Accelerated Redox Reactions Enable Stable Tin-Lead Mixed Perovskite Solar Cells.

The facile oxidation of Sn2+ to Sn4+ poses an inherent challenge that limits the efficiency and stability of tin-lead mixed (Sn-Pb) perovskite solar cells (PSCs) and all-perovskite tandem devices. In this work, we discover the sustainable redox reactions enabling self-healing Sn-Pb perovskites, where their intractable oxidation degradation can be recovered to their original state under light soaking. Quantitative and operando spectroscopies are used to investigate the redox chemistry, revealing that metallic Pb0 from the photolysis of perovskite reacts with Sn4+ to regenerate Pb2+ and Sn2+ spontaneously. Given the sluggish redox reaction kinetics, V3+ /V2+ ionic pair is designed as an effective redox shuttle to accelerate the recovery of Sn-Pb perovskites from oxidation. The target Sn-Pb PSCs enabled by V3+ /V2+ ionic pair deliver an improved power conversion efficiency (PCE) of 21.22 % and excellent device lifespan, retaining nearly 90 % of its initial PCE after maximum power point tracking under light for 1,000 hours.

Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with increasing incidence and geographic extent. The extent to which global climate change affects the incidence of SFTS disease remains obscure. We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in China. The spatial distribution of habitat suitability for the tick Haemaphysalis longicornis was predicted by applying a boosted regression tree model under four alternative climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) for the periods 2030-2039, 2050-2059, and 2080-2089. We incorporate the SFTS cases in the mainland of China from 2010 to 2019 with environmental variables and the projected distribution of H. longicornis into a generalized additive model to explore the current and future spatiotemporal dynamics of SFTS. Our results demonstrate an expanded geographic distribution of H. longicornis toward Northern and Northwestern China, showing a more pronounced change under the RCP8.5 scenario. In contrast, the environmental suitability of H. longicornis is predicted to be reduced in Central and Eastern China. The SFTS incidence in three time periods (2030-2039, 2050-2059, and 2080-2089) is predicted to be increased as compared to the 2010s in the context of various RCPs. A heterogeneous trend across provinces, however, was observed, when an increased incidence in Liaoning and Shandong provinces, while decreased incidence in Henan province is predicted. Notably, we predict possible outbreaks in Xinjiang and Yunnan in the future, where only sporadic cases have been reported previously. These findings highlight the need for tick control and population awareness of SFTS in endemic regions, and enhanced monitoring in potential risk areas.

Climate drives the spatiotemporal dynamics of scrub typhus in China.

Scrub typhus is a climate-sensitive and life-threatening vector-borne disease that poses a growing public health threat. Although the climate-epidemic associations of many vector-borne diseases have been studied for decades, the impacts of climate on scrub typhus remain poorly understood, especially in the context of global warming. Here we incorporate Chinese national surveillance data on scrub typhus from 2010 to 2019 into a climate-driven generalized additive mixed model to explain the spatiotemporal dynamics of this disease and predict how it may be affected by climate change under various representative concentration pathways (RCPs) for three future time periods (the 2030s, 2050s, and 2080s). Our results demonstrate that temperature, precipitation, and relative humidity play key roles in driving the seasonal epidemic of scrub typhus in mainland China with a 2-month lag. Our findings show that the change of projected spatiotemporal dynamics of scrub typhus will be heterogeneous and will depend on specific combinations of regional climate conditions in future climate scenarios. Our results contribute to a better understanding of spatiotemporal dynamics of scrub typhus, which can help public health authorities refine their prevention and control measures to reduce the risks resulting from climate change.

1D-2D Synergistic MXene-Nanotubes Hybrids for Efficient Perovskite Solar Cells.

Incorporation of 2D MXenes into the electron transporting layer (ETL) of perovskite solar cells (PSCs) has been shown to deliver high-efficiency photovoltaic (PV) devices. However, the ambient fabrication of the ETLs leads to unavoidable deterioration in the electrical properties of MXene due to oxidation. Herein, sorted metallic single-walled carbon nanotubes (m-SWCNTs) are employed to prepare MXene/SWCNTs composites to improve the PV performance of PSCs. With the optimized composition, a power conversion efficiency of over 21% is achieved. The improved photoluminescence and reduced charge transfer resistance revealed by electrochemical impedance spectroscopy demonstrated low trap density and improved charge extraction and transport characteristics due to the improved conductivity originating from the presence of nanotubes as well as the reduced defects associated with oxygen vacancies on the surface of the SnO2 . The MXene/SWCNTs strategy reported here provides a new avenue for realizing high-performance PSCs.

Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning.

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.

Simulating the Linkages Between Economy and Armed Conflict in India With a Long Short-Term Memory Algorithm.

This article analyzes the linkages between the economy and armed conflict in India using annual frequency data for the period 1989-2016, the maximum time period for which consistent data are available for the country. An adequate set of economic indicators was established to fully reflect the economic condition. Long short-term memory (LSTM), which is a machine-learning algorithm for time series, was employed to simulate the relationship between the economy and armed conflict events. In addition, LSTM was applied to predict the trend of armed conflict with two strategies: multiyear predictions and yearly predictions. The results show that both strategies can adequately simulate the relationship between the economy and armed conflict, with all simulation accuracies above 90%. The accuracy of the yearly prediction is higher than that of the multiyear prediction. Theoretically, the future state and trend of armed conflict can be predicted with LSTM and future economic data if future economic data can be predicted.

Spatiotemporal Patterns and Risk Factors for Scrub Typhus From 2007 to 2017 in Southern China.

BACKGROUND: Substantial outbreaks of scrub typhus, coupled with the discovery of this vector-borne disease in new areas, suggest that the disease remains remarkably neglected. The objectives of this study were to map the contemporary and potential transmission risk zones of the disease and to provide novel insights into the health burden imposed by scrub typhus in southern China. METHODS: Based on the assembled data sets of annual scrub typhus cases and maps of environmental and socioeconomic correlates, a boosted regression tree modeling procedure was used to identify the environmental niche of scrub typhus and to predict the potential infection zones of the disease. Additionally, we estimated the population living in the potential scrub typhus infection areas in southern China. RESULTS: Spatiotemporal patterns of the annual scrub typhus cases in southern China between 2007 and 2017 reveal a tremendous, wide spread of scrub typhus. Temperature, relative humidity, elevation, and the normalized difference vegetation index are the main factors that influence the spread of scrub typhus. In southern China, the predicted highest transmission risk areas of scrub typhus are mainly concentrated in several regions, such as Yunnan, Guangxi, Guangdong, Hainan, and Fujian. We estimated that 162 684 million people inhabit the potential infection risk zones in southern China. CONCLUSIONS: Our results provide a better understanding of the environmental and socioeconomic factors driving scrub typhus spread, and estimate the potential infection risk zones beyond the disease's current, limited geographical extent, which enhances our capacity to target biosurveillance and help public health authorities develop disease control strategies.

Estimating the potential of energy saving and carbon emission mitigation of cassava-based fuel ethanol using life cycle assessment coupled with a biogeochemical process model.

Global warming and increasing concentration of atmospheric greenhouse gas (GHG) have prompted considerable interest in the potential role of energy plant biomass. Cassava-based fuel ethanol is one of the most important bioenergy and has attracted much attention in both developed and developing countries. However, the development of cassava-based fuel ethanol is still faced with many uncertainties, including raw material supply, net energy potential, and carbon emission mitigation potential. Thus, an accurate estimation of these issues is urgently needed. This study provides an approach to estimate energy saving and carbon emission mitigation potentials of cassava-based fuel ethanol through LCA (life cycle assessment) coupled with a biogeochemical process model-GEPIC (GIS-based environmental policy integrated climate) model. The results indicate that the total potential of cassava yield on marginal land in China is 52.51 million t; the energy ratio value varies from 0.07 to 1.44, and the net energy surplus of cassava-based fuel ethanol in China is 92,920.58 million MJ. The total carbon emission mitigation from cassava-based fuel ethanol in China is 4593.89 million kgC. Guangxi, Guangdong, and Fujian are identified as target regions for large-scale development of cassava-based fuel ethanol industry. These results can provide an operational approach and fundamental data for scientific research and energy planning.

Long non-coding RNA TTN-AS1 promotes cell growth and metastasis in cervical cancer via miR-573/E2F3.

Long non-coding RNAs (lncRNAs) are a crucial member of non-coding RNA family, and increasing evidence demonstrates that lncRNAs participate in the initiation and progression of cancers. Our study aimed to explore the role of lncRNA TTN-AS1 in cervical cancer (CC) development. In the present study, our results showed that TTN-AS1 was substantially increased in CC tissues and cell lines, high expression of TTN-AS1 was correlated with advanced FIGO stage, poor differentiation, lymph node metastasis, and poor overall survival of CC patients. Function assays showed that TTN-AS1 inhibition decreased the proliferation and invasion of CC cells both in vitro and in vivo. Mechanistically, we revealed that TTN-AS1 could positively modulate E2F3 expression via sponging miR-573 in CC cells. Together, our study revealed that lncRNA TTN-AS1 was involved in the progression of CC cells by regulation of miR-573-E2F3 axis, which offered a new insight into the treatment strategies of CC.

Decreased levels of sRAGE in follicular fluid from patients with PCOS.

This study aimed to explore the association between soluble receptor for advanced glycation end products (sRAGE) levels in follicular fluid and the number of oocytes retrieved and to evaluate the effect of sRAGE on vascular endothelial growth factor (VEGF) in granulosa cells in patients with polycystic ovarian syndrome (PCOS). Two sets of experiments were performed in this study. In part one, sRAGE and VEGF protein levels in follicular fluid samples from 39 patients with PCOS and 35 non-PCOS patients were measured by ELISA. In part two, ovarian granulosa cells were isolated from an additional 10 patients with PCOS and cultured. VEGF and SP1 mRNA and protein levels, as well as pAKT levels, were detected by real-time PCR and Western blotting after cultured cells were treated with different concentrations of sRAGE. Compared with the non-PCOS patients, patients with PCOS had lower sRAGE levels in follicular fluid. Multi-adjusted regression analysis showed that high sRAGE levels in follicular fluid predicted a lower Gn dose, more oocytes retrieved, and a better IVF outcome in the non-PCOS group. Logistic regression analysis showed that higher sRAGE levels predicted favorably IVF outcomes in the non-PCOS group. Multi-adjusted regression analysis also showed that high sRAGE levels in follicular fluid predicted a lower Gn dose in the PCOS group. Treating granulosa cells isolated from patients with PCOS with recombinant sRAGE decreased VEGF and SP1 mRNA and protein expression and pAKT levels in a dose-dependent manner.

Follicular fluid soluble receptor for advanced glycation endproducts (sRAGE): a potential protective role in polycystic ovary syndrome.

PURPOSE: To explore the relationships between the soluble receptor for advanced glycation endproducts (sRAGE) and the outcome parameters following in vitro fertilization-embryo transfer (IVF-ET) in patients with polycystic ovary syndrome (PCOS) and investigate the protective effect of sRAGE in PCOS development regarding inflammation. METHODS: We conducted a prospective analysis of a subsample of 74 participants from the Reproductive Medical Center of the First Affiliated Hospital of Zhengzhou University. We quantified sRAGE, vascular endothelial growth factor (VEGF), tumor necrosis factor (TNF-α), interleukelin-6 (IL-6), and C-reactive protein (CPR) protein levels in the follicular fluid from 39 PCOS and 35 non-PCOS reproductive-age women. sRAGE and VEGF, TNF-α, IL-6, and CRP in follicular fluid aspirated without blood were measured by ELISA. RESULTS: sRAGE concentrations in the follicular fluid were significantly lower in the PCOS group compared to those in the control group, while VEGF, TNF-α, IL-6, and CRP concentrations were significantly higher in the PCOS group than in the control group (P < 0.05). sRAGE was significantly, inversely correlated with the total dose of gonadotropin (Gn) in the PCOS group undergoing IVF treatment (r = -0.451, P = 0.004). After adjusting for age and Gn dose (in international units used per cycle), sRAGE protein levels in the follicular fluid were significantly, inversely related to VEGF (r = -0.378, P = 0.018), TNF-α (r = -0.450, P = 0.004), IL-6 (r = -0.455, P = 0.004), and CRP (r = -0.375, P = 0.019). CONCLUSION: sRAGE in the follicular fluid might exert a protective effect against the inflammatory action of PCOS development.

Growth hormone co-treatment within a GnRH agonist long protocol improves implantation and pregnancy rates in patients undergoing IVF-ET.

OBJECTIVE: The efficacy of growth hormone (GH) co-treatment within a GnRH agonist long regimen, in women with a normal ovarian response to controlled ovarian hyperstimulation (COH), for IVF was assessed. METHODS: This retrospective clinical trial was performed in a private-assisted reproduction centre. The study involved 1114 patients who responded normally to high-dose gonadotropin treatment. The study group of 556 patients was given in a daily subcutaneous injection of 4.5 IU of GH co-treatment, starting from the initial day of gonadotropin treatment and lasting for 5 days. The control group of 558 patients received the same treatment protocol without the GH co-treatment. The participants were further divided into two subgroups: age ≥35 years and age <35 years. The primary endpoint of the study was IVF-ET outcomes. RESULTS: The demographic characteristics did not significantly differ between the groups. The implantation rate (36.7 vs. 20.4 %, P < 0.05) and clinical pregnancy rate (57.3 vs. 30.1 %, P < 0.05) were significantly higher in the study group than in the control group. An analysis using a multivariate logistic regression model showed that GH was a significant factor for predicting pregnancy outcomes (OR 3.125, 95 % CI 2.441-4.000). Furthermore, for the ≥35-year-old group, the endometrial thickness was significantly greater (11.99 ± 2.21 vs. 11.62 ± 2.45, P < 0.05) in the study group than in the control group; in contrast, for the <35-year-old group, the high-quality embryo rate was significantly higher (71.7 vs. 68.3 %, P < 0.05) in the study group than in the control group. CONCLUSION: Our study showed that co-treatment with GH in a GnRH agonist long protocol in patients who responded normally while undergoing IVF-ET could increase the implantation and pregnancy rates.

Reliability and validity of the multidimensional scale of perceived social support in Chinese mainland patients with methadone maintenance treatment.

INTRODUCTION: The multidimensional scale of perceived social support (MSPSS) is a valid tool for assessing perceived support from family, friends and significant others. However, evidence about reliability and validity of the MSPSS in Chinese mainland patients with methadone maintenance treatment (MMT) is lacking. METHODS: The patients (n=1212) being admitted to the first two largest MMT clinics in Xi'an were recruited in the study. Reliability was estimated with Cronbach's α and intra-class correlation (ICC). Convergent and discriminant validity was assessed using item-subscale correlation. Factorial validity was examined using exploratory and confirmatory factor analysis. The patients answered the questions of MSPSS at baseline and re-test after 6months, respectively. RESULTS: Cronbach's α of the overall MSPSS was 0.92 (subscales range: 0.84-0.89). ICC of the overall MSPSS was 0.65 (subscales range: 0.57-0.64). Better convergent validity (r≥0.40) was demonstrated by the satisfactory hypothesized item-subscale correlation. All of the hypothesized item-subscale correlations were higher than the correlations between the hypothesized items and other subscales, indicating better discriminant validity. Two factors were extracted from the 12 items, with factor 1 mainly covering friends and significant others subscales (explained 55.56% variance) and factor 2 mainly covering family subscale (explained 11.77% variance). In comparison with the proposed three-subscale model, the two-factor observed model did not fit well in this sample according to model fit indices. CONCLUSIONS: The MSPSS has acceptable reliability and convergent/discriminant validity in Chinese mainland MMT patients. The proposed three-factor model of MSPSS is much better fit than the two-factor observed model in this study. Findings of the study will provide evidence of psychometric properties of the MSPSS in MMT patient population and expand the use of the MSPSS in clinical MMT context.

Assessment of the GHG reduction potential from energy crops using a combined LCA and biogeochemical process models: a review.

The main purpose for developing biofuel is to reduce GHG (greenhouse gas) emissions, but the comprehensive environmental impact of such fuels is not clear. Life cycle analysis (LCA), as a complete comprehensive analysis method, has been widely used in bioenergy assessment studies. Great efforts have been directed toward establishing an efficient method for comprehensively estimating the greenhouse gas (GHG) emission reduction potential from the large-scale cultivation of energy plants by combining LCA with ecosystem/biogeochemical process models. LCA presents a general framework for evaluating the energy consumption and GHG emission from energy crop planting, yield acquisition, production, product use, and postprocessing. Meanwhile, ecosystem/biogeochemical process models are adopted to simulate the fluxes and storage of energy, water, carbon, and nitrogen in the soil-plant (energy crops) soil continuum. Although clear progress has been made in recent years, some problems still exist in current studies and should be addressed. This paper reviews the state-of-the-art method for estimating GHG emission reduction through developing energy crops and introduces in detail a new approach for assessing GHG emission reduction by combining LCA with biogeochemical process models. The main achievements of this study along with the problems in current studies are described and discussed.

Land-use classification based on high-resolution remote sensing imagery and deep learning models.

High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the performance of these models. In this study, we applied four well-established and robust deep learning models (FCN-8s, SegNet, U-Net, and Swin-UNet) to an open benchmark high-resolution remote sensing dataset to compare their performance in land-use mapping. The results indicate that FCN-8s, SegNet, U-Net, and Swin-UNet achieved overall accuracies of 80.73%, 89.86%, 91.90%, and 96.01%, respectively, on the test set. Furthermore, we assessed the generalization ability of these models using two measures: intersection of union and F1 score, which highlight Swin-UNet's superior robustness compared to the other three models. In summary, our study provides a systematic analysis of the classification differences among these four deep learning models through experiments. It serves as a valuable reference for selecting models in future research, particularly in scenarios such as land-use mapping, urban functional area recognition, and natural resource management.

Identification of nonvolatile organic compounds (NVOCs) in biopharmaceuticals through non-target analysis and quantification using complexation-precipitation extraction.

Single-use systems in biopharmaceutical manufacturing can potentially release chemical constituents (leachables) into drug products. Prior to conducting toxicological risk assessments, it is crucial to establish the qualitative and quantitative methods for these leachables. In this study, we conducted a comprehensive screening and structure elucidation of 23 leachables (nonvolatile organic compounds, NVOCs) in two antibody drugs using multiple (self-built and public) databases and mass spectral simulation. We identified 7 compounds that have not been previously reported in medical or medicinal extractables and leachables. The confidence levels for identified compounds were classified based on analytical standards, literature references, and fragment assignments. Most of the identified leachables were found to be plasticizers, antioxidants, slip agents or polymer degradants. Polysorbate (namely Tween) is commonly used as an excipient for protein stabilization in biopharmaceutical formulations, but its ionization in liquid chromatography-electrospray ionization mass spectrometry can interfere with compound quantification. To address this, we employed a complexation-precipitation extraction method to reduce polysorbate content and quantify the analytes. The developed quantitative method for target NVOCs demonstrated high sensitivity (limit of quantification: 20 or 50 µg/L), accuracy (recoveries: 77.2 to 109.5 %) and precision (RSD ≤ 8.2 %). Overall, this established method will facilitate the evaluation of NVOC safety in drug products.

Modeling analysis of armed conflict risk in sub-Saharan Africa, 2000-2019.

Sub-Saharan Africa has suffered frequent outbreaks of armed conflict since the end of the Cold War. Although several efforts have been made to understand the underlying causes of armed conflict and establish an early warning mechanism, there is still a lack of a comprehensive assessment approach to model the incidence risk of armed conflict well. Based on a large database of armed conflict events and related spatial datasets covering the period 2000-2019, this study uses a boosted regression tree (BRT) approach to model the spatiotemporal distribution of armed conflict risk in sub-Saharan Africa. Evaluation of accuracy indicates that the simulated models obtain high performance with an area under the receiver operator characteristic curve (ROC-AUC) mean value of 0.937 and an area under the precision recall curves (PR-AUC) mean value of 0.891. The result of the relative contribution indicates that the background context factors (i.e., social welfare and the political system) are the main driving factors of armed conflict risk, with a mean relative contribution of 92.599%. By comparison, the climate change-related variables have relatively little effect on armed conflict risk, accounting for only 7.401% of the total. These results provide novel insight into modelling the incidence risk of armed conflict, which may help implement interventions to prevent and minimize the harm of armed conflict.

The role of ion migration, octahedral tilt, and the A-site cation on the instability of Cs1-xFAxPbI3.

Organic-inorganic hybrid perovskites are promising materials for the next generation photovoltaics and optoelectronics; however, their practical application has been hindered by poor structural stability mainly caused by ion migration and external stimuli. Understanding the mechanism(s) of ion migration and structure decomposition is thus critical. Here we observe the sequence of structural changes at the atomic level that precede structural decomposition in the technologically important Cs1-xFAxPbI3 using ultralow dose transmission electron microscopy. We find that these changes differ, depending upon the A-site composition. Initially, there is a random loss of FA+, complemented by the loss of I-. The remaining FA+ and I- ions then migrate, unit cell by unit cell, into an ordered and more stable phase with a √2 x √2 superstructure. Further ion loss is accompanied by A-site dependent octahedral tilt modes and associated tetragonal phases with different stabilities. These observations of the loss of FA+/I- ion pairs, ion migration, octahedral tilt modes, and the role of the A-cation, provide insights into the atomic-scale structural mechanisms that drive and block ion loss and ion migration, opening pathways to inhibit ion loss, migration and improve structural stability.

Exploring the global geography of cybercrime and its driving forces.

Cybercrime is wreaking havoc on the global economy, national security, social stability, and individual interests. The current efforts to mitigate cybercrime threats are primarily focused on technical measures. This study considers cybercrime as a social phenomenon and constructs a theoretical framework that integrates the social, economic, political, technological, and cybersecurity factors that influence cybercrime. The FireHOL IP blocklist, a novel cybersecurity data set, is used to map worldwide subnational cybercrimes. Generalised linear models (GLMs) are used to identify the primary factors influencing cybercrime, whereas structural equation modelling (SEM) is used to estimate the direct and indirect effects of various factors on cybercrime. The GLM results suggest that the inclusion of a broad set of socioeconomic factors can significantly improve the model's explanatory power, and cybercrime is closely associated with socioeconomic development, while their effects on cybercrime differ by income level. Additionally, results from SEM further reveals the causal relationships between cybercrime and numerous contextual factors, demonstrating that technological factors serve as a mediator between socioeconomic conditions and cybercrime.