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Abstract To address the current problems of low accuracy and poor reliability of the discrete element model of cotton stalks, as well as the difficulty of guiding the design and optimization of the equipment through simulations, the discrete element modeling and physical-mechanical tests of cotton stalks in machine harvested film-stalk mixtures are carried out. The peak tensile force $$F_{\rm j}^{\max }$$ F j max , the peak pressure $$F_{\rm y}^{\max }$$ F y max , the peak bending force $$F_{\rm w}^{\max }$$ F w max , the peak shear force $$F_{\rm j}^{\max }$$ F j max , and the force-displacement (F–x) curves of cotton stalks are obtained from the physical tests. The discrete element model of double-layer cotton stalks based on the flat-joint model is established with the PFC $$^{\rm 3D}$$ 3 D software. The $$F_{\rm y}^{\max }$$ F y max is taken as the response value, and the microscopic parameters of the cotton stalk model are used as the test factors, then the Plackett–Burman test, the steepest climb test, and the Box–Behnken test are sequentially designed using Design-Expert software. The second-order regression model describing the relationship between the $$F_{\rm y}^{\max }$$ F y max and the microscopic parameters is established. The optimal parameter combinations of the microscopic parameters are obtained, and then they are utilized to construct the compression, bending, and shear models of cotton stalks and to carry out the validation tests. The results confirm that the established discrete element model could accurately characterize the biomechanical properties of cotton stalks and that the parameter calibration method is reasonable, which could provide a reference for the discrete element modeling of cotton stalks and other stalks, and also offer a theoretical basis for the research of the crushing and separation mechanism of the film-stalk mixtures and the development of the equipment.

The maintenance of host health and immune function is heavily dependent on the gut microbiota. However, the precise contribution of individual microbial taxa to regulating the overall functionality of the gut microbiome remains inadequately investigated. Chickens are commonly used as models for studying poultry gut microbiota, with high-throughput 16S rRNA sequencing has emerged as a valuable tool for assessing both its composition and functionality. The interactions between the gut’s microbial community and its host significantly influence health outcomes, disease susceptibility, and various mechanisms affecting gastrointestinal function. Despite substantial research efforts, the dynamic nature of this microbial ecosystem has led to inconsistencies in findings related to chicken gut microbiota, which is largely attributed to variations in rearing conditions. Consequently, the interaction between the chickens’ gut microflora and its host remains inadequately explored. This review highlights recent advances in understanding these relationships, with a specific focus on microbial composition, diversity, functional mechanisms, and their potential implications for improving poultry production.

Abstract Multi-agent reinforcement learning encounters a non-stationary challenge, where agents concurrently update their policies, leading to changes in the environment. Existing approaches have tackled this challenge through communication among agents to obtain their partners’ actions, but this introduces computational complexity known as partner sample complexity. An alternative approach is to develop partner models that generate samples instead of direct communication to mitigate this complexity. However, a discrepancy arises between the real policies distribution and the policy of partner models, termed as model bias, which can significantly impact performance when heavily relying on partner models. In order to achieve a trade-off between sample complexity and performance, a novel multi-agent model-based reinforcement learning algorithm called decentralized adaptive partner modeling (DAPM) is proposed, which utilizes fictitious self play (FSP) to construct partner models and update policies. Model bias is addressed by establishing an upper bound to restrict the usage of partner models. Coupled with that, an adaptive rollout approach is introduced, enabling real agents to dynamically communicate with partner models based on their quality, ensuring that agent performance can progressively improve with partner model samples. The effectiveness of DAPM is exhibited in two multi-agent tasks, showing that DAPM outperforms existing model-free algorithms in terms of partner sample complexity and training stability. Specifically, DAPM requires 28.5% fewer communications compared to the best baseline and exhibits reduced fluctuations in the learning curve, indicating superior performance.

Unmanned Surface Vehicles (USVs) are rapidly becoming mission-indispensable for a variety of naval operations, from search and rescue to environmental monitoring and surveillance. Path planning lies at the heart of the operational effectiveness of USVs, since it represents the key technology required to enable the vehicle to transit the unpredictable dynamics of the marine environment in an efficient and safe way. The paper develops a critical review of the most recent advances in USV path planning and a novel classification of algorithms according to operational complexity: Basic Pathfinders, Responsive Pathfinders, and Advanced Strategic Pathfinders. Each category can adapt to different requirements, from environmental predictability to the desired degree of human intervention, and from stable and controlled environments to highly dynamic and unpredictable conditions. The review includes current methodologies and points out the state-of-the-art algorithmic approaches in their experimental validations and real-time applications. Particular attention is paid to the description of experimental setups and navigational scenarios showing the realistic impact of these technologies. Moreover, this paper goes through the key, open challenges in the field and hints at the research direction to leverage in order to enhance the robustness and adaptability of path planning algorithms. This paper, by offering a critical analysis of the current state-of-the-art, lays down the foundation of future USV path planning algorithms.

Abstract Objectives This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment. Materials and methods In a training cohort of 121 NSCLC patients, radiomic features were extracted, selected from intra- and peri-tumoral regions, and used to build signatures (S1 and S2) using a Cox regression model. Deep learning features were obtained from three convolutional neural networks and utilized to build signatures (S3, S4, and S5) that were stratified into over- and under-expression subgroups for survival risk using X-tile. After univariate and multivariate Cox regression analyses, a nomogram incorporating the tumor, node, and metastasis (TNM) stages, radiomic signature, and deep learning signature was established to predict OS and PFS, respectively. The performance was validated using an independent cohort (61 patients). Results TNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40–3.67); PFS: (HR (95%), 2.23 (1.36–3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. For the OS nomogram, the C-index (95% CI) was 0.74 (0.70–0.79) and 0.72 (0.67–0.78) in the training and validation cohorts, respectively; for the PFS nomogram, the C-index (95% CI) was 0.71 (0.68–0.81) and 0.72 (0.66–0.79). The calibration curves for the 3- and 5-year OS and PFS were in acceptable agreement between the predicted and observed survival. The established nomogram presented a higher overall net benefit than the TNM stage for predicting both OS and PFS. Conclusion By integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients.

Abstract The purpose of this study was to explore the role of coixendide (Coix) combine with temozolomide (TMZ) in the treatment of Glioblastoma (GBM) and explore its possible mechanism. CCK-8 was used to determine the inhibitory rate of Coix group, TMZ group and drug combination group on GBM cells, and the combination index (CI) was calculated to determine whether they had synergistic effect. Then RNA was extracted from each group, transcriptome sequencing was performed, and differentially expressed genes (DEGs) were identified. The possible mechanism was analyzed by GO enrichment analysis and KEGG enrichment analysis. The CI of Coix and TMZ indicating a synergistic effect when TMZ concentration is 0.1 mg/ml and Coix concentration is 2 mg/ml. Transcriptome sequencing analysis showed that interferon (IFN) related genes were down-regulated by Coix and up-regulated by TMZ and combined drugs, however, the up-regulation induced by combined drugs was less than that of TMZ. Besides IFN related genes, cholesterol metabolism pathway were also been regulated. Coix and TMZ have synergistic effects in the treatment of GBM at certain doses. RNA-Seq results suggested that the abnormal on genetic materials caused by DNA damage induced by TMZ treatment can be sensed by IFN related genes and activates antiviral IFN signaling, causing the activation of repairing mechanism and drug resistance. Coix inhibits IFN related genes, thereby inhibits drug resistance of TMZ. In addition, the activation of ferroptosis and the regulation of DEGs in cholesterol metabolism pathway were also contributed to the synergistic effects of Coix and TMZ.

Preterm birth is the leading cause of infant morbidity and mortality. There has been an interest in developing prostaglandin F2α (PGF2α) antagonists as a new treatment for preterm birth, although much of the rationale for their use is based on studies in rodents where PGF2α initiates labour by regressing the corpus luteum and reducing systemic progesterone concentrations. How PGF2α antagonism would act in humans who do not have a fall in systemic progesterone remains unclear. One possibility, in addition to an acute stimulation of contractions, is a direct alteration of the myometrial smooth muscle cell state towards a pro-labour phenotype. In this study, we developed an immortalised myometrial cell line, MYLA, derived from myometrial tissue obtained from a pregnant, non-labouring patient, as well as a novel class of PGF2α receptor (FP) antagonist. We verified the functionality of the cell line by stimulation with PGF2α, resulting in Gαq-specific coupling and Ca2+ release, which were inhibited by FP antagonism. Compared to four published FP receptor antagonists, the novel FP antagonist N582707 was the most potent compound [Fmax 7.67 ± 0.63 (IC50 21.26 nM), AUC 7.30 ± 0.32 (IC50 50.43 nM), and frequency of Ca2+ oscillations 7.66 ± 0.41 (IC50 22.15 nM)]. RNA-sequencing of the MYLA cell line at 1, 3, 6, 12, 24, and 48 h post PGF2α treatment revealed a transforming phenotype from a fibroblastic to smooth muscle mRNA profile. PGF2α treatment increased the expression of MYLK, CALD1, and CNN1 as well as the pro-labour genes OXTR, IL6, and IL11, which were inhibited by FP antagonism. Concomitant with the inhibition of a smooth muscle, pro-labour transition, FP antagonism increased the expression of the fibroblast marker genes DCN, FBLN1, and PDGFRA. Our findings suggest that in addition to the well-described acute contractile effect, PGF2α transforms myometrial smooth muscle cells from a myofibroblast to a smooth muscle, pro-labour–like state and that the novel compound N582707 has the potential for prophylactic use in preterm labour management beyond its use as an acute tocolytic drug.

The Digital Elevation Model (DEM) super-resolution approach aims to improve the spatial resolution or detail of an existing DEM by applying techniques such as machine learning or spatial interpolation. Convolutional Neural Networks and Generative Adversarial Networks have exhibited remarkable capabilities in generating high-resolution DEMs from corresponding low-resolution inputs, significantly outperforming conventional spatial interpolation methods. Nevertheless, these current methodologies encounter substantial challenges when tasked with processing exceedingly high-resolution DEMs (256×256,512×512, or higher), specifically pertaining to the accurate restore maximum and minimum elevation values, the terrain features, and the edges of DEMs. Aiming to solve the problems of current super-resolution techniques that struggle to effectively restore topographic details and produce high-resolution DEMs that preserve coordinate information, this paper proposes an improved DEM super-resolution Transformer(DSRT) network for large-scale DEM super-resolution and account for geographic information continuity. We design a window attention module that is used to engage more elevation points in low-resolution DEMs, which can learn more terrain features from the input high-resolution DEMs. A GeoTransform module is designed to generate coordinates and projections for the DSRT network. We conduct an evaluation of the network utilizing DEMs of various types of terrains and elevation differences at resolutions of 64×64,256×256 and 512 × 512. The network demonstrated leading performance across all assessments in terms of root mean square error (RMSE) for elevation, slope, aspect, and curvature, indicating that Transformer-based deep learning networks are superior to CNNs and GANs in learning DEM features.

Background: The prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients. Objectives: To develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images. Materials and Methods: This retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0–3, 3–6, 6–9, 9–12, 12–15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere–shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts. Results: Among the five partitions, the model of 9–12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77–0.94). The AUC was 0.94 (0.85–0.98) for the feature fusion model and 0.91 (0.82–0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88–0.99) for the feature fusion method and 0.94 (0.85–0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81–0.97) and 0.89 (0.79–0.93) in two validation sets, respectively. Conclusions: This integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making.

Researchers typically leverage side information, such as social networks or the knowledge graph, to overcome the sparsity and cold start problem in collaborative filtering. To tackle the limitations of existing user interest modeling, we propose a knowledge-enhanced user multi-interest modeling for recommender systems (KEMIM). First, we utilize the user-item historical interaction as the knowledge graph’s head entity to create a user’s explicit interests and leverage the relationship path to expand the user’s potential interests through connections in the knowledge graph. Second, considering the diversity of a user’s interests, we adopt an attention mechanism to learn the user’s attention to each historical interaction and each potential interest. Third, we combine the user’s attribute features with interests to solve the cold start problem effectively. With the knowledge graph’s structural data, KEMIM could describe the features of users at a fine granularity and provide explainable recommendation results to users. In this study, we conduct an in-depth empirical evaluation across three open datasets for two different recommendation tasks: Click-Through rate (CTR) prediction and Top-K recommendation. The experimental findings demonstrate that KEMIM outperforms several state-of-the-art baselines.

Abstract Objectives To evaluate the discriminatory capability of spectral CT-based radiomics to distinguish benign from malignant solitary pulmonary solid nodules (SPSNs). Materials and methods A retrospective study was performed including 242 patients with SPSNs who underwent contrast-enhanced dual-layer Spectral Detector CT (SDCT) examination within one month before surgery in our hospital, which were randomly divided into training and testing datasets with a ratio of 7:3. Regions of interest (ROIs) based on 40-65 keV images of arterial phase (AP), venous phases (VP), and 120kVp of SDCT were delineated, and radiomics features were extracted. Then the optimal radiomics-based score in identifying SPSNs was calculated and selected for building radiomics-based model. The conventional model was developed based on significant clinical characteristics and spectral quantitative parameters, subsequently, the integrated model combining radiomics-based model and conventional model was established. The performance of three models was evaluated with discrimination, calibration, and clinical application. Results The 65 keV radiomics-based scores of AP and VP had the optimal performance in distinguishing benign from malignant SPSNs (AUC65keV-AP = 0.92, AUC65keV-VP = 0.88). The diagnostic efficiency of radiomics-based model (AUC = 0.96) based on 65 keV images of AP and VP outperformed conventional model (AUC = 0.86) in the identification of SPSNs, and that of integrated model (AUC = 0.97) was slightly further improved. Evaluation of three models showed the potential for generalizability. Conclusions Among the 40-65 keV radiomics-based scores based on SDCT, 65 keV radiomics-based score had the optimal performance in distinguishing benign from malignant SPSNs. The integrated model combining radiomics-based model based on 65 keV images of AP and VP with Zeff-AP was significantly superior to conventional model in the discrimination of SPSNs.

Abstract International flights have accelerated the global spread of Coronavirus Disease 2019 (COVID-19). Determination of the optimal quarantine period for international travelers is crucial to prevent the local spread caused by imported COVID-19 cases. We performed a retrospective epidemiological study using 491 imported COVID-19 cases in Chengdu, China, to describe the characteristic of the cases and estimate the time from arrival to confirmation for international travelers using nonparametric survival methods. Among the 491 imported COVID-19 cases, 194 (39.5%) were asymptomatic infections. The mean age was 35.6 years (SD = 12.1 years) and 83.3% were men. The majority (74.1%) were screened positive for SARS-CoV-2, conducted by Chengdu Customs District, the People’s Republic of China. Asymptomatic cases were younger than presymptomatic or symptomatic cases (P

Abstract The individual prognosis of chemotherapy is quite different in non-small cell lung cancer (NSCLC). There is an urgent need to precisely predict and assess the treatment response. To develop a deep multiple-instance learning (DMIL) based model for predicting chemotherapy response in NSCLC in pretreatment CT images. Two datasets of NSCLC patients treated with chemotherapy as the first-line treatment were collected from two hospitals. Dataset 1 (163 response and 138 nonresponse) was used to train, validate, and test the DMIL model and dataset 2 (22 response and 20 nonresponse) was used as the external validation cohort. Five backbone networks in the feature extraction module and three pooling methods were compared. The DMIL with a pre-trained VGG16 backbone and an attention mechanism pooling performed the best, with an accuracy of 0.883 and area under the curve (AUC) of 0.982 on Dataset 1. While using max pooling and convolutional pooling, the AUC was 0.958 and 0.931, respectively. In Dataset 2, the best DMIL model produced an accuracy of 0.833 and AUC of 0.940. Deep learning models based on the MIL can predict chemotherapy response in NSCLC using pretreatment CT images and the pre-trained VGG16 with attention mechanism pooling yielded better predictions.

Although it is well established that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be transmitted through aerosols, the mode of long-range aerosol transmission in high-rise buildings remains unclear. In this study, we analyzed an outbreak of coronavirus disease 2019 (COVID-19) that occurred in a high-rise building in China. Our objective was to investigate the plausibility of aerosol transmission of SARS-CoV-2 by testing relevant environmental variables and measuring the dispersion of a tracer gas in the drainage system of the building. The outbreak involved 7 infected families, of which 6 were from vertically aligned flats on different floors. Environmenìtal data revealed that 3 families’ bathrooms were contaminated by SARS-CoV-2. In our tracer experiment, we injected tracer gas (CO 2 ) into the dry floor drains and into water-filled toilets in the index case’ s bathroom. Our findings showed that the gas could travel through vertical pipes by the dry floor drains, but not through the water of the toilets. This indicates that dry floor drains might facilitate the transmission of viral aerosols through the sewage system. On the basis of circumstantial evidence, long-range aerosol transmission may have contributed to the community outbreak of COVID-19 in this high-rise building. The vertical transmission of diseases through aerosols in high-rise buildings demands urgent attention.

In the evolving landscape of portfolio management (PM), the fusion of advanced machine learning techniques with traditional financial methodologies has opened new avenues for innovation. Our study introduces a cutting-edge model combining deep reinforcement learning (DRL) with a non-stationary transformer architecture. This model is designed to decode complex patterns in financial time-series data, enhancing portfolio management strategies with deeper insights and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing the model’s ability to navigate the complexities of asset management. Rigorous testing demonstrates the model’s efficacy, highlighting the benefits of blending diverse data sources and sophisticated algorithmic approaches in mastering the nuances of PM.

Objective To analyze epidemiological and clinical characteristics of retest positive cases infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Chengdu city, Sichuan province and to provide evidences for the prevention and control of coronavirus disease 2019 (COVID-19). MethodsRelevant data on 147 SARS-CoV-2 nucleic acid retest positive cases were retrospectively collected from follow-up surveys conducted 2 and 4 weeks after hospital discharge among 977 COVID-19 patients in Chengdu city as of June 15, 2021. The epidemiological and clinical characteristics of the retest positive cases during hospitalization and at the retest were described. ResultsThe retest positive rate of SARS-CoV-2 nucleic acid was 15.05% and there was no significant type difference in the retest positive rate for a total of 440 various biological specimens from the cases (all P > 0.05). The chest images showed progressive lesions in 12 retest positive cases and another 12 cases presented clinical symptoms of COVID-19. Among 180 surface swab specimens sampled in living environment of the retest positive cases, two were positive for SARS-CoV-2 nucleic acid test. For all the retest positive cases, the average interval from discharge to the time of retest positive was 16.95 days and the average detoxification period was 56.58 days. The cases′ detection results of lymphocyte count, D-dimer, serum ferritin, creatine kinase isoenzymes at the time of retest positive and duration of hospitalization were significantly different from those at the time before the retest positivity (P < 0.05 for all). ConclusionRetest positive for SARS-CoV-2 nucleic acid exists and could be detected in various biological specimens among discharged COVID-19 cases. Detections of multiple specimens can facilitate the identification of the rest positive cases.

Building a high-precision bathymetry digital elevation model is essential for navigation planning, marine and lake resource planning, port construction, and underwater archaeological projects. However, existing bathymetry methods have yet to be effectively and comparatively analyzed. This paper comprehensively reviews state-of-the-art bathymetry methods, including data acquisition techniques, model accuracy, and interpolation algorithms for underwater terrain mapping. First, We assess the merits and drawbacks of novel data acquisition devices, such as single-beam/multi-beam echo sounders and light detection and ranging systems. After that, we analyze the accuracy of the ETOPO1, GEBCO_2022 and SRTM15 to provide valuable insights into their performance. Furthermore, we evaluate ANUDEM, Inverse Distance Weighting, Kriging and Nearest Neighbor interpolation algorithms in different underwater terrains by comparing their applicability, reliability, and accuracy in various underwater environments. Finally, we discuss the development trends and challenges in underwater bathymetry technology and offer a forward-looking perspective on the future of this essential field.

IntroductionThere have been few mid-term follow-up studies comparing arthroscopic and open Broström-Gould repair of the anterior talofibular ligament (ATFL). The purpose of this study was to evaluate the mid-term therapeutic effectiveness of arthroscopic ATFL repair with open Broström-Gould repair for chronic lateral ankle instability.MethodsWe retrospectively reviewed the database of patients with chronic lateral ankle instability undergoing repair of the ATFL from June 2014 to June 2018. The choice of surgical approach will depend on computer-generated randomization. In total, 49 patients underwent the arthroscopic Brostrom-Gould technique (group AB), while the other 50 patients underwent the open Broström-Gould technique (group OB). The surgery duration, hospitalization time, postoperative complications, the preoperative/postoperative manual anterior drawer test (ADT), Visual analog scale (VAS) scores, American Orthopaedic Foot & Ankle Society (AOFAS) scores, Karlsson-Peterson (K-P) scores, and Tegner activity scores were collected for comparative analysis during the follow-up period of 48 months.ResultsAt the final follow-up, the clinical outcomes, including ADT, VAS, AOFAS, K-P, and Tegner activity scores, were significantly improved after either arthroscopic or open treatment. Specifically, the AOFAS and K-P scores in the group AB were significantly higher than those in the group OB at 6 months post-surgery (P

Autonomous path planning along riverbanks is crucial for unmanned surface vehicles (USVs) to execute specific tasks such as levee safety detection and underwater pipe inspections, which are vital for riverbank safety and water environment protection. Given the intricate shapes of riverbanks, the dynamic nature of tidal influences, and constraints in real-time cartographic updates, there is a heightened susceptibility to inaccuracies during manual waypoint designation. These factors collectively impact the efficiency of USVs in following riverbank paths. We introduce a riverbank following planner (RBFP) for USVs to tackle this challenge. This planner, utilizing 2D LiDAR, autonomously selects the following point to follow riverbank shapes. Additionally, a PID controller is integrated to compensate for position and yaw errors. Our proposed method reduces the deviation between the USV’s planned path and the actual riverbank shape. We simulated straight, convex, and concave riverbanks in the Virtual RobotX (VRX) simulator while considering the impacts of wind, waves, and USV dynamics. The experimental result indicates the following performance of 96.92%, 67.30%, and 61.15% for straight, convex, and concave banks, respectively. The proposed RBFP can support a novel autonomous navigation scenario for autonomous paths following along the riverbank without any preplanned paths or destinations.

Secondary hydrocarbon migration is an important aspect of oil-gas accumulation research. While previous studies have relied on geological and fluid geochemical characteristics to predict migration direction, these results are often limited by the number of samples. In recent years, basin simulation has emerged as a valuable tool in hydrocarbon migration research due to its extensive algorithms and adaptable modeling capabilities. It has obvious technical advantages especially for resource evaluation in areas with a lower exploration degree and scarce data. The deep oil and gas in the Cambrian petroleum system in the Tarim Basin is a deep hydrocarbon challenging area in China. There has been no breakthrough in exploration due to the large burial depth (generally>6000 m) and the long accumulation and transformation processes. Therefore, predicting the secondary migration of Cambrian oil and gas has become the key to solving this problem. In this study, first, the thermal evolution of the Lower Cambrian source rocks in the Tarim Basin was recovered, and the three thermal evolution models were developed. The secondary migration process of the Cambrian petroleum system was restored using the geologic model of the source rocks and paleotectonic evolution and the latest fluid potential information. By comparing the simulation results, four secondary migration models of the ultradeep oil-gas migration and accumulation were developed: multisource, multiphase, multidirectional accumulation; multisource, multiphase, single-directional accumulation; single-source, multiphase, multidirectional accumulation; and single-source, multiphase, single-directional accumulation. The fluid potential simulation results indicate that the Cambrian oil and gas have salient inheritance characteristics. The dominant migration channels in the uplift and slope are beneficial to oil-gas migration and accumulation, and the Katake uplift and the west Bachu uplift have multisource charging accumulation. The east Bachu uplift and the Tabei uplift are oil-gas accumulation zones that are beneficial to the lower petroleum system due to the continuous charging of a single petroleum system. This fluid potential simulation provides a new solution for studying the secondary migration of deep oil and gas. It provides an important reference for studying hydrocarbon accumulation in deep and ultradeep areas.

With the development of the novel coronavirus disease 2019 (COVID-19) epidemic and the increase in cases, as a potential source of infection, the risk of close contact has gradually increased. However, few studies have analyzed the tracking and management of cross-regional personnel. In this study, we hope to understand the effectiveness and feasibility of existing close contact management measures in Chengdu, so as to provide a reference for further prevention and control of the epidemic. The close contact management mode and epidemiological characteristics of 40,425 close contacts from January 22, 2020, to March 1, 2022, in Chengdu, China, were analyzed. The relationship with index cases was mainly co-passengers (57.58%) and relatives (7.20%), and the frequency of contact was mainly occasional contact (70.39%). A total of 400 (0.99%) close contacts were converted into cases, which were mainly found in the first and second nucleic acid tests (53.69%), and the contact mode was mainly by sharing transportation (63.82%). In terms of close contact management time, both the supposed ((11.93 ± 3.00) days vs. (11.92 ± 7.24) days) and actual ((13.74 ± 17.47) days vs. (12.60 ± 4.35) days) isolation times in Chengdu were longer than those of the outer cities (P

Lower Carboniferous Tianeping Formation shale is a new shale gas exploration bed in Linayuan Sag of Xiangzhong Depression.Based on the filed geological investigation, shale gas drilling wells, sample collection and laboratory analysis, the shale gas accumulation conditions of Carboniferous Tianeping Formation in the sag were discussed in this paper, such as distribution characteristics, geochemical characteristics, reservoir characteristics and gas content.The results show that the total organic content of the shale is high and distributed from 0.78% to 2.84%.The organic matter types are mainly type Ⅱ1.The thermal evolution degree of the shale is mainly in high mature with the range of 1.48%-2.09%.The shale composition is mainly quartz and clay minerals which are mainly composed of illite/smectite, illite, and chlorite.The pore of organic matter is more developed in mud shale.The porosity and permeability of shale are 2.43% and 0.285×10-3 μm2 in average respectively, but the permeability of most of the samples is less than 0.1×10-3 μm2.The gas content of shale of Tianeping Formation in Well Xiangxindi 4 ranges from 1.64 m3/t to 4.29 m3/t, with an average of 2.69 m3/t (without residual gas).Combining with the regional sedimentary-tectonic background, we suggest the sedimentary environment and structural preservation conditions are the main controlling factors of shale gas accumulation conditions of Lower Carboniferous Tianeping Formation in Lianyuan Sag.The basin facies are the favorable facies belt for shale gas, and the syncline formed by the hedge structural style under the complex structural background is the most favorable for the preservation conditions of shale gas, which is the sweet spot area for shale gas exploration in the study area.The research results can provide reference for subsequent oil and gas exploration in this area.

PurposeThis study aims to evaluate the ability of peritumoral, intratumoral, or combined computed tomography (CT) radiomic features to predict chemotherapy response in non-small cell lung cancer (NSCLC).MethodsAfter excluding subjects with incomplete data or other types of treatments, 272 (Dataset 1) and 43 (Dataset 2, external validation) NSCLC patients who were only treated with chemotherapy as the first-line treatment were enrolled between 2015 and 2019. All patients were divided into response and nonresponse based on the response evaluation criteria in solid tumors, version 1.1. By using 3D slicer and morphological operations in python, the intra- and peritumoral regions of lung tumors were segmented from pre-treatment CT images (unenhanced) and confirmed by two experienced radiologists. Then radiomic features (the first order, texture, shape, et al.) were extracted from the above regions of interest. The models were trained and tested in Dataset 1 and further validated in Dataset 2. The performance of models was compared using the area under curve (AUC), confusion matrix, accuracy, precision, recall, and F1-score.ResultsThe radiomic model using features from the peritumoral region of 0–3 mm outperformed that using features from 3–6, 6–9, 9–12 mm peritumoral region, and intratumoral region (AUC: 0.95 versus 0.87, 0.86, 0.85, and 0.88). By the fusion of features from 0–3 and 3–6 mm peritumoral regions, the logistic regression model achieved the best performance, with an AUC of 0.97. This model achieved an AUC of 0.85 in the external cohort. Moreover, among the 20 selected features, seven features differed significantly between the two groups (p < 0.05).ConclusionsCT radiomic features from both the peri- and intratumoral regions can predict chemotherapy response in NSCLC using machine learning models. Combined features from two peritumoral regions yielded better predictions.

Abstract Background Human brucellosis is a neglected public health issue in China and reports of HIV-infected individuals complicated with brucellosis are rare. This report describes the case of an HIV-infected patient complicated with brucellosis. We want to raise awareness of clinical diagnosis of brucellosis among clinicians. Furthermore, we should be more concerned about cases with pyrexia of unknown origin, especially in non-epidemic areas of brucellosis in China. Case presentation We encountered the case of a 31-year-old HIV-infected male with a CD4+ T lymphocyte count of approximately 300. On May 1, 2019, the patient had onset of non-specific caustic irregular fever with body temperature reaching 41.0 °C. He was admitted to two medical institutions in Yunnan with pyrexia of unknown origin. Finally, on day 7 of hospitalization in the Public Health Clinical Medical Center in Chengdu City, he was diagnosed as having brucellosis infection based on blood culture results. Conclusions This is the first reported case of brucellosis concomitant with HIV infection in China. Laboratories in infectious disease hospitals and category A level III hospitals in the southern provinces of China should be equipped with reagents for clinical diagnosis of brucellosis and to strengthen the awareness of brucellosis diagnosis in China. Secondly, in provinces with a high incidence of AIDS and brucellosis such as Xinjiang and Henan, it is recommended to implement a joint examination strategy to ensure the early detection, diagnosis, and treatment of this infection.

Under partial shading conditions (PSC), the power-voltage (P-V) characteristic curve of photovoltaic (PV) strings exhibits multiple peaks. Such mismatching phenomenon brings challenges in controlling the output power. To analyze the electrical characteristics of PV strings in complex environments, a quantitative analysis method is required to characterize the PSC. This paper introduces the shading matrix to describe the shading rate and shading strength information. The proposed shading matrix would provide maximum power point tracking (MPPT) controllers with the essential environmental information to improve the global maximum power point (GMPP) tracking performance. A modified Tabu search (MTS) based identification method is proposed to estimate the shading matrix. The proposed modified method involves a preselection process of updating the Tabu list to optimize the searching efficiency. The accuracy and efficiency of the proposed analytical estimation expression are validated through simulations and experiments. By comparing with binary search (BS), golden-section search (GS) and Tabu search (TS) algorithms, the proposed MTS algorithm is demonstrated to perceive the shading information at least 18.75% faster.

In this study, based on the example of the ground vibration damage response of a thick and hard rock layer fracture type mine quake, and by applying the theories of mine pressure, rock mechanics and vibration energy principle, the concept of “vibration damage boundary” of mine quake ground, which uses particle vibration velocity to evaluate the vibration damage, is proposed. In addition, the quantitative prediction method of the ground vibration damage boundary is preliminarily established. The research results reveal that the elastic deformation of the fixed support end of the thick and hard rock layer structure in the stope is the main energy source for the formation of mine quakes, and that the particle vibration velocity caused by the propagation of focal energy to the ground can reasonably reflect the response degree of vibration damage. The proposed prediction method considers the “instantaneous” motion characteristics of mining thick and hard rock layer and the dynamic load effect of “mine quakes.” The method can deepen our understanding of mining ground damage prediction, and increase the reliability of ground damage boundary prediction. Finally, the results are used to predict the ground vibration damage boundary of limestone primary fracture in the longwall 16101 face of the Fuping Coal Mine.

Due to the rapidly increasing number of students at universities in recent years, universities may arrange students from different majors in the same class, which may bring several challenges in teaching and learning for students and teachers, such as poor class experience, less class discussion and more workload for teachers. In this paper, we investigate the learning achievements of students with different major backgrounds based on a large-size course of Introduction to Java Programming. Approaches of difficulty index, incrimination index, failure rate, median average mark, average mark and standard deviation are used to evaluate students’ learning achievements based on students’ coursework assignments, continuous assessment and the final exam. Results show that major backgrounds could significantly affect learning achievements even in the same teaching and learning environment. To achieve a better learning experience and performance, we finally divided the original module into two separate modules for students with different major backgrounds.

Most high-performance semantic segmentation networks are based on complicated deep convolutional neural networks, leading to severe latency in real-time detection. However, the state-of-the-art semantic segmentation networks with low complexity are still far from detecting objects accurately. In this paper, we propose a real-time semantic segmentation network, RecepNet, which balances accuracy and inference speed well. Our network adopts a bilateral architecture (including a detail path, a semantic path and a bilateral aggregation module). We devise a lightweight baseline network for the semantic path to gather rich semantic and spatial information. We also propose a detail stage pattern to store optimized high-resolution information after removing redundancy. Meanwhile, the effective feature-extraction structures are designed to reduce computational complexity. RecepNet achieves an accuracy of 78.65% mIoU (mean intersection over union) on the Cityscapes dataset in the multi-scale crop and flip evaluation. Its algorithm complexity is 52.12 GMACs (giga multiply–accumulate operations) and its inference speed on an RTX 3090 GPU is 50.12 fps. Moreover, we successfully applied RecepNet for blue-green algae real-time detection. We made and published a dataset consisting of aerial images of water surface with blue-green algae, on which RecepNet achieved 82.12% mIoU. To the best of our knowledge, our dataset is the world’s first public dataset of blue-green algae for semantic segmentation.

The Liupan Mountain (LPM) area is located on the northeast margin of the Qinghai–Tibet Plateau, which is the western part of the second ladder of China’s terrain. It is also an intersection area of two air currents, which are caused by the combined action of the Qinghai–Tibet Plateau, the middle and lower levels of the westerly belt and the edge of the monsoon area. LPM is one of the main air water vapour transport pathways in Northwest China as well as a main water conservation area for nearly ten million people. Research on atmospheric precipitable water vapour (PWV) variation characteristics in LPM is beneficial for understanding the mechanisms of orographic precipitation and improving the effects of weather modification. Based on the data from 10 Global Navigation Satellite System Meteorology (GNSS/MET) stations for 6 years and the data of automatic weather stations in the LPM, the temporal and spatial variation characteristics of PWV in the LPM were analysed, and the differences in PWV in the 24 h before and after precipitation were compared in this study. The results showed that the hourly, monthly and seasonal variations in PWV displayed obvious patterns. PWV increased rapidly 10 h ahead of precipitation, while it decreased rapidly within 10 h after precipitation, which was slower than that before precipitation. In terms of spatial distribution, PWV was larger in the south than in the north and larger in the east than in the west. Although the precipitation on the LPM peak was the highest in the whole LPM area, its PWV was always the lowest, indicating that the PWV was obviously affected by the air temperature. This showed that under the same water vapour condition, precipitation was more likely to form in the area with low temperature, and the precipitation was larger, which also provided a train of thought for improving the method of artificial precipitation enhancement by using the condensation catalyst.

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous disease. Emphysematous phenotype is the most common and critical phenotype, which is characterized by progressive lung destruction and poor prognosis. However, the underlying mechanism of this structural damage has not been completely elucidated. A total of 12 patients with COPD emphysematous phenotype (COPD-E) and nine patients with COPD non-emphysematous phenotype (COPD-NE) were enrolled to determine differences in differential abundant protein (DAP) expression between both groups. Quantitative tandem mass tag–based proteomics was performed on lung tissue samples of all patients. A total of 29 and 15 lung tissue samples from patients in COPD-E and COPD-NE groups, respectively, were used as the validation cohort to verify the proteomic analysis results using western blotting. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted for DAPs. A total of 4,343 proteins were identified, of which 25 were upregulated and 11 were downregulated in the COPD-E group. GO and KEGG analyses showed that wound repair and retinol metabolism–related pathways play an essential role in the molecular mechanism of COPD emphysematous phenotype. Three proteins, namely, KRT17, DHRS9, and FMO3, were selected for validation. While KRT17 and DHRS9 were highly expressed in the lung tissue samples of the COPD-E group, FMO3 expression was not significantly different between both groups. In conclusion, KRT17 and DHRS9 are highly expressed in the lung tissue of patients with COPD emphysematous phenotype. Therefore, these proteins might involve in wound healing and retinol metabolism in patients with emphysematous phenotype and can be used as phenotype-specific markers.

The heterogeneity and complexity of non-small cell lung cancer (NSCLC) tumors mean that NSCLC patients at the same stage can have different chemotherapy prognoses. Accurate predictive models could recognize NSCLC patients likely to respond to chemotherapy so that they can be given personalized and effective treatment. We propose to identify predictive imaging biomarkers from pre-treatment CT images and construct a radiomic model that can predict the chemotherapy response in NSCLC. This single-center cohort study included 280 NSCLC patients who received first-line chemotherapy treatment. Non-contrast CT images were taken before and after the chemotherapy, and clinical information were collected. Based on the Response Evaluation Criteria in Solid Tumors and clinical criteria, the responses were classified into two categories: response (n = 145) and progression (n = 135), then all data were divided into two cohorts: training cohort (224 patients) and independent test cohort (56 patients). In total, 1629 features characterizing the tumor phenotype were extracted from a cube containing the tumor lesion cropped from the pre-chemotherapy CT images. After dimensionality reduction, predictive models of the chemotherapy response of NSCLC with different feature selection methods and different machine-learning classifiers (support vector machine, random forest, and logistic regression) were constructed. For the independent test cohort, the predictive model based on a random-forest classifier with 20 radiomic features achieved the best performance, with an accuracy of 85.7% and an area under the receiver operating characteristic curve of 0.941 (95% confidence interval, 0.898–0.982). Of the 20 selected features, four were first-order statistics of image intensity and the others were texture features. For nine features, there were significant differences between the response and progression groups (p < 0.001). In the response group, three features, indicating heterogeneity, were overrepresented and one feature indicating homogeneity was underrepresented. The proposed radiomic model with pre-chemotherapy CT features can predict the chemotherapy response of patients with non-small cell lung cancer. This radiomic model can help to stratify patients with NSCLC, thereby offering the prospect of better treatment.

Purpose: Hematologic toxicity (HT) during chemoradiation therapy (CRT) for anal cancer can lead to treatment breaks that compromise efficacy. We hypothesized that CRT-induced HT correlates with changes in active bone marrow (ABM) characterized by pre-/post-CRT positron emission tomography (PET)/computed tomography. Methods and materials: Data from 36 patients with anal cancer who were treated with 18F-fluorodeoxyglucose PET/computed tomography scans 2 weeks before and 6 to 16 weeks after CRT were analyzed. Complete blood counts with differential within 2 weeks from, weekly during, and 2 week after treatment were obtained. HT was defined as baseline complete blood count change to nadir and posttreatment recovery. Total bone marrow was segmented into 2 subregions: lumbosacral (LS) pelvis (L5 vertebrae, sacrum, and coccyx) and lower pelvis (LP) (ilium, femoral head/neck, and greater and lesser trochanter). PET ABM was characterized as the volume having standard uptake value (SUV) greater than the mean uptake of unirradiated extrapelvic bone marrow. PET variables of pre-/post-CRT and HT predictors were analyzed by linear regression. Results: Average pelvic ABM was significantly reduced from 52% to 41% in pre- to post-CRT PET scans for all patients (P = .0012). Regional analysis indicated significant post-CRT reduction of LS-ABM (P 50 Gy) in terms of acute hematologic ΔWBC (P = .021) and ΔANC(P = .028). HT increased with increasing volume of ABM receiving 40 Gy. The results also suggest that ABM V40 Gy ≤ 20% to 25% may significantly reduce the risk of HT. Conclusions: HT was significantly associated with ΔABM in patients with anal cancer who were treated with CRT. LS-ABM was a robust surrogate for evaluating CRT-induced HT. Our results suggest implementation of ABM dosimetric constraints, V40 Gy ≤ 20-25%, may significantly reduce HT and lead to decreased treatment delays associated with clinical outcomes.

Abstract Background Pulmonary lobectomy has been a well-established curative treatment method for localized lung cancer. After left upper pulmonary lobectomy, the upward displacement of remaining lower lobe causes the distortion or kink of bronchus, which is associated with intractable cough and breathless. However, the quantitative study on structural and functional alterations of the tracheobronchial tree after lobectomy has not been reported. We sought to investigate these alterations using CT imaging analysis and computational fluid dynamics (CFD) method. Methods Both preoperative and postoperative CT images of 18 patients who underwent left upper pulmonary lobectomy are collected. After the tracheobronchial tree models are extracted, the angles between trachea and bronchi, the surface area and volume of the tree, and the cross-sectional area of left lower lobar bronchus are investigated. CFD method is further used to describe the airflow characteristics by the wall pressure, airflow velocity, lobar flow rate, etc. Results It is found that the angle between the trachea and the right main bronchus increases after operation, but the angle with the left main bronchus decreases. No significant alteration is observed for the surface area or volume of the tree between pre-operation and post-operation. After left upper pulmonary lobectomy, the cross-sectional area of left lower lobar bronchus is reduced for most of the patients (15/18) by 15–75%, especially for 4 patients by more than 50%. The wall pressure, airflow velocity and pressure drop significantly increase after the operation. The flow rate to the right lung increases significantly by 2–30% (but there is no significant difference between each lobe), and the flow rate to the left lung drops accordingly. Many vortices are found in various places with severe distortions. Conclusions The favorable and unfavorable adaptive alterations of tracheobronchial tree will occur after left upper pulmonary lobectomy, and these alterations can be clarified through CT imaging and CFD analysis. The severe distortions at left lower lobar bronchus might exacerbate postoperative shortness of breath.

Abstract Background Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesions presenting various appearances. Segmentation of lung parenchyma can help locate and analyze the neighboring lesions, but is not well studied in the framework of machine learning. Methods We proposed to segment lung parenchyma using a convolutional neural network (CNN) model. To reduce the workload of manually preparing the dataset for training the CNN, one clustering algorithm based method is proposed firstly. Specifically, after splitting CT slices into image patches, the k-means clustering algorithm with two categories is performed twice using the mean and minimum intensity of image patch, respectively. A cross-shaped verification, a volume intersection, a connected component analysis and a patch expansion are followed to generate final dataset. Secondly, we design a CNN architecture consisting of only one convolutional layer with six kernels, followed by one maximum pooling layer and two fully connected layers. Using the generated dataset, a variety of CNN models are trained and optimized, and their performances are evaluated by eightfold cross-validation. A separate validation experiment is further conducted using a dataset of 201 subjects (4.62 billion patches) with lung cancer or chronic obstructive pulmonary disease, scanned by CT or PET/CT. The segmentation results by our method are compared with those yielded by manual segmentation and some available methods. Results A total of 121,728 patches are generated to train and validate the CNN models. After the parameter optimization, our CNN model achieves an average F-score of 0.9917 and an area of curve up to 0.9991 for classification of lung parenchyma and non-lung-parenchyma. The obtain model can segment the lung parenchyma accurately for 201 subjects with heterogeneous lung diseases and CT scanners. The overlap ratio between the manual segmentation and the one by our method reaches 0.96. Conclusions The results demonstrated that the proposed clustering algorithm based method can generate the training dataset for CNN models. The obtained CNN model can segment lung parenchyma with very satisfactory performance and have the potential to locate and analyze lung lesions.

Path-keeping requires unmanned surface vehicles (USVs) to follow a planned path in autonomous navigation. It is essential for USVs to carry out autonomous tasks such as collecting various data of water quality and surrounding terrain for exploration and protection of water environments. However, due to obstacle avoidance and other factors such as wind, water waves, and dynamics of USVs, USVs usually deviate from the original planned path during autonomous navigation. This paper proposes a novel path-keeping algorithm based on the artificial potential field method (PK-APF) for USVs. To minimize the deviation between the actual path and the original planned path, the vertical distance and the virtual foot points of the current position of USVs to the original path (a line connecting the previous navigation point and the next navigation point) are calculated. When the vertical distance is larger than a threshold, we regard the vertical foot point as a virtual goal point to guide the USVs to navigate the original path in real-time to achieve high-precision path-keeping. Obstacle avoidance is simulated in the MATLAB and Virtual RobotX (VRX) simulators, and the influence of wind and water waves is considered in VRX. Experiments are conducted in typical scenarios and results show that PK-APF outperforms the traditional APF by at least 22%. The work provides an important basis for real-life environments. Substantial further work is planned for applying the method on a physical USV.

The emergence of a photogrammetry-based 3D reconstruction technique enables rapid 3D modeling at a low cost and uncovers many applications in documenting the geometric dimensions of the environment. Although the theoretical accuracy of photogrammetry-based reconstruction has been studied intensively in the literature, the problem remains in evaluating the accuracy of the generated point cloud in practice. Typically, checking the coordinates of ground control points (GCPs) using a total station is considered a promising approach; however, the GCPs have clear and identifiable features and consistent normal vectors or less roughness, which cannot be considered as a typical sample for an accuracy evaluation of the point cloud. Meanwhile, the cloud-to-cloud (C2C) and cloud-to-mesh (C2M) comparison methods usually consider either the closest point or the neighboring points within a fixed searching radius as the “ground truth”, which may not reflect the actual accuracy; therefore, the present paper proposes an adaptive cloud-to-cloud (AC2C) comparison method to search the potential “ground truth” in the theoretical error space. The theoretical error space of each point is estimated according to the position of the corresponding visible cameras and their distances to a target point. A case study is carried out to investigate the feasibility of the proposed AC2C comparison method. The results presented basically the same error distribution range from 0 to 20 mm with the C2C and C2M methods, but with a higher mean value and a much smaller standard deviation. Compared to the existing methods, the proposed method provides new thinking in evaluating the accuracy of SfM-MVS by including the theoretical error constraints.

Abstract Background The oriental fruit fly, Bactrocera dorsalis (Hendel) has been considered to be one of the most important agricultural pest around the world. As a holometabolous insect, larvae must go through a metamorphosis process with dramatic morphological and structural changes to complete their development. To better understand the molecular mechanisms of these changes, RNA-seq of B. dorsalis from wandering stage (WS), late wandering stage (LWS) and white puparium stage (WPS) were performed. Results In total, 11,721 transcripts were obtained, out of which 1914 genes (578 up-regulated and 1336 down-regulated) and 2047 genes (655 up-regulated and 1392 down-regulated) were found to be differentially expressed between WS and LWS, as well as between WS and WPS, respectively. Of these DEGs, 1862 and 1996 genes were successfully annotated in various databases. The analysis of RNA-seq data together with qRT-PCR validation indicated that during this transition, the genes in the oxidative phosphorylation pathway, and genes encoding P450s, serine protease inhibitor, and cuticular proteins were down-regulated, while the serine protease genes were up-regulated. Moreover, we found some 20-hydroxyecdysone (20E) biosynthesis and signaling pathway genes had a higher expression in the WS, while the genes responsible for juvenile hormone (JH) synthesis, degradation, signaling and transporter pathways were down-regulated, suggesting these genes might be involved in the process of larval pupariation in B. dorsalis. For the chitinolytic enzymes, the genes encoding chitinases (chitinase 2, chitinase 5, chitinase 8, and chitinase 10) and chitin deacetylase might play the crucial role in the degradation of insect chitin with their expressions significantly increased during the transition. Here, we also found that chitin synthase 1A might be involved in the chitin synthesis of cuticles during the metamorphosis in B. dorsalis. Conclusions Significant changes at transcriptional level were identified during the larval pupariation of B. dorsalis. Importantly, we also obtained a vast quantity of RNA-seq data and identified metamorphosis associated genes, which would all help us to better understand the molecular mechanism of metamorphosis process in B. dorsalis.

Large interactive displays can provide suitable workspaces for learners to conduct collaborative learning tasks with visual information in co-located settings. In this research, we explored the use of these displays to support collaborative engagement and exploratory tasks with visual representations. Our investigation looked at the effect of four factors (number of virtual workspaces within the display, number of displays, position arrangement of the collaborators, and collaborative modes of interaction) on learners’ knowledge acquisition, engagement level, and task performance. To this end, a user study was conducted with 72 participants divided into 6 groups using an interactive tool developed to support the collaborative exploration of 3D visual structures. The results of this study showed that learners with one shared workspace and one single display can achieve better user performance and engagement levels. In addition, the back-to-back position with learners sharing their view and control of the workspaces was the most favorable. It also led to improved learning outcomes and engagement levels during the collaboration process.

Referred to the new recognition from petroleum exploration of the Sinian to Cambrian in South China, it could be considered that the distribution of the early Cambrian source rocks was controlled by the palaeo-geomorphology at the end of Neoproterozoic in the Tarim Basin. Based on the zircon U-Pb dating of pyroclastic rock samples from the clastic rock stratum under the bottom of Cambrian carbonate rocks, the stratigraphic correlation of the Sinian to Cambrian was conducted to build the palaeo-geomorphology framework at the end of Neoproterozoic in Tarim Basin. Lastly, according to the development mode of source rocks at the beginning of Cambrian, the distribution of source rocks was predicted initially through the division of seismic facies. The youngest zircon concordia age of pyroclastic rocks from the bottom of well Tong 1 is 707±8Ma. It was revealed by the strata framework of the Sinian to Cambrian, the palaeo-geomorphology at the end of Neoproterozoic in Tarim Basin was characterized by an uplift highland in Bachu-Tazhong area, the south north high-low, and the west is higher than the east. The distribution of source rocks in the bottom of the Cambrian on the palaeo-platform and slopes was coincident with the Upper Sinian dolomite basically. But the contemporaneous sediment happened to be absent or changed in sedimentary facies on the uplift and its edges. From the seismic facies of the strata under the bottom of Cambrian, it could be concluded that source rocks in the type of the Xishanbraque Group (∈1xs) was limited in the Manjiaer Depression, while the source rocks in the type of the Yuertusi Group (∈1y) are widely distributed in south of Tabei Uplift, east Awat Depression, and even the Maigt Slope. However, among the west Awat Depression and western Tanguzibasi Depression, and the middle area of the Bachu-Tazhong Uplifts, the contemporaneous source rocks may have changed into sedimentary facies of tidal flat and lagoon, instead of deep shelf. Keywords: Tarim Basin, Sinian, Palaeo-geomorphology, Resource rocks, Seismic facies

[Background] Virtual reality (VR) technology can provide unique immersive experiences for group users, and especially for analytics tasks with visual information in learning. Providing a shared control/view may improve the task performance and enhance the user experience during VR collaboration. [Objectives] Therefore, this research explores the effect of collaborative modes and user position arrangements on task performance, user engagement, and collaboration behaviors and patterns in a VR learning environment that supports immersive collaborative tasks. [Method] The study involved two collaborative modes (shared and non-shared view and control) and three position arrangements (side-by-side, corner-to-corner, and back-to-back). A user study was conducted with 30 participants divided into three groups (Single, Shared, and Non-Shared) using a VR application that allowed users to explore the structural and transformational properties of 3D geometric shapes. [Results] The results showed that the shared mode would lead to higher task performance than single users for learning analytics tasks in VR. Besides, the side-by-side position got a higher score and more favor for enhancing the collaborative experience. [Conclusion] The shared view would be more suitable for improving task performance in collaborative VR. In addition, the side-by-side position may provide a higher user experience when collaborating in learning VR. From these results, a set of guidelines for the design of collaborative visualizations for VR environments are distilled and presented at the end of the paper. All in all, although our experiment is based on a colocated setting with two users, the results are applicable to both colocated and distributed collaborative scenarios with two or more users.

Path planning and collision avoidance during autonomous navigation in unknown environments is a crucial issue for unmanned surface vehicles (USVs). This paper improves the traditional D* Lite algorithm and achieves multi-goal path planning and collision avoidance for USVs in unknown and complex environments. By expanding the adjacent search range and setting a safe distance for USVs, we solve the issue of limited steering maneuverability in USVs with fewer DOF during autonomous navigation. We propose an approach to optimize the planned path during navigation by comparing the estimated distance with the actual distance between the current waypoint and the goal waypoint. A minimum binary heap is used to optimize the priority queue of the D* Lite and significantly reduce the path search time. Simulation results show that the improved D * Lite can significantly reduce the path planning time, optimize the planned path and solve the issue of limited steering maneuverability in USVs. We apply the algorithm to a real USV for further tests. Experimental results show that the USV can plan an optimized path while avoiding both static and dynamic obstacles in complex environments with a safe distance during autonomous navigation.

Heteroatom doping is regarded as a promising approach to enhance the electrochemical performance of carbon materials, while the poor controllability of heteroatoms remains the main challenge. In this context, sulfur-doped graphdiyne (S-GDY) was successfully synthesized on the surface of copper foil using a sulfur-containing multi-acetylene monomer to form a uniform film. The S-GDY film possesses a porous structure and abundant sulfur atoms decorated homogeneously in the carbon skeleton, which facilitate the fast diffusion and storage of lithium ions. The lithium-ion batteries (LIBs) fabricated with S-GDY as anode exhibit excellent performance, including the high specific capacity of 920 mA h g−1 and superior rate performances. The LIBs also show long-term cycling stability under the high current density. This result could potentially provide a modular design principle for the construction of high-performance anode materials for lithium-ion batteries.

Uncertainty is intrinsic in most of the complex systems, especially when the systems have to interact with the physical environment; therefore, handling uncertainty is critical in the Internet of Things (IoT). In this paper, we propose a semantic-based approach to build the belief network in IoT systems to handle the uncertainties. Semantics is the functionality description of any system component. Semantic Match mechanisms can construct the appropriate structures to compare the consistency between different sources of data based on the same functionality. In the approach, we define the belief property of every system component and develop the related algorithms to update the belief value. Furthermore, the related mechanisms and algorithms for data fusion and fault detection based on the belief property are described to explain how the approach works in the IoT systems. Several simulation experiments are used to evaluate the proposed approach, and the results indicate that the approach can work as expected. More accurate data are fused from the inaccurate devices and the fault in one node is automatically detected.

Juvenile hormone (JH) prevents metamorphosis during insect larval stages and promotes adult reproductive processes. Krüppel-homolog 1 (Kr-h1), a zinc finger transcription factor assumed to be induced by JH via the JH receptor methoprene-tolerant (Met), mediates the antimetamorphic effect of JH in insects, but its function in JH-mediated reproductive processes has not been fully explored. In this study, Met and Kr-h1 involved in the JH signaling pathway were first cloned and identified from the oriental fruit fly, Bactrocera dorsalis, an important pest infesting fruit and vegetables worldwide. Subsequent spatiotemporal expression analysis revealed that Met and Kr-h1 were both highly expressed in 7-day-old adults and fat body of female adults, respectively. Treatment with a JH analog (methoprene) significantly induced the expression of JH signaling and vitellogenin (Vg) genes and accelerated ovary development. RNA interference (RNAi) further revealed that either Met or Kr-h1 depletion at the adult stage of B. dorsalis impeded ovary development, with significantly lower egg production noted as well. In addition, rescue through methoprene application after RNAi stimulated the expression of JH signaling and Vg genes. Although there were still differences in ovary phenotype between rescued insects and the pre-RNAi control, ovary redevelopment with a larger surface area was observed, consistent with the spatiotemporal expression and phenotypes recorded in the original methoprene experiment. Our data reveal the involvement of Met and Kr-h1 in insect vitellogenesis and egg production, thus indicating the crucial role of the JH signaling pathway in insect reproduction.

INTRODUCTION:Overexpression of the androgen receptor (AR) characterizes a distinct molecular subset of triple negative breast carcinomas (TNBC). The role of AR as a prognostic/predictive biomarker in TNBC is controversial, but increasing evidence suggests that this subset may respond to therapeutic agents targeting AR. Evaluation of AR has not been standardized, and criteria for selection of patients for antiandrogen therapy remain controversial. In this study we determine the appropriate threshold of AR immunoreactivity to define AR positive (AR+) TNBC, describe the clinicopathologic features of AR+ TNBC, and discuss the utility of AR positivity as a prognostic and predictive marker in TNBC. MATERIALS AND METHODS:135 invasive TNBC processed in accordance with ASCO/CAP guidelines, were immunostained for AR. Clinicopathologic features of AR+ TNBC were analyzed and compared to AR negative (AR-) TNBC. Patients' age, tumor size, tumor grade, lymph node status, proliferation rate, immunopositivity for EGFR, CK5/6, Ki-67, and disease free survival (DFS) were evaluated statistically. RESULTS:A 1% cutpoint was confirmed as the appropriate threshold for AR positivity. Using this cutpoint 41% of 135 TNBC were AR+. AR+ TNBC occurred in older women, were larger, had lower mean proliferation rate and increased incidence of axillary metastasis than AR- TNBC. 76% of TNBC with apocrine morphology were AR+. A subset of AR+TNBC expressed basal markers (EGFR and CK5/6). A prognostic model was created. SUMMARY:AR identifies a heterogeneous group of TNBC. Additional evaluation of EGFR expression allowed us to stratify TNBCs into 3 risk groups with significant differences in DFS and therapeutic implications: low-risk (AR+ EGFR-) which represents the LAR molecular subtype with the best prognosis and may benefit the most from anti-androgen therapies; high-risk (AR- EGFR+) which represents the basal molecular subtype with the worst prognosis and may benefit the most from chemotherapy regimens; intermediate-risk (AR+EGFR+ and AR-EGFR-) TNBC with an intermediate prognosis. Prospective trials are required to further validate this prognostic and predictive grouping.