GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs.

Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.

Controlling the graphite-like microcrystalline structure of lignin-based ultrafine carbon fibers via the design of condensed structures.

Obtaining lignin-based graphite-like microcrystallites at a relatively low carbonization temperature is still very challenging. In this work, we report a new method based on condensed structures, for regulating graphite-like microcrystalline structures via the incorporation of 4,4'-diphenylmethane diisocyanate (MDI) into the main structure of lignin. The effects of MDI on the thermal properties of lignin and the graphite-like microcrystalline structure of lignin-based ultrafine carbon fibers were extensively studied and investigated. The incorporation of MDI decreased the thermal stability of lignin, increased the carbon yield and enhanced the formation of graphite-like microcrystallites, which are beneficial for reducing energy consumption during the preparation of lignin-based carbon fibers. The modified lignin-based ultrafine carbon fibers (M-LCFs) demonstrated satisfactory electrochemical performance, including high specific capacitance, low charge transfer resistance, and good cycle performance. The M-LCFs-3/2 electrode had a specific capacitance of 241.3 F g-1 at a current density of 0.5 A g-1, and a residual ratio of 90.2 % after 2000 charge and discharge cycles. This study provides a new approach to control the graphite-like microcrystalline structure and electrochemical performance while also optimizing the temperature.

Large-language models facilitate discovery of the molecular signatures regulating sleep and activity.

Sleep, locomotor and social activities are essential animal behaviors, but their reciprocal relationships and underlying mechanisms remain poorly understood. Here, we elicit information from a cutting-edge large-language model (LLM), generative pre-trained transformer (GPT) 3.5, which interprets 10.2-13.8% of Drosophila genes known to regulate the 3 behaviors. We develop an instrument for simultaneous video tracking of multiple moving objects, and conduct a genome-wide screen. We have identified 758 fly genes that regulate sleep and activities, including mre11 which regulates sleep only in the presence of conspecifics, and NELF-B which regulates sleep regardless of whether conspecifics are present. Based on LLM-reasoning, an educated signal web is modeled for understanding of potential relationships between its components, presenting comprehensive molecular signatures that control sleep, locomotor and social activities. This LLM-aided strategy may also be helpful for addressing other complex scientific questions.

Dual specific phosphatase 4 suppresses ferroptosis and enhances sorafenib resistance in hepatocellular carcinoma.

AIMS: This investigation aims to elucidate the mechanism underlying sorafenib-induced ferroptosis in hepatocellular carcinoma (HCC). METHODS: The role of dual specificity phosphatase 4 (DUSP4) in sorafenib-treated HCC was investigated using comprehensive assessments both in vitro and in vivo, including Western blotting, qRT-PCR, cell viability assay, lipid reactive oxygen species (ROS) assay, immunohistochemistry, and xenograft tumor mouse model. Additionally, label-free quantitative proteomics was employed to identify potential proteins associated with DUSP4. RESULTS: Our study revealed that suppression of DUSP4 expression heightens the susceptibility of HCC cells to ferroptosis inducers, specifically sorafenib and erastin, in both in vitro and in vivo settings. Furthermore, we identified DUSP4-mediated regulation of key ferroptosis-related markers, such as ferritin light chain (FTL) and ferritin heavy chain 1 (FTH1). Notably, label-free quantitative proteomics unveiled the phosphorylation of threonine residue T148 on YTH Domain Containing 1 (YTHDC1) by DUSP4. Further investigations unraveled that YTHDC1, functioning as an mRNA nuclear export regulator, is a direct target of DUSP4, orchestrating the subcellular localization of FTL and FTH1 mRNAs. Significantly, our study highlights a strong correlation between elevated DUSP4 expression and sorafenib resistance in HCC. CONCLUSIONS: Our findings introduce DUSP4 as a negative regulator of sorafenib-induced ferroptosis. This discovery opens new avenues for the development of ferroptosis-based therapeutic strategies tailored for HCC treatment.

Differences in Nurses' Satisfaction and Demand for Spatial Design Among Different Departments of Nursing Units: A Case Study in China.

OBJECTIVES: The study aimed to extend original research and identify operational and space-related requirements for specialization from the perspectives of nursing behavior and process. BACKGROUND: Studies related to the specialty of different nursing units have been widely conducted in nursing science, while few studies have explored the specialized requirements for the physical environments of nursing units in different departments. METHOD: Questionnaire survey data were collected from 125 nurses in 11 clinical departments, and nurse shadowing (approximately 68 hr) was conducted in four clinical departments. RESULTS: The questionnaire survey showed that satisfaction with care activity, visibility, and physical environment within the existing nursing unit environment was rated differently among different departments of nursing units. However, nurses in different groups of age, education, work experience, and position indicated no statistically significant difference. Behavioral observations demonstrated that the spatial and temporal distributions of activities, spatial linkages, and communication patterns varied in distinct departments. CONCLUSION: This research found that nurses in different departments had different evaluations of satisfaction and environmental characteristics. It also explains the differences in nursing work behaviors and processes found in various departments and sheds light on specialized requirements from the behavior perspective. The findings could help optimize the design of efficient and satisfactory nursing units in different departments.

Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study.

PURPOSE: There are undetectable levels of fat in fat-poor angiomyolipoma. Thus, it is often misdiagnosed as renal cell carcinoma. We aimed to develop and evaluate a multichannel deep learning model for differentiating fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC). METHODS: This two-center retrospective study included 320 patients from the First Affiliated Hospital of Sun Yat-Sen University (FAHSYSU) and 132 patients from the Sun Yat-Sen University Cancer Center (SYSUCC). Data from patients at FAHSYSU were divided into a development dataset (n = 267) and a hold-out dataset (n = 53). The development dataset was used to obtain the optimal combination of CT modality and input channel. The hold-out dataset and SYSUCC dataset were used for independent internal and external validation, respectively. RESULTS: In the development phase, models trained on unenhanced CT images performed significantly better than those trained on enhanced CT images based on the fivefold cross-validation. The best patient-level performance, with an average area under the receiver operating characteristic curve (AUC) of 0.951 ± 0.026 (mean ± SD), was achieved using the "unenhanced CT and 7-channel" model, which was finally selected as the optimal model. In the independent internal and external validation, AUCs of 0.966 (95% CI 0.919-1.000) and 0.898 (95% CI 0.824-0.972), respectively, were obtained using the optimal model. In addition, the performance of this model was better on large tumors (≥ 40 mm) in both internal and external validation. CONCLUSION: The promising results suggest that our multichannel deep learning classifier based on unenhanced whole-tumor CT images is a highly useful tool for differentiating fp-AML from RCC.

Hsa_circ_0057105 modulates a balance of epithelial-mesenchymal transition and ferroptosis vulnerability in renal cell carcinoma.

BACKGROUND: The incidence of renal cell carcinoma (RCC) has increased in recent years. Metastatic RCC is common and remains a major cause of mortality. A regulatory role for circular RNAs (circRNAs) in the occurrence and progression of RCC has been identified, but their function, molecular mechanisms, and potential clinical applications remain poorly understood. METHODS: High-throughput RNA sequencing was used to explore the differential expression of circRNAs and their related pathways in RCC patients. Transwell and CCK-8 assays were used to assess the function of hsa_circ_0057105 in RCC cells. The clinical relevance of hsa_circ_0057105 was evaluated in a cohort of RCC patients. The hsa_circ_0057105 regulatory axis was defined using RNA pull-down, luciferase reporter assays, and fluorescence in situ hybridization assays, and the in vivo effect of hsa_circ_0057105 was validated using animal experiments. RESULTS: Single-sample gene set enrichment analysis and correlation analysis of RNA-seq data showed that hsa_circ_0057105 was potentially oncogenic and may serve to regulate epithelial-mesenchymal transition (EMT) activation in RCC. Hsa_circ_0057105 expression was associated with advanced TNM stages and was an independent prognostic factor for poor RCC patient survival. Phenotypic studies show that hsa_circ_0057105 can enhance the migration and invasion abilities of RCC cells. Further, hsa_circ_0057105 was shown to inhibit the expression of miR-577, a miRNA that regulated the expression of both COL1A1, which induced EMT activation, and VDAC2, which modulated ferroptosis sensitivity. The dual regulatory roles of hsa_circ_0057105 on EMT and ferroptosis sensitivity were verified using rescue experiments. Animal studies confirmed that hsa_circ_0057105 increased the metastatic ability and ferroptosis sensitivity of RCC cells in vivo. CONCLUSIONS: In RCC, hsa_circ_0057105 regulates COL1A1 and VDAC2 expression through its sponge effect on miR-577, acting like a 'double-edged sword'. These findings provide new insight into the relationship between EMT and ferroptosis in RCC and provide potential biomarkers for RCC surveillance and treatment.

Changes in air pollution, land surface temperature, and urban heat islands during the COVID-19 lockdown in three Chinese urban agglomerations.

COVID-19 has notably impacted the world economy and human activities. However, the strict urban lockdown policies implemented in various countries appear to have positively affected pollution and the thermal environment. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and aerosol optical depth (AOD) data were selected, combined with Sentinel-5P images and meteorological elements, to analyze the changes and associations among air pollution, LST, and urban heat islands (UHIs) in three urban agglomerations in mainland China during the COVID-19 lockdown. The results showed that during the COVID-19 lockdown period (February 2020), the levels of the AOD and atmospheric pollutants (fine particles (PM2.5), NO2, and CO) significantly decreased. Among them, PM2.5 and NO2 decreased the most in all urban agglomerations, by >14 %. Notably, the continued improvement in air pollution attributed to China's strict control policies could lead to overestimation of the enhanced air quality during the lockdown. The surface temperature in all three urban agglomerations increased by >1 °C during the lockdown, which was mainly due to climate factors, but we also showed that the lockdown constrained positive LST anomalies. The decrease in the nighttime urban heat island intensity (UHIInight) in the three urban agglomerations was greater than that in the daytime quantity by >25 %. The reduction in surface UHIs at night was mainly due to the reduced human activities and air pollutant emissions. Although strict restrictions on human activities positively affected air pollution and UHIs, these changes were quickly reverted when lockdown policies were relaxed. Moreover, small-scale lockdowns contributed little to environmental improvement. Our results have implications for assessing the environmental benefits of city-scale lockdowns.

Environmentally friendly strategy to access self-healable, reprocessable and recyclable chitin, chitosan, and sodium alginate based polysaccharide-vitrimer hybrid materials.

Natural polysaccharides show enviable advantages for preparation of sustainable hybrid materials. However, in most cases, complex chemical modifications of natural polysaccharides are required, which not only causes changes of the inherent properties of polysaccharides, but also increases the manufacturing costs of the final materials. Therefore, it is highly desired to develop efficient and low-cost ways to access polysaccharides-containing hybrid materials. In this work, we report the environmentally friendly preparation of a new kind of polysaccharide-based materials, called polysaccharide-vitrimer hybrid materials, for the first time. The vitrimer synthesis and hybridization with polysaccharides can be achieved via a convenient one-pot method in absence of solvent and catalyst. In addition, time-consuming and labor-intensive physical/chemical modifications of natural polysaccharides are completely avoided. The resultant hybrid materials show good mechanical performance (tensile toughness is up to 13.7 MJ/m3), high thermal stability (Td,max is up to 457 °C), fast self-healing ability (self-healing efficiency is up to 99 % within 20s at 80 °C) and excellent reprocessability and recyclability (at least three cycles). Especially, conductive polysaccharide-vitrimer hybrid materials could be readily prepared from the resultant materials, exhibiting novel applications as flexible sensors and electromagnetic shielding materials (the EMI SE is up to 24.93 dB).

Antiphotoaging Effect of Micronized Fat in Ultraviolet B-Induced Human Dermal Fibroblasts.

BACKGROUND: Adipose-derived stromal vascular fraction (SVF) and mesenchymal stem cells have been proven to reduce the effects of skin photoaging. However, there is no standardized protocol for their preparation. This study aimed to investigate the skin rejuvenation potential of micronized fat, obtained using a novel device attached with a trifoliate blade, in the ultraviolet B (UV-B)-induced human dermal fibroblast model. METHODS: Micronized fat was prepared to obtain adipose-derived SVF, and the adipose-derived mesenchymal stem cell-to-SVF ratio was determined by flow cytometry. The UV-B-induced human dermal fibroblasts model was constructed to identify the characteristics of the human dermal fibroblasts using vimentin and S-100 immunostaining, observe their morphology, and measure the levels of photoaging-related factors. After the previous steps were completed, different cell groups were co-cultured with UV-B-induced human dermal fibroblasts, and the extent of improvement of photoaging was evaluated. RESULTS: Micronized fat had a higher adipose-derived mesenchymal stem cell-to-SVF ratio than the control fat preparations. The UV-B-induced human dermal fibroblasts model showed lowered levels of type I collagen and transforming growth factor-ß and increased expression of matrix metalloproteinases (MMPs), which are the characteristics of photoaging in normal human dermal fibroblasts. Compared with different cell groups co-cultured with UV-B-induced human dermal fibroblasts, micronized fat could lower the expression of MMPs and increase the level of type I collagen but lower the level of transforming growth factor-ß. CONCLUSIONS: Obtaining micronized fat is more effortless and clinically safer. Micronized fat has an antiphotoaging effect by inhibiting the expression of MMPs by means of the mitogen-activated protein kinases signaling pathway. CLINICAL RELEVANCE STATEMENT: The authors' work has potential clinical applications in fat grafting for facial rejuvenation.

EHBP1L1 Drives Immune Evasion in Renal Cell Carcinoma through Binding and Stabilizing JAK1.

High lymphocyte infiltration and immunosuppression characterize the tumor microenvironment (TME) in renal cell carcinoma (RCC). There is an urgent need to elucidate how tumor cells escape the immune attack and to develop novel therapeutic targets to enhance the efficacy of immune checkpoint blockade (ICB) in RCC. Overactivated IFN-γ-induced JAK/STAT signaling involves in such TME, but the underlying mechanisms remain elusive. Here, EH domain-binding protein 1-like protein 1 (EHBP1L1) is identified as a crucial mediator of IFN-γ/JAK1/STAT1/PD-L1 signaling in RCC. EHBP1L1 is highly expressed in RCC, and high EHBP1L1 expression levels are correlated with poor prognosis and resistance to ICB. EHBP1L1 depletion significantly inhibits tumor growth, which is attributed to enhanced CD8+ T cell-mediated antitumor immunity. Mechanistically, EHBP1L1 interacts with and stabilizes JAK1. By competing with SOCS1, EHBP1L1 protects JAK1 from proteasomal degradation, which leads to elevated JAK1 protein levels and JAK1/STAT1/PD-L1 signaling activity, thereby forming an immunosuppressive TME. Furthermore, the combination of EHBP1L1 inhibition and ICB reprograms the immunosuppressive TME and prevents tumor immune evasion, thus significantly reinforcing the therapeutic efficacy of ICB in RCC patient-derived xenograft (PDX) models. These findings reveal the vital role of EHBP1L1 in immune evasion in RCC, which may be a potential complement for ICB therapy.

The relationship between the CUN-BAE body fatness index and incident diabetes: a longitudinal retrospective study.

BACKGROUND: The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) index has been recommended as an ideal indicator of body fat and exhibited significant correlation with cardiometabolic risk factors. However, whether the CUN-BAE index correlates with incident diabetes in Asian populations is unknown. Therefore, this longitudinal study was designed to evaluate the association between baseline CUN-BAE index and type 2 diabetes mellitus (T2DM). METHODS: This retrospective longitudinal study involved 15,464 participants of 18-79 years of age in the NAGALA (NAfld in the Gifu Area Longitudinal Analysis) study over the period of 2004-2015. Cox proportional hazards regression was performed to test the relationship between the baseline CUN-BAE index and diabetes incidence. Further stratification analysis was conducted to ensure that the results were robust. The diagnostic utility of the CUN-BAE index was tested by the receiver operating characteristic (ROC) curve. RESULTS: Over the course of an average follow-up of 5.4 years, 373 (2.41%) participants developed diabetes. A higher diabetes incidence was associated with higher CUN-BAE quartiles (P for trend< 0.001). Each 1 unit increase in CUN-BAE index was associated with a 1.08-fold and 1.14-fold increased risk of diabetes after adjustment for confounders in males and females, respectively (both P < 0.001). Stratification analysis demonstrated a consistent positive correlation between baseline CUN-BAE and diabetes incidence. Moreover, based on ROC analysis, CUN-BAE exhibited a better capacity for diabetes prediction than both body mass index (BMI) and waist circumference (WC) in both sexes. CONCLUSIONS: The baseline CUN-BAE level was independently related to the incidence of diabetes. Increased adiposity determined by CUN-BAE could be used as a strong nonlaboratory predictor of incident diabetes in clinical practice.

Grating-like DBD plasma for air disinfection: Dose and dose-response characteristics.

Atmospheric pressure dielectric barrier discharge (DBD) plasma is an emerging technique for effective bioaerosol decontamination and is promising to be used in indoor environments to reduce infections. However, fundamental knowledge of the dose and dose-response characteristics of plasma-based disinfection technology is very limited. By examining the single-pass removal efficiency of S. lentus aerosol by in-duct grating-like DBD plasma reactors with varied discharge setups (gap distance, electrode size, number of discharge layers, frequency, dielectric material), it was found that the specific input energy (SIE) could be served as the dose for disinfection, and the efficiency was exponentially dependent on SIE in most cases. The corresponding susceptibility constants (Z values) were obtained hereinafter. Humidity was a prominent factor boosting the efficiency with a Z value of 0.36 L/J at relative humidity (RH) of 20% and 1.68 L/J at RH of 60%. MS2 phage showed a much higher efficiency of 2.66-3.08 log10 of reduction than those of S. lentus (38-85%) and E. coli (42%-95%) under the same condition. Using SIE as the dose, the performance of plasma reactors in the literature was compared and evaluated. This work provides a theoretical and engineering basis for air disinfection by plasma-based technology.

A Perovskite Memristor with Large Dynamic Space for Analog-Encoded Image Recognition.

Reservoir computing (RC) is a computational architecture capable of efficiently processing temporal information, which allows low-cost hardware implementation. However, the previously reported memristor-based RC mostly utilized binarized data sets to reduce the difficulty of signal processing of the memristor, which inevitably induces data distortion to a certain extent, leading to poor network computing performance. Here, we report on a RC system in a fully memristive architecture based on solution-processed perovskite memristors. The perovskite memristor exhibits 10000 conductance states with a modulation range of more than 4 orders of magnitude. The obtained tens of thousands of finely spaced conductance states with a near-ideal analog property provide a sufficiently large dynamic range and enough intermediate states, which were further applied as a reservoir to map the feature information on different sequential inputs in an analog way. The computing capability of the image classification task of a Fashion-MNIST data set with a high recognition accuracy of up to 90.1% shows that the excellent analog and short-term properties of our perovskite memristor allow the hardware implementation of neuromorphic computing with a reduced training cost.

Organic radicals stabilization above 300 °C in Eu-based coordination polymers for solar steam generation.

Organic radicals feature unpaired electrons, and these compounds may have applications in biomedical technology and as materials for solar energy conversion. However, unpaired electrons tend to pair up (to form chemical bonds), making radicals unstable and hampering their applications. Here we report an organic radical system that is stable even at 350 °C, surpassing the upper temperature limit (200 °C) observed for other organic radicals. The system reported herein features a sulfur-rich organic linker that facilitates the formation of the radical centers; on the solid-state level, the molecules are crystallized with Eu(III) ions to form a 3D framework featuring stacks of linker molecules. The stacking is, however, somewhat loose and allows the molecules to wiggle and transform into sulfur-stabilized radicals at higher temperatures. In addition, the resulting solid framework remains crystalline, and it is stable to water and air. Moreover, it is black and features strong broad absorption in the visible and near IR region, thereby enhancing both photothermal conversion and solar-driven water evaporation.

m6A-immune-related lncRNA prognostic signature for predicting immune landscape and prognosis of bladder cancer.

BACKGROUND: N6-methyladenosine (m6A) related long noncoding RNAs (lncRNAs) may have prognostic value in bladder cancer for their key role in tumorigenesis and innate immunity. METHODS: Bladder cancer transcriptome data and the corresponding clinical data were acquired from the Cancer Genome Atlas (TCGA) database. The m6A-immune-related lncRNAs were identified using univariate Cox regression analysis and Pearson correlation analysis. A risk model was established using least absolute shrinkage and selection operator (LASSO) Cox regression analyses, and analyzed using nomogram, time-dependent receiver operating characteristics (ROC) and Kaplan-Meier survival analysis. The differences in infiltration scores, clinical features, and sensitivity to Talazoparib of various immune cells between low- and high-risk groups were investigated. RESULTS: Totally 618 m6A-immune-related lncRNAs and 490 immune-related lncRNAs were identified from TCGA, and 47 lncRNAs of their intersection demonstrated prognostic values. A risk model with 11 lncRNAs was established by Lasso Cox regression, and can predict the prognosis of bladder cancer patients as demonstrated by time-dependent ROC and Kaplan-Meier analysis. Significant correlations were determined between risk score and tumor malignancy or immune cell infiltration. Meanwhile, significant differences were observed in tumor mutation burden and stemness-score between the low-risk group and high-risk group. Moreover, high-risk group patients were more responsive to Talazoparib. CONCLUSIONS: An m6A-immune-related lncRNA risk model was established in this study, which can be applied to predict prognosis, immune landscape and chemotherapeutic response in bladder cancer.

Integrated System for On-Site Rapid and Safe Screening of COVID-19.

Since the outbreak of coronavirus disease 2019 (COVID-19), the epidemic has been spreading around the world for more than 2 years. Rapid, safe, and on-site detection methods of COVID-19 are in urgent demand for the control of the epidemic. Here, we established an integrated system, which incorporates a machine-learning-based Fourier transform infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based disinfection modules to prevent potential secondary infections. A partial least-squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset containing 857 training serum samples. Furthermore, the sensitivity, specificity, and prediction accuracy could all reach over 94% from the results of the field test regarding 968 blind testing samples. Additionally, the disinfection modules achieved an inactivation efficiency of 99.9% for surface and airborne tested bacteria. The proposed system is conducive and promising for point-of-care and on-site COVID-19 screening in the mass population.

Mapping Local Climate Zones in the Urban Environment: The Optimal Combination of Data Source and Classifier.

The novel concept of local climate zones (LCZs) provides a consistent classification framework for studies of the urban thermal environment. However, the development of urban climate science is severely hampered by the lack of high-resolution data to map LCZs. Using Gaofen-6 and Sentinel-1/2 as data sources, this study designed four schemes using convolutional neural network (CNN) and random forest (RF) classifiers, respectively, to demonstrate the potential of high-resolution images in LCZ mapping and evaluate the optimal combination of different data sources and classifiers. The results showed that the combination of GF-6 and CNN (S3) was considered the best LCZ classification scheme for urban areas, with OA and kappa coefficients of 85.9% and 0.842, respectively. The accuracy of urban building categories is above 80%, and the F1 score for each category is the highest, except for LCZ1 and LCZ5, where there is a small amount of confusion. The Sentinel-1/2-based RF classifier (S2) was second only to S3 and superior to the combination of GF-6 and random forest (S1), with OA and kappa coefficients of 64.4% and 0.612, respectively. The Sentinel-1/2 and CNN (S4) combination has the worst classification result, with an OA of only 39.9%. The LCZ classification map based on S3 shows that the urban building categories in Xi'an are mainly distributed within the second ring, while heavy industrial buildings have started to appear in the third ring. The urban periphery is mainly vegetated and bare land. In conclusion, CNN has the best application effect in the LCZ mapping task of high-resolution remote sensing images. In contrast, the random forest algorithm has better robustness in the band-abundant Sentinel data.

Burden of tuberculosis and its association with socio-economic development status in 204 countries and territories, 1990-2019.

Background: Tuberculosis (TB) always runs in the forefront of the global burden when it comes to infectious diseases. Tuberculosis, which can lead to impairment of quality of life, financial hardship, discrimination, marginalization, and social barriers, is a major public health problem. The assessment of TB burden and trend can provide crucial information for policy decision and planning, and help countries in the world to achieve the goal of sustainable development of ending the epidemic of TB in 2030. Methods: All data are from the Global Burden of Disease 2019 (GBD 2019) database, which analyzed the burden trend of age-standardized incidence, DALYs, and deaths rate in TB and HIV/AIDS-infected TB over the past 30 years. Also, GBD 2019 not only analyzed the burden distribution of TB in 204 countries and main regions of the world but also analyzed the relationship between the burden of global TB and the socio-demographic Index (SDI). Results: The age-standardized incidence, age-standardized disability-adjusted life years (DALYs), and age-standardized deaths rate for HIV-negative TB were 10,671.45 (9,395.60-12,194.10), 59,042.45 (53,684.78-64,641.53), and 1,463.62 (1,339.24-1,602.71) (95% CI, per 100,000 person-years) in 2019, respectively. Age-standardized incidence, age-standardized DALYs, and age-standardized deaths rate of HIV/AIDS-XDR-TB (95% CI, per 1,000 person-years) were 2.10 (1.51-2.90), 64.23 (28.64-117.74), and 1.01 (0.42-1.86), respectively. We found that TB is inversely proportional to SDI, the age-standardized incidence, DALYs, and deaths rate low burden countries were in high SDI areas, while high burden countries were in low SDI areas. The global TB showed a slow decline trend, but the age-standardized incidence of HIV-positive TB was increasing, and mainly distributed in sub-Saharan Africa. Conclusion: Age-standardized incidence, age-standardized DALYs, and age-standardized deaths rate of TB is related to SDI, and the burden of low SDI countries is lighter than that of high SDI countries. Without effective measures, it will be difficult for countries around the world to achieve the goal of ending the TB epidemic by 2030. Effective control of the spread of TB requires concerted efforts from all countries in the world, especially in the countries with low SDI, which need to improve the diagnosis and preventive measures of TB and improve the control of HIV/AIDS-TB.

Hardwood Kraft lignin-derived carbon microfibers with enhanced electrochemical performance.

It is of great challenge to prepare lignin-derived carbon microfibers with suitable graphite crystallites due to the volatilization of incorporated polymers. In this work, we proposed a simple method for the construction of graphite crystallites based on the regulation of the hydrogen-bonding interaction between hardwood Kraft lignin (HKL) and poly(m-phenylene isophthalamide) (PMIA). The strong hydrogen-bonding interaction demonstrated by the results of TG, FTIR, XPS, Raman and XRD increased the graphite crystal size and perfected the crystal structure of HKL-based carbon microfibers, which further enhanced the electrochemical performance of HKL/PMIA-based carbon microfibers electrodes, especially for the increase of capacitance and cycle performance and the decrease of charge transfer resistance. The specific capacitance, energy density and power density of P2H2-based (HKL/PMIA = 1:1) carbon microfibers electrode were up to 190.8 F g-1, 34.4 Wh kg-1 and 540 W kg-1 at a current density of 0.5 A g-1, respectively, which were comparable to or even higher than those of lignin composites-based carbon fibers electrodes. This work reveals the relationship between hydrogen-bonding interaction and crystalline structure, which can be further considered in the preparation of lignin-based carbon fibers electrodes.