypertrophy in spontaneously hypertensive rats with GBE and amlodipine as positive controls. The results showed that total flavonoid aglycones (TFAs) of GBLCG was mainly composed of quercetin (QCT), kaempferol (KMF) and isorhamnetin (ISR); Total terpenoid lactones (TTLs) of GBLCG might be a novel cocrystal composed of Ginkgolide A (GA), Ginkgolide B (GB) Ginkgolide C (GC), Ginkgolide J (GJ) and bilobalide (BB). The hypotensive activity of GBLCG (4.4 mg/kg) group was better than that of GBE group (p

The development of object detection technology makes it possible for robots to interact with people and the environment, but the changeable application scenarios make the detection accuracy of small and medium objects in the practical application of object detection technology low. In this paper, based on multi-scale feature fusion of indoor small target detection method, using the device to collect different indoor images with angle, light, and shade conditions, and use the image enhancement technology to set up and amplify a date set, with indoor scenarios and the SSD algorithm in target detection layer and its adjacent features fusion. The Faster R-CNN, YOLOv5, SSD, and SSD target detection models based on multi-scale feature fusion were trained on an indoor scene data set based on transfer learning. The experimental results show that multi-scale feature fusion can improve the detection accuracy of all kinds of objects, especially for objects with a relatively small scale. In addition, although the detection speed of the improved SSD algorithm decreases, it is faster than the Faster R-CNN, which better achieves the balance between target detection accuracy and speed.

The analysis of robot inverse kinematic solutions is the basis of robot control and path planning, and is of great importance for research. Due to the limitations of the analytical and geometric methods, intelligent algorithms are more advantageous because they can obtain approximate solutions directly from the robot’s positive kinematic equations, saving a large number of computational steps. Particle Swarm Algorithm (PSO), as one of the intelligent algorithms, is widely used due to its simple principle and excellent performance. In this paper, we propose an improved particle swarm algorithm for robot inverse kinematics solving. Since the setting of weights affects the global and local search ability of the algorithm, this paper proposes an adaptive weight adjustment strategy for improving the search ability. Considering the running time of the algorithm, this paper proposes a condition setting based on the limit joints, and introduces the position coefficient k in the velocity factor. Meanwhile, an exponential product form modeling method (POE) based on spinor theory is chosen. Compared with the traditional DH modeling method, the spinor approach describes the motion of a rigid body as a whole and avoids the singularities that arise when described by a local coordinate system. In order to illustrate the advantages of the algorithm in terms of accuracy, time, convergence and adaptability, three experiments were conducted with a general six-degree-of-freedom industrial robotic arm, a PUMA560 robotic arm and a seven-degree-of-freedom robotic arm as the research objects. In all three experiments, the parameters of the robot arm, the range of joint angles, and the initial attitude and position of the end-effector of the robot arm are given, and the attitude and position of the impact point of the end-effector are set to verify whether the joint angles found by the algorithm can reach the specified positions. In Experiments 2 and 3, the algorithm proposed in this paper is compared with the traditional particle swarm algorithm (PSO) and quantum particle swarm algorithm (QPSO) in terms of position and direction solving accuracy, operation time, and algorithm convergence. The results show that compared with the other two algorithms, the algorithm proposed in this paper can ensure higher position accuracy and orientation accuracy of the robotic arm end-effector. the position error of the algorithm proposed in this paper is 0 and the maximum orientation error is 1.29 × 10–8. while the minimum position error of the other two algorithms is −1.64 × 10–5 and the minimum orientation error is −4.03 × 10–6. In terms of operation time, the proposed algorithm in this paper has shorter operation time compared with the other two algorithms. In the last two experiments, the computing time of the proposed algorithm is 0.31851 and 0.30004s respectively, while the shortest computing time of the other two algorithms is 0.33359 and 0.30521s respectively. In terms of algorithm convergence, the proposed algorithm can achieve faster and more stable convergence than the other two algorithms. After changing the experimental subjects, the proposed algorithm still maintains its advantages in terms of accuracy, time and convergence, which indicates that the proposed algorithm is more applicable and has certain potential in solving the multi-arm inverse kinematics solution. This paper provides a new way of thinking for solving the multi-arm inverse kinematics solution problem.

Background and ObjectiveAn increasing number of evidence has revealed that the gut microbiome functions in immunity, inflammation, metabolism, and homeostasis and is considered to be crucial due to its balance between human health and diseases such as cancer, leading to the emergence of treatments that target intestinal microbiota. Probiotics are one of them. However, many challenges remain regarding the effects of probiotics in cancer treatment. Berberine (BBR), a natural extract of Rhizoma Coptidis and extensively used in the treatment of gastrointestinal diseases, has been found to have antitumor effects in vivo and in vitro by many recent studies, but its definite mechanisms are still unclear. This study aimed to explore the inhibitory effect of BBR and probiotics on the growth of colon cancer cells in vitro and in vivo, and the regulatory influence on the gut microbiome and butyrate production.MethodsColon cancer cell line HT29 was used to establish a xenograft model of nude mice and an in vitro model. A total of 44 nude mice and HT29 cells were divided into control, model, model + BBR, model + probiotics, and model + combination of BBR with probiotics (CBPs). Live combined Bifidobacterium, Lactobacillus, and Enterococcus powder (LCBLEP) was used as a probiotic preparation. LCBLEP was cultured in the liquid medium under anaerobic conditions (the number of viable bacteria should reach 1 × 108CFU), and the supernatant was collected, and it is called probiotic supernatant (PS). Model + BBR and model + probiotics groups were treated with BBR and LCBLEP or PS for 4 weeks in vivo or 48, 72, and 96 h in vitro, respectively. Tumor volume or cell proliferation was measured. Gut microbiota was pyrosequenced using a 16S rDNA amplicon. HDAC1 mRNA level in HT29 cells and sodium butyrate (SB) expression in the serum of mice was detected by QPCR and ELISA.ResultsThe treatment of BBR and CBP reduced the growth of neoplasms in mice to a different extent (p > 0.05), especially at 14 days. The inhibitory effect of LCBLEP on tumor growth was more significant, especially at 11–21 days (p < 0.05). Inhibition of BBR on in vitro proliferation was concentration-dependent. The suppression of 75% probiotic supernatant (PS) on the proliferation was the most significant. The supplement of LCBLEP significantly increased the richness and evenness of the gut microbe. BBR dramatically increased the abundance of Bacteroidetes and Proteobacteria, with reduced Ruminococcus, followed by the LCBLEP. The LCBLEP reduced the relative abundance of Verrucomicrobia and Akkermansia, and the CBP also promoted the relative level of Bacteroidetes but reduced the level of Verrucomicrobia and Akkermansia. BBR and LCBLEP or CBP improved the alpha and beta diversity and significantly affected the biomarker and metabolic function of the gut microbe in nude mice with colon cancer. The level of HDAC1 mRNA was reduced in HT29 cells treated with BBR or PS (p < 0.05), the mice treated with BBR revealed a significantly increased concentration of SB in serum (p < 0.05), and the inhibitory effect of SB on the proliferation of HT29 cells was stronger than panobinostat and TSA.ConclusionAlthough the combination of BBR and probiotics has no advantage in inhibiting tumor growth compared with the drug alone, BBR can be used as a regulator of the intestinal microbiome similar to the probiotics by mediating the production of SB during reducing the growth of colon cancer.

In order to solve the problems of poor image quality, loss of detail information and excessive brightness enhancement during image enhancement in low light environment, we propose a low-light image enhancement algorithm based on improved multi-scale Retinex and Artificial Bee Colony (ABC) algorithm optimization in this paper. First of all, the algorithm makes two copies of the original image, afterwards, the irradiation component of the original image is obtained by used the structure extraction from texture via relative total variation for the first image, and combines it with the multi-scale Retinex algorithm to obtain the reflection component of the original image, which are simultaneously enhanced using histogram equalization, bilateral gamma function correction and bilateral filtering. In the next part, the second image is enhanced by histogram equalization and edge-preserving with Weighted Guided Image Filtering (WGIF). Finally, the weight-optimized image fusion is performed by ABC algorithm. The mean values of Information Entropy (IE), Average Gradient (AG) and Standard Deviation (SD) of the enhanced images are respectively 7.7878, 7.5560 and 67.0154, and the improvement compared to original image is respectively 2.4916, 5.8599 and 52.7553. The results of experiment show that the algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of the image.

Abstract Background In lung cancer management, differential diagnosis between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IMP) is a critical point that is of direct therapeutic and clinical importance. However, this process often suffers from absence of a gold standard, resulting in equivocal cases. Herein, we present a series of three cases, in which genomic alteration patterns revealed by next-generation sequencing (NGS) facilitated the differential diagnosis between MPLC and IMP. Case presentation Case 1 was a 57-year-old female with two separate lesions in the upper lobe and the lower lobe of left lung, which were both histopathologically determined as T2aN0M0 adenocarcinomas. NGS identified an EGFR L858R in one lesion and an EGFR 20 exon insertion in the other one, suggestive of double primary malignancies. The patient underwent wedge resections and received an adjuvant treatment of icotinib and chemotherapy. She had a disease-free survival (DFS) of 19 months and counting. Case 2 was a 55-year-old female with multiple small lesions in both lungs. Histopathological examinations of resected lesions from right upper lobe revealed three subtypes: atypical adenomatous hyperplasia of alveolar epithelium, adenocarcinomas in situ and minimally invasive adenocarcinoma. NGS identified two different BRAF driver mutations G466E and V600_K601delinsE in two lesions of adenocarcinoma in situ, and a BRAF K601E in a lesion of minimally invasive adenocarcinoma. Case 3, a 68-year-old male, had the right upper lobe lesion histophathologically classified as a stage T3NxM0 mixed adenoneuroendocrine carcinoma and the left upper lobe lesion as a stage T1aN0M0 adenocarcinoma. NGS performed with different loci of surgical tissues revealed a rare sensitizing EGFR mutation G719A shared by the right upper lobe lesion and lymph node, and two EGFR mutations L861Q and G719S in left upper lobe lesion. The patient received icotinib treatment postoperatively and achieved a stable disease with a progression-free survival of 5 months. Conclusion Our cases provide evidence for utility of NGS in facilitating diagnosis and treatment decisions.

Abstract Objective To address the challenge of computational identification of cell type-specific regulatory elements on a genome-wide scale. Results We propose SeqEnhDL, a deep learning framework for classifying cell type-specific enhancers based on sequence features. DNA sequences of “strong enhancer” chromatin states in nine cell types from the ENCODE project were retrieved to build and test enhancer classifiers. For any DNA sequence, positional k-mer (k = 5, 7, 9 and 11) fold changes relative to randomly selected non-coding sequences across each nucleotide position were used as features for deep learning models. Three deep learning models were implemented, including multi-layer perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). All models in SeqEnhDL outperform state-of-the-art enhancer classifiers (including gkm-SVM and DanQ) in distinguishing cell type-specific enhancers from randomly selected non-coding sequences. Moreover, SeqEnhDL can directly discriminate enhancers from different cell types, which has not been achieved by other enhancer classifiers. Our analysis suggests that both enhancers and their tissue-specificity can be accurately identified based on their sequence features. SeqEnhDL is publicly available at https://github.com/wyp1125/SeqEnhDL .

The value assessment of job skills is critical for companies to select and retain the right talent and for individuals to develop them. Here the authors show that a data-driven method based on an enhanced neural network can assign meaningful value to job skills in a quantitative way and outperforms benchmark models for job salary prediction.

Background and objective Lung cancer is the highest incidence of cancer in the world, which seriously threatens human health. Early diagnosis and treatment of lung cancer is particularly important for the survival of lung cancer patients. Serum tumor markers have been widely used as an important method for early diagnosis of tumor. However, there are few early diagnostic markers for lung cancer. Therefore, the aim of this study was to investigate the expression level of Lipocalin-2 and its clinical significance in serum of patients with lung cancer. Methods The serum levels of Lipocalin-2 in 60 lung cancer patients and 63 healthy people were detected by enzyme-linked immunosorbent assay (ELISA), and the relationship between the expression level of Lipocalin-2 and the clinical characteristics of lung cancer was analyzed. Results The expression level of Lipocalin-2 in peripheral blood serum of patients with lung cancer was significantly higher than that of healthy people, and the difference was statistically significant (P