Improving the Performance of Si/PEDOT:PSS Hybrid Solar Cells with More Economical and Environmentally Friendly Alcohol Ether Solvents.

The photoelectric characteristics of poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) films significantly affect the power conversion efficiency and stability of Si/PEDOT:PSS hybrid solar cells. In this paper, we investigated PEDOT:PSS modification with alcohol ether solvents (dipropylene glycol methyl ether (DPM) and propylene glycol phenyl ether (PPH)). The reduction of PSS content and the transformation of the PEDOT chain from benzene to a quinone structure in PEDOT:PSS induced by doping with DPM or PPH are the reasons for the improved conductivity of PEDOT:PSS films. DPM and PPH doping improves the quality of silicon with the PEDOT:PSS heterojunction and silicon surface passivation, thereby reducing the surface recombination of charge carriers, which improves the photovoltaic performance of Si/PEDOT:PSS solar cells. Comparing the power conversion performance (PCE) and air stability of Si/PEDOT:PSS solar cells with DPM (13.24%), DPH (13.51%), ethylene glycol (EG, 13.07%), and dimethyl sulfoxide (DMSO, 12.62%), it is suggested that doping with DPM and DPH can replace DMSO and EG to enhance the performance of Si/PEDOT:PSS solar cells. The EG and DMSO solvents not only have a certain toxicity to the human body but also are not environmentally friendly. In comparison to DMSO and EG, DPM and DPH are more economical and environmentally friendly, helping to reduce the manufacturing cost of Si/PEDOT:PSS solar cells and making them more conducive to their commercial applications.

Political factors, carbon emissions, and life expectancy in India: Implications for the Sustainable Development Goal 3.

This study examines the nexus among political factors, carbon emissions, and life expectancy between 1990 and 2020 in India. Data for this study was extracted from the World Bank Development indicators, after which it was subjected to econometrics analysis. The results showed that on averages, between 1990 and 2020, India experienced a life expectancy around 65 years. Fossil fuel energy consumption represents a small proportion of total energy consumption in India. However, carbon emissions and life expectancy have a positive and significant relationship. Fossil fuel usage and life expectancy possess a significantly positive relationship (FFEC = 0.044128, P-value = 0.0023) Moreover, government effectiveness and life expectancy have a significant direct relationship. Political stability and life expectancy have a significant negative relationship in the country. We conclude that policymakers in India should ensure that carbon emissions and fossil fuel usage in India do not pose a threat to life expectancy. Efforts should be put in place by policymakers in India to increase life expectancy , a strategic component of SDG 3- good health and well being for all at all ages, by ensuring stable political climate, good governance and efficient health enhanced public policies.

Cette étude examine le lien entre les facteurs politiques, les émissions de carbone et l'espérance de vie entre 1990 et 2020 en Inde. Les données de cette étude ont été extraites des indicateurs de développement de la Banque mondiale, après quoi elles ont été soumises à une analyse économétrique. Les résultats ont montré qu'en moyenne, entre 1990 et 2020, l'Inde a connu une espérance de vie d'environ 65 ans. La consommation d'énergie fossile représente une petite proportion de la consommation totale d'énergie en Inde. Cependant, les émissions de carbone et l'espérance de vie ont une relation positive et significative. L'utilisation de combustibles fossiles et l'espérance de vie possèdent une relation significativement positive (FFEC = 0,044128, valeur P = 0,0023). De plus, l'efficacité du gouvernement et l'espérance de vie ont une relation directe significative. La stabilité politique et l'espérance de vie ont une relation négative significative dans le pays. Nous concluons que les décideurs politiques indiens devraient veiller à ce que les émissions de carbone et l'utilisation de combustibles fossiles en Inde ne constituent pas une menace pour l'espérance de vie. Des efforts devraient être mis en place par les décideurs politiques indiens pour augmenter l'espérance de vie, une composante stratégique de l'ODD 3 - bonne santé et bien-être pour tous à tout âge, en garantissant un climat politique stable, une bonne gouvernance et des politiques publiques efficaces et améliorées en matière de santé.

Preexposure to one social threat alters responses to another social threat: Behavioral and electrophysiological evidence.

A recent Cyberball study has indicated that the experience of loss of control can affect how people process subsequent social exclusion. This "preexposure effect" supports the idea of a common cognitive system involved in the processing of different types of social threats. To test the validity of this assumption in the current study, we reversed the sequence of the preexposure setup. We measured the effects of social exclusion on the subsequent processing of loss of control utilizing event-related brain potentials (ERPs) and self-reports. In the control group (CG, n = 26), the transition to loss of control elicited significant increases in both the P3 amplitude and the self-reported negative mood. Replicating the results of the previous preexposure study, these effects were significantly reduced by the preexposure to an independent social threat (here: social exclusion). In contrast to previous findings, these effects were not modulated by the discontinuation (EG1disc, n = 25) or continuation (EG2cont, n = 24) of the preexposure threat. Given that the P3 effect is related to the violation of subjective expectations, these results support the notion that preexposure to a specific social threat has widespread effects on the individuals' expectancy of upcoming social participation and control.

Dipeptidyl peptidase-4 inhibitors alleviate cognitive dysfunction in type 2 diabetes mellitus.

BACKGROUND: Patients with type 2 diabetes mellitus (T2DM) are commonly at high risk for developing cognitive dysfunction. Antidiabetic agents might be repurposed for targeting cognitive dysfunction in addition to modulation on glucose homeostasis. This study aimed to evaluate the impact of dipeptidyl peptidase-4 inhibitors (DPP-4i) on cognitive function in T2DM. METHODS: PubMed, Embase, Cochrane Library and Web of Science were systematically searched from inception to September 30, 2023. Weighted mean differences were calculated using the Mantel-Haenszel (M-H) fixed or random effects model based on the degree of heterogeneity among studies. Heterogeneity was evaluated using a Chi-squared test and quantified with Higgins I2. Sensitivity analysis was performed with the leave-one-out method, and publication bias was evaluated according to Begg's and Egger's tests. RESULTS: Six clinical trials involving 5,178 participants were included in the pooled analysis. Administration of DPP-4i generally correlated with an increase of Mini-Mental State Examination (MMSE) scores (1.09, 95% CI: 0.22 to 1.96). DPP-4i alleviated cognitive impairment in the copying skill subdomain of MMSE (0.26, 95% CI: 0.12 to 0.40). Treatment with DPP-4i also resulted in an increase of Instrumental Activities of Daily Living (IADL) scores (0.82, 95% CI: 0.30 to 1.34). However, DPP-4i produced no significant effects on Barthel Activities of Daily Living (BADL) scores (0.37, 95% CI: -1.26 to 1.99) or other test scores. CONCLUSIONS: DPP-4i treatment favourably improved cognitive function in patients with T2DM. Further trials with larger samples should be performed to confirm these estimates and investigate the association of different DPP-4i with cognitive function among diabetic patients. TRIAL REGISTRATION IN PROSPERO: CRD42023430873.

MOnitoring human papillomavirus Vaccine effect on Infection and cErvical diseases (MOVIE): Protocol for a cohort study using electronic health records from Yinzhou, China.

Cervical cancer is the fourth most common cancer in women, with a high disease burden worldwide. Human papillomavirus (HPV) vaccination reduces HPV-related infection and associated cervical lesions and cancers. Few studies have explored HPV vaccination impact in real-world settings in China. This study aims to monitor HPV vaccine uptake and its effects on HPV-related diseases, evaluating vaccine effectiveness in a real-world context and complementing clinical trial results. Electronic health records (EHRs) from 2010 to 2020 from the Yinzhou Regional Health Information Platform (YRHIP) will be queried/extracted to identify and monitor HPV vaccine uptake in females aged 9-45 years, and HPV-related screening and prevalence (i.e., cervical HPV infection, cervical intraepithelial neoplasia [CIN] grades 1-3, and cervical cancer) in a cohort of females aged 9-70 years. Cervical cancer screening guidelines and expert consultation will be used for intra-database validation, to determine the best algorithm for identifying HPV-related disease. Pre-launch (2010-2016) and post-launch (2018-2020) periods are predefined. A time trend analysis will be performed to describe the vaccination impact on disease prevalence and, if prerequisite conditions are met, vaccine effectiveness will be computed using logistic regression, adjusting for age, calendar year, history of screening and HPV infection. Cohort study design, outcomes validation, data linkage, and multi-step statistical analyses could provide valuable experience for designing other real-world studies in the future. The study outcomes can help inform policy-makers about uptake and HPV vaccination policy in girls and women in Yinzhou District, and provide insights on progress toward achieving goals set by the World Health Organization.

AI-assisted accelerated MRI of the ankle: clinical practice assessment.

BACKGROUND: High-spatial resolution magnetic resonance imaging (MRI) is essential for imaging ankle joints. However, the clinical application of fast spin-echo sequences remains limited by their lengthy acquisition time. Artificial intelligence-assisted compressed sensing (ACS) technology has been recently introduced as an integrative acceleration solution. We compared ACS-accelerated 3-T ankle MRI to conventional methods of compressed sensing (CS) and parallel imaging (PI) . METHODS: We prospectively included 2 healthy volunteers and 105 patients with ankle pain. ACS acceleration factors for ankle protocol of T1-, T2-, and proton density (PD)-weighted sequences were optimized in a pilot study on healthy volunteers (acceleration factor 3.2-3.3×). Images of patients acquired using ACS and conventional acceleration methods were compared in terms of acquisition times, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality, and diagnostic agreement. Shapiro-Wilk test, Cohen κ, intraclass correlation coefficient, and one-way ANOVA with post hoc tests (Tukey or Dunn) were used. RESULTS: ACS acceleration reduced the acquisition times of T1-, T2-, and PD-weighted sequences by 32-43%, compared with conventional CS and PI, while maintaining image quality (mostly higher SNR with p < 0.004 and higher CNR with p < 0.047). The diagnostic agreement between ACS and conventional sequences was rated excellent (κ = 1.00). CONCLUSIONS: The optimum ACS acceleration factors for ankle MRI were found to be 3.2-3.3× protocol. The ACS allows faster imaging, yielding similar image quality and diagnostic performance. RELEVANCE STATEMENT: AI-assisted compressed sensing significantly accelerates ankle MRI times while preserving image quality and diagnostic precision, potentially expediting patient diagnoses and improving clinical workflows. KEY POINTS: • AI-assisted compressed sensing (ACS) significantly reduced scan duration for ankle MRI. • Similar image quality achieved by ACS compared to conventional acceleration methods. • A high agreement by three acceleration methods in the diagnosis of ankle lesions was observed.

Driving rural industry revitalization in the digital economy era: Exploring strategies and pathways in China.

In the context of the digital economy, achieving rural industrial revitalization in China hinges on digitization. This study delves into the synergistic mechanisms of diverse factors that contribute to Rural Industrial Revitalization across three dimensions: technology, organization, and environment. To investigate these mechanisms, a combination of the necessary condition analysis method and the fuzzy set qualitative comparative analysis method is employed. The research findings indicate that no necessary conditions exist for achieving high-level rural industrial revitalization. However, digital infrastructure and the digital financial environment have a universally significant impact. The study identifies four distinct pathways driving high-level rural industrial revitalization: digitaldriven, digital-government-talentdriven, digital-enterprisedriven, and digital-enterprise-talentdriven. Furthermore, significant variations exist in the driving pathways for rural industrial revitalization among the eastern, central, and western regions of China. By unveiling the multifaceted mechanisms underpinning the revitalization of rural industries, this research provides valuable practical insights for the future development of rural industries in China.

The impact of government reimbursement negotiation on targeted anticancer medicines use and cost in China: A cohort study based on national health insurance data.

Background: High prices of targeted anticancer medicines (TAMs) result in financial toxicity for patients and the health insurance system. How national price negotiation and reimbursement policy affect the accessibility of TAMs for cancer patients remains unknown. Methods: In this population-based cohort study, we used national health insurance claims data in 2017 and identified adult patients with cancer diagnoses for which price-negotiated TAMs were indicated. We estimated the half-month prevalence of price-negotiated TAMs use before and after the policy implementation in September 2017. We calculated direct medical costs, out-of-pocket (OOP) costs, and the proportion of OOP cost for each cancer patient to measure their financial burden attributable to TAMs use. We performed segmented linear and multivariable logistic regression to analyse the policy impact. Results: We included 39 391 of a total 118 655 cancer beneficiaries. After September 2017, the prevalence of price-negotiated TAMs use increased from 1.4%-2.1% to 2.9%-3.1% (P = 0.005); TAMs users' daily medical costs increased from US$261.3 to US$292.5 (P < 0.001), while median daily OOP costs (US$68.2 vs US$65.7; P = 0.134) and OOP costs as a proportion of daily medical costs persisted (28.5% vs 28.5%; P = 0.995). Compared with resident beneficiaries, the relative probability of urban employee beneficiaries on TAMs uses decreased after the policy (adjusted odds ratio (aOR) = 2.4 vs aOR = 2.2). Conclusions: The government price negotiation and reimbursement policy improved patient access to TAMs and narrowed disparities among insurance schemes. China's approach to promoting the affordability of expensive medicines provides valuable experience for health policy decision-makers.

Utilization of lipidic food waste as low-cost nutrients for enhancing the potentiality of biofuel production from engineered diatom under temperature variations.

The scarcity of natural fossil fuels presents a promising opportunity for the development of renewable microalgae-based biofuels. However, the current microalgae cultivation is unable to effectively address the high costs of the production of biofuels. To tackle this challenge, this study focused on recruiting engineered Phaeodactylum tricornutum (FabG-OE) to enhance biomass accumulation and lipid production by employing food waste hydrolysate under temperature variations. The biomass and lipid accumulations of FabG-OE were improved effectively in mixed culture medium and food waste hydrolysate at a volume ratio (v/v) of 80:20 at 30 °C. It was found that oxidative stress might contribute to the overexpression of lipogenic genes, thereby leading to lipogenesis at 30 °C. Upscaling cultivation of FabG-OE at 30 °C using a semi-continuous strategy and batch strategy was conducted to achieve 0.73 and 0.77 g/L/d of biomass containing 0.35 and 0.38 g/L/d of lipid, respectively. In summary, these findings provide valuable insights for advancing microalgae-based biofuel production.

Performance assessment of variant UNet-based deep-learning dose engines for MR-Linac-based prostate IMRT plans.

Objective. UNet-based deep-learning (DL) architectures are promising dose engines for traditional linear accelerator (Linac) models. Current UNet-based engines, however, were designed differently with various strategies, making it challenging to fairly compare the results from different studies. The objective of this study is to thoroughly evaluate the performance of UNet-based models on magnetic-resonance (MR)-Linac-based intensity-modulated radiation therapy (IMRT) dose calculations.Approach. The UNet-based models, including the standard-UNet, cascaded-UNet, dense-dilated-UNet, residual-UNet, HD-UNet, and attention-aware-UNet, were implemented. The model input is patient CT and IMRT field dose in water, and the output is patient dose calculated by DL model. The reference dose was calculated by the Monaco Monte Carlo module. Twenty training and ten test cases of prostate patients were included. The accuracy of the DL-calculated doses was measured using gamma analysis, and the calculation efficiency was evaluated by inference time.Results. All the studied models effectively corrected low-accuracy doses in water to high-accuracy patient doses in a magnetic field. The gamma passing rates between reference and DL-calculated doses were over 86% (1%/1 mm), 98% (2%/2 mm), and 99% (3%/3 mm) for all the models. The inference times ranged from 0.03 (graphics processing unit) to 7.5 (central processing unit) seconds. Each model demonstrated different strengths in calculation accuracy and efficiency; Res-UNet achieved the highest accuracy, HD-UNet offered high accuracy with the fewest parameters but the longest inference, dense-dilated-UNet was consistently accurate regardless of model levels, standard-UNet had the shortest inference but relatively lower accuracy, and the others showed average performance. Therefore, the best-performing model would depend on the specific clinical needs and available computational resources.Significance. The feasibility of using common UNet-based models for MR-Linac-based dose calculations has been explored in this study. By using the same model input type, patient training data, and computing environment, a fair assessment of the models' performance was present.

Dynamic effect of economic growth on the persistence of suicide rates.

Positive and negative economic growth is closely related to the suicide rate. To answer the question whether economic development has a dynamic impact on this rate, we used a panel smooth transition autoregressive model to evaluate the threshold effect of economic growth rate on the persistence of suicide. The research period was from 1994 to 2020, and the results show that the suicide rate had a persistent effect, which varied over time depending on the transition variable within different threshold intervals. However, the persistent effect was manifested in different degrees with the change in the economic growth rate and as the lag period of the suicide rate increased, the effect of the influence gradually decreased. We investigated different lag periods and noted that the impact on the suicide rate was the strongest in the first year after an economic change and then reduced to be only marginal after three years. This means that the growth momentum of the suicide rate within the first two years after a change in the economic growth rate, should be included in policy considerations of how to prevent suicides.

Assessment on the declining degree of farmland shelter forest in a desert oasis based on LiDAR and hyperspectrum imagery.

We examined the growth decline and health status of farmland protective forest belt (Populus alba var. pyramidalis and Populus simonii shelterbelts) in Ulanbuh Desert Oasis by using airborne hyperspectral and ground-based LiDAR to collect the hyperspectral images and point cloud data of the whole forest belt respectively. Through correlation analysis and stepwise regression analysis, we constructed the evaluation model of the decline degree of farmland protection forest with the spectral differential value, vegetation index, and forest structure parameters as independent variables and the tree canopy dead branch index of the field survey as dependent variables. We further tested the accuracy of the model. The results showed that the evaluation accuracy of the decline degree of P. alba var. pyramidalis and P. simonii by LiDAR method was better than that by hyperspectral method, and that the evaluation accuracy of the combined LiDAR and hyperspectral method was the highest. Using the LiDAR method, hyperspectral method, the combined method, the optimal model of P. alba var. pyramidalis was all light gradient boosting machine model, with the overall classification accuracy being 0.75, 0.68, 0.80, and Kappa coefficient being 0.58, 0.43, 0.66, respectively. The optimal model of P. simonii was random forest model, random forest model, and multilayer perceptron model, with the overall classification accuracy being 0.76, 0.62, 0.81, and Kappa coefficient being 0.60, 0.34, 0.71, respectively. This research method could accurately check and monitor the decline of plantations.

Invasiveness assessment by artificial intelligence against intraoperative frozen section for pulmonary nodules ≤ 3 cm.

PURPOSE: To investigate the performance of an artificial intelligence (AI) algorithm for assessing the malignancy and invasiveness of pulmonary nodules in a multicenter cohort. METHODS: A previously developed deep learning system based on a 3D convolutional neural network was used to predict tumor malignancy and invasiveness. Dataset of pulmonary nodules no more than 3 cm was integrated with CT images and pathologic information. Receiver operating characteristic curve analysis was used to evaluate the performance of the system. RESULTS: A total of 466 resected pulmonary nodules were included in this study. The areas under the curves (AUCs) of the deep learning system in the prediction of malignancy as compared with pathological reports were 0.80, 0.80, and 0.75 for all, subcentimeter, and solid nodules, respectively. Additionally, the AUC in the AI-assisted prediction of invasive adenocarcinoma (IA) among subsolid lesions (n = 184) was 0.88. Most malignancies that were misdiagnosed by the AI system as benign diseases with a diameter measuring greater than 1 cm (26/250, 10.4%) presented as solid nodules (19/26, 73.1%) on CT. In an exploratory analysis involving nodules underwent intraoperative pathologic examination, the concordance rate in identifying IA between the AI model and frozen section examination was 0.69, with a sensitivity of 0.50 and specificity of 0.97. CONCLUSION: The deep learning system can discriminate malignant diseases for pulmonary nodules measuring no more than 3 cm. The AI model has a high positive predictive value for invasive adenocarcinoma with respect to intraoperative frozen section examination, which might help determine the individualized surgical strategy.

[Spatial Distribution Characteristics and Assessment of Heavy Metal Pollution in Cultivated Soil of Agricultural Small Watershed in Typical Mining Area in South China].

In order to clarify the spatial distribution characteristics and the present situation of heavy metal pollution in cultivated soil of a typical mining basin in southern China, the contents of Cd, As, Mn, Cu, Zn, and Pb in cultivated soil in the selected small watershed were determined, and the spatial distribution characteristics of heavy metals were analyzed using the spatial interpolation method. Further, the ecological risk was evaluated using the geo-accumulation index and potential ecological risk coefficient (PERC). The results showed that the average contents of Pb, Zn, Cu, As, and Cd in the topsoil (0-20 cm) in the small watershed were approximately 11.9, 3.8, 8.2, 4.7, and 14.2 times that of the background value in Hunan soil, respectively. Compared with the soil risk screening value of agricultural land, the over-standard rates of Pb, Zn, Cu, As, and Cd were approximately 24%, 56%, 75%, 44%, and 48%, respectively. Zn and Mn showed medium variation, whereas Pb, As, Cu, and Cd showed strong variation, which indicated that there was an obvious enrichment of heavy metals in the small watershed. The spatial analysis results showed that there was an obvious consistent characteristic of six heavy metals in the topsoil, that is, there was enrichment in the mining activity area and its downstream and the township streets in the west of the small watershed. Additionally, there were high value points of some heavy metals on both sides of the main river, indicating that the main pollution sources in the study area were mining and metallurgy activities and sewage irrigation. The results of the geo-accumulation index and potential ecological risk index showed that there was compound pollution of heavy metals, in which the degree of Cd pollution was the highest, and the soil of both sides of the river could pose the strongest potential ecological risk.

Assessment of community efforts to advance network-based prediction of protein-protein interactions.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

An ultra-fast deep-learning-based dose engine for prostate VMAT via knowledge distillation framework with limited patient data.

Objective. Deep-learning (DL)-based dose engines have been developed to alleviate the intrinsic compromise between the calculation accuracy and efficiency of the traditional dose calculation algorithms. However, current DL-based engines typically possess high computational complexity and require powerful computing devices. Therefore, to mitigate their computational burdens and broaden their applicability to a clinical setting where resource-limited devices are available, we proposed a compact dose engine via knowledge distillation (KD) framework that offers an ultra-fast calculation speed with high accuracy for prostate Volumetric Modulated Arc Therapy (VMAT).Approach. The KD framework contains two sub-models: a large pre-trained teacher and a small to-be-trained student. The student receives knowledge transferred from the teacher for better generalization. The trained student serves as the final engine for dose calculation. The model input is patient computed tomography and VMAT dose in water, and the output is DL-calculated patient dose. The ground-truth \dose was computed by the Monte Carlo module of the Monaco treatment planning system. Twenty and ten prostate cases were included for model training and assessment, respectively. The model's performance (teacher/student/student-only) was evaluated by Gamma analysis and inference efficiency.Main results. The dosimetric comparisons (input/DL-calculated/ground-truth doses) suggest that the proposed engine can effectively convert low-accuracy doses in water to high-accuracy patient doses. The Gamma passing rate (2%/2 mm, 10% threshold) between the DL-calculated and ground-truth doses was 98.64 ± 0.62% (teacher), 98.13 ± 0.76% (student), and 96.95 ± 1.02% (student-only). The inference time was 16 milliseconds (teacher) and 11 milliseconds (student/student-only) using a graphics processing unit device, while it was 936 milliseconds (teacher) and 374 milliseconds (student/student-only) using a central processing unit device.Significance. With the KD framework, a compact dose engine can achieve comparable accuracy to that of a larger one. Its compact size reduces the computational burdens and computing device requirements, and thus such an engine can be more clinically applicable.

Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin.

With the intensification of conflicts between different ecosystem services, how to achieve a win-win situation between socio-economic development and ecological protection is an important issue that needs to be addressed nowadays. In particular, how to better quantify and assess the intensity of ecosystem service trade-offs and their relative benefits, and to identify the influencing factors are issues that need to be studied in depth. Based on the INVEST model, this paper analyzed the evolution of spatial and temporal patterns of ecosystem services such as Carbon Storage (CS), Food Production (FP), Habitat Quality (HQ), and Water Yield (WY) in the Shandong Yellow River Basin (SYRB) in 2000, 2010 and 2020. Next, we quantitatively measured the trade-off intensity and revealed the key influencing factors of the trade-off intensity evolution using automatic linear models, root mean square deviation, and geographically weighted regressions. Subsequently, we further analyzed the impact of the correlation between environmental and socio-economic factors on the trade-off intensity of ecosystem services. The results indicated that the temporal and spatial changes of the four main ecosystem services in SYRB area were inconsistent. WY showed a fluctuating trend, with a large interannual gap. CS and FP are on the rise, while HQ is on the decline. Spatially, WY and HQ showed a decreasing distribution from the center to the periphery, while FP and CS showed a decreasing distribution from the southwest to the northeast. The location characteristics of SYRB's four ecosystem services and their trade-offs were obvious. FP had absolute location advantage in ecosystem service trade-offs. Most of the four ecosystem services showed significant trade-offs, and the trade-off intensity had significant spatial heterogeneity, but the trade-off between FP and CS was relatively weak. At the same time, there were also differences in the trends of trade-off intensities. Counties with low trade-off intensity were mostly located in mountainous areas; these areas are less disturbed by human activities, and most of them are areas without prominent services. Counties with high trade-off intensities were mostly concentrated in areas with relatively developed agriculture; these areas are more disturbed by human activities and are mostly prominent in FP. The trade-off intensity of ecosystem services in SYRB was affected by several factors together, and there were difference characteristics in the degree and direction of influence of each factor. Moreover, these influencing factors have gradually changed over 20 years. In terms of the spatial distribution at the county scale, the influence areas of the dominant drivers of different trade-off types varied greatly, among which the areas with NDVI, CON, and PRE as the dominant factors were the largest. In the future, in effectively balancing regional economic development and ecological environmental protection, quantifiable correspondence strategies should be developed from the administrative perspective of counties and regions based on comprehensive consideration of the locational advantages of each ecosystem service and changes in trade-offs.

Spatiotemporal Interaction and Socioeconomic Determinants of Rural Energy Poverty in China.

This study investigated the energy poverty spatiotemporal interaction characteristics and socioeconomic determinants in rural China from 2000 to 2015 using exploratory time-space data analysis and a geographical detector model. We obtained the following results. (1) The overall trend of energy poverty in China's rural areas was "rising first and then declining", and the evolution trend of energy poverty in the three regions formed a "central-west-east" stepwise decreasing pattern. (2) There was a dynamic local spatial dependence and unstable spatial evolution process, and the spatial agglomeration of rural energy poverty in China had a relatively higher path dependence and locked spatial characteristics. (3) The provinces with negative connections were mainly concentrated in the central and western regions. Anhui and Henan, Inner Mongolia and Jilin, Jilin and Heilongjiang, Hebei and Shanxi, and Liaoning and Jilin constituted a strong synergistic growth period. (4) From a long-term perspective, the disposable income of rural residents had the greatest determinant power on rural energy poverty, followed by per capita GDP, rural labor education level, regulatory agencies, and energy investment. In addition, our findings showed that the selected driving factors all had enhanced effects on rural energy poverty in China through interaction effects.

Prognostic Value of Preoperative Nutritional Assessment and Neutrophil-to-Lymphocyte Ratio in Patients With Thymic Epithelial Tumors.

Introduction: Systemic nutrition and immune inflammation are the key factors in cancer development and metastasis. This study aimed to compare and assess four nutritional status and immune indicators: prognostic nutritional index (PNI), nutritional risk index (NRI), neutrophil-to-lymphocyte ratio (NLR), and the systemic immune-inflammatory index (SII) as prognostic indicators for patients with thymic epithelial tumors. Materials: We retrospectively reviewed 154 patients who underwent thymic epithelial tumor resection at our hospital between 2004 and 2015. The optimal cutoff value for each nutritional and immune index was obtained using the X-tile software. Kaplan-Meier curves and Cox proportional hazards models were used for survival analysis. Results: Univariate analysis showed that PNI, NRI, NLR, SII, albumin (ALB), the albumin/globulin ratio (A/G), WHO stage, T stage, and drinking history were associated with the overall survival (OS) of patients (P < 0.05). The NRI, NLR, A/G, ALB, T stage, and WHO stage were significant independent prognostic factors of OS in multivariate analysis (P < 0.05). Finally, we constructed a coNRI-NLR model to predict OS and recurrence-free survival (RFS). Conclusions: This study suggests that the preoperative NRI, NLR, and coNRI-NLR model may be important prognostic factors for patients with thymic epithelial tumors who undergo surgical resection.