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Abstract Mumps is a vaccine-preventable disease with high contagious capability. Its incidence declined rapidly since one dose of mumps vaccine was introduced into Expanded Program of Immunization (EPI) in 2008 in China. Nonetheless, the outbreaks of mumps remain frequent in China. Here we aim to assess herd immunity level followed by one-dose mumps ingredient vaccine and to elucidate the genetic characteristics of mumps viruses circulating in the post vaccine era in Jiangsu province of China. The complete sequences of mumps virus small hydrophobic(SH) gene were amplified and sequenced; coalescent-based Bayesian method was used to perform phylogenetic analysis with BEAST 1.84 software. Commercially available indirect enzyme-linked immune-sorbent IgG assay was used for the quantitative detection of IgG antibody against mumps virus. Our results show that genotype F was the predominant mumps viruses and belonged to indigenous spread, and most of Jiangsu sequences clustered together and formed a monophyly. The prevalence of mumps reached a peak in 2012 and subsequently declined, which presented an obvious different trajectory with virus circulating in other regions of China. The gene diversity of viruses circulating in Jiangsu province was far less than those in China. The antibody prevalence reached 70.42% in the general population during 2018 to 2020. The rising trend of antibody level was also observed. Although mumps antibody prevalence does not reach expected level, mumps virus faces higher pressure in Jiangsu province than the whole of China. To reduce further the prevalence of mumps viruses, two doses of mumps vaccine should be involved into EPI.

The Weirong shale gas in the Sichuan Basin, buried at depths of 3 600-3 850 m, represents a prototypical deep shale gas reservoir. Drilling operations face challenges such as a complex pressure system, developed fault-dissolved bodies in certain areas of the Maokou Formation, poor drillability of formations, and high strata temperatures in the Longmaxi Formation. To address these complex geological conditions, this study focused on the safe and accelerated drilling. By combining mature technologies with pilot testing, continuous efforts were made to advance drilling engineering techniques. Through deepening the understanding of the geology of the passing strata sections, targeted solutions for each section were implemented to enhance the precision of engineering techniques. The Weirong shale gas field had gone through three stages: exploration and evaluation, development phaseⅠ, and Ⅱ. This led to the development of safe drilling technologies that emphasize optimization of well structure, track designs avoiding fault-dissolved bodies, wellbore pressure control, and synchronized drilling andfracturing to prevent interference between wells. Additionally, accelerated drilling technologies were cultivated, focusing on a "2D + small 3D" three-dimensional track profile, integrated geological guidance, optimal rock-breaking tools, and enhanced drilling parameters. Applied to over 150 well drillings in the Weirong shale gas field, these technologies increased the rate of penetration from 6.32 m/h at the start of development to 9.12 m/h, reduced the drilling cycle from 106.68 days to 68.75 days (a reduction of 35.56%), achieving the goal of acce-lerated and cost-effective deep shale gas drilling in Weirong area. These technologies provide significant reference value for the development of similar gas reservoirs.

ABSTRACTIn naval and civilian domains, meticulous ship classification and detection are paramount. Nevertheless, predominant research has gravitated toward leveraging Convolutional Neural Network (CNN)-centered methodologies, often overlooking the diverse granularity inherent in ship samples. In our pursuit to holistically extract features from ship images across varying granularities, we present a transformative architecture: the Vision Transformer and Multi-Grain Feature Vector Feature Pyramid Network (ViT-MGFV-FPN). This model synergistically melds the merits of MGFV-FPN with an augmented Vision Transformer (ViT) for a comprehensive image feature extraction. To cater to the extraction of broader image features whilst sidestepping the innate quadratic complexity of traditional ViT, we unveil an enhanced version christened the Global Swin Transformer. Concurrently, the MGFV-FPN is orchestrated to harness the prowess of CNNs in distilling intricate ship attributes. Rigorous empirical evaluations underscore our model’s superiority in juxtaposition with extant CNN and transformer-based paradigms for nuanced ship categorization.

Abstract Objectives This study aims to compare the diagnostic efficacy of metagenomic next-generation sequencing (mNGS) to traditional diagnostic methods in patients with lower respiratory tract infections (LRTIs), elucidate the etiological spectrum of these infections, and explore the impact of mNGS on guiding antimicrobial therapy. Methods We retrospectively analyzed data from 128 patients admitted to the Respiratory Department of Anqing 116 Hospital between July 2022 and July 2023. All patients had undergone both mNGS and conventional microbiological techniques (CMT) for LRTI diagnosis. We assessed the diagnostic performance of these methods and examined the influence of mNGS on antimicrobial decision-making. Results Overall, mNGS demonstrated superior sensitivity (96.8%) and accuracy (96.8%) compared to CMT. For Mycobacterium tuberculosis detection, the accuracy and sensitivity of mNGS was 88.8% and 77.6%, which was lower than the 94.7% sensitivity of the T-spot test and the 79.6% sensitivity of CMT. In fungal pathogen detection, mNGS showed excellent sensitivity (90.5%), specificity (86.7%), and accuracy (88.0%). Bacteria were the predominant pathogens detected (75.34%), with Mycobacterium tuberculosis (41.74%), Streptococcus pneumoniae (21.74%), and Haemophilus influenzae (16.52%) being most prevalent. Bacterial infections were most common (62.10%), followed by fungal and mixed infections (17.74%). Of the 118 patients whose treatment regimens were adjusted based on mNGS results, 102 (86.5%) improved, 7 (5.9%) did not respond favorably, and follow-up was lost for 9 patients (7.6%). Conclusions mNGS offers rapid and precise pathogen detection for patients with suspected LRTIs and shows considerable promise in diagnosing Mycobacterium tuberculosis and fungal infections. By broadening the pathogen spectrum and identifying polymicrobial infections, mNGS can significantly inform and refine antibiotic therapy.

This study proposes a method of autonomous navigation UAV for oil and gas pipeline (OGP) dial detection based on the improved YOLOv7 model. The canny edge detection algorithm is applied in identifying the edges of the pipeline, and the Hough transform algorithm is used to detect the pipeline in a straight line. The intelligent UAV P600 is guided to patrol the oil and gas dials (OGD) along the pipeline, and the trained improved YOLOv7-based model is adopted to identify the OGD data. Dial recognition is divided into two stages, that is, dial contour detection and dial reading recognition. For the dial recognition rate (RR), the Levenstein distance, a commonly used method, is introduced, thereby calculating the distance between two character sequences. Meanwhile, an integrated global attention mechanism (GAM) is proposed based on the YOLOv7 model, aiming at extracting more informative features. With this mechanism, the channel and spatial aspects of the features are effectively captured, and the importance of cross-dimensional interactions is increased. By introducing GAM attention mechanism in the backbone and head of YOLOv7, the network’s ability in efficiently extracting depth and primary features is enhanced. ACmix (a hybrid model combining the advantages of self-attentiveness and convolution) is also included, with ACmix module improved. The improved ACmix module has the objectives of enhancing feature extraction capability of backbone network and accelerating network convergence. By substituting 3 × 3 convolutional block with 3 × 3 ACmixBlock and adding a jump connection and a 1 × 1 convolutional structure between the ACmixBlock modules, E-ELAN module in YOLOv7 network is also improved, thus optimizing E-ELAN network, enriching features extracted by E-ELAN network, and reducing inference time of YOLOv7 model. As indicated by comparing the experimental results of the six model algorithms (improved YOLOv7, YOLOv7, YOLOX, YOLOv5, YOLOv6 and Faster R-CNN), the improved YOLOv7 model has higher mAP, faster RR, faster network convergence, and higher IOU. In addition, a generic real dataset, called custom dial reading dataset, is presented. With well-defined evaluation protocol, this dataset allows for a fair comparison of various methods in future work.

BackgroundMany studies have explored the risk factors associated with cognitive impairment in patients with Type 2 diabetes mellitus (T2DM). However, research on determining the optimal threshold for these risk factors and comparative studies on the therapeutic effects of insulin and metformin is limited. This study aims to establish the optimal threshold for cognitive impairment risk factors in T2DM patients and compare the efficacy of insulin and metformin in treating mild cognitive impairment (MCI).MethodsA total of 308 patients with T2DM were included. The optimal threshold for cognitive impairment risk factors was determined using receiver operating characteristic curve and binary logistic regression models. MCI patients were divided into three groups: insulin, metformin, and insulin with metformin. The treatment effect was evaluated after a 6-month follow-up.ResultsThe study identified several factors that influenced cognitive function in T2DM patients, including female gender, duration of diabetes >13.50 years, years of education >7.50 years, and serum sodium level > 141.90 mmol/L. Metformin and insulin with metformin showed superior therapeutic effects compared to insulin alone, but no difference was observed between metformin and combination therapy.ConclusionSpecial attention should be given to female and those with diabetes duration >13.50 years, as well as to individuals with educational level ≤ 7.50 years and serum sodium concentration ≤ 141.90 mmol/L. Metformin and insulin with metformin effectively improve MCI in patients with T2DM and outperform insulin monotherapy. The efficacy of metformin and combination therapy was found to be comparable.

The attention mechanism can extract salient features in images, which has been proved to be effective in improving the performance of person re-identification (Re-ID). However, most of the existing attention modules have the following two shortcomings: On the one hand, they mostly use global average pooling to generate context descriptors, without highlighting the guiding role of salient information on descriptor generation, resulting in insufficient ability of the final generated attention mask representation; On the other hand, the design of most attention modules is complicated, which greatly increases the computational cost of the model. To solve these problems, this paper proposes an attention module called self-supervised recalibration (SR) block, which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask. In particular, a special ''Squeeze-Excitation'' (SE) unit is designed in the SR block to further process the generated intermediate masks, both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels. Furthermore, we combine the most commonly used ResNet-50 to construct the instantiation model of the SR block, and verify its effectiveness on multiple Re-ID datasets, especially the mean Average Precision (mAP) on the Occluded-Duke dataset exceeds the state-of-the-art (SOTA) algorithm by 4.49%.

Magnitude estimation is a critical task in seismology, and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution. In this context, we propose the Earthquake Graph Network (EQGraphNet) to enhance the performance of single-station magnitude estimation. The backbone of the proposed model consists of eleven convolutional neural network layers and ten RCGL modules, where a RCGL combines a Residual Connection and a Graph convolutional Layer capable of mitigating the over-smoothing problem and simultaneously extracting temporal features of seismic signals. Our work uses the STanford EArthquake Dataset for model training and performance testing. Compared with three existing deep learning models, EQGraphNet demonstrates improved accuracy for both local magnitude and duration magnitude scales. To evaluate the robustness, we add natural background noise to the model input and find that EQGraphNet achieves the best results, particularly for signals with lower signal-to-noise ratios. Additionally, by replacing various network components and comparing their estimation performances, we illustrate the contribution of each part of EQGraphNet, validating the rationality of our approach. We also demonstrate the generalization capability of our model across different earthquakes occurring environments, achieving mean errors of ±0.1 units. Furthermore, by demonstrating the effectiveness of deeper architectures, this work encourages further exploration of deeper GNN models for both multi-station and single-station magnitude estimation.

ABSTRACTEffectiveness of heterologous booster regimes with ad5 vectored COVID-19 vaccine in a large, diverse population during the national-scale outbreak of SARS-CoV-2 omicron predominance in China has not been reported, yet. We conducted a large-scale cohort-control study in six provinces in China, and did a retrospective survey on the COVID-19 attack risk during this outbreak. Participant aged ≥18 years in five previous trials who were primed with 1 to 3 doses of ICV received heterologous booster with either intramuscular or orally inhaled ad5 vectored COVID-19 vaccine were included in the heterologous-trial cohort. We performed propensity score-matching at a ratio of 1:4 to match participants in the heterologous-trial cohort individually with the community individuals who received three-dose of ICV as a control (ICV-community cohort). From February 4 to April 10, 2023, 41504 (74.5%) of 55710 individuals completed the survey. The median time since the most recent vaccination to the onset of the symptoms of COVID-19 was 303.0 days (IQR 293.0-322.0). The attack rate of COVID-19 in the heterologous-trial cohort was 55.8%, while that in the ICV-community cohort was 64.6%, resulting in a relative effectiveness of 13.7% (95% CI 11.9 to 15.3). In addition, a higher relative effectiveness against COVID-19 associated outpatient visits, and admission to hospital was demonstrated, which was 25.1% (95% CI 18.9 to 30.9), and 48.9% (95% CI 27.0 to 64.2), respectively. The heterologous booster with ad5 vectored COVID-19 vaccine still offered some additional protection in preventing COVID-19 breakthrough infection versus homologous three-dose regimen with ICV, 10 months after vaccination.

ABSTRACTThis study employed sero-epidemiological methods to estimate the incidence of pertussis within a healthy population located in eastern China. The aim was to gain deeper insights into the epidemiological characteristics and burden of pertussis within the country. Blood samples were collected from healthy individuals in Jiangsu Province between June 2019 and December 2022. The levels of IgG antibodies against pertussis toxin (anti-PT) and filamentous hemagglutinin (anti-FHA) in the serum were quantitatively measured using enzyme-linked immunosorbent assay (ELISA). Additionally, pertussis case data reported in Jiangsu Province were collected from the China Information System for Disease Control and Prevention and compared with the results of this study. In 2022, the reported incidence of pertussis stood at 1.0 per 100,000 individuals, marking the highest rate observed in the past two decades. Among 1,909 patients examined, the geometric mean concentration (GMC) of anti-PT IgG antibody was 20.2 (18.5–21.9) IU/ml, while that of anti-FHA IgG antibody was 27.0 (25.4–28.7) IU/ml. The IgG-PT and IgG-FHA seropositivity rate (>20.0 IU/ml) was highest in the 1 ~ 2 y old group and decreased rapidly to the lowest in the 3 ~ 4 y old group and then increased gradually with age. The estimated rate of pertussis infection based on seroprevalence was approximately 25,625‐fold higher than the reported notification rate in the ≥15 year age group. Our findings highlight decreased immunity post-vaccination, stressing the importance of additional booster shots for adolescents and adults to maintain immunity and reduce severe illness. Additionally, they offer vital guidance for policymakers to enhance immunization strategies.

Tuning the surface properties of catalysts is an effective method for accelerating water electrolysis. Herein, we propose a directional doping and interfacial coupling strategy to design two surface-functionalized Schottky junction catalysts for coordinating the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Directional doping with B/S atoms endows amphiphilic g-C3N4 with significant n-/p-type semiconductor properties. Further coupling with Fe3C modulates the energy band levels of B–C3N4 and S–C3N4, thus resulting in functionalized Schottky junction catalysts with specific surface-adsorption properties. The space-charge region generated by the dual modulation induces a local “OH−- and H+-enriched” environment, thus selectively promoting the kinetic behavior of the OER/HER. Impressively, the designed B–C3N4@Fe3C||S–C3N4@Fe3C pair requires only a low voltage of 1.52 ​V to achieve efficient water electrolysis at 10 ​mA ​cm−2. This work highlights the potential of functionalized Schottky junction catalysts for coordinating redox reactions in water electrolysis, thereby resolving the trade-off between catalytic activity and stability.

ObjectiveTo investigate the feasibility and evaluate the safety and effectiveness of Computed Tomography (CT) guided125I radioactive particle implantation for treating lymph node metastases in radioiodine-refractory differentiated thyroid cancer (RAIR-DTC). To verify the accuracy of the computerized three-dimensional treatment planning system (TPS) in treating lymph node metastasis using125I particle implantation at the dosimetric level.MethodsA retrospective analysis was conducted on 42 patients with RAIR-DTC and lymph node metastases who were admitted to the General Hospital of the Northern Theater Command between December 2016 and January 2019. During this analysis, physicians utilized preoperative CT images to design an intraoperative plan using TPS. The dosimetric parameters of the postoperative plan were then compared to the preoperative plan. Additionally, this study examined the changes in tumor size and tumor-related marker Thyroglobulin (Tg) values in patients at 2, 6, and 12 months after the operation.ResultsThe number of125I radioactive particles implanted in 42 patients was 226, with an average of 14.5 (range 2.0–30.0) particles implanted per lesion. The local remission rates were 97.62% (41/42), 88.10% (37/42), and 85.71% (36/42) at 2, 6, and 12 months postoperatively, respectively. The volume of the lesions was (4.44 ± 1.57) cm3, (4.20 ± 1.70) cm3, and (4.23 ± 1.77) cm3at 2, 6, and 12 months after treatment, respectively, which significantly decreased from the preoperative baseline level of (6.87 ± 1.67) cm3(t-values: 9.466, 9.923, 7.566, all P0.05], and postoperative dose parameters delivered to 100% of the gross tumor volume (GTV)(D100) (6,881.5 ± 1,381.8) cGy, the volume percentages of GTV receiving 150% of the prescribed dose(V150) (58.5 ± 18.40)%) were lower than the preoperative plan D100 (8,085.8 ± 2,330.0) cGy, V150 (66.5 ± 17.70)%; t-value=8.913 and 3.032, both P0.05).ConclusionReferring to the TPS preoperative plan, the125I particle implantation therapy for RAIR-DTC lymph node metastasis can achieve the expected dose distribution, ensuring precise short-term local tumor control efficacy.

Pathological scars (PS), including hypertrophic scars (HTS) and keloids, are a common complication of poor wound healing that significantly affects patients’ quality of life. Currently, there are several treatment options for PS, including surgery, drug therapy, radiation therapy, and biological therapy. However, these treatments still face major challenges such as low efficacy, high side effects, and a high risk of recurrence. Therefore, the search for safer and more effective treatments is particularly urgent. New materials often have less immune rejection, good histocompatibility, and can reduce secondary damage during treatment. New technology can also reduce the side effects of traditional treatments and the recurrence rate after treatment. Furthermore, derivative products of new materials and biomaterials can improve the therapeutic effect of new technologies on PS. Therefore, new technologies and innovative materials are considered better options for enhancing PS. This review concentrates on the use of two emerging technologies, microneedle (MN) and photodynamic therapy (PDT), and two novel materials, photosensitizers and exosomes (Exos), in the treatment of PS.

The cigarette filter is an essential component of modern cigarettes and studying the flow distribution within the cigarette filter is of great significance in reducing the harm of cigarettes and optimizing smoking sensations. As the object of numerical simulation research, a three-dimensional model of the cigarette was accurately constructed through micro-CT reverse engineering, achieving a scanning accuracy of 4.05 μm. An overall porous media model of the cigarette filter was established to characterize the pressure distribution inside the filter. Based on the three-dimensional reconstruction, a local simulation model of the cavity-filtered filter was created by extracting a 1/36 geometric model. The simulation results of the overall porous media model of the cigarette filter were used as the pressure boundary conditions for the local simulation model of the cavity-filtered filter, and the effects of the wrapped paper and cavity on the flow field were analyzed. The results show that the simulated pressure drop in the overall porous media model of the cigarette filter had a deviation of less than 3.5% compared to the experimental results. This suggests that the porous media model can effectively predict the changes in pressure drop within the filter. When both wrapped paper and cavity were present, the velocity at the interface between acetate fiber and wrapped paper increased by 141.54%, while the pressure approached 0 Pa. Similarly, at the interface between acetate fiber and cavity, the velocity increased by 130.77%. It indicates that both wrapped paper and cavity significantly influenced the flow field characteristics within the cigarette filter. Additionally, as the porosity of the wrapped paper gradually increased from 0.69 to 0.99 in the radial direction, the fluid velocity increased by 14.46%, while the fluid pressure decreased by 29.09%. These changes were particularly evident when the porosity was below 0.87.

To augment the accuracy, stability, and qualification rate of wind power prediction, thereby fostering the secure and economical operation of wind farms, a method predicated on quadratic decomposition and multi-objective optimization for ultra-short-term wind power prediction is proposed. Initially, the original wind power signal is decomposed using a quadratic decomposition method constituted by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Fuzzy Entropy (FE), and Symplectic Geometry Mode Decomposition (SGMD), thereby mitigating the randomness and volatility of the original signal. Subsequently, the decomposed signal components are introduced into the Deep Bidirectional Long Short-Term Memory (DBiLSTM) neural network for time series modeling, and the Sand Cat Swarm Optimization Algorithm (SCSO) is employed to optimize the network hyperparameters, thereby enhancing the network’s predictive performance. Ultimately, a multi-objective optimization loss that accommodates accuracy, stability, and grid compliance is proposed to guide network training. Experimental results reveal that the employed quadratic decomposition method and the proposed multi-objective optimization loss can effectively bolster the model’s predictive performance. Compared to other classical methods, the proposed method achieves optimal results across different seasons, thereby demonstrating robust practicality.

The concrete deck at the negative bending moment region of a continuous steel-concrete composite girder bridge is the weakest part of the structure. Introducing ultra-high performance concrete (UHPC) to the hogging region may overcome the shortage and break through the bottleneck. This paper explores the cracking performance of steel-concrete composite girders with concrete slabs topped by a thin layer of UHPC subjected to a negative bending moment.Areal continuous composite girder bridge is briefly introduced as the engineering background, and the cracking characteristic of the concrete deck over the middle piers of the bridge is numerically modeled. Approaches to strengthen the cracking performance of the concrete deck at the hogging region through topping UHPC overlays are proposed. The effectiveness of the approaches is examined by conducting a series of numerical and experimental tests. Numerical results indicate that the normal concrete (NC) deck near the middle forums of the bridge would crack due to the large tensile stress from negative bending moments. Replacing the top concrete with an identical-thick UHPC overlay can increase the cracking resistance of the deck under the moment. As the thickness of the UHPC overlay increased from 6.0 cm to 12.0 cm, the maximum shear stress at the UHPC overlay-to-NC substrate interface under different load combinations was decreased by 56.3%~65.3%. Experimental results show that the first-cracking load of the composite beam usingan NC-UHPC overlaid slab was 2.1 times that using an NC slab. The application of a UHPC overlaid deck can significantly improve the crack performance of the steel-concrete composite girder bridge.

Seismic inversion, such as velocity and impedance, is an ill-posed problem. To solve this problem, swarm intelligence (SI) algorithms have been increasingly applied as the global optimization approach, such as differential evolution (DE) and particle swarm optimization (PSO). Based on the well logs, the sparse probability distribution (PD) of the reflectivity distribution is spatial stationarity. Therefore, we proposed a general SI scheme with constrained by a priori sparse distribution of the reflectivity, which helps to provide more accurate potential solutions for the seismic inversion. In the proposed scheme, as two key operations, the creating of probability density function library and probability transformation are inserted into standard SI algorithms. In particular, two targeted DE-PD and PSO-PD algorithms are implemented. Numerical example of Marmousi2 model and field example of gas hydrates show that the DE-PD and PSO-PD estimate better inversion solutions than the results of the original DE and PSO. In particular, the DE-PD is the best performer both in terms of mean error and fitness value of velocity and impendence inversion. Overall, the proposed SI with sparse distribution scheme is feasible and effective for seismic inversion.

Strategies to improve the efficiency of piezoelectric catalysis have long focused on piezo-optical coupling and construction of heterojunctions. However, it is a challenge to reinforce the performance of piezoelectric catalysis in a single material. Herein the built-in nanopores in single-crystal ZnO rods are employed to form stress to intensify piezo-catalytic efficiency. The piezo-catalytic efficiency of the ZnO rods with built-in nanopores (holey ZnO NRs) for degrading dyes was about 1.7 times that of the ZnO rods without built-in nanopores (ZnO NRs). X-ray diffraction and Raman peaks of holey ZnO NRs appeared blue-shifted in comparison to ZnO NRs, uncovering the existence of tensile stress in holey ZnO NRs. The piezoelectric coefficient d33 of holey ZnO NRs increased by 1.92 times, triggering the amplification of piezoelectric catalytic property. Additionally, the piezoelectric current, carrier lifetime, and diffusion length of holey ZnO NRs were larger than that of ZnO NRs, respectively. These factors all contribute to the enhanced piezoelectric catalytic efficiency of holey ZnO NRs. This work demonstrates that the method of induced stress with built-in nanopores is a promising strategy for improving the piezoelectric catalytic efficiency of single-crystal ZnO rods.

Objectives: During the COVID-19 pandemic, there was a decline in vaccine coverage, and the implementation of combined vaccines and co-administration strategies emerged as potential solutions to alleviate this predicament. Our objective is to delve into the concurrent administration of the sabin-strain-based inactivated poliovirus vaccine (sIPV), the diphtheria-tetanus-acellular pertussis vaccine (DTaP), and measles-mumps-rubella vaccine (MMR), with the intention of bridging the evidentiary gap pertaining to vaccine co-administration in Chinese infants, and to ensure a safe and effective vaccination strategy, ultimately leading to an augmentation in immunization coverage. Methods: This study was a follow-up trial of the “Immunogenicity and safety of concomitant administration of the sIPV with the DTaP vaccine in children: a multicenter, randomized, non-inferiority, controlled trial.” Blood samples were collected on day 0 and day 30, and serum antibody levels were detected to measure antibody responses to each of the antigens. Local and systemic adverse events were monitored and compared among groups. This study is the first to fill the knowledge gap in China regarding the safe and effective combined vaccination of sIPV, DTaP, and MMR vaccines. Results: The geometric mean titer of the poliovirus types I, II, and III neutralizing antibodies were 1060.22 (95% CI: 865.73-1298.39), 1537.06 (95% CI: 1324.27-1784.05), and 1539.10 (95% CI: 1296.37-1827.29) in group I on day 30; geometric mean titer of antibodies against DTaP and MMR in the simultaneous vaccination group was non-inferior to those in the DTaP alone and MMR alone group. Reporting rates of local and systemic adverse reactions were similar between groups and no serious adverse events were reported throughout the clinical study period. Conclusion: Co-administration of the sIPV, DTaP, and MMR was safe and did not impact immunogenicity, which would help to mitigate administrative costs and enhance vaccine coverage rates.

Lignocellulose nanofibers (LCNFs) as a new material is attracting extensive attention. The pretreatment and mechanical fibrillation are the two main stages involved in the preparation of LCNFs, and lignin plays the important role of these two stages. This review discussed the interaction between lignin and chemicals in the pretreatment stage, and discovered the general law of the effect of lignin in the mechanical fibrillation stage. Lignin exhibits both promotion and inhibition effects on mechanical fibrillation, and the mutual competition between the two effects ultimately affects the energy consumption, morphology and yield of LCNFs. Furthermore, the recent research progress related to the contributions of lignin on the functional application of LCNFs was summarized, aiming to provide profound guidance for the preparation and application of LCNFs.

Purposes A deep understanding of the pore structure of cement paste is important for improving the performance of cement-based materials. Methods In this paper, terahertz time-domain spectroscopy (THz-TDS) system was used to test the terahertz time-domain signal of dry cement paste with water cement ratio of 0.3, 0.4, 0.5 and hydration ages of 7, 28 and 56 d in the frequency range of 0.2~2 THz. The refractive index and dielectric constant of cement paste were calculated by using the delay time of time-domain signal. Findings The test results show that the dielectric constant of cement paste decreases with the increase of water-cement ratio at the same age. The dielectric constant of cement paste with the same water cement ratio does not change obviously with age. The relationship between the effective dielectric constant and pore structure of cement paste is established by using the General Effective Media (GEM) model. Through the fitting of experimental data, the dielectric constant of the solid phase of hydration products in cement paste is 8.73. Conclusions According to the GEM model parameter analysis, the porosity of cement paste is the most important factor affecting its effective dielectric constant.

Abstract Multi-object tracking (MOT) aims to locate and identify objects in videos. As deep learning brings excellent performances to object detection, the tracking-by-detection (TBD) has gradually become a mainstream tracking framework. However, some drawbacks still exist in the current TBD framework: (1) inaccurate prediction of the bounding boxes would occur in the detection part, which is caused by overlooking the actual pedestrian ratio in the surveillance scene. (2) The width of the bounding boxes in the next frame might be indirectly predicted by the aspect ratio, which increases the error of width prediction in the motion prediction part. (3) Association is only performed for high-confidence detection boxes, and the low-confidence boxes caused by occlusion are discarded in the data association part, resulting in fragmentation of trajectories. To address the above issues, we propose a multi-target tracking model incorporating motion estimation and multi-stage association (MEMA). First, the aspect ratio of the ground-true bounding box is introduced to improve the fit of the detection and the ground-true bounding box, and we design the elliptical Gaussian kernel to improve the positioning accuracy of the object center point. Then, the prediction state vector of the Kalman filter is modified to predict the width and its corresponding velocity directly. It can reduce the width error of the prediction box and eliminate the velocity error of the motion estimation, which leads to a more pedestrian-friendly prediction bounding box. Finally, we propose a multi-stage association strategy to correlate different confidence boxes. Without using the appearance feature, the strategy can reduce the impact of occlusion and improve the tracking performance. On the MOT17 test set, the method proposed in this paper achieves a MOTA of 74.3% and an IDF1 of 72.4%, outperforming the current SOTA.

In this paper, we introduce a multi-modal sensor fusion system designed for biomedical engineering, specifically geared toward optimizing physical training by collecting detailed body movement data. This system employs inertial measurement units, flex sensors, electromyography sensors, and Microsoft's Kinect V2 to generate an in-depth analysis of an individual's physical performance. We incorporate a gated recurrent unit- recurrent neural network algorithm to achieve highly accurate body and hand motion estimation, thus surpassing the performance of traditional machine learning algorithms in terms of accuracy, precision, recall, and F1 score. The system's integration with the PICO 4 VR environment creates a rich, interactive experience for physical training. Unlike conventional motion capture systems, our sensor fusion system is not limited to a fixed workspace, allowing users to engage in exercise within a flexible, free-form environment.

Abstract Tight carbonate reservoirs in the Western Qaidam Basin have complex lithologies and pore structures. The oil–water mobility law in reservoirs has not yet been completely determined, restricting the formulation of rational reservoir development methods. To bridge this gap, in this study, we used several test methods, such as casting thin sections, mercury intrusion, and nuclear magnetic resonance, to obtain the pore structure and oil–water displacement characteristics of tight carbonate reservoirs in the Western Qaidam Basin. The pore structures of the reservoirs could be categorized into three types: microfractures + dissolved pores + micropores (MFD), microfractures + micropores (MF), and matrix (M). The characteristics of single‐phase oil seepage and water flooding in reservoirs with various pore structures differed evidently. For the MF‐ and M‐types, the water‐locking effect caused by the strong capillary force affected oil charging in the micropores. The effect of the pressure drop on the MFD‐type algal limestone was less than that on the MF‐type limestone (dolomite) because of the occurrence of a non‐Darcy flow. The MFD‐type, which contained microfractures, had preferential seepage channels, resulting in obvious fluid channeling and low water displacement efficiency. Oil−water displacement mainly occurred in the dissolved pores and microfractures, suggesting that starting oil accumulation in the micropores was crucial. This study will assist in efficient development of tight carbonate reservoirs in the Western Qaidam Basin.

Abstract Contactless bubble manipulation with a high spatiotemporal resolution brings a qualitative leap forward in a variety of applications. Despite considerable advances, light-induced bubble maneuvering remains challenging in terms of robust transportation, splitting and detachment. Here, a photopyroelectric slippery surface (PESS) with a sandwich structure is constructed to achieve the versatile bubble manipulation. Due to the generated dielectric wetting and nonuniform electric field under the irradiation of near infrared (NIR) light, a bubble is subject to both the Laplace force and dielectrophoresis force, enabling a high-efficiency bubble steering. We demonstrate that the splitting, merging and detachment of underwater bubbles can be achieved with high flexibility and precision, high velocity and agile direction maneuverability. We further extend the capability of bubble control to microrobots for cargo transportation, micropart assembly and transmission of gear structures. We envision this robust bubble manipulation strategy on the PESS would provide a valuable platform for various bubble-involved processes, ranging from microfluidic devices to soft robotics.

This study explores the learning effects of color cues in video lectures and their underlying mechanisms. With the rapid growth of online education, lifelong learning, and blended learning, video lectures have become integral to teaching and learning. Color, a crucial element in visual design, directs attention, organizes content, and integrates information. Evaluating 78 college students, we assessed learning performance by comparing video content with no-color, single-color, and multi-color cues using eye-tracking technology and cognitive load scales. Results indicate that students viewing videos with color cues demonstrated better retention and transfer test performance, while absence or excess of color cues increased cognitive load. These findings have practical implications for video producers and provide a theoretical foundation for enhancing learners’ viewing experience and overall effectiveness. This study not only offers an in-depth analysis of color cue utilization in video lectures, highlighting their positive impact on learning outcomes but also introduces fresh perspectives for educational technology and cognitive psychology research. Future investigations should consider color cue effects in diverse cultural contexts and subject areas, exploring varied strategies to optimize the learning experience.

Flexoelectric effect describes the electromechanical coupling between the strain gradient and its internal polarization in all dielectrics. Despite this universality, the resulting flexoelectric field remains small at the macroscopic level. However, in nanosystems, the size-dependent effect of flexoelectricity becomes increasingly significant, leading to a notable flexoelectric field that can strongly influence the material’s physical properties. This review aims to explore the flexoelectric effect specifically at the nanoscale. We achieve this by examining strain gradients generated through two distinct methods: internal inhomogeneous strain and external stimulation. In addition, advanced synthesis techniques are utilized to enhance the properties and functionalities associated with flexoelectricity. Furthermore, we delve into other coupled phenomena observed in thin films, including the coupling and utilization of flexomagnetic and flexophotovoltaic effects. This review presents the latest advancements in these areas and highlights their role in driving further breakthroughs in the field of flexoelectricity.

Although Severe Acute Respiratory Syndrome Coronavirus 2 infection (SARS-CoV-2) is primarily recognized as a respiratory disease, mounting evidence suggests that it may lead to neurological and cognitive impairments. The current study used three eye-tracking tasks (free-viewing, fixation, and smooth pursuit) to assess the oculomotor functions of mild infected cases over six months with symptomatic SARS-CoV-2 infected volunteers. Fifty symptomatic SARS-CoV-2 infected, and 24 self-reported healthy controls completed the eye-tracking tasks in an initial assessment. Then, 45, and 40 symptomatic SARS-CoV-2 infected completed the tasks at 2- and 6-months post-infection, respectively. In the initial assessment, symptomatic SARS-CoV-2 infected exhibited impairments in diverse eye movement metrics. Over the six months following infection, the infected reported overall improvement in health condition, except for self-perceived mental health. The eye movement patterns in the free-viewing task shifted toward a more focal processing mode and there was no significant improvement in fixation stability among the infected. A linear discriminant analysis shows that eye movement metrics could differentiate the infected from healthy controls with an accuracy of approximately 62%, even 6 months post-infection. These findings suggest that symptomatic SARS-CoV-2 infection may result in persistent impairments in oculomotor functions, and the employment of eye-tracking technology can offer valuable insights into both the immediate and long-term effects of SARS-CoV-2 infections. Future studies should employ a more balanced research design and leverage advanced machine-learning methods to comprehensively investigate the impact of SARS-CoV-2 infection on oculomotor functions.

Electrically heated tobacco products (EHTPs) could release effective aerosol components from tobacco materials at relatively low temperatures without a burning phenomenon. It is essential to grasp the temperature distribution and release mechanism of key components in heated tobacco materials. The existing experimental studies have provided initial insights into the thermodynamic behavior of tobacco materials under various conditions. However, current numerical models are still in their early stages of development, with the majority failing to correlate heat transfer with component release. Based on this, a coupled numerical model of gas flow, heat transfer, and the release of key components in the electrically heated tobacco product is established in this study, which exhibits improvements in revealing the internal heat and mass transfer characteristics in the porous media of tobacco and is capable of evaluating the influence of component contents and product design parameters. The release rates of water, glycerol, and nicotine components are quantitatively described by the first-order Arrhenius formula, and the transport of heat and gas flow is simulated using the Navier-Stokes equation. The accuracy of the model is validated through experiments, including temperature monitoring at multiple measurement points and determination of residual contents in the tobacco substrate after each puff. The simulation results suggest that an appropriate component ratio and tobacco filler mass can enhance both the release amount and release efficiency of key components, and reducing either the diameter or length of the tobacco section can help to improve the heat transfer performance. A slower heating rate matched with longer preheating times enables the complementary release of water and glycerol components, which helps to regulate the uniformity of component content in the aerosol to some extent. This study helps to provide suggestions for the design and optimization of electrically heated tobacco products.

Background: This study presents the initial case of phlegmonous enteritis following endoscopic submucosal dissection (ESD), a rare and potentially fatal complication. Additionally, a comprehensive review of relevant literature is provided. Case report: A 66-year-old female patient, diagnosed with Hashimoto's thyroiditis and thrombocytopenia, underwent ESD to address a laterally spreading tumor located in the ascending colon. After the procedure, the patient manifested abdominal pain and a high fever, was diagnosed with peritonitis, necessitating an emergency exploratory laparotomy and right hemicolectomy. Subsequent histological examination indicated a significant presence of neutrophil infiltration across all layers of the intestines. The ascites culture yielded the growth of Escherichia coli. Literature review: A search was conducted in the PubMed database to identify case reports conforming to the definition of phlegmonous enteritis proposed by Rokitansky et al. We retrieved about 30 studies regarding phlegmonous enteritis from 1951 to 2022, with around 39 cases. Among these, only 28 patients had comprehensive medical data available. Subsequently, an examination of the literature was undertaken to explore the pathogenesis, prevention, and treatment of phlegmonous enteritis. Conclusion: The possibility of phlegmonous enteritis should be taken into consideration in cases of unexplained acute abdomen, particularly in patients with compromised immunity, in order to provide active surgical and antibiotic interventions.

Background: The incidence of hepatitis B virus (HBV) has decreased year by year in China after the expansion of vaccination, but there is still a high disease burden in Jiangsu Province of China. Methods: The year-by-year incidence data of HBV in Jiangsu Province from 1990 to 2021 were collected. The incidence rates of males and females age groups were clustered by systematic clustering, and the incidence rates of each age group were analyzed and studied by using Joinpoint regression model and age-period-cohort effect model (APC). Results: Joinpoint regression model and APC model showed a general decrease in HBV prevalence in both males and females. In addition, the results of the APC model showed that the age, period, and cohort effects of patients all affected the incidence of HBV, and the incidence was higher in males than in females. The incidence is highest in the population between the ages of 15 and 30 years (mean: 21.76/100,000), especially in males (mean: 31.53/100,000) than in females (mean:11.67/100,000). Another high-risk group is those over 60 years of age (mean: 21.40/100,000), especially males (mean: 31.17/100,000) than females (mean: 11.63/100,000). The period effect of the APC model suggests that HBV vaccination is effective in reducing the incidence of HBV in the population. Conclusions: The incidence of HBV in Jiangsu Province showed a gradual downward trend, but the disease burden in males was higher than that in females. The incidence is higher and increasing rapidly in the population between the ages of 15 and 30 years and people over 60 years of age. More targeted prevention and control measures should be implemented for males and the elderly.

As one of the important lakes in the “One Lake and Two Seas” of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake.

The main aim of this study was to utilize remote sensing data to establish regression models through machine learning to predict locust density in the upcoming year. First, a dataset for monitoring grassland locust density was constructed based on meteorological data and multi-source remote sensing data in the study area. Subsequently, an SVR (support vector regression) model, BP neural network regression model, random forest regression model, BP neural network regression model with the PCA (principal component analysis), and deep belief network regression model were built on the dataset. The experimental results show that the random forest regression model had the best prediction performance among the five models. Specifically, the model achieved a coefficient of determination (R2) of 0.9685 and a root mean square error (RMSE) of 1.0144 on the test set, which were the optimal values achieved among all the models tested. Finally, the locust density in the study area for 2023 was predicted and, by comparing the predicted results with actual measured data, it was found that the prediction accuracy was high. This is of great significance for local grassland ecological management, disaster warning, scientific decision-making support, scientific research progress, and sustainable agricultural development.

As one of the most important and economically enhanced oil-recovery technologies, water flooding has been applied in various oilfields worldwide for nearly a century. Stratified water injection is the key to improving water-flooding performance. In water flooding, the water-injection rate is normally optimized based on the reservoir permeability and thickness. However, this strategy is not applicable after oilfields enter the ultra-high-water-cut period. In this study, an original method for optimizing water-flooding parameters for developing multi-layer sandstone reservoirs in the entire flooding process and in a given period is proposed based on reservoir engineering theory and optimization technology. Meanwhile, optimization mathematical models that yield maximum oil recovery or net present value (NPV) are developed. The new method is verified by water-flooding experiments using Berea cores. The results show that using the method developed in this study can increase the total oil recovery by approximately 3 percent compared with the traditional method using the same water-injection amounts. The experimental results are consistent with the results from theoretical analysis. Moreover, this study shows that the geological reserves of each layer and the relative permeability curves have the greatest influence on the optimized water-injection rate, rather than the reservoir properties, which are the primary consideration in a traditional optimization method. The method developed in this study could not only be implemented in a newly developed oilfield but also could be used in a mature oilfield that has been developed for years. However, this study also shows that using the optimized water injection at an earlier stage will provide better EOR performance.

Abstract The disaster information collection mission should be executed after the disaster occurs to provide details for the decision-makers. During the execution of the information collection mission, some disruptions may occur and prevent the resource used for information collection from completing the mission as planned. It is difficult for decision-makers to make reactive resource scheduling plan that optimize the mission’s execution time, quality, and cost at the same time under such circumstances. This article focuses on designing the reactive decision support algorithm for the disaster information collection resource scheduling, which aims to provide multi high-quality scheduling plans for decision-makers to choose. The problem studied in this article is modeled as an extension of Resource-Constrained Project Scheduling Problem (RCPSP). First, the basic problem formulation for a normal schedule and two disruption recovery models are presented. Second, a novel framework of a parallel pareto local search based on decomposition is designed to repair the schedule within the time limit. Third, two solution acceptance criteria based on constraint handling and negative correlation are specially designed to maintain high-quality population with diversity. The experiments show that the proposed method outperforms the other competitors with respect to Inverted Generational Distance, Spacing, and Hypervolume, which means that the proposed method can help decision-makers to make better decisions.

IntroductionDespite the growing body of evidence, the link between the gut microbiota and different types of tumors, such as colorectal, gastric, and liver cancer, is becoming more apparent. The gut microbiota can be used as a reference for evaluating various diseases, including cancer, and can also act as risk factors or preventive factors. However, the specific connection between the gut microbiota and the advancement of esophageal cancer has yet to be investigated. Therefore, the aim of this research is to clarify the possible causal influence of intestinal microorganisms on the vulnerability to esophageal cancer through the utilization of Mendelian randomization (MR) studies.MethodsIn this study, we employed a two-sample Mendelian randomization approach to evaluate the unbiased causal association between 150 different gut microbiota types and the occurrence of esophageal cancer. Following the selection from the IEU GWAS database and SNP filtration, we utilized various MR statistical techniques on the suitable instrumental variables. These included IVW methods, employing inverse variance weighting. Additionally, we performed a range of sensitivity analyses to confirm the heterogeneity and pleiotropy of the instrumental variables, thus ensuring the reliability of the outcomes.ResultsThe increased likelihood of developing esophageal cancer is linked to the genetically predicted high levels of Gordonibacter, Oxalobacter, Coprobacter, Veillonella, Ruminiclostridium 5, Ruminococcus 1, and Senegalimasilia genera. Conversely, a decreased risk of esophageal cancer is associated with the high abundance of Turicibacter, Eubacterium oxidoreducens group, Romboutsia, and Prevotella 9 genera. No heterogeneity and pleiotropy were detected in the sensitivity analysis.DiscussionWe found that 11 types of gut microbial communities are associated with esophageal cancer, thereby confirming that the gut microbiota plays a significant role in the path.

In the atomic cell of the spin exchange optical pumping system, the optical pumping inevitably introduces a magnetic field gradient, which affects the measurement result of the nuclear spin relaxation time a lot. To address this issue, our method achieves the measurement while excluding the influence of this magnetic field gradient. In this method, the pump laser is turned off after the spins of 87Rb and 129Xe have been effectively polarized, which results in the spin polarization of the 87Rb atoms being provided only by the spin exchange with 129Xe. Ultimately, the transverse and longitudinal components of the polarization of 129Xe are detected by the in situ 87Rb atom magnetometer to obtain the T1 and T2 of 129Xe under different conditions. Using our method, since the influence of inhomogeneity caused by the pump laser is eliminated, the relaxation characteristic of atomic cells can be measured more quickly, essentially, and conveniently, which helps compare the performance of different atomic cells and find the best cell fabrication process further.

BackgroundThis study is to evaluate the safety of two kinds of PCV13 carriers by monitoring the occurrence of adverse event following immunization (AEFI) after the launch of two kinds of PCV13 carriers in Jiangsu Province, China.MethodsThe AEFI Information System (CNAEFIS) of mainland China was used to monitor the incidence and classification of adverse reactions of the CRM197-carrier protein PCV13 and TT-carrier protein PCV13 vaccines.ResultsThere was no statistical difference between the cumulative reported incidence of AEFI between the two vaccines from 2020 to 2022 (χ2 = 1.991, p

Spin-based comagnetometers have essential applications in studying new physical effects such as the fifth force, Lorentz violations, and spin-gravity interactions. In this paper, a transfer function model of the spin precession phase (frequency) is derived to analyze the comagnetometer's noise mechanism. The theory combined with experiments reveals that the output frequency noise of the spin oscillator above 5 Hz is mainly phase noise from the phase-locked loop. The noise below 5 Hz is mainly from the biased magnetic field noise. When building a comagnetometer using two spin oscillators, the symmetry of the spin oscillator is significant. When the intrinsic physical parameters cannot be symmetrical, the comagnetometer's common-mode suppression capability to magnetic field fluctuations can be enhanced by optimizing the control parameters. In addition, the closed-loop control of Bz can significantly weaken the effect of system asymmetry in the low-frequency band. Finally, when considering the comagnetometer system in nuclear magnetic resonance gyroscopes, a trade-off between the bandwidth and sensitivity can be achieved using theory. This paper is an excellent reference for both the research and application of comagnetometers.

Open biomass burning (OBB) in agriculture presents a significant and well-documented challenge, posing severe consequences for both environmental and human health. OBB releases air pollutants that degrade air quality and contribute to climate change, leading to premature deaths in regions with high concentrations of open crop straw burning (OCSB) emissions. Although policies aimed at prohibiting OBB are in place, the efficacy of these regulations in mitigating OCSB emissions remains ambiguous. Consequently, early prevention and monitoring of open biomass combustion are imperative for environmental preservation. Traditional monitoring techniques, reliant on fixed-position cameras, are constrained by their location and monitoring intensity, making concealed fire recognition a complex problem. To address this limitation and monitor the human living environment more flexibly and accurately, we propose a new method to identify straw fires in UAV Aerial Image Using CNN Branch Reinforce Transformer which named BranTNet, enabling early detection and rapid response to crop straw fires. By integrating computer vision technology and deep learning algorithms, straw fires in UAV-acquired aerial survey images can be detected and categorized. In the realm of artificial intelligence algorithms, we skillfully merge convolution and attention mechanisms, harnessing the full potential of both methodologies. Moreover, we seamlessly incorporate transfer learning, skillfully unifying self-training convolution modules with pre-trained transformer modules. This strategic amalgamation not only minimizes time costs but also ensures optimal experimental outcomes. Regarding data, we meticulously collected a substantial number of authentic samples, ensuring the sufficiency of our experimental dataset. The experimental results demonstrate that our proposed method exhibits exceptional accuracy and robustness in detecting and identifying straw fires in UAV aerial survey images. Our approach outperforms the use of convolution or attention mechanisms alone. By integrating this approach with drone technology, we unlock the potential for developing more versatile and precise monitoring solutions, expanding the application of drones to diverse domains. This progress contributes significantly to the early detection and prevention of crop straw fires, fundamentally reducing environmental pollution, curbing carbon emissions, and advancing the cause of carbon neutrality. This innovative technique for monitoring and preventing OBB holds substantial promise in mitigating the adverse effects of OBB on the environment and human health.

Abstract In bearings-only localization, clustering-based methods have been widely used to remove spurious intersections by fusing multiple bearing measurements from different observation stations. Existing clustering methods, including fuzzy C-mean (FCM) clustering and density-based spatial clustering of applications with noise (DBSCAN), must specify the number of clusters and the threshold for defining the neighborhood density, respectively, which are always unknown and difficult to estimate. Moreover, in dense radiation source scenes, existing clustering methods for removal of spurious intersections all deteriorate significantly. Therefore, we propose a novel density-based clustering method called K-M-DBSCAN, which combines the minimum K distance algorithm with Mahalanobis distance-based DBSCAN clustering. Firstly, K-M-DBSCAN uses minimum K distance algorithm for preprocessing to remove most of the spurious intersections and reduce the computational complexity of clustering. Mahalanobis distance-based DBSCAN is used for clustering and spurious intersections recognition. In order to adapt the large variations of sample density in clustering, we use Mahalanobis distance to define an explicit neighborhood of DBSCAN instead of traditional Euclidean distance. Simulation results show that the proposed K-M-DBSCAN performs better than FCM and DBSCAN in removing of spurious intersections.

Abstract Background Chemotherapy resistance is an important bottleneck affecting the efficacy of chemotherapy in colon cancer. Therefore, improving the chemotherapy sensitivity of colorectal cancer cells is of great significance for improving the prognosis of patients with colon cancer. Methods CCK-8 assay was employed to examine the cell viability of colorectal cancer cell lines. Realtime-PCR and western blot were used to explore toll-like receptor 4 (TLR4) expression in colorectal cancer cell lines. The functions of TLR4 in the stemness of the colorectal cancer cell lines were analyzed by infecting cells with lentivirus containing TLR4 siRNA. Results We found that aspirin could effectively enhance the chemosensitivity of CT26 and HCT116 colorectal cancer cell lines. Aspirin can also inhibit the stemness of colorectal cancer cell including inhibiting the number of clone formation and reducing the volume and number of cell spheres and inducing the down-regulation of stemness-related genes. Besides that, aspirin also lead to down-regulation of TLR4 expression in colorectal cancer cells. The TLR4 positive colorectal cancer cells demonstrated a higher chemotherapy resistance potential than TLR4 negative colorectal cancer cells. In addition, the stemness of TLR4 positive colorectal cancer cells is stronger than TLR4 negative colorectal cancer cells. Conclusion The results of our study indicate that aspirin increases chemosensitivity of colorectal cancer cells and inhibits the expression of toll-like receptor 4.

Piceatannol (PIC) and epigallocatechin gallate (EGCG) are polyphenolic compounds with applications in the treatment of various diseases such as cancer, but their stability is poor. β-lactoglobulin (β-LG) is a natural carrier that provides a protective effect to small molecule compounds and thus improves their stability. To elucidate the mechanism of action of EGCG, PIC, and palmitate (PLM) in binding to β-LG individually and jointly, this study applied molecular docking and molecular dynamics simulations combined with in-depth analyses including noncovalent interaction (NCI) and binding free energy to investigate the binding characteristics between β-LG and compounds of PIC, EGCG, and PLM. Simulations on the binary complexes of β-LG + PIC, β-LG + EGCG, and β-LG + PLM and ternary complexes of (β-LG + PLM) + PIC, (β-LG + PLM) + EGCG, β-LG + PIC) + EGCG, and (β-LG + EGCG) + PIC were performed for comparison and characterizing the interactions between binding compounds. The results demonstrated that the co-bound PIC and EGCG showed non-beneficial effects on each other. However, the centrally located PLM was revealed to be able to adjust the binding conformation of PIC, which led to the increase in binding affinity with β-LG, thus showing a synergistic effect on the co-bound PIC. The current study of β-LG co-encapsulated PLM and PIC provides a theoretical basis and research suggestions for improving the stability of polyphenols.

The energy inside the Earth can not only be released outward through earthquakes and volcanoes but also can be used by humans in the form of geothermal energy. Is there a correlation between different forms of energy release? In this contribution, we perform detailed seismic and geothermal research in the Beijing area. The results show that the geothermal resources in Beijing belong to typical medium-low temperature geothermal resources of the sedimentary basin, and some areas are controlled by deep fault activities (e.g., Xiji geothermal well (No. 17)). The heat sources are upper mantle heat, radioactive heat in granite, and residual heat from magma cooling. The high overlap of earthquakes and geothermal field locations and the positive correlation between the injection water and earthquakes indicate that the exploitation and injection water will promote the release of the earth’s energy. The energy releases are partitioned into multiple microearthquakes, avoiding damaging earthquakes (ML ≥ 5) due to excessive energy accumulation. Therefore, the exploitation of geothermal resources may be one way to reduce destructive earthquakes. Furthermore, the use of geothermal resources can also reduce the burning of fossil energy, which is of great significance in dealing with global warming.

ABSTRACT Malonyl-CoA reductase (MCR) is a NADPH-dependent bi-functional enzyme that performs alcohol dehydrogenase and aldehyde dehydrogenase (CoA-acylating) activities in the N- and C-terminal fragments, respectively. It catalyzes the two-step reduction of malonyl-CoA to 3-hydroxypropionate (3-HP), a key reaction in the autotrophic CO2 fixation cycles of Chloroflexaceae green non-sulfur bacteria and the archaea Crenarchaeota. However, the structural basis underlying substrate selection, coordination, and the subsequent catalytic reactions of full-length MCR is largely unknown. For the first time, we here determined the structure of full-length MCR from the photosynthetic green non-sulfur bacterium Roseiflexus castenholzii (RfxMCR) at 3.35 Å resolution. Furthermore, we determined the crystal structures of the N- and C-terminal fragments bound with reaction intermediates NADP+ and malonate semialdehyde (MSA) at 2.0 Å and 2.3 Å, respectively, and elucidated the catalytic mechanisms using a combination of molecular dynamics simulations and enzymatic analyses. Full-length RfxMCR was a homodimer of two cross-interlocked subunits, each containing four tandemly arranged short-chain dehydrogenase/reductase (SDR) domains. Only the catalytic domains SDR1 and SDR3 incorporated additional secondary structures that changed with NADP+–MSA binding. The substrate, malonyl-CoA, was immobilized in the substrate-binding pocket of SDR3 through coordination with Arg1164 and Arg799 of SDR4 and the extra domain, respectively. Malonyl-CoA was successively reduced through protonation by the Tyr743–Arg746 pair in SDR3 and the catalytic triad (Thr165–Tyr178–Lys182) in SDR1 after nucleophilic attack from NADPH hydrides. IMPORTANCE The bi-functional MCR catalyzes NADPH-dependent reduction of malonyl-CoA to 3-HP, an important metabolic intermediate and platform chemical, from biomass. The individual MCR-N and MCR-C fragments, which contain the alcohol dehydrogenase and aldehyde dehydrogenase (CoA-acylating) activities, respectively, have previously been structurally investigated and reconstructed into a malonyl-CoA pathway for the biosynthetic production of 3-HP. However, no structural information for full-length MCR has been available to illustrate the catalytic mechanism of this enzyme, which greatly limits our capacity to increase the 3-HP yield of recombinant strains. Here, we report the cryo-electron microscopy structure of full-length MCR for the first time and elucidate the mechanisms underlying substrate selection, coordination, and catalysis in the bi-functional MCR. These findings provide a structural and mechanistic basis for enzyme engineering and biosynthetic applications of the 3-HP carbon fixation pathways.

BackgroundThe occurrence of surgical site infection (SSI) can prolong the postoperative hospital stay, increase the economic burden of patients, and even endanger their lives. The purpose of this study was to investigate the incidence, risk factors, and microbiology of SSI after colorectal surgery (CRS) and to provide a basis for the prevention and control of SSI.MethodsA single-center, prospective, cross-sectional study of adult patients undergoing CRS was conducted from 2010–2019. Univariate and multivariate logistic regression models were used to collect and analyze demographic information, hospital characteristics, and potential perioperative risk factors of SSI.ResultsA total of 3,302 eligible patients were included in this study, with 213 cases experiencing SSIs, resulting in an infection rate of 6.45%. Notably, the incidence of SSI decreased from 13.33% in 2010 to 3.56% in 2019 (Ptrend

This paper deals with an unstirred competitive chemostat model with the Beddington–DeAngelis functional response. With the help of the linear eigenvalue theory and the monotone dynamical system theory, we establish a relatively clear dynamic classification of this system in terms of the growth rates of two species. The results indicate that there exist several critical curves, which may classify the dynamics of this system into three scenarios: 1) extinction; 2) competitive exclusion; and 3) coexistence. Comparing with the classical chemostat model [26], our theoretical results reveal that under the weak–strong competition cases, the role of intraspecific competition can lead to species coexistence. Moreover, the simulations suggest that under different competitive cases, coexistence can occur for suitably small diffusion rates and some intermediate diffusion rates. These new phenomena indicate that the intraspecific competition and diffusion have a great influence on the dynamics of the unstirred chemostat model of two species competing with the Beddington–DeAngelis functional response.

Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering the strong potential of deep neural network for extracting data features, this paper proposes a data-driven model, ResGraphNet, which improves the prediction accuracy of time series by an embedded residual module in GraphSAGE layers. The experimental results of a global mean temperature dataset, HadCRUT5, show that compared with 11 traditional prediction technologies, the proposed ResGraphNet obtains the best accuracy. The error indicator predicted by the proposed ResGraphNet is smaller than that of the other 11 prediction models. Furthermore, the performance on seven temperature datasets shows the excellent generalization of the ResGraphNet. Finally, based on our proposed ResGraphNet, the predicted 2022 annual anomaly of global temperature is 0.74722 °C, which provides confidence for limiting warming to 1.5 °C above pre-industrial levels.

Seismic data interpolation, especially irregularly sampled data interpolation, is a critical task for seismic processing and subsequent interpretation. Recently, with the development of machine learning and deep learning, convolutional neural networks (CNNs) are applied for interpolating irregularly sampled seismic data. CNN based approaches can address the apparent defects of traditional interpolation methods, such as the low computational efficiency and the difficulty on parameters selection. However, current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data, which fail to consider the frequency features of seismic data, i.e., the multi-scale features. To overcome these drawbacks, we propose a wavelet-based convolutional block attention deep learning (W-CBADL) network for irregularly sampled seismic data reconstruction. We firstly introduce the discrete wavelet transform (DWT) and the inverse wavelet transform (IWT) to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic data. Moreover, we propose to adopt the convolutional block attention module (CBAM) to precisely restore sampled seismic traces, which could apply the attention to both channel and spatial dimensions. Finally, we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and effectiveness. The results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.