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Malignant glioma (MG) is the most common type of primary malignant brain tumors. Surgical resection of MG remains the cornerstone of therapy and the extent of resection correlates with patient survival. A limiting factor for resection, however, is the difficulty in differentiating the tumor from normal tissue during surgery. Fluorescence imaging is an emerging technique for real-time intraoperative visualization of MGs and their boundaries. However, most clinical grade neurosurgical operative microscopes with fluorescence imaging ability are hampered by low adoption rates due to high cost, limited portability, limited operation flexibility, and lack of skilled professionals with technical knowledge. To overcome the limitations, we innovatively integrated miniaturized light sources, flippable filters, and a recording camera to the surgical eye loupes to generate a wearable fluorescence eye loupe (FLoupe) device for intraoperative imaging of fluorescent MGs. Two FLoupe prototypes were constructed for imaging of Fluorescein and 5-aminolevulinic acid (5-ALA), respectively. The wearable FLoupe devices were tested on tumor-simulating phantoms and patients with MGs. Comparable results were observed against the standard neurosurgical operative microscope (PENTERO® 900) with fluorescence kits. The affordable and wearable FLoupe devices enable visualization of both color and fluorescence images with the same quality as the large and expensive stationary operative microscopes. The wearable FLoupe device allows for a greater range of movement, less obstruction, and faster/easier operation. Thus, it reduces surgery time and is more easily adapted to the surgical environment than unwieldy neurosurgical operative microscopes. Clinical and Translational Impact Statement—The affordable and wearable fluorescence imaging device developed in this study enables neurosurgeons to observe brain tumors with the same clarity and greater flexibility compared to bulky and costly operative microscopes.

Summary: This study proposes a directed acyclic graph (DAG)-based framework for generalized variance decomposition for investigating the heterogeneous return spillovers in financial system and measuring the systemic importance of financial institutions among 34 listed Chinese financial institutions from 2011 to 2023. Findings indicate pronounced information spillovers among institutions within the same sector due to contemporaneous causal relationships. Both static and dynamic financial network analyses highlight the significance of the securities sector. Dynamic structural characteristics align with macroeconomic development and are sensitive to internal and external shocks. Systemic importance assessment reveals that market size alone doesn’t determine importance, with notable disparities between banking and non-banking sectors. State-owned and joint-stock commercial banks play a vital role in banking, while local government and private capital-controlled institutions are crucial in the securities sector. This research aids regulatory efforts in maintaining a balanced regulatory environment, ensuring market efficiency, and reducing operational costs.

Abstract Large river deltas are usually ecologically important wetland habitats, but also fertile agricultural exploitation areas, creating a conflict of long‐running substantial interest between agricultural expansion and wetland conservation. Over the past several years, large‐scale cultivation of water‐consuming rice has been growing rapidly in the semi‐arid climate of the Yellow River Delta (YRD). Timely monitoring of rice cultivation dynamics is of great significance for sustainable ecological conservation of the delta, which has insufficient freshwater resources. This study proposed a stratified metrics‐based method that integrates statistical spectral indices and phenological metrics at different growing stages to improve the accuracy of rice paddy classification in areas where rice and wetlands coexist. We applied the method to time‐series Sentinel‐1/2 images to produce annual rice paddy maps of the YRD from 2016 to 2021. Together with rice paddy data from 2011 to 2015 from Statistical Yearbooks of Dongying Bureau of Statistics, we investigated the expansion dynamics over the past decade and in this paper discuss the advantages and disadvantages of rice cultivation expansion over wetland ecosystem conservation. Rapid expansion of rice cultivation intensifies water conflicts, and adversely affects wetland restoration in the YRD. Considering the important ecological services of rice paddies as alternative habitats, we argue for maintaining a reasonable scale of rice paddies and optimizing their distribution as a potential solution to achieving the overall sustainable conservation of the YRD in the context of water scarcity.

Coronary artery calcification (CAC) is common in dialysis patients and is associated with a higher risk of future cardiovascular events. Sodium thiosulfate (STS) is effective for calciphylaxis in dialysis patients; however, the influence of STS on CAC in dialysis patients remains unclear. This systematic review and meta-analysis were conducted to evaluate the effects of STS on CAC in patients undergoing dialysis. PubMed, Embase, Cochrane Library, CNKI, and Wanfang databases were searched from inception to 22 March 2023 for controlled studies comparing the influence of STS versus usual care without STS on CAC scores in dialysis patients. A random effects model incorporating the potential influence of heterogeneity was used to pool the results. Nine studies, including two non-randomized studies and seven randomized controlled trials, were included in the meta-analysis. Among these, 365 patients on dialysis were included in the study. Compared with usual care without STS, intravenous STS for 3–6 months was associated with significantly reduced CAC scores (mean difference [MD] = −180.17, 95% confidence interval [CI]: −276.64 to −83.70, p 0.05). In conclusion, intravenous STS may be effective in attenuating CAC in dialysis patients.

Abstract How climate change in the middle to late Holocene has influenced the early human migrations in Central Asian Steppe remains poorly understood. To address this issue, we reconstructed a multiproxy-based Holocene climate history from the sediments of Kanas Lake and neighboring Tiewaike Lake in the southern Altai Mountains. The results show an exceptionally warm climate during ~6.5–3.6 kyr is indicated by the silicon isotope composition of diatom silica (δ30Sidiatom) and the biogenic silica (BSi) content. During 4.7-4.3 kyr, a peak in δ30Sidiatom reflects enhanced lake thermal stratification and periodic nutrient limitation as indicated by concomitant decreasing BSi content. Our geochemical results indicate a significantly warm and wet climate in the Altai Mountain region during 6.5–3.6 kyr, corresponding to the Altai Holocene Climatic Optimum (AHCO), which is critical for promoting prehistoric human population expansion and intensified cultural exchanges across the Central Asian steppe during the Bronze Age.

In recent years, biomedical optics technology has developed rapidly. The current widespread use of biomedical optics was made possible by the invention of optical instruments. The advantages of being non-invasive, portable, effective, low cost, and less susceptible to system noise have led to the rapid development of functional near-infrared spectroscopy (fNIRS) technology for hemodynamics detection, especially in the field of functional brain imaging. At the same time, laboratories and companies have developed various fNIRS-based systems. The safety, stability, and efficacy of fNIRS systems are key performance indicators. However, there is still a lack of comprehensive and systematic evaluation methods for fNIRS instruments. This study uses the fNIRS system developed in our laboratory as the test object. The test method established in this study includes system validation and performance testing to comprehensively assess fNIRS systems' reliability. These methods feature low cost and high practicality. Based on this study, existing or newly developed systems can be comprehensively and easily evaluated in the laboratory or workspace.

Abstract Emerging SARS-CoV-2 variants, particularly the Omicron variant and its sublineages, continually threaten the global public health. Small molecule antivirals are an effective treatment strategy to fight against the virus. However, the first-generation antivirals either show limited clinical efficacy and/or have some defects in pharmacokinetic (PK) properties. Moreover, with increased use of these drugs across the globe, they face great pressure of drug resistance. We herein present the discovery and characterization of a new generation antiviral drug candidate (SY110), which is a potent and selective inhibitor of SARS-CoV-2 main protease (Mpro). This compound displayed potent in vitro antiviral activity against not only the predominant SARS-CoV-2 Omicron sublineage BA.5, but also other highly pathogenic human coronaviruses including SARS-CoV-1 and MERS-CoV. In the Omicron-infected K18-hACE2 mouse model, oral treatment with SY110 significantly lowered the viral burdens in lung and alleviated the virus-induced pathology. Importantly, SY110 possesses favorable PK properties with high oral drug exposure and oral bioavailability, and also an outstanding safety profile. Furthermore, SY110 exhibited sensitivity to several drug-resistance Mpro mutations. Collectively, this investigation provides a promising new drug candidate against Omicron and other variants of SARS-CoV-2.

Abstract In recent years, the sustainable livelihood of farmers has been threatened by various events such as the COVID-19 pandemic, which has significantly impacted efforts to alleviate poverty. Therefore, it is vitally important to increase farmers’ sustainable livelihood resilience to enhance the stability and sustainability of poverty alleviation efforts. In this study, to scientifically measure and analyze farmers’ sustainable livelihood resilience, we designed an analytical framework that captures the characteristics of farmers’ sustainable livelihood resilience from the three dimensions of buffer capacity, self-organization capacity, and learning capacity. We then constructed an index system of farmers’ sustainable livelihood resilience and a cloud-model-based multi-level fuzzy comprehensive evaluation model. Finally, the coupling coordination degree and decision tree methods were used to identify the level of development and relationships among the three abovementioned dimensions of farmers’ sustainable livelihood resilience. A case study from Fugong County, Yunnan Province, China revealed that the spatial and temporal distributions of farmers’ sustainable livelihood resilience were heterogeneous across various regions. Furthermore, the spatial distribution of the coordinated development level of farmers’ sustainable livelihood resilience is similar to that of its overall level because the three dimensions of buffer capacity, self-organization capacity, and learning capacity interact with each other and develop synergistically, and the lack of any one of these affects the overall development of farmers’ sustainable livelihood resilience. In addition, the sustainable livelihood resilience of farmers in various villages is in a state of stable promotion, benign promotion, stagnation, mild recession, severe recession, or chaotic period, indicating a lack of balance in terms of the state of development. However, sustainable livelihood resilience will gradually improve in response to targeted support policies formulated by the national or local governments.

Ferroptosis is a promising therapeutic target for a variety of diseases, but a majority of known ferroptosis inhibitors belong to either antioxidants or iron chelators. Here, the authors discover a new non-classical small molecule inhibitor that is a PHB2 binder and show it ameliorates liver damage in an acute liver injury model.

To assess the impact of vaccines on clinical outcomes among hospitalized COVID-19-infected patients requiring oxygen supplementation during the Beijing Omicron outbreak. We conducted a retrospective cohort study at Beijing Chaoyang Hospital, Capital Medical University, from November 15, 2022, to March 31, 2023. Vaccination statuses were categorized into 3 doses, 2 doses, and unvaccinated (0 dose). The primary outcome was 28-day all-cause mortality. Secondary outcomes included poor outcomes, intensive care unit admission, cardiovascular thromboembolism events, and hospital readmission. Among the included patients, 117 were 2 doses, 285 received booster doses, and 503 were unvaccinated. After propensity score inverse probability weighting, the 3 doses group showed a significantly lower 28-day all-cause mortality compared to the unvaccinated group (inverse probability of treatment weighting-adjusted HR: 0.64, 95% CI: 0.50–0.81). No significant difference was observed in all-cause mortality between the 2 doses and unvaccinated groups. No significant differences were observed in secondary outcome analyses when comparing the 3 doses or 2 doses group to the unvaccinated group. Subgroup analysis revealed significant benefits of booster vaccination in patients with shorter symptom duration, lower Charlson Comorbidity Index, and without immunosuppression status. Our study highlights the significant reduction in all-cause mortality among hospitalized Omicron-infected patients who received a third dose vaccine. These findings underscore the importance of prioritizing booster vaccinations, especially among the elderly. Further research is warranted to confirm and extend these observations.

Abstract. Background:. Hypertrophic cardiomyopathy (HCM) is an underdiagnosed genetic heart disease worldwide. The management and prognosis of obstructive HCM (HOCM) and non-obstructive HCM (HNCM) are quite different, but it also remains challenging to discriminate these two subtypes. HCM is characterized by dysmetabolism, and myocardial amino acid (AA) metabolism is robustly changed. The present study aimed to delineate plasma AA and derivatives profiles, and identify potential biomarkers for HCM. Methods:. Plasma samples from 166 participants, including 57 cases of HOCM, 52 cases of HNCM, and 57 normal controls (NCs), who first visited the International Cooperation Center for HCM, Xijing Hospital between December 2019 and September 2020, were collected and analyzed by high-performance liquid chromatography–mass spectrometry based on targeted AA metabolomics. Three separate classification algorithms, including random forest, support vector machine, and logistic regression, were applied for the identification of specific AA and derivatives compositions for HCM and the development of screening models to discriminate HCM from NC as well as HOCM from HNCM. Results:. The univariate analysis showed that the serine, glycine, proline, citrulline, glutamine, cystine, creatinine, cysteine, choline, and aminoadipic acid levels in the HCM group were significantly different from those in the NC group. Four AAs and derivatives (Panel A; proline, glycine, cysteine, and choline) were screened out by multiple feature selection algorithms for discriminating HCM patients from NCs. The receiver operating characteristic (ROC) analysis in Panel A yielded an area under the ROC curve (AUC) of 0.83 (0.75–0.91) in the training set and 0.79 (0.65–0.94) in the validation set. Moreover, among 10 AAs and derivatives (arginine, phenylalanine, tyrosine, proline, alanine, asparagine, creatine, tryptophan, ornithine, and choline) with statistical significance between HOCM and HNCM, 3 AAs (Panel B; arginine, proline, and ornithine) were selected to differentiate the two subgroups. The AUC values in the training and validation sets for Panel B were 0.83 (0.74–0.93) and 0.82 (0.66–0.98), respectively. Conclusions:. The plasma AA and derivatives profiles were distinct between the HCM and NC groups. Based on the differential profiles, the two established screening models have potential value in assisting HCM screening and identifying whether it is obstructive.

Abstract Background Fractional vegetation coverage (FVC) is a crucial parameter in determining vegetation structure. Automatic measurement of FVC using digital images captured by mobile smart devices is a potential direction for future research on field survey methods in plant ecology, and this algorithm is crucial for accurate FVC measurement. However, there is a lack of insight into the influence of illumination on the accuracy of FVC measurements. Therefore, the main objective of this research is to assess the adaptiveness and performance of different algorithms under varying light conditions for FVC measurements and to deepen our understanding of the influence of illumination on FVC measurement. Methods and results Based on a literature survey, we selected four algorithms that have been reported to have high accuracy in automatic FVC measurements. The first algorithm (Fun01) identifies green plants based on the combination of $$R/G$$ R / G , $$B/G$$ B / G , and $$ExG$$ ExG ( $$R$$ R , $$G$$ G , and $$B$$ B are the actual pixel digital numbers from the images based on each RGB channel, $$ExG$$ ExG is the abbreviation of the Excess Green index), the second algorithm (Fun02) is a decision tree that uses color properties to discriminate plants from the background, the third algorithm (Fun03) uses $$ExG-ExR$$ E x G - E x R ( $$ExR$$ ExR is the abbreviation of the Excess Red index) to recognize plants in the image, and the fourth algorithm (Fun04) uses $$ExG$$ ExG and $$O{\text{tsu}}$$ O tsu to separate the plants from the background. $$Otsu$$ Otsu is an algorithm used to determine a threshold to transform the image into a binary image for the vegetation and background. We measured the FVC of several surveyed quadrats using these four algorithms under three scenarios, namely overcast sky, solar forenoon, and solar noon. FVC values obtained using the Photoshop-assisted manual identification method were used as a reference to assess the accuracy of the four algorithms selected. Results indicate that under the overcast sky scenario, Fun01 was more accurate than the other algorithms and the MAPE (mean absolute percentage error), BIAS, relBIAS (relative BIAS), RMSE (root mean square error), and relRMSE (relative RMSE) are 8.68%, 1.3, 3.97, 3.13, and 12.33%, respectively. Under the scenario of the solar forenoon, Fun02 (decision tree) was more accurate than other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 22.70%, − 2.86, − 7.70, 5.00, and 41.23%. Under the solar noon scenario, Fun02 was also more accurate than the other algorithms, and the MAPE, BIAS, relBIAS, RMSE, and relRMSE are 20.60%, − 6.39, − 20.67, 7.30, and 24.49%, respectively. Conclusions Given that each algorithm has its own optimal application scenario, among the four algorithms selected, Fun01 (the combination of $$R/G$$ R / G , $$B/G$$ B / G , and $$ExG$$ ExG ) can be recommended for measuring FVC on cloudy days. Fun02 (decision tree) is more suitable for measuring the FVC on sunny days. However, it considerably underestimates the FVC in most cases. We expect the findings of this study to serve as a useful reference for automatic vegetation cover measurements.

Purpose – China has established 14 marine economic development demonstration zones in 2018, which has become an important step in exploring the high-quality development of the marine economy. The paper statistically analyzes the documents related to the field of marine economy and special economic zones (SEZs) and strives to find out the hot spots, intersections and related development contexts of the two in the research direction, so as to provide some ideas for later research. Design/methodology/approach – By taking the bibliographic information data of “Marine Economy” and “Special Economic Zone” in the Web of Science database as a sample, the paper applies the Citespace bibliometric tool to analyze the evolution of disciplinary distribution, research country, author collaboration and research hotspot trends in the two fields. Findings – It can be found from the paper results that the current research on marine economy and SEZs involves the intersection of many disciplines. China and the United States are the leaders in this field. However, there is more extensive cooperation between authors from different countries, but the cooperation depth needs to be strengthened. At the same time, compared with the terrestrial economy, the trend of its research hotspots is lagging to a certain extent. Moreover, there is still no systematic and professional research paradigm on marine economy. Research limitations/implications – At present, there are a few research studies on marine economic development demonstration areas or marine SEZs, and related bibliographical references are incomplete, which leads to insufficient samples, and bibliometric methods cannot fully reveal the general research rules and development trends in this field. Originality/value – The research on the marine economic development demonstration area is still in its infancy. The paper jointly analyzes the literature in the two fields of marine economy and SEZs, aims to find the intersection and related research hotspots of the two, and provides references for the future research of marine development demonstration areas, which is of certain practical significance.

Integration of diagnostic and therapeutic modalities into a single nanoplatform can play an important role in personalized cancer therapy. In this work, an all-in-one theranostic nanoplatform (HPCT nanomicelles) for breast cancer was fabricated, composed of the synthetic polymer hyaluronan-b-poly(ε-caprolactone) (HA-PCL), drug curcumin (Cur) and adjuvant tocopheryl polyethylene glycol succinate (TPGS). HPCT nanomicelles presented adequate drug loading efficiency, excellent stability, and attractive hyaluronidase-sensitive release features. Imaging agent radionuclide 99mTc was loaded into HPCT nanomicelles (denoted as 99mTc-HPCT nanomicelles) and in vivo SPECT/CT imaging verified the HA-based nanomicelles could actively target breast cancer in mice. In vivo anti-tumor pharmacodynamic studies indicated that HPCT nanomicelles exhibited desirable anti-tumor efficacy with favorable biosafety. 99mTc-HPCT nanomicelles, integrating imaging and therapeutic modalities, have the promising potential to be an all-in-one theranostic nanoplatform for breast cancer.

Spatial distribution information offers a valuable resource for the growing research on evaluating and managing aquaculture and the mechanism of its interaction with the environment, especially concerning landside clustering aquaculture ponds (LCAP). However, studies aimed at obtaining data on global LCAP distribution remains limited. This study combined edge detection and the morphology implemented on Google Earth Engine (GEE) platform to produce the first global spatial distribution data of LCAP using 4,015,054 tiles of the 10-m Sentinel-2 time-series images collected throughout 2020. The study findings showed a total area of global LCAP of 55,337.03 km2. Asia, led by China, had the highest distribution area of LCAP, accounting for 89.12 % of global LCAP. The main LCAP distribution area was the landside 30-km buffer region of coastline, accounting for 75.57 % of global LCAP. Meanwhile, the greatest proportion of LCAP (69.63 %) was located in China. Accuracy verification revealed that the precision and recall error of the results were 83.91 % and 92.49 % respectively, and F1 score was 0.88. A comparison of China, Vietnam, and India, based on data drawn from most of the existing local studies that used remote sensing methods, showed that these countries’ aquaculture area differs by less than 10 %. Despite some variation from FAO statistics or official national statistics, the results of our remote sensing-based method show promise in such aspects as global coverage and temporal coherency when compared with the classical statistical method.

To achieve reproduction, male solitary mammals need to locate females using chemical communication with high levels of precision. In the case of giant pandas, the total estrus period of females was usually 15 days each year, however, successful mating activity is finished within 3 days from respective home range. The mating pattern of giant pandas, where multiple males compete for each female requires females employ efficient systems to communicate their estrus phases. To verifying whether the scent secretions of giant pandas changes by gender and estrus progression, the microbiota and compounds in 29 anogenital gland samples from 14 individuals during estrus were analyzed by 16S rRNA sequencing and GC-MS. We show that the microbiota communities covary by gender with 4 particular compounds of scent secretions. Among 597 genera, 34 were identified as biomarkers that could be used to distinguish between different estrus phases. By bacterial-compounds co-analysis, 3 fatty ester acids and squalene compounds covaried with the development of estrus in the bacterial communities of female giant pandas. This study helps clarify how a large, solitary mammal expresses accurate information to improve the likelihood of successful reproduction by changing the composition of microbiota and odor compounds of anogenital glands during estrus.

Introduction: Diabetic nephropathy (DN) is the leading cause of kidney failure worldwide. To explore the pathogenesis and effective biological target of DN is beneficial to seeking novel treatment strategies. Objective: This study aimed to investigate the role of the lncRNA Dlx6os1/SOX6/EZH2 axis in DN progression. Methods: PAS staining was performed to evaluate extracellular matrix accumulation; ELISA was carried out to assess the levels of urine microalbumin and blood glucose concentration; RT-qPCR was carried out to detect the levels of lncRNA Dlx6os1, TNF-α, IL-1β, IL-6, SOX6, and EZH2. Western blot was performed to assess the levels of Col-IV, FN, TGF-β1, and SOX6 proteins. RIP assay was carried out to verify the interaction between lncRNA Dlx6os1 and EZH2. ChIP-qPCR was conducted to verify the interaction between EZH2 and SOX6 promoter. Results: Our results illustrated that lncRNA Dlx6os1 was highly expressed in DN mice and HG-induced SV40 MES13 cells. LncRNA Dlx6os1 knockdown inhibited HG-induced SV40 MES13 cell proliferation, fibrosis, and inflammatory cytokine release. LncRNA Dlx6os1 inhibited SOX6 expression by recruiting EZH2 in HG-SV40 MES13 cells, and SOX6 mediated the effects of lncRNA Dlx6os1 on proliferation, fibrosis, and inflammatory factor release of HG-induced SV40 MES13 cells. Conclusion: LncRNA Dlx6os1 accelerates the progression of DN by epigenetically repressing SOX6 via recruiting EZH2.

Synthesis of aryl phosphorus compounds with electron-withdrawing substituents on the arene is difficult to do simply. Here the authors show a method to form aryl carbon-phosphorus bonds through electrochemistry, without requiring any catalyst or external oxidant.

Abstract Southern corn rust is a destructive maize disease caused by Puccinia polysora Underw that can lead to severe yield losses. However, genomic information and microsatellite markers are currently unavailable for this disease. In this study, we generated a total of 27,295,216 high-quality cDNA sequence reads using Illumina sequencing technology. These reads were assembled into 17,496 unigenes with an average length of 1015 bp. The functional annotation indicated that 8113 (46.37%), 1933 (11.04%) and 5516 (31.52%) unigenes showed significant similarity to known proteins in the NCBI Nr, Nt and Swiss-Prot databases, respectively. In addition, 2921 (16.70%) unigenes were assigned to KEGG database categories; 4218 (24.11%), to KOG database categories; and 6,603 (37.74%), to GO database categories. Furthermore, we identified 8,798 potential SSRs among 6653 unigenes. A total of 9 polymorphic SSR markers were developed to evaluate the genetic diversity and population structure of 96 isolates collected from Guangdong Province in China. Clonal reproduction of P. polysora in Guangdong was dominant. The YJ (Yangjiang) population had the highest genotypic diversity and the greatest number of the multilocus genotypes, followed by the HY (Heyuan), HZ (Huizhou) and XY (Xinyi) populations. These results provide valuable information for the molecular genetic analysis of P. polysora and related species.

BackgroundHypertrophic cardiomyopathy (HCM) is a widely distributed, but clinically heterogeneous genetic heart disease, affects approximately 20 million people worldwide. Nowadays, HCM is treatable with the advancement of medical interventions. However, due to occult clinical presentations and a lack of easy, inexpensive, and widely popularized screening approaches in the general population, 80–90% HCM patients are not clinically identifiable, which brings certain safety hazards could have been prevented. The majority HCM patients showed abnormal and diverse electrocardiogram (ECG) presentations, it is unclear which ECG parameters are the most efficient for HCM screening.ObjectiveWe aimed to develop a pragmatic prediction model based on the most common ECG features to screen for HCM.MethodsBetween April 1st and September 30th, 2020, 423 consecutive subjects from the International Cooperation Center for Hypertrophic Cardiomyopathy of Xijing Hospital [172 HCM patients, 251 participants without left ventricular hypertrophy (non-HCM)] were prospectively included in the training cohort. Between January 4th and February 30th, 2021, 163 participants from the same center were included in the temporal internal validation cohort (62 HCM patients, 101 non-HCM participants). External validation was performed using retrospectively collected ECG data from Xijing Hospital (3,232 HCM ECG samples from January 1st, 2000, to March 31st, 2020; 95,184 non-HCM ECG samples from January 1st to December 31st, 2020). The C-statistic was used to measure the discriminative ability of the model.ResultsAmong 30 ECG features examined, all except abnormal Q wave significantly differed between the HCM patients and non-HCM comparators. After several independent feature selection approaches and model evaluation, we included only two ECG features, T wave inversion (TWI) and the amplitude of S wave in lead V1 (SV1), in the HCM prediction model. The model showed a clearly useful discriminative performance (C-statistic > 0.75) in the training [C-statistic 0.857 (0.818–0.896)], and temporal validation cohorts [C-statistic 0.871 (0.812–0.930)]. In the external validation cohort, the C-statistic of the model was 0.833 [0.825–0.841]. A browser-based calculator was generated accordingly.ConclusionThe pragmatic model established using only TWI and SV1 may be helpful for predicting the probability of HCM and shows promise for use in population-based HCM screening.

Mariculture is an important part of aquaculture, and it is important to address global food security and nutrition issues. However, seawater environmental conditions are complex and variable, which causes large uncertainties in the remote sensing spectral features. At the same time, mariculture types are distinct because of the different types of aquaculture (cage aquaculture and raft aquaculture). These factors bring great challenges for mariculture extraction and mapping using remote sensing. In order to solve these problems, an optical remote sensing aquaculture index named the marine aquaculture index (MAI) is proposed. Based on this spectral index, using time series Sentinel-1 and Sentinel-2 satellite data, a random forest classification scheme is proposed for mapping mariculture by combining spectral, textural, geometric, and synthetic aperture radar (SAR) backscattering. The results revealed that (1) MAI can emphasize the difference between mariculture and seawater; (2) the overall accuracy of mariculture in the Bohai Rim is 94.10%, and the kappa coefficient is 0.91; and (3) the area of cage aquaculture and raft aquaculture in the Bohai Rim is 16.89 km2 and 1206.71 km2, respectively. This study details an effective method for carrying out mariculture monitoring and ensuring the sustainable development of aquaculture.

Monitoring tropical and subtropical forest soil CO2 emission efflux (FSCO2) is crucial for understanding the global carbon cycle and terrestrial ecosystem respiration. In this study, we addressed the challenge of low spatiotemporal resolution in FSCO2 monitoring by combining data fusion and model methods to improve the accuracy of quantitative inversion. We used time series Landsat 8 LST and MODIS LST fusion images and a linear mixed effect model to estimate FSCO2 at watershed scale. Our results show that modeling without random factors, and the use of Fusion LST as the fixed predictor, resulted in 47% (marginal R2 = 0.47) of FSCO2 variability in the Monthly random effect model, while it only accounted for 19% of FSCO2 variability in the Daily random effect model and 7% in the Seasonally random effect model. However, the inclusion of random effects in the model’s parameterization improved the performance of both models. The Monthly random effect model that performed optimally had an explanation rate of 55.3% (conditional R2 = 0.55 and t value > 1.9) for FSCO2 variability and yielded the smallest deviation from observed FSCO2. Our study highlights the importance of incorporating random effects and using Fusion LST as a fixed predictor to improve the accuracy of FSCO2 monitoring in tropical and subtropical forests.

Abstract Background Acute kidney injury (AKI) is a major kidney disease with poor clinical outcome. SP1, a well-known transcription factor, plays a critical role in AKI and subsequent kidney repair through the regulation of various cell biologic processes. However, the underlying mechanism of SP1 in these pathological processes remain largely unknown. Methods An in vitro HK-2 cells with anoxia-reoxygenation injury model (In vitro simulated ischemic injury disease) and an in vivo rat renal ischemia-reperfusion injury model were used in this study. The expression levels of SP1, miR-205 and PTEN were detected by RT-qPCR, and the protein expression levels of SP1, p62, PTEN, AKT, p-AKT, LC3II, LC3I and Beclin-1 were assayed by western blot. Cell proliferation was assessed by MTT assay, and the cell apoptosis was detected by flow cytometry. The secretions of IL-6 and TNF-α were detected by ELISA. The targeted relationship between miR-205 and PTEN was confirmed by dual luciferase report assay. The expression and positioning of LC-3 were observed by immunofluorescence staining. TUNEL staining was used to detect cell apoptosis and immunohistochemical analysis was used to evaluate the expression of SP1 in renal tissue after ischemia-reperfusion injury in rats. Results The expression of PTEN was upregulated while SP1 and miR-205 were downregulated in renal ischemia-reperfusion injury. Overexpression of SP1 protected renal tubule cell against injury induced by ischemia-reperfusion via miR-205/PTEN/Akt pathway mediated autophagy. Overexpression of SP1 attenuated renal ischemia-reperfusion injury in rats. Conclusions SP1 overexpression restored autophagy to alleviate acute renal injury induced by ischemia-reperfusion through the miR-205/PTEN/Akt pathway.

Abstract Background Dysregulation of both mitochondrial biogenesis and mitophagy is critical to sustain oncogenic signaling pathways. However, the mechanism of mitophagy in promoting hepatocellular carcinoma (HCC) progression remains poorly understood. In this study, we investigated the clinical significance and biological involvement of mitochondrial inner membrane protein STOML2 in HCC. Methods STOML2 was identified by gene expression profiles of HCC tissues and was measured in tissue microarray and cell lines. Gain/loss-of-function experiment was applied to study the biological function of STOML2 in HCC. Flow cytometry, Western blotting, laser confocal microscopy, transmission electron microscopy, and co-immunoprecipitation were used to detect and analyze mitophagy. ChIP and luciferase reporter assay were conducted to evaluate the relationship between STOML2 and HIF-1α. The sensitivity to lenvatinib was assessed in HCC both in vitro and in vivo. Results Increased expression of STOML2 was found in HCC compared with paired peritumoral tissues. It was more significant in HCC with metastasis and correlated with worse overall survival and higher probability of recurrence after hepatectomy. Upregulation of STOML2 accelerated HCC cells colony formation, migration and invasion. Mechanically, TCGA dataset-based analysis showed enrichment of autophagy-related pathways in STOML2 highly-expressed HCC. Next, STOML2 was demonstrated to interact and stabilize PINK1 under cellular stress, amplify PINK1-Parkin-mediated mitophagy and then promote HCC growth and metastasis. Most interestingly, HIF-1α was upregulated and transcriptionally increased STOML2 expression in HCC cells under the treatment of lenvatinib. Furthermore, higher sensitivity to lenvatinib was found in HCC cells when STOML2 was downregulated. Combination therapy with lenvatinib and mitophagy inhibitor hydroxychloroquine obtained best efficacy. Conclusions Our findings suggested that STOML2 could amplify mitophagy through interacting and stabilizing PINK1, which promote HCC metastasis and modulate the response of HCC to lenvatinib. Combinations of pharmacologic inhibitors that concurrently block both angiogenesis and mitophagy may serve as an effective treatment for HCC.

Rubber plantations in southeast Asia have grown at an unprecedented rate in recent decades, leading to drastic changes in regional carbon storage. To this end, this study proposes a systematic approach for quantitatively estimating and assessing the impact of rubber expansions on regional carbon storage. First, using Sentinel-1 and Sentinel-2 satellite data, the distributions of forest and rubber, respectively, were extracted. Then, based on the Landsat time series (1999–2019) remote sensing data, the stand age estimation of rubber plantations was studied with the improved shapelet algorithm. On this basis, the Ecosystem Services and Tradeoffs model (InVEST) was applied to assess the regional carbon density and storage. Finally, by setting up two scenarios of actual planting and hypothetical non-planting of rubber forests, the impact of the carbon storage under these two scenarios was explored. The results of the study showed the following: (1) The area of rubber was 1.28 × 105 ha in 2019, mainly distributed at an elevation of 200–400 m (accounting for 78.47% of the total of rubber). (2) The average age of rubber stands was 13.85 years, and the total newly established rubber plantations were converted from cropland and natural forests, accounting for 54.81% and 45.19%, respectively. (3) With the expansion of rubber plantations, the carbon density increased from only 2.25 Mg·C/ha in 1999 to more than 15 Mg·C/ha in 2018. Among them, the carbon sequestration increased dramatically when the cropland was replaced by rubber, while deforestation and replacement of natural forests will cause a significant decrease. (4) The difference between the actual and the hypothetical carbon storage reached −0.15 million tons in 2018, which means that the expansion of rubber led to a decline in carbon storage in our study area. These research findings can provide a theoretical basis and practical application for sustainable regional rubber forest plantation and management, carbon balance maintenance, and climate change stabilization.

Traffic flow prediction is a key issue in intelligent transportation systems. The growing trend in data disclosure has created more potential sources for the input for predictive models, posing new challenges to the prediction of traffic flow in the era of big data. In this study, the prediction of urban traffic flow was regarded as a spatiotemporal prediction problem, focusing on the traffic speed. A Graph LSTM (Long Short-Term Memory) Spatiotemporal Neural Network (GLSNN) model was constructed to perform a multi-scale spatiotemporal fusion prediction based on the multi-source input data. The GLSNN model consists of three parts: MS-LSTM, LZ-GCN, and LSTM-GRU. We used the MS-LSTM module to scale the traffic timing data, and then used the LZ-GCN network and the LSTM-GRU network to capture both the time and space dependencies. The model was tested on a real traffic dataset, and the experiment results verified the superior performance of the GLSNN model on both a high-precision and multi-scale prediction of urban traffic flow.

Accurate examination of poverty-causing factors and their mechanisms of poverty-stricken farmer households from a fine scale is conducive to policy implementation and long-term effective poverty reduction. The spatial effects in most previous studies are not fully considered, resulting in less reliability of detection results. Therefore, by fully considering background effects and spatial–temporal effects, this study designs a hierarchical spatial–temporal regressive model (HSTRM) to accurately identify the factors as well as mechanisms that cause poverty more reasonably. The empirical study of Fugong County, Yunnan Province, China, shows that: (1) There has been a certain degree of spatial effects in the study area over the years; therefore, spatial effects should be considered. (2) The poverty degree of farmer households in the study area is affected by individual factors and background factors. Therefore, poverty-causing factors should be observed at different levels. (3) Poverty-causing factors feature different action mechanisms. The influence of the village-level factors on poverty is greater than that of the household level. In addition, the village-level factors have a certain impact on the contribution of household-level factors to poverty. This study offers technical support and policy guidance for sustainable poverty reduction and development of poor farmer households.

It is necessary to upgrade and transform the sorting equipment in the industrial production line. In order to improve production efficiency and reduce labor intensity, a high-speed lightweight parallel mechanism control system for the high-speed sorting and packaging of light items was studied. A fuzzy PID controller based on genetic algorithm (GA) optimization is proposed according to the nonlinear and strong coupling characteristics of the parallel mechanism (PM) control system. The inverse kinematic analysis was conducted to map the workspace trajectory tracking problem to the joint space. It was transformed into the trajectory planning and solving problems in the joint space. The motion trajectory was obtained utilizing quintic polynomial interpolation. Finally, the servo control system model was established, and the PID control parameters were optimized and self-tuned by the GA. They were applied to the fuzzy PID controller for simulation experiments. The simulation results showed that the GA-optimized fuzzy PID control system compared with the fuzzy PID control system had a 23.39% shorter rise time, 22.32% less regulation time, and 7.18% less steady-state error. The control system had a good dynamic and steady-state performance.

In this paper, we apply an RV coefficient network to investigate the topological structure of China’s new energy stock market via daily prices of 60 component stocks of CSI (China Stock Index) New Energy Index spanning the period January 4, 2012 to March 29, 2019. Compared with the Pearson correlation coefficient, RV coefficient can better reflect the similarity between stocks from the perspective of multi-dimensional data. The empirical result indicates that (1) the scale-free characteristics of China’s new energy stock market are not significant; (2) the new energy storage is the leading sub-sector of the new energy sector and the new energy interactive equipment plays a connecting role between renewable energy production and new energy storage; (3) the most influential stock in the network is Group DMEGC Magnetics Co., Ltd., Xiamen Tungsten Co., Ltd. and GEM Co., Ltd. play an important role in the network connection. These findings are of great significance to understand the interaction between Chinese new energy stocks and the pricing mechanism of stocks. The authority should pay more attention to the new energy storage industry. Investor’s portfolios can be optimized according to the influence assessment of stocks and sub-sectors. First published online 26 May 2020

Modeling the direct economic losses of storm surge disasters can assess the disaster situation in a timely manner and improve the efficiency of post-disaster management in practice, which is acknowledged as one of the most significant issues in clean production. However, improving the forecasting accuracy of direct economic losses caused by storm surge disasters remains challenging, which is also a major concern in the field of disaster risk management. In particular, most of the previous studies have mainly focused on individual models, which ignored the significance of reduction and optimization. Therefore, a novel direct economic loss forecasting system for storm surge disasters is proposed in this study, which includes reduction, forecasting, and evaluation modules. In this system, a forecasting module based on an improved machine learning technique is proposed, which improves the generalization ability and robustness of the system. In addition, the key attributes and samples are selected by the proposed reduction module to further improve the forecasting performance from the two innovative perspectives. Moreover, an evaluation module is incorporated to comprehensively evaluate the superiority of the developed forecasting system. Data on the storm surge disasters from three typical provinces are utilized to conduct a case study, and the performance of the proposed forecasting system is analyzed and compared with eight comparison models. The experimental results show that the mean absolute percentage error (MAPE) predicted by the Extreme Learning Machine (ELM) model was 16.5293%, and the MAPE predicted by the proposed system was 1.0313%. Overall, the results show that the performance of the proposed forecasting system is superior compared to other models, and it is suitable for the forecasting of direct economic losses resulting from storm surge disasters.

Introduction: Diabetic nephropathy (DN) is a serious complication of diabetes mellitus and is considered to be a sterile inflammatory disease. Increasing evidence suggest that pyroptosis and subsequent inflammatory response play a key role in the pathogenesis of DN. However, the underlying cellular and molecular mechanisms responsible for pyroptosis in DN are largely unknown. Methods: The rat models of DN were successfully established by single 65 mg/kg streptozotocin treatment. Glomerular mesangial cells were exposed to 30 mmol/L high glucose media for 48 h to mimic the DN environment in vitro. Gene and protein expressions were determined by quantitative real-time PCR and Western blot. Cell viability and pyroptosis were measured by MTT assay and flow cytometry analysis, respectively. The relationship between lncRNA NEAT1, miR-34c, and Nod-like receptor protein-3 (NLRP3) was confirmed by luciferase reporter assay. Results: We found that upregulation of NEAT1 was associated with the increase of pyroptosis in DN models. miR-34c, as a target gene of NEAT1, mediated the effect of NEAT1 on pyroptosis in DN by regulating the expression of NLRP3 as well as the expressions of caspase-1 and interleukin-1β. Either miR-34c inhibition or NLRP3 overexpression could reverse the accentuation of pyroptosis and inflammation by sh-NEAT1 transfection in the in vitro model of DN. Conclusions: Our findings suggested NEAT1 and its target gene miR-34c regulated cell pyroptosis via mediating NLRP3 in DN, providing new insights into understanding the molecular mechanisms of pyroptosis in the pathogenesis of DN.

Abstract Background The purpose of this study is to investigate whether or not the complement system is systemically activated and to specify the clinical and prognostic implications of its components during hepatitis B virus related acute-on-chronic liver failure (HBV-ACLF). Methods Blood samples were taken from twenty-seven patients diagnosed with HBV-ACLF, twenty-five patients diagnosed with chronic hepatitis B but without liver failure (CHB), and nine healthy volunteers (the control group). Plasma complement components were measured with Enzyme-linked immunosorbent assay. Correlative analysis were assessed between the levels of complement components and the liver failure related index. Results The concentrations of C3 was 6568 μg/ml in the HBV-ACLF group, 8916 μg/ml in the CHB group and 15,653 μg/ml in the control group, respectively (P

The seasonal effect on land cover classification has been widely recognized. It is important to use the imagery acquired at key points of vegetation phenological development to obtain a higher classification accuracy for land cover. This study compared the effect of seasons on landscape classification and the quasi-circular vegetation patches (QVPs) detection from four fused Gaofen 1 images acquired in the different seasons by using the pixel-based random forest (RF) and object-based support vector machine (SVM) methods over the Yellow River Delta, China. The results from this study demonstrated that the seasonal effect on classifying landscapes and detecting the QVPs is significant, especially for the pixel-based RF method. The object-based SVM method was more appropriate for classifying landscape from the non-growing season images, while the pixel-based RF approach was more suitable for classifying the growing-season images. The spring data (April imagery; overall accuracy = 99.8%) and the winter data (February imagery; F measure = 65.9%) yielded the best results for landscape classification and QVP detection, respectively, by using the object-based SVM approach. Therefore, in practice, we recommend the use of February to April imagery with the object-based SVM approach to map the QVPs in the future.

Accurately identifying and delineating urban functional areas has seen increasing demand in smart urban planning, landscape design, and resource allocation. Recently, POI (point of interest) data have been increasingly applied to identify urban functional areas. However, heterogeneity in urban spaces or the corresponding POI data has not been fully considered in previous studies. In this study, we proposed a new scheme for urban-functional-area identification by combining POI data, OpenStreetMap (OSM) datasets, and high-resolution remote-sensing imagery. A function-intensity index that integrates the quantitative-density index and average-nearest-neighbor index (ANNI) of POIs was built for representing the urban function. The results show that the proposed function-intensity index can balance the impact of the spatial heterogeneity of each type of POI on determining the functional characteristics of the urban units. In Futian District, Shenzhen, China, the method was effective in distinguishing functional areas with fewer POI amounts but high ANNIs from those functional areas with dense POIs. The overall accuracy of the proposed method is about 11% higher than that of the method using the POI density only. This paper argues for considering both the quantitative density and spatial heterogeneity of POIs to improve urban-functional-area identification.

Because of a decrease in water provision from the Yellow River and the influence of various human actions, the Shandong Yellow River Delta Wetland, selected a Ramsar Wetland of International Importance in 2013, has undergone substantial degradation. Although some restoration work has been undertaken, more adaptive active restoration strategies should be developed based on researches on spontaneous revegetation in this region. In this regard, the quasi-circular vegetation patches (QVPs), characterized by a rapid rate of succession, are thus considered as ideal research targets. The spatial and temporal quantification of QVPs is not only fundamental to determining QVP formation mechanism but also contributes to the development of adaptive restoration strategies for the degraded ecosystems. Therefore, we examined QVP dynamics over seven time periods spanning 22 years using one aerial photograph and seven high-spatial resolution satellite images. We found that although the total number of QVPs remained almost unchanged, the total area had an increasing trend, and that the shape index (SIi=Peremeteri2πAreai) of QVPs tended to initially increase and subsequently decrease, with QVPs finally attaining an almost circular shape. Observations for the period between 2000 and 2016 indicated that the area and shape of QVPs could reach maximum extents within about three years. This knowledge could provide a valuable basis for studying the interactions of QVPs with environment, and the implications/consequences to ecosystems, e.g., mechanisms of the evolution of ecosystems, ecological services etc. We accordingly suggest that developing a long-term remote sensing monitoring and ground-based survey program would make a significant contribution to developing a nature-based solution for the degraded ecosystems in the Yellow River Delta, China.

United Nations Sustainable Development Goal SDG11.3.1—the ratio of land consumption rate (LCR) to population growth rate (PGR) (LCRPGR)—aims to measure the efficiency and sustainability of urban land use. In recent years, SDG11.3.1 has been widely used in sustainable urban development research. However, previous studies have focused on the urban core area, while the sustainable development status of the urban peripheral areas (suburban and rural areas) that contribute significantly to the ecological environment has been neglected. To this end, relying on land use/cover change (LUCC) data obtained from high-resolution remote sensing satellite images rather than the single impervious surface data used in traditional research, according to the multiple functions of the land use type, the city is divided into three types of space: production, living, and ecological spaces. Research from the perspective of multi-scale coordination is of great significance for gaining a comprehensive understanding of the sustainable development status of urban space. Taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China as an example, in this paper, LUCC remote sensing data and comprehensive population and gross domestic product (GDP) data are used. From the multi-functional production-living-ecological space perspective, based on the original land use efficiency indicator, the ratio of land consumption rate (LCR) to economic growth rate (EGR) (LCREGR) is introduced and the analytic hierarchy process (AHP) is used to comprehensively evaluate the sustainable development level (SDL) of the space between 2000–2010 and 2010–2020 on the urban agglomeration and prefecture-level city scales. The results show that (1) the level of and changes in the spatial sustainable development are significantly different at different scales; (2) the division of the production-living-ecological spaces can guide cities to optimize different types of spaces in the future. This paper proposes a new evaluation method for spatial sustainable development, which provides a useful reference for any country or region in the world.

With the rapid development of remote sensing with small, lightweight unmanned aerial vehicles (UAV), efficient and accurate crop spike counting, and yield estimation methods based on deep learning (DL) methods have begun to emerge, greatly reducing labor costs and enabling fast and accurate counting of sorghum spikes. However, there has not been a systematic, comprehensive evaluation of their applicability in cereal crop spike identification in UAV images, especially in sorghum head counting. To this end, this paper conducts a comparative study of the performance of three common DL algorithms, EfficientDet, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv4), for sorghum head detection based on lightweight UAV remote sensing data. The paper explores the effects of overlap ratio, confidence, and intersection over union (IoU) parameters, using the evaluation metrics of precision P, recall R, average precision AP, F1 score, computational efficiency, and the number of detected positive/negative samples (Objects detected consistent/inconsistent with real samples). The experiment results show the following. (1) The detection results of the three methods under dense coverage conditions were better than those under medium and sparse conditions. YOLOv4 had the most accurate detection under different coverage conditions; on the contrary, EfficientDet was the worst. While SSD obtained better detection results under dense conditions, the number of over-detections was larger. (2) It was concluded that although EfficientDet had a good positive sample detection rate, it detected the fewest samples, had the smallest R and F1, and its actual precision was poor, while its training time, although medium, had the lowest detection efficiency, and the detection time per image was 2.82-times that of SSD. SSD had medium values for P, AP, and the number of detected samples, but had the highest training and detection efficiency. YOLOv4 detected the largest number of positive samples, and its values for R, AP, and F1 were the highest among the three methods. Although the training time was the slowest, the detection efficiency was better than EfficientDet. (3) With an increase in the overlap ratios, both positive and negative samples tended to increase, and when the threshold value was 0.3, all three methods had better detection results. With an increase in the confidence value, the number of positive and negative samples significantly decreased, and when the threshold value was 0.3, it balanced the numbers for sample detection and detection accuracy. An increase in IoU was accompanied by a gradual decrease in the number of positive samples and a gradual increase in the number of negative samples. When the threshold value was 0.3, better detection was achieved. The research findings can provide a methodological basis for accurately detecting and counting sorghum heads using UAV.

Abstract Vascular calcification (VC) plays as a critical role on cardiovascular disease (CVD) and acts as a notable risk factor in cardiovascular system. Vascular smooth muscle cells (VSMCs) calcification can be triggered by high phosphate treatment; however, the explicit mechanism remains unclear. In the present study, we isolated VSMCs from primary rat artery, applied β‐GP (β‐glycerophosphate) for inducing VSMCs calcification in vitro to explore the mechanism of phosphate‐induced calcification in VSMCs. Alizarin red staining was performed to assess the mineralization in VSMCs. Calcium deposition experiment was taken to evaluate the calcium content. ALP staining was determined to assess the ALP activity. The recombinant adenoviruses were constructed for the overexpression of Klotho and FGF23, respectively. qRT‐PCR and western blot analysis were subjected to measure the expression of Klotho/FGF23 and correlated genes among Wnt7b/β‐catenin pathway. We found that the calcium content was obviously increased and Alizarin red staining was positive in calcification group exposure with high phosphate in a time‐dependent manner. The expression of Klotho and FGF23 was significantly decreased in the calcification group. However, overexpression of Klotho and FGF23 markedly reversed VSMCs calcification stimulating with high phosphate treatment. Moreover, Wnt7b/β‐catenin inhibitor DKK1 could partly attenuate the effect of high phosphate on calcified VSMCs. These findings demonstrated that Klotho/FGF23 axis could modulate high phosphate‐induced VSMCs calcification via Wnt7b/β‐catenin signaling pathway. Our findings unravel that Klotho/FGF23‐ Wnt7b/β‐catenin axis functions as a crucial role in the VSMCs calcification.

High-precision spatial mapping of paddy planting areas is very important for food security risk assessment and agricultural monitoring. Previous studies have mainly been based on multi-source satellite imagery, the fusion of Synthetic Aperture Radar (SAR) with optical data, and the combined use of multi-scale and multi-source sensors. However, there have been few studies on paddy spatial mapping using collaborative multi-source remote sensing product information, especially in tropical regions such as Southeast Asia. Therefore, based on the Google Earth Engine (GEE) platform, in this study, Cambodia, which is dominated by agriculture, was taken as the study area, and an extraction scheme for paddy planting areas was developed from collaborative multi-source information, including multi-source remote sensing images (Sentinel-1 and Sentinel-2), multi-source remote sensing land cover products (GFSAD30SEACE, GLC_FCS30-2015, FROM_GLC2015, SERVIR MEKONG, and GUF), paddy phenology information, and topographic features. Evaluation and analysis of the extraction results and the SERVIR MEKONG and ESACCI-LC paddy products revealed that the accuracy of the paddy planting areas extracted using the proposed method is the highest, with an overall accuracy of 89.90%. The results of the proposed method are better than those of the other products in terms of the outline of the paddy planting areas and the description of the road information. The results of this study provide a reference for future high-precision paddy mapping.

As a result of global climate change, the frequency and intensity of heat waves have increased significantly. According to the World Meteorological Organization (WMO), extreme temperatures in southwestern Pakistan have exceeded 54 °C in successive years. The identification and assessment of heat-health vulnerability (HHV) are important for controlling heat-related diseases and mortality. At present, heat waves have many definitions. To better describe the heat wave mortality risk, we redefine the heat wave by regarding the most frequent temperature (MFT) as the minimum temperature threshold for HHV for the first time. In addition, different indicators that serve as relevant evaluation factors of exposure, sensitivity and adaptability are selected to conduct a kilometre-level HHV assessment. The hesitant analytic hierarchy process (H-AHP) method is used to evaluate each index weight. Finally, we incorporate the weights into the data layers to establish the final HHV assessment model. The vulnerability in the study area is divided into five levels, high, middle-high, medium, middle-low and low, with proportions of 3.06%, 46.55%, 41.85%, 8.53% and 0%, respectively. Health facilities and urbanization were found to provide advantages for vulnerability reduction. Our study improved the resolution to describe the spatial heterogeneity of HHV, which provided a reference for more detailed model construction. It can help local government formulate more targeted control measures to reduce morbidity and mortality during heat waves.

Accurately identifying poverty-contributing factors of farmer households in an all-round way is the critical prerequisite and guarantee for taking targeted measures in poverty alleviation. From the combined perspectives of multi-level comprehensive detection and human-nature sustainable development, this study has designed a multi-level index system of household-level, village-level, and town-level, and constructed a nested three-level hierarchical linear model to examine the poverty-contributing factors of farmer households, and to reveal the significant ones and their multi-level interaction mechanism. The case test from Fugong County shows that: (1) Poverty-contributing factors are multi-level, showing both individual and background effects. 77.14% of the poverty is caused by household-level factors, 6.24% by village-level ones and 16.62% by town-level factors. (2) Significant poverty-contributing factors at different levels are different, identifying different contribution degrees to poverty gaps of farmer households. Five household-level factors show significant influence on poverty degree and account for 70.95% of the overall poverty gap among poor households, 11.70% for four village-level significant factors and 86.80% for two town-level ones, respectively. (3) Higher-level factors have different degrees of influence on the contribution difference of lower-level ones. The two town-level factors, terrain relief and town per capita annual income have explained 59.38% of the difference of village-level proportion of migrant workers' contribution to poverty degree among towns and 89.89% of the difference of household-level per capita annual income's contribution to poverty degree among towns respectively. (4) Measures such as improving the type of access to roads, developing characteristic planting and breeding, and implementing relocation projects, can help poor households in the study area to reduce poverty. This study provides a new perspective for identifying farmers' poverty-contributing factors and technical reference and decision support for local departments to plan and implement targeted assistance and household-specific development policies.

Background/Aims: Carnitine palmitoyltransferase 1A (CPT1A) is a rate-limiting enzyme in the transport of long-chain fatty acids for β-oxidation. Increasing evidence has indicated that CPT1A plays an important role in carcinogenesis. However, the expression and prognostic value of CPT1A in hepatocellular carcinoma (HCC) have not been extensively studied. Methods: Here, we collected 66 post-operative liver cancer tissue samples. Gene profile expression was tested by RT-PCR. Receiver operating characteristic (ROC) analysis was performed and multivariate analysis with Cox’s Proportional Hazard Model was used for confirming the selected markers’ predictive efficiency for HCC patients’ survival. A simple risk scoring system was created based on Cox’s regression modeling and bootstrap internal validation. Results: Cox multivariate regression analysis demonstrated that CPT1A, tumor size, intrahepatic metastasis, TNM stage and histological grade were independent risk factors for the prognosis of HCC patients after surgery. Our genetic and clinical data-based (GC) risk scoring system revealed that HCC patients whose total score≥3 are more likely to relapse and die than patients whose total score < 3. Finally, the good discriminatory power of our risk scoring model was validated by bootstrap internal validation. Conclusions: The genetic and clinical data-based risk scoring model can be a promising predictive tool for liver cancer patients’ prognosis after operation.

Vegetation, hydrology and geomorphology are three major elements of the floodplain ecosystem on Earth. Although the normalized difference vegetation index (NDVI) has been used extensively to characterize floodplain vegetation growth, vigour and biomass, methods for quantifying the various distinct responses of floodplain vegetation to hydro-geomorphological changes in different lateral belts in arid regions are still needed. In this study, the Linhe reach was divided into four lateral belts based on their hydro-geomorphological characteristics, and the Moderate Resolution Imaging Spectroradiometer (MODIS)-NDVI time series statistical indicators were used to characterise the distinct changing the patterns of vegetation growth in different belts. The response of floodplain vegetation to river hydro-geomorphology in each belt was analysed. The result showed that the average maximum NDVI value in the regular inundation area was 0.23 and higher than that in the other lateral belts. The correlation between the water persistence time and peak NDVI value in the regular water inundation area was significant (ρ = 0.84), indicating that in contrast to highly frequent or extremely rare water inundation, regular water inundation provides significant benefits to floodplains. Continuous or highly frequent inundation may cause decreased vegetation productivity. Overall, our results suggest that the vegetation greenness response to the river hydro-geomorphology is different from the river to the edge of the floodplain. Thus, a better understanding of the interactions between the floodplain vegetation and river hydro-morphology and river water resource management in arid-region floodplains.

Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification “smarter”. In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing for their ability to build extensible relationships between geographic entities. This paper implements a classification scheme considering the neighborhood relationship of land cover by extracting information from a graph. First, a graph representing the spatial relationships of land cover types was built based on an existing land cover map. Empirical probability distributions of the spatial relationships were then extracted using this graph. Second, an image was classified based on an object-based fuzzy classifier. Finally, the membership of objects and the attributes of their neighborhood objects were joined to decide the final classes. Two experiments were implemented. Overall accuracy of the two experiments increased by 5.2% and 0.6%, showing that this method has the ability to correct misclassified patches using the spatial relationship between geo-entities. However, two issues must be considered when applying spatial relationships to image classification. The first is the “siphonic effect” produced by neighborhood patches. Second, the use of global spatial relationships derived from a pre-trained graph loses local spatial relationship in-formation to some degree.

Background/AimsTo investigate the role of selected serum microRNA (miRNA) levels as potential noninvasive biomarkers for differentiating S0–S2 (early fibrosis) from S3–S4 (late fibrosis) in patients with a chronic hepatitis B virus (HBV) infection.Methods : One hundred twenty-three treatment-naive patients with a chronic HBV infection who underwent a liver biopsy were enrolled in this study. The levels of selected miRNAs were measured using a real-time quantitative polymerase chain reaction assay. A logistic regression analysis was performed to assess factors associated with fibrosis progression. Receiver operating characteristic (ROC) curve and discriminant analyses validated these the ability of these predicted variables to discriminate S0–S2 from S3–S4.Results : Serum miR-29, miR-143, miR-223, miR-21, and miR-374 levels were significantly downregulated as fibrosis progressed from S0–S2 to S3–S4 (p

With the widespread implementation of COVID-19 prevention and control measures during the pandemic, online classes have become a normal and indispensable part of college students' lives. Analysis of the factors affecting college students' behavioural intention towards online classes will help improve online class quality. This paper sets up a model to analyse the factors that may affect such intentions . The results show that social influence, performance expectancy, effort expectancy, perceived usefulness and perceived ease of use have a significant positive effect on college students' behavioural intention, and enabling environment(i.e. the infeasibility of offline teaching due to the pandemic)has a significant negative effect on it. Gender and voluntariness, as moderator variables, also clearly influence behavioural intention. Based on pandemic prevention and control measures, along with education reform and modernisation, this paper offers valuable suggestions to online learning platform and education administrators.

In some cloudy and rainy regions, the cloud cover is high in moderate-high resolution remote sensing images collected by satellites with a low revisit cycle, such as Landsat. This presents an obstacle for classifying land cover in cloud-covered parts of the image. A decision fusion scheme is proposed for improving land cover classification accuracy by integrating the complementary information of MODIS (Moderate-resolution Imaging Spectroradiometer) time series data with Landsat moderate-high spatial resolution data. The multilevel decision fusion method includes two processes. First, MODIS and Landsat data are pre-classified by fuzzy classifiers. Second, the pre-classified results are assembled according to their assessed performance. Thus, better pre-classified results are retained and worse pre-classified results are restrained. For the purpose of solving the resolution difference between MODIS and Landsat data, the proposed fusion scheme employs an object-oriented weight assignment method. A decision rule based on a compromise operator is applied to assemble pre-classified results. Three levels of data containing different types of information are combined, namely the MODIS pixel-level and object-level data, and the Landsat pixel-level data. The multilevel decision fusion scheme was tested on a site in northeast Thailand. The fusion results were compared with the single data source classification results, showing that the multilevel decision fusion results had a higher overall accuracy. The overall accuracy is improved by more than 5 percent. The method was also compared to the two-level combination results and a weighted sum decision rule-based approach. A comparison experiment showed that the multilevel decision fusion rule had a higher overall accuracy than the weighted sum decision rule-based approach and the low-level combination approach. A major limitation of the method is that the accuracy of some of the land covers, where areas are small, are not as improved as the overall accuracy.

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta (YRD) region using moderate resolution imaging spectroradiometer (MODIS) time-series data. The normalized difference vegetation index (NDVI) was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.