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The integration of landsenses ecology encompasses both the ecological and perceptual effects on the ecological environment. Although the theoretical framework has gradually matured, challenges persist, including a lack of research strength, incomplete evaluation systems, and ambiguous disciplinary boundaries. Therefore, a systematic review of landsense research is necessary to analyze its characteristics and explore future research trends to ensure that landscape ecology will flourish. This study conducted a bibliometric analysis of 37 Chinese articles from China National Knowledge Infrastructure (CNKI) and 46 English articles from Web of Science (WOS). As an emerging field in ecology, landsenses has grown in recent years. However, interconnectedness among the research teams is weak. The CNKI literature tends to be concentrated, with 70.27 % of the articles appearing in the journal Acta Ecologica Sinica. Researchers are predominantly affiliated with the Chinese Academy of Sciences, with fewer contributions from other institutions and universities. Future directions for landsenses ecology are outlined, including (1) enhancing interdisciplinary collaboration with emerging technologies, such as VR and AI, (2) refining quantifiable landsense metrics and evaluation systems, and (3) establishing a normal model and criteria for landsense creation services. By discussing the disciplinary boundaries and application of landsenses ecology, this study contributes a theoretical reference value for its development. This study proposes the concepts and associated contents of landsenses ecology and mixed marching data and explains the role of the meliorization model and Internet of Things (IoT) in landsenses ecology-based land-use planning, construction, and management. Utilizing the results of bibliometric research, this study proposes recommendations for the future application and theoretical development of landsenses ecology. These suggestions aim to bolster landsenses ecology application practices and theoretical foundations, enabling a more significant contribution to ecosystem services and facilitating sustainable development strategies.

Abstract This review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration of high‐throughput computational screening (HTCS) and machine learning (ML) accelerates materials discovery, guided by algorithms categorizing RFBs. A collaborative exploration, spanning macroscopic to mesoscopic scales, combines quantum machine learning with reinforcement learning, transfer learning, time series analysis, Bayesian optimization, active learning and various generative models. The collaborative integration of ML with computational techniques and experimental methods, anchored in experimentally validated Density Functional Theory (DFT) calculations and molecular dynamics (MD) simulations, proves indispensable for cost‐effective RFBs. Data collection and feature engineering are explored, emphasizing the integration of optimization goals and precise data collection within the ML framework. Feature analysis importance is highlighted, utilizing methods such as the filter, embedded, wrapper and deep learning methods for efficient energy materials exploration. Computational perspectives on materials features and operating conditions encompass membrane characteristics, fluid dynamics, temperature dependence and pressure sensitivity. Time‐dependent features and ML‐generated insights are crucial for understanding cycling performance intricacies, providing a comprehensive understanding of RFB materials.

Abstract Background Cell-based immunotherapy shows the therapeutic potential in sarcomas, in addition to angiogenesis-targeted tyrosine kinase inhibitor (TKI) and immune checkpoint inhibitor (ICI). Multi-antigen stimulated cell therapy-I (MASCT-I) technology is a sequential immune cell therapy for cancer, which composes of multiple antigen-loaded dendritic cell (DC) vaccines followed by the adoptive transfer of anti-tumor effector T-cells. Methods In this phase 1 study, we assessed MASCT-I plus camrelizumab (an ICI against PD-1) and apatinib (a highly selective TKI targeting VEGFR2) in patients with unresectable recurrent or metastatic bone and soft-tissue sarcoma after at least one line of prior systemic therapy. One MASCT-I course consisted of 3 DC subcutaneous injections, followed by 3 active T cell infusions administered 18–27 days after each DC injection. In schedule-I group, 3 DC injections were administered with a 28-day interval in all courses; in schedule-II group, 3 DC injections were administered with a 7-day interval in the first course and with a 28-day interval thereafter. All patients received intravenous camrelizumab 200 mg every 3 weeks and oral apatinib 250 mg daily. Results From October 30, 2019, to August 12, 2021, 19 patients were enrolled and randomly assigned to schedule-I group (n = 9) and schedule-II group (n = 10). Of the 19 patients, 11 (57.9%) experienced grade 3 or 4 treatment-related adverse events. No treatment-related deaths occurred. Patients in schedule-II group showed similar objective response rate (ORR) with those in schedule-I group (30.0% versus 33.3%) but had higher disease control rate (DCR; 90.0% versus 44.4%) and longer median progression-free survival (PFS; 7.7 versus 4.0 months). For the 13 patients with soft-tissue sarcomas, the ORR was 30.8%, DCR was 76.9%, and median PFS was 12.9 months; for the 6 patients with osteosarcomas, the ORR was 33.3%, the DCR was 50.0%, and median PFS was 5.7 months. Conclusions Overall, MASCT-I plus camrelizumab and apatinib was safe and showed encouraging efficacy in advanced bone and soft-tissue sarcoma, and schedule-II administration method was recommended. Trial registration ClinicalTrials.gov, NCT04074564.

Abstract Objectives Abdominal palpation is an essential examination to diagnose various digestive system diseases. This study aimed to develop an objective and standardized test based on abdominal palpation simulators, and establish a credible pass/fail standard of basic competency. Methods Two tests were designed using the newly developed Jucheng abdominal palpation simulator (test 1) and the AbSim simulator (test 2), respectively. Validity evidence for both tests was gathered according to Messick’s contemporary framework by using experts to define test content and then administering the tests in a highly standardized way to participants of different experience. Different simulator setups modified by the built-in software were selected from hepatomegaly, splenomegaly, positive McBurney’s sign plus rebound tenderness, gallbladder tenderness (Murphy’s sign), pancreas tenderness, and a normal setup without pathologies, with six sets used in test 1 and five sets used in test 2. Different novices and experienced were included in the tests, and test 1 was also administered to an intermediate group. Scores and test time were collected and analyzed statistically. Results The internal consistency reliability of test 1 and test 2 showed low Cronbach’s alphas of 0.35 and -0.41, respectively. Cronbach’s alpha for palpation time across cases were 0.65 for test 1 and 0.76 for test 2. There was no statistical difference in total time spent and total scores among the three groups in test 1 (P-values (ANOVA) were 0.53 and 0.35 respectively), nor between novices and experienced groups in test 2 (P-values (t-test) were 0.13 and 1.0 respectively). It was not relevant to try to establish pass/fail standards due to the low reliability and lack of discriminatory ability of the tests. Conclusions It was not possible to measure abdominal palpation skills in a valid way using either of the two standardized, simulation-based tests in our study. Assessment of the patient’s abdomen using palpation is a challenging clinical skill that is difficult to simulate as it highly relies on tactile sensations and adequate responsiveness from the patients.

Scientific researchers are known for their demanding intellectual efforts and are particularly susceptible to mental health disorders. Urban green spaces (UGSs) are beneficial for people’s mental health, yet there is limited research on what types of UGSs and activities are effective for improving researchers’ mental health. Here, we investigate if researchers’ mental health can be enhanced by visiting affiliated green spaces, pocket parks, comprehensive parks, and coastal beaches. The research hypothesis was that the natural and social pathways of recreational ecosystem services jointly reduce researchers’ mental stress and attentional fatigue. The results suggest that nature contact, nature connectedness, and social interaction directly or indirectly have positive impacts on scientific researchers’ mental health. Both nature connectedness and social interaction serve as crucial mediating variables between nature contact and mental health. In terms of direct impact, high-frequency recreational activities in small UGSs with high accessibility, markedly relieve stress, whereas visit frequency in large UGSs with low accessibility has a limited effect on long-term mental stress relief. In terms of indirect impact, small UGSs alleviate attentional fatigue through the mediation of nature connectedness, while large UGSs mainly mitigate mental stress and attentional fatigue through the mediation of social interaction. This study provides a natural-social integrated perspective on recreational ecosystem services to enhance the well-being of scientific researchers. We provide recommendations for researchers to mitigate work-related stress and fatigue and to offer information useful for decision-making support to create a healthy environment for scientific researchers.

Studies have shed light on the impact of the co-application of inorganic fertilizer and biochar on soil fertility, health, and crop growth performance and yield. However, insufficient literature exists regarding the appropriate nitrogen reduction ratio for enhancing soil quality and maximizing crop nitrogen utilization following the application of biochar in a continuous tobacco-rice rotation field. Here, we explored nitrogen absorption and utilization patterns of tobacco crops, as well as the response characteristics of functional genes related to soil nitrogen cycling subjected to the interaction of reduced nitrogen utilization ratios following biochar application in a long-term tobacco-rice rotation field. The results showed that the treatments with 10% (T2) and 20% (T3) nitrogen reduction combined with biochar (30 t∙ha−1) promoted nitrogen utilization efficiency and nitrogen harvest index of tobacco plants. In the second year of the experiment, T2 and T3 significantly increased the nitrogen harvest index by 3.85% and 5.78% compared with the conventional nitrogen application treatment (T1), respectively. We believe that the increase in abundance of nitrification, nitrogen fixation, and ammonification genes, including nxrA, nifH, and UreC in the rhizosphere soil, precipitate the high nitrogen absorption and utilization efficiency in the biochar combined with nitrogen reduction treatments, respectively. This suggests that biochar application at a rate of 30 t·ha−1, nitrogen fertilizer usage can be reduced by 10% and 20% to achieve optimal and sustainable tobacco production.

Abstract Background Colorectal cancer (CRC) is a prevalent malignancy of the gastrointestinal. Circular RNAs (circRNAs) act as important roles in CRC malignant progression. However, the role of circ_0039857 in CRC is still unclear. Therefore, this study aimed to explore the function and mechanism of hsa_circ_0039857 in the CRC. Methods The mRNA and protein expression were measured via RT-qPCR. RNase R assay and Actinomycin D were employed to evaluate the stability of circ_0039857. Functional experiments, such as proliferation and apoptosis, were applied to study the function of circ_0039857 in CRC cells. The underlying mechanisms of circ_0039857 were then analyzed by bioinformatics, dual-luciferase reporter gene assay, RNA pull-down and rescue experiments. Results We revealed that circ_0039857 was significantly enhanced in CRC. Circ_0039857 was stabler than linear RNA in cells and valuable for the disease diagnosis. In addition, circ_0039857 knockdown inhibited proliferation and promoted apoptosis. Mechanistically, circ_0039857 positively regulated the expression of RAB32 via sponging miR-338-3p. Conclusion This study demonstrated that circ_0039857 knockdown suppressed CRC malignant progression through miR-338-3p/RAB32 axis. Most importantly, this will help us to better understand the circRNA network in CRC, and may find potential biomarkers and targets for CRC clinical treatment.

Objectives To investigate the impact of marriage on multiple myeloma (MM) survival, and the role of factors such as age, sex, and income in this relationship. Material and Methods The Surveillance, Epidemiology, and End Results (SEER) database was searched for eligible MM patients between 2007 and 2016. We compared overall survival (OS) and cancer-specific survival (CSS) differences by the Kaplan–Meier method and log-rank tests. The hazard ratios (HRs) with 95% confidence intervals (CIs) for OS and CSS were estimated by Cox regression models. A 1:1 propensity score matching (PSM) analysis was used to minimize the covariates differences between married and unmarried group. Results This study identified 48,952 eligible patients diagnosed with MM, comprising 29,607 married patients, 5,147 divorced/separated patients, 6,851 single patients, and 7,347 widowed patients. Married MM patients were found to be an independent protective prognostic factor for OS and CSS (all P

Summary: Background: Ultrasonography is the most widely used technique to diagnose echinococcosis; however, challenges in using this technique and the demand on medical resources, especially in low-income or remote areas, can delay diagnosis. We aimed to develop a deep convolutional neural network (DCNN) model based on ultrasonography to identify echinococcosis and its types, especially alveolar echinococcosis. Methods: This retrospective, large-scale, multicentre study used ultrasound images from patients assessed at 84 hospitals in China, obtained between Jan 1, 2002, and Dec 31, 2021. Patients with a diagnosis of cystic echinococcosis, alveolar echinococcosis, or seven other types of focal liver lesions were included. We tested ResNet-50, ResNext-50, and VGG-16 as the backbone network architecture for a classification DCNN model and input the perinodular information from the ultrasound images. We trained and validated the DCNN model to diagnose and classify echinococcosis using still greyscale ultrasound images of focal liver lesions in four stages: differentiating between echinococcosis and other focal liver lesions (stage one); differentiating cystic echinococcosis, alveolar echinococcosis, and other focal liver lesions (stage two); differentiating cystic echinococcosis, alveolar echinococcosis, benign other focal liver lesions, and malignant focal liver lesions (stage three); and differentiating between active and transitional cystic echinococcosis and inactive cystic echinococcosis (stage four). We then tested the algorithm on internal, external, and prospective test datasets. The performance of DCNN was also compared with that of 12 radiologists recruited between Jan 15, 2022, and Jan 28, 2022, from Qinghai, Xinjiang, Anhui, Henan, Xizang, and Beijing, China, with different levels of diagnostic experience for echinococcosis and other focal liver lesions in a subset of ultrasound data that were randomly chosen from the prospective test dataset. The study is registered at ClinicalTrials.gov (NCT03871140). Findings: The study took place between Jan 1, 2002, and Dec 31, 2021. In total, to train and test the DCNN model, we used 9631 liver ultrasound images from 6784 patients (2819 [41·7%] female patients and 3943 [58·3%] male patients) from 87 Chinese hospitals. The DCNN model was trained with 6328 images, internally validated with 984 images, and tested with 2319 images. The ResNet-50 network architecture outperformed VGG-16 and ResNext-50 and was generalisable, with areas under the receiver operating characteristic curve (AUCs) of 0·982 (95% CI 0·960–0·994), 0·984 (0·972–0·992), and 0·913 (0·886–0·935) in distinguishing echinococcosis from other focal liver lesions; 0·986 (0·966–0·996), 0·962 (0·946–0·975), and 0·900 (0·872–0·924) in distinguishing alveolar echinococcosis from cystic echinococcosis and other focal liver lesions; and 0·974 (0·818–1·000), 0·956 (0·875–0·991), and 0·944 (0·844–0·988) in distinguishing active and transitional cystic echinococcosis from inactive echinococcosis in the three test datasets. Specifically, in patients with the hepatitis B or hepatitis C virus, the model could distinguish alveolar echinococcosis from hepatocellular carcinoma with an AUC of 0·892 (0·812–0·946). In identifying echinococcosis, the model showed significantly better performance compared with senior radiologists from a high-endemicity area (AUC 0·942 [0·904–0·967] vs 0·844 [0·820–0·866]; p=0·027) and improved the diagnostic ability of junior, attending, and senior radiologists before and after assistance with AI with comparison of AUCs of 0·743 (0·714–0·770) versus 0·850 (0·826–0·871); p

Abstract Purpose Emerging evidence shows that immune checkpoint inhibitors lead to durable responses in a variety of cancers, including nasopharyngeal carcinoma (NPC), however, combination approaches (i.e., stereotactic body radiation therapy, SBRT) are required to extend this benefit beyond a subset of patients. This study retrospectively evaluated eight recurrent/metastatic NPC patients, to investigate how radiation could potentiate PD-1 checkpoint inhibition therapy. Methods Between September 2016 and July 2017, eight consecutive cases with histologically confirmed PDL1-positive status, for which prior standard therapy had been ineffective (five patients), were treated at our institution and Macao Clinics and two patients had disease progression within 6 months of completion of definitive chemoradiation, or one patient refused to receive chemoradiotherapy. All received PD-1 inhibitors first, seven of them accepted SBRT with an unmodified PD-1 inhibitors regimen after first evaluation as they were unresponsive to PD-1 inhibitors alone. Treatment was discontinued as long as patients were experiencing a clinical benefit in the opinion of the physicians and at least five cycles were given before stoppage. Results Median follow-up time was 56.7 months. The confirmed objective response rate based on RECIST-v1.1 at first evaluation was 12.5% (1/8). For the seven cases who received SBRT, six of them experience an objective response (6/7, 85.7%) after SBRT. Only one patient showed rapid progress and die within 95 days after the initiation of SBRT intervention. Three patients who did not have all lesions exposed to irradiation were available to evaluate the incidence of an abscopal effect, however, it did not occur as expected. Median PFS and OS for the seven patients were 8.0 and 30.8 months after SBRT intervention, respectively. Two-year OS as indicated was 71.0%. Conclusions PD-1 inhibitors combined with SBRT demonstrated promising antitumor activity in patients with PD-L1 positive RM-NPC. Patients may benefit from continue immunotherapy beyond disease progression when SBRT was introduced.

IntroductionThere is increasing awareness of the role of adipose tissue in breast cancer occurrence and development, but no comparison of adipose adjacent to breast cancer tissues and adipose adjacent to normal breast tissues has been reported.MethodsSingle-nucleus RNA sequencing (snRNA-seq) was used to analyze cancer-adjacent and normal adipose tissues from the same breast cancer patient to characterize heterogeneity. SnRNA-seq was performed on 54513 cells from six samples of normal breast adipose tissue (N) distant from the tumor and tumor-adjacent adipose tissue (T) from the three patients (all surgically resected).Results and discussionSignificant diversity was detected in cell subgroups, differentiation status and, gene expression profiles. Breast cancer induces inflammatory gene profiles in most adipose cell types, such as macrophages, endothelial cells, and adipocytes. Furthermore, breast cancer decreased lipid uptake and the lipolytic phenotype and caused a switch to lipid biosynthesis and an inflammatory state in adipocytes. The in vivo trajectory of adipogenesis revealed distinct transcriptional stages. Breast cancer induced reprogramming across many cell types in breast cancer adipose tissues. Cellular remodeling was investigated by alterations in cell proportions, transcriptional profiles and cell-cell interactions. Breast cancer biology and novel biomarkers and therapy targets may be exposed.

Abstract Objective The study conducted a multicenter study in China to explore the learning curve of contrast enhanced ultrasound (CEUS) for sentinel lymph nodes (SLNs), the feasibility of using this technique for the localization of SLNs and lymphatic channels (LCs) and its diagnostic performance for lymph node metastasis. Method Nine hundred two patients with early invasive breast cancer from six tertiary class hospitals in China were enrolled between December 2016 and December 2019. Each patient received general ultrasound scanning and SLN-CEUS before surgery. The locations and sizes of LCs and SLNs were marked on the body surface based on observations from SLN-CEUS. These body surface markers were then compared with intraoperative blue staining in terms of their locations. The first 40 patients from each center were included in determining the learning curve of SLN-CEUS across sites. The remaining patients were used to investigate the diagnostic efficacy of this technique in comparison with intraoperative blue staining and pathology respectively. Result The ultrasound doctor can master SLN-CEUS after 25 cases, and the mean operating time is 22.5 min. The sensitivity, specificity, negative predictive value, and positive predictive value of SLN-CEUS in diagnosing lymph node metastases were 86.47, 89.81, 74.90, and 94.97% respectively. Conclusion Ultrasound doctors can master SLN-CEUS with a suitable learning curve. SLN-CEUS is a feasible and useful approach to locate SLNs and LCs before surgery and it is helpful for diagnosing LN metastases.

BackgroundLeukemia inhibitory factor (LIF) exhibits significant tumor-promoting function, while its cognate receptor (LIFR) is considered to act as either a tumor promoter or suppressor. Dysregulation of LIF and LIFR is associated with the initiation, progression and metastasis of multiple cancer entities. Although increasing numbers of studies are revealing an indispensable critical role of LIFR in tumorigenesis for various different cancers, no systematic analysis of LIFR has appeared thus far.MethodsHere, we comprehensively analyzed the expression profile and prognostic value of LIFR, and correlations between LIFR and the infiltration of immune cells and clinicopathological parameters across different tumor types using several bioinformatic tools. The expression profile of LIFR in various tumor types and clinical stages was investigated using the TIMER2 and GEPIA2 databases. Genetic alternations of LIFR were extracted from cBioPortal. The prognostic value of LIFR was assessed using GEPIA2 and Sanger box databases, and correlations between LIFR expression and immune infiltration were analyzed using the CIBERSORT method and TIMER2 database. The correlations between LIFR expression and immune and stromal scores were assessed using ESTIMATE. We also analyzed correlations between LIFR and immunoregulators. Finally, we detected an effect of LIFR on Uterine Corpus Endometrial Carcinoma (UCEC) and evaluated the expression level of LIFR in clinical UCEC samples.ResultsAberrant expression of LIFR in cancers and its prognosis ability, especially in UCEC was documented. Significantly lower levels of LIFR expression level correlated with better prognosis in multiple tumor types. LIFR expression was positively correlated with the abundance of cancer-associated fibroblasts (CAFs) and endothelial cells in the tumor microenvironment. Additionally, LIFR expression was strongly associated with the presence of immune modulators and checkpoint genes. Overexpression of LIFR suppressed the migration and invasion of UCEC cells in vitro.ConclusionOur pan-cancer detection data provided a novel understanding of the roles of LIFR in oncogenesis.

Profiling the spatial distributions and tissue changes of characteristic compounds with interspecific differences is critical to elucidate the complex species identification during tree species traceability, wood anti-counterfeiting verification and timber trade control. In this research, in order to visualize the spatial position of characteristic compounds in two species with similar morphology (Pterocarpus santalinus and Pterocarpus tinctorius), a high coverage MALDI-TOF-MS imaging method was used to found the mass spectra fingerprints of different wood species. 2-Mercaptobenzothiazole matrix was used to spray wood tissue section to enhance the detection effect of metabolic molecules, and the mass spectrometry imaging data were obtained. Based on this technology, the spatial location of fifteen potential chemical markers with remarkable interspecific differences in 2 Pterocarpus timber species were successfully obtained. Distinct chemical signatures obtained from this method can promote rapid identification at the wood species level. Thus, matrix-assisted laser desorption/time-of-flight/ionization mass spectrometry imaging (MALDI-TOF-MSI) provides a spatial-resolved way for traditional wood morphological classification and breaking through the limitations of traditional wood identification technology.

Abstract Purpose Malignant pleural effusion (MPE) is an adverse prognostic factor in patients with osteoblastic osteosarcoma; however, the cellular contexts of MPE are largely unknown. Experimental design We performed single‐cell RNA‐sequencing (scRNA‐seq) on 27 260 cells from seven MPE samples and 91 186 cells from eight osteosarcoma tissues, including one recurrent, one lung metastasis and six primary tumour (PT) samples, to characterize their tumour microenvironment. Results Thirteen main cell groups were identified in osteosarcoma tumour and MPE samples. Immune cells dominate the cellular contexts in MPE with more T/NK cells and less osteoclasts compared to PT samples. Of T/NK cells, CD8+GNLY+, CD8+KLRC2+ T cells and FCGR3A+NK cells were enriched in MPE but CD4+FOXP3+ Tregs were enriched in PT samples. Naïve IGHD+ B and immune regulatory IGHA1+ B cells were largely identified in MPE, whereas bone metabolism‐related CLEC11A+ B cells were significantly enriched in osteosarcoma PT. M2‐type TAMs, including CLEC11A_TAM, C1QC_TAM and Prolif_TAMs, among myeloid cells were enriched in PT, which may suppress cytotoxicity activities of T cells through multiple ligand–receptor interactions. Mature LAMP3+ DCs were transformed from CD1C+ DC and CLEC9A+ DC sub‐clusters when exposure to tumour alloantigens, which may improve T cell cytotoxicity activities on tumour cells under anti‐PD‐L1 treatments. In further, immune cells from MPE usually present up‐regulated glycolysis and down‐regulated oxidative phosphorylation and riboflavin metabolism activities compared to those in PT samples. Conclusions Our study provided a novel cellular atlas of MPE and PT in patients with advanced osteosarcoma, which may provide potential therapeutic targets in the future.

To improve tobacco leaf quality, excessive K2SO4 fertilizers were applied to soils in major tobacco-planting areas in China. However, the effects of K2SO4 application on soil microbial community and functions are still unclear. An eight-year field experiment with three kinds of K2SO4 amounts (low amount, K2O 82.57 kg hm-2, LK; moderate amount, K2O 165.07 kg hm-2, MK; high amount, K2O 247.58 kg hm-2, HK) was established to assess the effects of K2SO4 application on the chemical and bacterial characteristics of tobacco-planting soil using 16S rRNA gene and metagenomic sequencing approaches. Results showed that HK led to lower pH and higher nitrogen (N), potassium (K), sulfur(S) and organic matter contents of the soil than LK. The bacterial community composition of HK was significantly different from those of MK and LK, while these of MK and LK were similar. Compared to LK, HK increased the relative abundance of predicted copiotrophic groups (e.g. Burkholderiaceae, Rhodospirillaceae families and Ellin6067 genus) and potentially beneficial bacteria (e.g. Gemmatimonadetes phylum and Bacillus genus) associated with pathogens and heavy metal resistance, N fixation, dissolution of phosphorus and K. While some oligotrophic taxa (e.g. Acidobacteria phylum) related to carbon, N metabolism exhibited adverse responses to HK. Metagenomic analysis suggested that the improvement of pathways related to carbohydrate metabolism and genetic information processing by HK might be the self-protection mechanism of microorganisms against environmental stress. Besides, the redundancy analysis and variation partitioning analysis showed that soil pH, available K and S were the primary soil factors in shifting the bacterial community and KEGG pathways. This study provides a clear understanding of the responses of soil microbial communities and potential functions to excessive application of K2SO4 in tobacco-planting soil.

A model of environmental evolution of cities, especially in East Asia, is said to follow four stages. The pathways to the fourth stage of eco-city are not always clear, but need to be contextual. This research extended the original stage model of environmental evolution to describe urban development and its impact on the environment, especially integrated a model comprise of density, mix and accessibility (DMA) for urbanization and applied Ecological Footprint (EF) concept for pressure on eco-environment. The research analyzed the Xiamen City in China through the first three stages in order to gain insights how the city might be able to hasten its transition to the fourth stage of an eco-city. As expected, the development of Xiamen City and its environmental impacts has very obvious stage characteristic. Before 2010, Xiamen City got through a long time of Stage I (poverty stage); and in the period of 2010 to 2019, Xiamen experienced the Stage II (industrial pollution stage) and Stage III (mass consumption stage) crossly. The new built-up land in the decade of 2000–2010 is much larger than the previous 10 years from 1989 to 2000, 5 times more. Meanwhile, compared with 1980–1995, the ecological deficit also enlarged greatly in period of 1995–2010. Considering up to 2019 Xiamen still has large amount of energy consumption and high level output of industry solid waste, Xiamen should take measures of adjusting the industrial structure, promoting the green industry and planning the urban growth boundary to achieve the eco-city stage.

BackgroundThe Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) is a widely used developmental assessment tool for children aged 0–6 years in China. The communication warning behavior subscale of CNBS-R2016 is used to assess the symptoms of autism spectrum disorder (ASD), and its value of >30 points indicates ASD based on CNBS-R2016. However, we observed that children with relatively lower values were also diagnosed with ASD later on in clinical practice. Thus, this study aimed to identify the suitable cutoff value for ASD screening recommended by the communication warning behavior of CNBS-R2016.Materials and methodsA total of 90 typically developing (TD) children and 316 children with developmental disorders such as ASD, developmental language disorder (DLD), and global developmental delay (GDD; 130 in the ASD group, 100 in the DLD group, and 86 in the GDD group) were enrolled in this study. All subjects were evaluated based on the CNBS-R2016. The newly recommended cutoff value of communication warning behavior for screening ASD was analyzed with receiver operating curves.ResultsChildren in the ASD group presented with lower developmental levels than TD, DLD, and GDD groups in overall developmental quotient assessed by CNBS-R2016. We compared the consistency between the scores of communication warning behavior subscale and Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS), Autism Diagnostic Observation Schedule, second edition (ADOS-2), and clinical diagnosis for the classification of ASD at a value of 30 based on the previously and newly recommended cutoff value of 12 by the CNBS-R2016. The Kappa values between the communication warning behavior and ABC, CARS, ADOS-2, and clinical diagnosis were 0.494, 0.476, 0.137, and 0.529, respectively, with an agreement rate of 76.90%, 76.26%, 52.03%, and 82.27%, respectively, when the cutoff point was 30. The corresponding Kappa values were 0.891, 0.816, 0.613, and 0.844, respectively, and the corresponding agreement rate was 94.62%, 90.82%, 90.54%, and 93.10%, respectively, when the cutoff point was 12.ConclusionThe communication warning behavior subscale of CNBS-R2016 is important for screening ASD. When the communication warning behavior score is 12 points or greater, considerable attention and further comprehensive diagnostic evaluation for ASD are required to achieve the early detection and diagnosis of ASD in children.

Background: High-grade serous ovarian cancer (HGSOC) treatment is facing clinical challenges. The tumor immune microenvironment (TME) has recently been shown to perform a critical function in the prediction of clinical outcomes as well as the effectiveness of treatment. Leukocyte migration is enhanced in malignant tumors and promotes immunity. However, its role in how to underlie the migration of immune cells into the TME remains to be further explained in HGSOC. Methods: We built a prognostic multigene signature with leukocyte migration-related differentially expressed genes (LMDGs), which is associated with TME by single-sample gene set enrichment analysis (ssGSEA), in the The Cancer Genome Atlas (TCGA) cohort. Furthermore, we systematically correlated risk signature with immunological characteris-tics in TME, mutational profiles of HGSOC, and potential value in predicting efficacy of platinum-based chemotherapy and immunotherapy. Screening of the most important prognostic factor among risk signatures by Friends analysis, and immunofluorescence was employed to examine both the expression of CD2 as well as its relationship with CD8 and PD-1. Results: LMDGs-related prognostic model showed good prediction performance. Patients who had high-risk scores exhibited significantly reduced progression-free survival (PFS) and overall survival (OS) than those with low-risk scores, according to the results of the survival analysis (p < 0.001). In the TCGA cohort, the risk signature was found to have independent prognostic sig-nificance for HGSOC (HR =1.829, 95% CI = 1.460–2.290, p < 0.001) and validated in the Gene Expression Omnibus (GEO) cohort. Samples with high-risk scores had lower levels of CD8+ T cells infiltration. The low-risk signature shapes an inflamed TME in HGSOC. Furthermore, immune therapy might be effective for the low-risk subtype of HGSOC patients (p < 0.001). Friends analysis revealed that CD2 was the most important prognostic gene among risk signatures. Real-time quantitative PCR analysis showed the expression of CD2 was greater in tumor cells as opposed to normal ovarian cells. CD8, PD-1, and CD2 were shown to be co-localized in HGSOC tissues, according to immunofluorescence analyses. CD2 was significantly correlated with CD8 (r = 0.47). Conclusions: Our study identified and validated a promising LMDGs signature associated with inflamed TME, which might offer some prospective clinical implications for the treatment of SOC. CD2 might be a novel biomarker to predict immune efficacy.

Zinc-based hybrid-flow batteries are considered as a promising alternative to conventional electrochemical energy-storage systems for medium- to large-scale applications due to their high energy densities, safety, and abundance. However, the performance of these batteries has been limited by issues such as dendritic growth and passivation of zinc anodes during charge–discharge cycling. To address this challenge, a variety of two- and three-dimensional zinc anodes have been investigated. While two-dimensional zinc anodes have been extensively studied, there has been limited investigation into three-dimensional zinc anodes for hybrid-flow batteries. This study highlights the potential of three-dimensional zinc anodes to mitigate overpotentials and improve the mass transport of active species to promote negative electrode reactions. The performance of a membraneless flow battery based on low-cost zinc and organic quinone was herein evaluated using experimental and numerical approaches. Specifically, the use of zinc fiber was shown to yield an average coulombic efficiency of approximately 90% and an average voltage efficiency of approximately 82% over the course of 100 cycles at a current density of 30 mA cm−2. These results indicate the viability of using zinc fiber anodes to improve the performance of existing hybrid-flow batteries.

The land surface temperature (LST) is an important indicator reflecting the ecological environment condition. As a sensitive area to climate change, mastering the spatial and temporal changes of summer LST in the Bosten Lake basin (BLB) helps gain insight into the evolution of the thermal environment in the Bosten Lake basin and for long-term monitoring of the basic ecological changes in the basin. Based on MOD11A1 data from 2005 to 2020, this paper investigates the diurnal LST spatiotemporal series variation and its influencing factors in the Bosten Lake basin by using surface temperature class classification, trending analysis, the Hurst index, and geographic probes. The results show that (1) the wetland grasslands in and around the Bayinbruck steppe in the northwestern part of the study area exhibit a heat island effect during the day, while the opposite is true at night. In terms of temporal changes, LST changes in the BLB fluctuate widely, having a general rising and then decreasing trend. (2) The decreasing trend of LST from 2005 to 2020 is significant during the daytime and vice versa at night, and the change at night is greater than during the day. The areas with significantly higher diurnal LST in the future have all expanded compared to the area occupied by them now, with an overall trend of a steady increase. (3) The dominant factor of LST variation has the strongest explanatory power when altitude and NDVI are combined during the daytime and the strongest explanatory power when NPP and temperature are combined at night.

Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use.

Accuracy assessment is essential in all image classification-related fields, ranging from molecular imaging to earth observation. However, existing accuracy metrics are too sensitive to class imbalance or lack explicit interpretations for assessing classification performance. Consequently, their scores may be misleading when they are applied to compare classification algorithms that address different image data sources. These limitations jeopardize the widespread application of deep learning classification methods for classifying different image types. We introduce the metrics of image classification efficacy from medicine and pharmacology to overcome the limitations of accuracy metrics. We include a baseline classification to derive the metrics of image classification efficacy and apply real-world and hypothetical examples to further examine their usefulness. Image classification efficacies can be applied at the map and class levels and for binary and multiclass classifications. The interpretability and comparability of image classification efficacies facilitate reliable classification method evaluation across data sources. We detail the procedures of classification efficacy assessment for image classification researchers and classifier users.

The COVID-19 pandemic caused drastic changes in human activities and nighttime light (NTL) at various scales, providing a unique opportunity for exploring the pattern of the extreme responses of human community. This study used daily NTL data to examine the spatial variations and temporal dynamics of human activities under the influence of COVID-19, taking Chinese mainland as the study area. The results suggest that the change in the intensity of NTL is not correlated to the number of confirmed cases, but reflects the changes in human activities and the intensity of epidemic prevention and control measures within a region. During the outbreak period, the major provincial capitals and urban agglomerations were affected by COVID-19 more than smaller cities. During the recovery, different regions showed different recovery processes. The cities in West and Northeast China recovered steadily while the recovery in coastal cities showed relatively greater fluctuations due to an increase in imported cases. Wuhan, the most seriously affected city in China, did not recover until the end of March. Nevertheless, as of 31 March, the overall NTL across China had recovered to an 89.5% level of the same period in the previous year. The high consistency between the big data of travel intensity and NTL further proved the validity of the results of this study. These findings imply that daily NTL data are effective for rapidly monitoring the dynamic changes in human activities, and can help evaluate the effects of control measures on human activities during major public health events.

ObjectiveAnlotinib, a novel multitarget kinase inhibitor of VEGFR, FGFR, PDGFR and c-Kit, has proven to be effective and safe for refractory soft tissue sarcoma patients, but has not been examined in recurrent or metastatic primary malignant bone tumors in a clinical trial setting.MethodsThis is a multicenter single-arm trial. Patients with pathologically proven recurrent or metastatic primary malignant bone tumors were eligible. Anlotinib was administered orally at 12 mg per day. Each cycle consisted of 2 weeks of treatment followed by 1-week off-treatment. The primary endpoint was progression-free survival (PFS), as assessed in the intention-to-treat (ITT) population. Secondary endpoints included objective response rate (ORR), disease control rate (DCR) and overall survival (OS). Adverse events (AEs) were assessed per NCI CTCAE version 4.03.ResultsA total of 42 patients were enrolled. Median PFS was 5.3 months (95% CI 3.5-8.4 months) in the overall analysis, 4.8 months (95%CI 3.5-7.1 months) in osteosarcoma patients and 2.8 months [95%CI 1.3 months to not reached (NR)] in chondrosarcoma patients. The median OS was 11.4 months (95% CI 10.1 months to NR) in the overall analysis, not reached (95% CI, NR, NR) in osteosarcoma patients and 11.4 months (95% CI 1.8 to 21.1 months) in chondrosarcoma patients. The ORR was 9.52% and DCR was 78.57%. Grade 3 or above AEs occurred in 54.76% of the patients, and included hypertension (19.05%), hypertriglyceridemia (9.52%) and pustulosis palmaris et plantaris (7.14%). No treatment-related death was reported.ConclusionAnlotinib demonstrated promising antitumor activities in recurrent or metastatic primary malignant bone tumors with manageable AEs.

Osteosarcomas are difficult to treat and have a limited response to immunotherapy. Here, the authors analyse osteosarcomas at the single-cell level, and identify both the transdifferentiation of malignant cells and an array of immune cells that could have implications for metastasis and immunotherapy.

Abstract Background Squamous cell carcinoma (SCC) is a main pathological type of non-small cell lung cancer. It is common among elderly patients with poor prognosis. We aimed to establish an accurate nomogram to predict survival for elderly patients (≥ 60 years old) with SCC based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods The gerontal patients diagnosed with SCC from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The independent prognostic factors were identified using multivariate Cox proportional hazards regression analysis, which were utilized to conduct a nomogram for predicting survival. The novel nomogram was evaluated by Concordance index (C-index), calibration curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Results 32,474 elderly SCC patients were included in the analysis, who were randomly assigned to training cohort (n = 22,732) and validation cohort (n = 9742). The following factors were contained in the final prognostic model: age, sex, race, marital status, tumor site, AJCC stage, surgery, radiation and chemotherapy. Compared to AJCC stage, the novel nomogram exhibited better performance: C-index (training group: 0.789 vs. 0.730, validation group: 0.791 vs. 0.733), the areas under the receiver operating characteristic curve of the training set (1-year AUC: 0.846 vs. 0.791, 3-year AUC: 0.860 vs. 0.801, 5-year AUC: 0.859 vs. 0.794) and the validation set (1-year AUC: 0.846 vs. 0.793, 3-year AUC: 0.863 vs. 0.806, 5-year AUC: 0.866 vs. 0.801), and the 1-, 3- and 5-year calibration plots. Additionally, the NRI and IDI and 1-, 3- and 5-year DCA curves all confirmed that the nomogram was a great prognosis tool. Conclusions We constructed a novel nomogram that could be practical and helpful for precise evaluation of elderly SCC patient prognosis, thus helping clinicians in determining the appropriate therapy strategies for individual SCC patients.

Osteosarcoma (OS) is the most common primary bone sarcoma, chemoresistance becomes an obstacle to its treatment. Metabolic reprogramming is a hallmark of malignancy, targeting the metabolic pathways might provide a reasonable therapeutic strategy for OS. Here we demonstrated that Ailanthone (AIL), a major component of the Chinese medicine Ailanthus altissima, significantly suppressed OS cell growth in vitro and in vivo. Furthermore, AIL dose-dependently inhibited cell migration and invasion, induced cell cycle arrest and apoptosis in OS cells. Combined transcriptomics, proteomics and metabolomics analyses revealed that AIL induced widespread changes in metabolic programs in OS cells, while the serine biosynthetic pathway (SSP) was the most significantly altered pathway. qRT-PCR and Western blot assay confirmed that the transcript and protein levels of the SSP genes (PHGDH, PSAT1 and PSPH) were downregulated dose-dependently by AIL. In addition, we found out that many downstream pathways of the SSP including the one-carbon pool by folate, purine metabolism, pyrimidine metabolism, DNA replication and sphingolipid metabolism were downregulated after AIL treatment. In the revere test, PHGDH overexpression but not exogenous serine supplementation clearly attenuated the effects of AIL on OS cells. Taken together, AIL exerts antitumor effects on OS through mediating metabolic reprogramming, at least in part, by suppressing the SSP. Our findings suggest that AIL could emerge as a potential therapeutic strategy in OS.

Introduction: The present study aimed to evaluate the influence of biological characteristics of hepatocellular carcinoma (HCC) on the Liver Imaging Reporting and Data System (LI-RADS) v2017 category of contrast-enhanced ultrasound (CEUS) in patients with high risk and compare the outcomes among different categories after radical resection. Methods: Between June 2017 and December 2020, standardized CEUS data of liver nodules were prospectively collected from multiple centers across China. We conducted a retrospective analysis of the prospectively collected data on HCCs measuring no more than 5 cm as diagnosed by pathology. LI-RADS categories were assigned after thorough evaluation of CEUS features. Then, CEUS LI-RADS categories and major features were compared in different differentiation, Ki-67 and microvascular invasion (MVI) statuses. Differences in recurrence-free survival (RFS) among different LI- RADS categories were further analyzed. Results: A total of 293 HCC nodules in 293 patients were included. This study revealed significant differences in the CEUS LI-RADS category of HCCs among differentiation (p 0.05). With a median follow-up of 23 months, HCCs assigned to different CEUS LI-RADS classes showed no significant differences in RFS after resection. Conclusions: Biological characteristics of HCC, including differentiation and level of Ki-67 expression could influence major features of CEUS and impact the CEUS LI-RADS category. HCCs in different CEUS LI-RADS categories showed no significant differences in RFS after resection.

Alternative splicing (AS) occurs extensively in eukaryotes as an essential mechanism for regulating transcriptome complexity and diversity, but the AS landscape regulating potassium (K) use efficiency in plants is unclear. In this study, we performed high-throughput transcriptome sequencing of roots and shoots from allopolyploid Nicotiana tabacum under K+ deficiency. Preliminary physiological analysis showed that root system architecture was dramatically changed due to potassium deficiency and that IAA content was significantly reduced in root and shoot. AS analysis showed that a total of 28,179 genes exhibited 54,457 AS events, and 1,510 and 1,732 differentially alternatively spliced (DAS) events were identified in shoots and roots under low K+ stress. Nevertheless, only 120 DAS events occurred in both shoots and roots, implying that most DAS events were tissue-specific. Both in shoot and the root, the proportion of DAS genes in differentially expressed (DE) genes equaled that in non-DE genes, which indicated that AS might play a unique regulatory role in response to low potassium. Gene ontology analysis further indicated that transcription regulation and AS modulation worked independently in response to low K+ stress in tobacco, as their target biological processes were different. Totally 45 DAS transcription factors (TFs) were found, which were involved in 18 TF families. Five Auxin response factor (ARF) TFs were significantly DAS in root, suggesting that response to auxin was probably subject to AS regulation in the tobacco root. Our study shows that AS variation occurs extensively and has a particular regulatory mechanism under K+ deficiency in tobacco. The study also links changes in root system architecture with the changes in AS of ARF TFs, which implied the functional significance of these AS events for root growth and architecture.

Background: Training parents to implement language and communication intervention strategies is an effective approach to promote language development for children with language delay. Aims: This study introduces an online parent training program conducted in Hubei province, China, which was designed to help parents of language-delayed children with a diagnosis of autism spectrum disorder (ASD), developmental language disorder (DLD) or global developmental delay (GDD) apply language intervention strategies into daily interactions and promote their children's language development at home. Methods & Procedures: The Bethel Hearing and Speaking Training Center Family Training for Early Communication & Language Development (Bethel Family Training Program, BFT) (Bethel HSTC, 2020) was designed to improve the language and communication skills for children with language delay in a naturalistic way. The caregivers (including parents, grandparents and other main caregivers) participated in an 8-h online program, including lectures on milestones in child language development, common misunderstandings of child language development, and three basic family language intervention strategies ('Looking together, playing together, and talking together') incorporating active learning through video analysis and discussion. Tongji Hospital in Hubei then continued with 3 months of online home intervention monitoring to all the caregivers via weekly online Q&As led by BFT certified speech therapists' team. The Gesell Developmental Schedules (GDS) was carried out before the online parent training program and after the 3-month online home intervention monitoring. Outcomes & Results: 146 families whose children aged 12-68 months with language delay participated in the online training program. The results of the GDS assessments conducted before and after the program showed that not only did the developmental quotient (DQ) of language improve, but so did the DQ of social behaviour and adaptive behaviour (p < 0.001). There is no between-group difference in the application of three strategies between the ASD group and the DLD or GDD group (p > 0.05). Furthermore, both caregivers' ability to apply 'looking together, playing together, talking together' strategies and the effective interaction time played important roles in improving the child's language abilities. Conclusion & Implications: The online parent training focusing on improving daily interaction with children through speech-language stimulation strategies promoted the development of language skills. It is an economic and practical approach for children with language delay who have limited access to local language intervention programs.

Compact development is one of the most effective solutions for sustainable urbanization under the rapid growth of the urban population. Great efforts have been made to measure urban physical compactness while limited attention has been paid to functional zoning of urban areas. Here, we introduce a novel index, called the functional compactness index (FCI), to quantify urban functional compactness through the integration of geospatial data sources, including Points of Interest (POIs) data, Road Network of OpenStreetMap (RNO) data, and National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data. The FCI does not require the analysis of the grid scale and thus, is technically simpler than conventional compactness index (CI). We examined the effectiveness of FCI on estimating urban compactness under four land use scenarios and in four Chinese cities. The results suggest that: (1) the FCI can comprehensively reflect the intensity of human activity, the differentiation between residential zones and other functional zones, and the mixing degree of different functional zones; (2) the FCI is not affected by the service radius of residential zones; (3) the FCI can reflect the overall and local-scale functional compactness of a city; and (4) the FCI can be used to effectively compare spatial characteristics of functional compactness among different cities. In conclusion, the FCI considers the rationality of urban functional layout, which not only is helpful for urban planning, but also enriches the quantitative methods of urban compactness evaluation.

Rapid urbanization in China has led to an excessive urban expansion of built-up areas, which makes quantitative research on compact city important. We adopted density and the degree of mixed land use to measure the compactness of 160 Chinese cities. Spatial autocorrelation analysis was performed to identify spatial clustering patterns, and the relationships between compactness and five variables were explored through regression models. The result shows that in nearly half of the cases, the calculated values of two indices are less than the average. The high or low values of density and the degree of mixed land use tend to be spatially clustered. The hot spot regions of density and the degree of mixed land use lie mainly in the south of China, while the north present as cold spots or the insignificant regions. Urban compactness can be affected by multifaceted factors and the relationships between compactness and five variables are not consistent throughout the areas of analysis. The GWR model can identify this phenomenon and provides a better fit than the OLS model. This study proposed a new approach to measure the compactness, and the results of GWR analysis can conducive to appropriate policy-making based on different local conditions.

Triple-negative breast cancer (TNBC) is the most aggressive and fatal sub-type of breast cancer. This study aimed to identify metastasis-associated genes that could serve as biomarkers for TNBC diagnosis and prognosis. RNA-seq data and clinical information on TNBC from the Cancer Genome Atlas were used to conduct analyses. Expression data were used to establish co-expression modules using average linkage hierarchical clustering. We used weighted gene co-expression network analysis to explore the associations between gene sets and clinical features and to identify metastasis-associated candidate biomarkers. The K-M plotter website was used to explore the association between the expression of candidate biomarkers and patient survival. In addition, receiver operating characteristic curve analysis was used to illustrate the diagnostic performance of candidate genes. The pale turquoise module was significantly associated with the occurrence of metastasis. In this module, 64 genes were identified, and its functional enrichment analysis revealed that they were mainly associated with transcriptional misregulation in cancer, microRNAs in cancer, and negative regulation of angiogenesis. Further, 4 genes, IGSF10, RUNX1T1, XIST , and TSHZ2 , which were negatively associated with relapse-free survival and have seldom been reported before in TNBC, were selected. In addition, the mRNA expression levels of the 4 candidate genes were significantly lower in TNBC tumor tissues compared with healthy tissues. Based on the K-M plotter, these 4 genes were correlated with poor prognosis of TNBC. The area under the curve of IGSF10, RUNX1T1, TSHZ2 , and XIST was 0.918, 0.957, 0.977, and 0.749. These findings provide new insight into TNBC metastasis. IGSF10, RUNX1T1, TSHZ2 , and XIST could be used as candidate biomarkers for the diagnosis and prognosis of TNBC metastasis.

Background: The diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. We aimed to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification of malignant from benign FLLs. Materials and methods: This study was conducted in 13 hospitals and finally 2143 patients with 24,343 US images were enrolled. Patients who had non-cystic FLLs with pathological results were enrolled. The FLLs from 11 hospitals were randomly divided into training and internal validations (IV) cohorts with a 4:1 ratio for developing and evaluating DCNN-US. Diagnostic performance of the model was verified using external validation (EV) cohort from another two hospitals. The diagnosis value of DCNN-US was compared with that of contrast enhanced computed tomography (CT)/magnetic resonance image (MRI) and 236 radiologists, respectively. Findings: The AUC of ModelLBC for FLLs was 0.924 (95% CI: 0.889–0.959) in the EV cohort. The diagnostic sensitivity and specificity of ModelLBC were superior to 15-year skilled radiologists (86.5% vs 76.1%, p = 0.0084 and 85.5% vs 76.9%, p = 0.0051, respectively). Accuracy of ModelLBC was comparable to that of contrast enhanced CT (both 84.7%) but inferior to contrast enhanced MRI (87.9%) for lesions detected by US. Interpretation: DCNN-US with high sensitivity and specificity in diagnosing FLLs shows its potential to assist less-experienced radiologists in improving their performance and lowering their dependence on sectional imaging in liver cancer diagnosis.

The random vibration failure of an array power supply for near-space SAR was analyzed. The fracture mechanism and the fracture reason of fracture formation in the specimen were investigated. The results show that antishock MOS pin breaks first, and the power supply is still in the working state during the process of random vibration. This caused dischargings at the tip of the fracture and melting of the tip of the broken pin which form a river-shaped fracture and granular tissue. The plastic fracture with typical dimple morphology of the pins for the resistor tube occurred during the random vibration. The intergranular fracture appeared at the welding part of the electronic components for array power supply, which presented a brittle fracture mechanism. The fracture was dominated by a ductile fracture for components when the stress produced by the vibration was close to the yield strength of the material. The fracture was dominated by a brittle fracture for components when the stress produced by the vibration was far beyond the yield strength of the material. A simulation evaluation system based on the high-confidence model was proposed. The stress of the electronic components for array power supply and its welding was much lower than the allowable strength of the material by the optimization of the structure and the form of the welding for the array power supply. The sample was successfully tested and verified without any further fracture problems.

Abstract This study reports an acupuncture needle modified with an iron-porphyrin functionalized graphene composite (FGPC) for real-time monitoring of nitric oxide (NO) release in acupoints of rats. A gold film was first deposited to the needle surface to enhance the conductivity. The FGPC was prepared via hydrothermal synthesis, and subsequently applied to the tip surface of acupuncture needle by electrochemical deposition method. The functionalized needle enabled a specific and sensitive detection of NO based on the favorably catalytic properties of iron-porphyrin and the excellent conductivity of graphene. Amperometric data showed that the needle achieved not only a low detection limit down to 3.2 nM in PBS solution, but also a satisfactory selectivity. Interestingly, the functionalized needle could be inserted into the acupoints of rats for real-time monitoring of NO in vivo. It was found that a remarkable response to NO was respectively obtained in different acupoints when stimulated by L-arginine (L-Arg), revealing that the release of NO was detectable in acupoints. We expect this work would showcase the applications of acupuncture needle in detecting some important signaling molecules in vivo, and exploring the mechanism of acupuncture treatment.

Abstract Accumulating evidence suggests an association between the SIRT1 gene and human psychiatric disorders. The aim of the study was to investigate the association between SIRT1 and predisposition to antisocial personality traits (ASP) in Chinese adolescents. Participants consisted of 327 controls and 261 juvenile offenders who were diagnosed with predisposition to ASP according to the Personality Diagnostic Questionnaire. Four tag single nucleotide polymorphisms (tagSNPs) of SIRT1, namely rs12778366, rs7896005, rs10823112, and rs4746720, were genotyped. Association analysis between individual SNPs and ASP risk revealed the CC genotype of rs4746720 to be significantly associated with reduced risk of ASP (OR = 0.51, 95% CI = 0.33–0.77, adjusted P = 0.007). Haplotype analysis showed the TAAC haplotype was associated with reduced susceptibility to ASP (OR = 0.72, 95% CI = 0.57–0.91, P = 0.005). Moreover, rs4746720 variants were found to not only have a direct impact on ASP susceptibility but also modulate the effect of alcohol consumption (Y = 0.022X + 0.431 vs. Y = −0.066X + 0.387). The present study is the first to report a significant association between SIRT1 polymorphisms and ASP in adolescents. This finding is expected to aid in the development of effective interventions for this socially and personally costly disorder.

Osteosarcoma (OS) is one of the most common primary bone tumors in children and young adults. The majority of osteosarcoma patients have limited alternative therapeutic options and metastatic patients generally have a poor prognosis. Thus, it is important to explore novel effective therapeutic targets in the treatment of osteosarcoma. Diacylglycerol kinase zeta (DGKZ) is a recently identified gene potentially associated with certain human carcinogenesis. However, the role of DGKZ in proliferation of osteosarcoma is still unclear. In this study, DGKZ's expression was firstly investigated in OS tumor samples and correlated with poor outcome in OS patients. Silence of DGKZ by shRNA hampered osteosarcoma cell growth and promoted cell apoptosis in vitro. In vivo, DGKZ's knockout also suppressed xenograft tumor proliferation as determined by bioluminescence imaging and weight/volume measurements. Meanwhile, Affymetrix GeneChip and Ingenuity Pathway Analysis (IPA) revealed that DGKZ knockdown resulted in a decreased activity of MYC pathway, and several target genes expression in MYC pathway were altered, including CCND1, CDKN2B, CDK6, PCNA, and EGR1. Furthermore, immunoprecipitation coupled with mass spectrometry (IP-MS) analysis was used to identify proteins that interacted with DGKZ in OS cells and revealed ERK1/2, a key MYC-interactor, to associate with DGKZ. Together, our study demonstrated that DGKZ might act as an oncogene in osteosarcoma via its possible interaction with ERK1/2 and MYC pathway.

The timely and accurate recognition of damage to buildings after destructive disasters is one of the most important post-event responses. Due to the complex and dangerous situations in affected areas, field surveys of post-disaster conditions are not always feasible. The use of satellite imagery for disaster assessment can overcome this problem. However, the textural and contextual features of post-event satellite images vary with disaster types, which makes it difficult to use models that have been developed for a specific disaster type to detect damaged buildings following other types of disasters. Therefore, it is hard to use a single model to effectively and automatically recognize post-disaster building damage for a broad range of disaster types. Therefore, in this paper, we introduce a building damage detection network (BDD-Net) composed of a novel end-to-end remote sensing pixel-classification deep convolutional neural network. BDD-Net was developed to automatically classify every pixel of a post-disaster image into one of non-damaged building, damaged building, or background classes. Pre- and post-disaster images were provided as input for the network to increase semantic information, and a hybrid loss function that combines dice loss and focal loss was used to optimize the network. Publicly available data were utilized to train and test the model, which makes the presented method readily repeatable and comparable. The protocol was tested on images for five disaster types, namely flood, earthquake, volcanic eruption, hurricane, and wildfire. The results show that the proposed method is consistently effective for recognizing buildings damaged by different disasters and in different areas.

Decomposing main drivers of CO2 emissions and predicting the trend of it are the key to promoting low-carbon development for coping with climate change based on controlling GHG emissions. Here, we decomposed six drivers of CO2 emissions in Changxing County using the Logarithmic Mean Divisia Index (LMDI) method. We then constructed a model for CO2 emissions prediction based on a revised version of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and used it to simulate energy-related CO2 emissions in five scenarios. Results show that: (1) From 2010 to 2017, the economic output effect was a significant, direct, dominant, and long-term driver of increasing CO2 emissions; (2) The STIRPAT model predicted that energy structure will be the decisive factor restricting total CO2 emissions from 2018 to 2035; (3) Low-carbon development in the electric power sector is the best strategy for Changxing to achieve low-carbon development. Under the tested scenarios, Changxing will likely reach peak total CO2 emissions (17.95 million tons) by 2030. Measures focused on optimizing the overall industrial structure, adjusting the internal industry sector, and optimizing the energy structure can help industry-oriented counties achieve targeted carbon reduction and control, while simultaneously achieving rapid economic development.

Due to the nearly identical phenotypes and chemical constituents, it is often very challenging to accurately differentiate diverse species of a Chinese herbal genus. Although technologies including DNA barcoding have been introduced to help address this problem, they are generally time-consuming and require expensive sequencing. Herein, we present a simple sequencing-free electrochemical biosensor, which enables easy differentiation between two closely related Fritillaria species. To improve its differentiation capability using trace amounts of DNA sample available from herbal extracts, a stepwise electrochemical deposition of reduced graphene oxide (RGO) and gold nanoparticles (AuNPs) was adopted to engineer a synergistic nanostructured sensing interface. By using such a nanofeatured electrochemical DNA (E-DNA) biosensor, two Chinese herbal species of Fritillaria (F. thunbergii and F. cirrhosa) were successfully discriminated at the DNA level, because a fragment of 16-mer sequence at the spacer region of the 5S-rRNA only exists in F. thunbergii. This E-DNA sensor was capable of identifying the target sequence in the range from 100 fM to 10 nM, and a detection limit as low as 11.7 fM (S/N = 3) was obtained. Importantly, this sensor was applied to detect the unique fragment of the PCR products amplified from F. thunbergii and F. cirrhosa, respectively. We anticipate that such a direct, sequencing-free sensing mode will ultimately pave the way towards a new generation of herb-identification strategies.

To estimate the prevalence of suicide attempts and explore the shared and unique factors influencing suicide risk in left-behind children (LBC) and non-left-behind children (NLBC) in rural China, this study collected data using a multi-stage cluster random sampling method from 13,952 children including 6,034 LBC and 7,918 NLBC. Sociodemographic characteristics, suicide attempts, neglect and physical abuse, negative life events, and loneliness were measured by self-reported questionnaires. Data were analyzed using logistic regression models. Gender and mother's education level were unique influential factors for NLBC while family structure type was a unique influential factor for LBC. The study provides two novel findings regarding NLBC specifically: 1. Children with optimal family socioeconomic status are more likely to report suicide attempts (odds ratio OR = 1)than are those in the general children population, OR 0.52 (95% CI: 0.39-0.70), and 2. Children with higher mother's education level are subject to higher suicide rates in high school, OR 1.67 (95% CI: 1.13-2.46), and post-secondary education, OR 2.14 (95% CI: 1.37-3.37). The unique characteristics of LBC and NLBC in China suggest that investigating risk factors and determining the factors that might be targeted in intervention programs are urgently needed currently.

Information about urban built-up areas is important for urban planning and management. However, obtaining accurate information about urban built-up areas is a challenge. This study developed a general-purpose built-up area intelligent classification (BAIC) system that supports various types of data and classifiers. All of the steps in the BAIC were implemented using Python modules including Numpy, Pandas, matplotlib, and scikit-learn. We used the BAIC to conduct a classification experiment that involved seven types of input data; namely, Point of Interest (POI), Road Network (RN), nighttime light (NTL), a combination of POI and RN data (POI_RN), a combination of POI and NTL data (POI_NTL), a combination of RN and NTL data (RN_NTL), and a combination of POI, RN, and NTL data (POI_RN_NTL), and five classifiers, namely, Logistic Regression (LR), Decision Tree (DT), Random Forests (RF), Gradient Boosted Decision Trees (GBDT), and AdaBoost. The results show the following: (1) among the 35 combinations of the five classifiers and seven types of input data, the overall accuracy (OA) ranged from 76 to 89%, F1 values ranged from 0.73 to 0.86, and the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.83 to 0.95. The largest F1 value and OA were obtained using the POI_RN_NTL data and AdaBoost, while the largest AUC was obtained using POI_RN_NTL and POI_NTL data against AdaBoost, LR, and RF; and (2) the advantages of the BAIC include its support for multi-source input data, its objective accuracy assessment, and its robust classifiers. The BAIC can quickly and efficiently realize the automatic classification of urban built-up areas at a reasonably low cost and can be readily applied to other urban areas in the world where any kind of POI, RN, or NTL data coverage is available. The results of this study are expected to provide timely and effective reference information for urban planning and urban management departments, and could also potentially be used to develop large-scale maps of urban built-up areas in the future.

Rapid assessment of natural disasters is essential for disaster analysis and spatially explicit strategic decisions of post-disaster reconstruction but requires timely available data. The recent daily data of the National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provide new opportunities to detect and evaluate natural disasters. Here, we introduce an application of NPP-VIIRS DNB daily data for rapidly assessing the damage of a severe typhoon that struck the urban agglomerations along the western Taiwan Straits in China. Our research explored the methods of rapid identification and extraction of the areas based on changes in nighttime light (NTL) after the typhoon disaster by using a statistical radiation-normalization method. We analyzed the correlations of NTL image derivatives with human population, population density, and gross domestic product (GDP). The strong correlations were found between NTL image light density and population density (R2 = 0.83) and between the total nighttime light intensity and GDP (R2 = 0.96) at the prefecture level. In addition, we examined the interrelationships between changes in NTL images and the areas affected by the typhoon and proposed a method to predict the affected population. Finally, the affected area and the affected population in the study area could be rapidly retrieved based on the proposed remote sensing method. The overall accuracy was 83.2% for the detection of the affected population after disaster and the recovery rate of the affected area was 86.9% in the third week after the typhoon. This research demonstrates that the NTL image-based change detection method is simple and effective, and further explains that the NPP-VIIRS DNB daily data are useful for rapidly assessing affected areas and affected populations after typhoon disasters, and for timely quantifying the degree of recovery at a large spatial scale.

Land carrying capacity (LCC) explains whether the local land resources are effectively used to support economic activities and/or human population. LCC can be evaluated commonly with two approaches, namely ecological footprint analysis (EFA) and the index system method (ISM). EFA is helpful to investigate the effects of different land categories whereas ISM can be used to evaluate the contributions of social, environmental, and economic factors. Here we compared the two LCC-evaluation approaches with data collected from Xiamen City, a typical region where rapid economic growth and urbanization are found in China. The results show that LCC assessments with EFA and ISM not only complement each other but also are mutually supportive. Both assessments suggest that decreases in arable land and increasingly high energy consumption have major negative effects on LCC and threaten sustainable development for Xiamen City. It is important for the local policy makers, planners and designers to reduce ecological deficits by controlling fossil energy consumption, protecting arable land and forest land from converting into other land types, and slowing down the speed of urbanization, and to promote sustainability by controlling rural-to-urban immigration, increasing hazard-free treatment rate of household garbage, and raising energy consumption per unit industrial added value. Although EFA seems more appropriate for estimating LCC for a resource-output or self-sufficient region and ISM is more suitable for a resource-input region, both approaches should be employed when perform LCC assessment in any places around the world.

Accurate mapping of temporal changes in urban land use and land cover (LULC) is important for monitoring urban expansion and changes in LULC, urban planning, environmental management, and environmental modeling. In this study, we present a feature-based approach of the decision tree classification (FBA-DTC) method for mapping LULC based on spectral and topographic information. Landsat 5 TM and Land 8 OLI images were employed, and the technique was applied to the coastal city of Xiamen, China. The method integrates multi-spectral features such as SAVI (soil adjustment vegetation index), NDWI (normalized water index), MNDBaI (modified normalized difference barren index), BI (brightness index), and WI (wetness index), with topographic features including DEM and slope. In addition, the new approach distinguishes between fallow land and cropland, and separates high-rise buildings from beaches and water bodies. Several of the FBA-DTC parameters (or rules) from 1997 to 2015 remained constant (i.e., DEM and slope), whereas others changed slightly. WI was negatively related to percent area of beach land, and BI was negatively related to percent area of arable land. The FBA-DTC method had an average user’s accuracy (UA) of 91.36% for built-up land, an average overall accuracy (OA) of 92.13%, and a Kappa coefficient (KC) of 0.90 for the period from 2003 to 2015, representing respective increases of 15.87%, 10.17%, and 0.13, compared with values calculated using maximum likelihood classification (MLC). Over the past 12 years, built-up land increased from 23.67% to 43.17% owing to occupation of coastal reclamation, arable land, and forest land. The FBA-DTC method presented here is a valuable technique for evaluating urban growth and changes in LULC classification for coastal cities.