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Abstract Saturated three-dimensional carbocycles have gained increasing prominence in synthetic and medicinal chemistry. In particular, bicyclo[2.1.1]hexanes (BCHs) have been identified as the molecular replacement for benzenes. Here, we present facile access to a variety of BCHs via a stepwise two-electron formal (3 + 2) cycloaddition between silyl enol ethers and bicyclo[1.1.0]butanes (BCBs) under Lewis acid catalysis. The reaction features wide functional group tolerance for silyl enol ethers, allowing the efficient construction of two vicinal quaternary carbon centers and a silyl-protected tertiary alcohol unit in a streamlined fashion. Interestingly, the reaction with conjugated silyl dienol ethers can provide access to bicyclo[4.1.1]octanes (BCOs) equipped with silyl enol ethers that facilitate further transformation. The utilities of this methodology are demonstrated by the late-stage modification of natural products, transformations of tertiary alcohol units on bicyclo[2.1.1]hexane frameworks, and derivatization of silyl enol ethers on bicyclo[4.1.1]octanes, delivering functionalized bicycles that are traditionally inaccessible.

Abstract Background This study aims to explore the bidirectional causal relationship between immune cell phenotypes and chronic periodontitis using a Mendelian randomization framework. Materials and methods Through a two-sample Mendelian randomization analysis, this research examined genetic data related to 731 immune cell traits and chronic periodontitis. Instrumental variables were chosen based on their genetic links to either immune traits or periodontitis. Various statistical techniques, including MR-Egger regression, weighted median, and inverse-variance weighted (IVW) analysis, were employed to determine the causal connections. Results Predominantly using the IVW method, 26 distinct immune phenotypes were identified as potentially influencing periodontitis (P

Abstract Background To explore the influence of cross-sectional type and morphological parameters at the mandibular molar sites on lingual plate perforation (LPP) during the immediate implant placement (IIP). Methods 181 implants were virtually placed in the mandibular molar sites on the cone beam computed tomography (CBCT). Each cross-section of the implantation site was divided into the Undercut (U)/Parallel (P)/Convex (C) types. Morphologically relevant parameters were measured on the cross-sections, including width of the upper end (Wb), width of the lower end (Wc), vertical height (V), angle between the natural crown axis and the alveolar bone axis (∠β), LC depth (LCD), LC height, and angle between the horizontal line and the line connecting the most prominent point and the most concave point of lingual plate (∠α). Besides, the distance from the end of the virtual implant and the lingual bone plate of the cross-section (DIL) was calculated. Relationships between all the morphologically relevant parameters and the DIL were further analyzed. Results A total of 77 (42.5%) cross-sections were classified as U-type, which was the most common one, accounting for 63% of the second molar regions. All LPP cases and most of the nearly LPP (87.9%) cases occurred at the U-type cross-sections, and the relationship between the DIL and the morphological parameters can be expressed by a multivariate linear equation. Conclusions The occurrence rate of U-type cross-sections in the second molar region was very high, and the risk of LPP should be considered during IIP. Except for the U-type, significant large LCD, small Wc, and large ∠β were the important relevant factors. CBCT and multivariate linear equations could help to assess the LPP risk and provide a reference for implant placement design pre-surgery.

Abstract Background Accurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein–protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data. Results In this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively. Conclusions We demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.

Abstract Background The accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine and pharmaceuticals. Computationally predicting protein function has been studied in the past decades. Plagued by noise and errors in protein–protein interaction (PPI) networks, researchers have undertaken to focus on the fusion of multi-omics data in recent years. A data model that appropriately integrates network topologies with biological data and preserves their intrinsic characteristics is still a bottleneck and an aspirational goal for protein function prediction. Results In this paper, we propose the RWRT (Random Walks with Restart on Tensor) method to accomplish protein function prediction by applying bi-random walks on the tensor. RWRT firstly constructs a functional similarity tensor by combining protein interaction networks with multi-omics data derived from domain annotation and protein complex information. After this, RWRT extends the bi-random walks algorithm from a two-dimensional matrix to the tensor for scoring functional similarity between proteins. Finally, RWRT filters out possible pretenders based on the concept of cohesiveness coefficient and annotates target proteins with functions of the remaining functional partners. Experimental results indicate that RWRT performs significantly better than the state-of-the-art methods and improves the area under the receiver-operating curve (AUROC) by no less than 18%. Conclusions The functional similarity tensor offers us an alternative, in that it is a collection of networks sharing the same nodes; however, the edges belong to different categories or represent interactions of different nature. We demonstrate that the tensor-based random walk model can not only discover more partners with similar functions but also free from the constraints of errors in protein interaction networks effectively. We believe that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information on protein correlations.

Land-use change and regional carbon emissions are closely related. In-depth research on the mechanism of land-use carbon emissions is conducive for clarifying the direction and focus of future low-carbon construction work. Carbon emissions calculation models were used to calculate total carbon emissions in Guizhou Province and reveal their spatiotemporal distribution characteristics. The results were as follows. (1) The land-use structure of Guizhou Province underwent a significant change from 2009 to 2019, in particular with regard to cultivated and construction land. (2) Land-use carbon emissions in Guizhou Province were increasing annually, and this was related to urbanization, population density, and energy consumption. (3) There were spatial differences in the regional distribution of carbon emissions, their intensity, and carbon emissions per capita in Guizhou Province. Additionally, there were spatial clusters of cities with high or low emissions intensity. Therefore, there is scope for constructive proposals concerning sustainable land use and development, such as controlling the total amount and intensity of construction land, optimizing the structure of land use, managing the rational distribution of the population, constantly advancing the optimization of industrial structure, and improving the relevant policies.

Environmental and occupational low-dose radiation (LDR) exposure may be harmful for health but the previous reports regarding effect of LDR on cognition are contradictory. Here we investigated the effect of long-term LDR exposure on cognition. In this study, male Balb/c mice’ cognitive functions were tested at 15 weeks after being exposed to 0.5 Gy LDR in 10 fractions at each dose of 0.05 Gy. The results demonstrated that long-term LDR exposure increases escape latency and the time spent in finding exits in mice compared with non LDR exposure. Meanwhile, the inflammation-related proteins including NFκB and p38 also increased. Lipopolysaccharide (LPS) increased and short-chain fatty acid (SCFA) levels decreased following long term LDR exposure. Treatment with microbiota-derived LPS and SCFAs reversed these effects in mice. Furthermore, the gut barrier integrity was damaged in a time-dependent manner with the decreased expression of intestinal epithelial-related biomarkers such as ZO-1 and occludin. Mechanistically, long after exposure to LDR, increased LPS levels may cause cognitive impairment through the regulation of Akt/mTOR signaling in the mouse hippocampus. These findings provide new insight into the clinical applications of LDR and suggest that the gut microbiota–plasma LPS and SCFAs–brain axis may underlie long-term LDR-induced cognition effects.

Polistes hebraeus (Smith) is a common paper wasp species of subfamily Polistinae. The clinical potential allergic reaction after stings and valuable venom components of P. hebraeus make it a medical importance species. Here, the mitochondrial genome of P. hebraeus was analyzed. The mitogenome was 19,262 bp in length including 22 transfer RNAs (tRNA) and 2 ribosomal RNAs (rRNA). The protein coding gene (PCGs) number and order are consistent with existing records of Polistidae. The nucleotide composition is AT: 84.5% and CG: 15.5% which are largely similar with other complete Polistidae mitogenome (15.27 ∼ 19.16%). The available mitogenome resources of Polistidae species and P. hebraeus were used to construct the phylogenetic tree. It showed that the genus Polistes could clearly seperated from Parapolybia, and the relationship in Polistes was consistent with previous studies. This mitogenome resource can contribute to further phylogenetic and taxonomic study on paper wasp.

Objective: To study the effects of the novel vanillin derivative BVAN08 on the radiosensitivity of hepatic cancer cells for the purpose of providing evidence for its potential use as a potential radiosensitive drug. Methods: Hepatic cancer Huh-7 ​cells labeled with luciferase were used to investigate the radiosensitivity induced by BVAN08 in vivo. Colony formation assays and flow cytometry were used to measure the apoptosis and radiosensitivity of Huh-7 ​cells in vitro produced by BVAN08. Histology and immunohistochemistry were used to evaluate the toxicity of BVAN08 in vivo. Results: BVAN08 induced apoptosis and cell cycle arrest of Huh-7 ​cells in a time-dependent manner. Moreover, BVAN08 combined with radiation increased the sensitivity of Huh-7 ​cells to γ-ray radiation and significantly inhibited tumor growth in vivo. The tumor inhibition rates of the BVAN08 treatment group, irradiation treatment group, and combined therapy group were 58%, 38%, and 85%, respectively. The DNA-PKcs expression in tumor tissues of the BVAN08 treatment group was lower than that of the control group (P ​

Abstract Background The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation. Results We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation. Conclusions We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the “small-world” feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.

In this study, the CAR-like multivalent aptamer nanoparticles (X-polymers) were assembled with the dimer of murine CD28 RNA aptamer (CD28Apt7), the tetramer of CTLA-4 (cytotoxic T-lymphocyte-associated protein 4) RNA aptamer (Del60), and a folic acid labeled ssDNA fragment in a stable nucleic acid three-way junction scaffold (3WJ). Results showed that the X-polymers could recognize both the mCD28 and mCTLA-4 molecules. Confocal imaging and flow cytometry assays showed that the X-polymers could target both T cells and B16 cells in vitro. With the first costimulatory signals provided by the CD3 antibodies, the X-polymers could increase T cell proliferation and reverse the inhibitory effect of interleukin-2 (IL-2) secreting caused by exogenous B7.1 molecules on T cells in vitro. Results of our study also showed that X-polymers could inhibit mouse melanoma B16 cell growth both in vitro and in vivo. Our study demonstrated for the first time that the multivalent aptamer nanoparticle-activated T cells could fulfill the function of CAR-T, which promised a novel approach to developing a multi-functional design of aptamer drugs with potential CAR-like characteristics to enhance the safety of CAR-T cell immunotherapy.

Abstract Background Essential proteins are an important part of the cell and closely related to the life activities of the cell. Hitherto, Protein-Protein Interaction (PPI) networks have been adopted by many computational methods to predict essential proteins. Most of the current approaches focus mainly on the topological structure of PPI networks. However, those methods relying solely on the PPI network have low detection accuracy for essential proteins. Therefore, it is necessary to integrate the PPI network with other biological information to identify essential proteins. Results In this paper, we proposed a novel random walk method for identifying essential proteins, called HEPT. A three-dimensional tensor is constructed first by combining the PPI network of Saccharomyces cerevisiae with multiple biological data such as gene ontology annotations and protein domains. Then, based on the newly constructed tensor, we extended the Hyperlink-Induced Topic Search (HITS) algorithm from a two-dimensional to a three-dimensional tensor model that can be utilized to infer essential proteins. Different from existing state-of-the-art methods, the importance of proteins and the types of interactions will both contribute to the essential protein prediction. To evaluate the performance of our newly proposed HEPT method, proteins are ranked in the descending order based on their ranking scores computed by our method and other competitive methods. After that, a certain number of the ranked proteins are selected as candidates for essential proteins. According to the list of known essential proteins, the number of true essential proteins is used to judge the performance of each method. Experimental results show that our method can achieve better prediction performance in comparison with other nine state-of-the-art methods in identifying essential proteins. Conclusions Through analysis and experimental results, it is obvious that HEPT can be used to effectively improve the prediction accuracy of essential proteins by the use of HITS algorithm and the combination of network topology with gene ontology annotations and protein domains, which provides a new insight into multi-data source fusion.

Abstract Background Persistent infection with hepatitis B virus (HBV) accounts for the majority of hepatocellular carcinoma (HCC), but the molecular mechanisms underlying liver carcinogenesis are still not completely understood. Increasing evidence demonstrates that microRNAs (miRNAs) play significant functional roles in virus–host interactions. The aim of this study was to explore differentially expressed miRNA profiles and investigate the molecular mechanism of miR-0308-3p in HBV-positive HCC carcinogenesis. Methods High-throughput sequencing was used to detect novel miRNAs in three samples of HBV-positive HCC tissue compared to matched HBV-negative HCC tissue. The Cancer Genome Atlas (TCGA) database was used to mine miRNAs related to HBV-positive HCC. Bioinformatics analyses were conducted to predict the miRNAs’ possible biological and pathway regulatory functions. Quantitative polymerase chain reaction (qPCR) was then applied to evaluate the expression levels of randomly selected miRNAs. CCK-8 was used to measure cell proliferation and cell cycles were analyzed using flow cytometry. A dual luciferase reporter gene assay was used to confirm the downstream targets of miR-0308-3p. Results In total, there were 34 overlapping miRNAs in both our miRNA-seq data and the TCGA database. We found two overlapping miRNAs in both the HBV-positive HCC samples and the TCGA database, and 205 novel pre-miRNA sequences were predicted. miR-522 and miR-523 were markedly overexpressed in HBV-positive HCC and were associated with a significantly poorer long-term prognosis (miR-522, HR 2.19, 95% CI 1.33–3.6, p = 0.0015; miR-523HR 1.5, 95% CI 1–2.44, p = 0.0047). Of note, we found that the novel miR-0308-3p was markedly downregulated in HBV-positive HCC samples and HCC cancer cell lines compared with HBV-negative HCC samples and adjacent normal hepatic tissue. Moreover, elevated expression of miR-0308-3p was found to inhibit proliferation of cancer cells by promoting G1/S cell cycle arrest but did not influence the apoptosis of cancer cells. A dual luciferase reporter activity assay identified that miR-0308-3p acted directly on the target sequence of the CDK6 and Cyclin D1 mRNA 3ʹUTR to suppress CDK6 and Cyclin D1 expression. Conclusions MiR-0308-3p upregulation dramatically suppressed HCC cell proliferation and induced G1/S cell cycle arrest by directly targeting CDK6/Cyclin D1. These findings reveal a novel molecular mechanism for activation of G1/S arrest in HCC and may prove clinically useful for developing new therapeutic targets.

Essential proteins are important for the survival and reproduction of organisms. Many computational methods have been proposed to identify essential proteins, due to the production of vast amounts of protein-protein interaction (PPI) data. It has been demonstrated that PPI networks have graph-theoretic characteristics as so-called small-world and scale-free. The traditional metrics cannot really reflect the relationship between proteins when identifying essential proteins from PPI networks. In this paper, we construct a diffusion distance network (DSN) by combining PPI topology characteristics with orthologous proteins and sub-cellular localization information of proteins. Taking the modularity feature of essential proteins into account, we proposed a new essential proteins prediction method based on DSN. We employed our DSN method and ten other state-of-the-art methods to predict essential proteins. The precision-recall curve, jackknife methodology and so on are used to test the performance of these methods. Experimental results show that our method outperform ten other competitive methods. The row data and the software are freely available at: https://github.com/husaiccsu/DSN.

Carbon emissions are critical to climate change, and land-use change is an essential source of growth in carbon emissions. Research on land-use carbon emissions has become one of the hotspots in academic research. To explore the research hotspots and development trends of land-use carbon emissions in the last 20 years, CiteSpace software was used to conduct a quantitative analysis of relevant literature. This paper was based on the China National Knowledge Infrastructure (CNKI) and Web of Science (WoS) database literature on land-use carbon emissions from 2001 to 2020. The scientific research cooperation network CiteSpace software, with keyword co-occurrence, clustering, and burst word detection, was used to systematically analyze the main research strengths, hotspots and frontiers and clarify the research progress. The research results are as follows: (1) the amount of literature and the depth of research on land-use carbon emissions have increased yearly. However, there is little cooperation between research institutions and scholars, and there is still a lack of large-scale and stable research teams. (2) At the research hotspot level, the English literature focuses on building models and theoretical frameworks to study the internal mechanisms and driving factors of carbon emissions and climate change. The Chinese literature focuses on achieving regional carbon emissions reductions and carbon cycle goals and optimizing a low-carbon economy, transportation and land-use structure. (3) Research frontiers and trends show that the English literature first explored carbon sequestration, organic carbon, and carbon accounting. In China, the research frontiers are gradually becoming focused on influencing factors, decoupling analysis, and the built environment. The study will strengthen the intensity and depth of global carbon emission research and provide a reference for improving global climate change, protecting ecology and balancing economic development.

Human societal growth has greatly pressured available land resources. The key to reducing land pressure and fostering regional synergistic development is revealing the transmission effect of land pressure. We used a modified gravity model to construct a spatial correlation network (SCN) of the land pressure in the Yangtze River Delta region (YRDR) for the years 1995, 2000, 2005, 2010, 2015 and 2020. To examine how the land pressure is transmitted throughout the cities in the YRDR, we used a social network analysis to examine the overall network structure, individual network characteristics and spatial clustering characteristics. Finally, the center of gravity-GTWR model that coupled the inter-city interactions and the temporal non-smoothness further revealed the spatiotemporal evolution and the different patterns of the influencing factors. The results revealed that (1) the spatial correlation structure of the land pressure in the YRDR was relatively stable. Nanjing, Shanghai, Suzhou, Hangzhou and Changzhou played a significant role as linkages. (2) The YRDR was beyond the geographical limit for the land pressure transmission effect and each block had a considerable and mostly steady transmission impact. (3) The center of gravity-GTWR model that coupled the inter-city interactions and the temporal non-stationarity was a viable method for analyzing the factors that influence the land pressure. (4) There were significant regional and temporal variations in the factors influencing land pressure. The influencing factors differed in intensity and direction from city to city. Our results can provide a new perspective on relieving land pressure from the perspective of urban agglomerations and help accomplish the sustainable development of regional land resources.

Abstract Background High occupational burnout among general practitioners (GPs) is an important challenge to China’s efforts to strengthen its primary healthcare delivery; however, data to help understand this issue are unavailable. This study aimed to investigate the prevalence of burnout and associated factors among GPs. Methods A cross-sectional design was used to collect data from December 12, 2014, to March 10, 2015, with a self-administered structured questionnaire from 1015 GPs (response rate, 85.6%) in Hubei Province, Central China. Burnout was measured using a 22-item Maslach Burnout Inventory-Human Services Survey (MBI-HSS). MBI-HSS scores and frequency were analyzed by the three dimensions of emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA). Factors associated with burnout among GPs were estimated using a multiple linear regression model. Results Of the respondents, 2.46% had a high level of burnout in all three dimensions, 24.83% reported high levels of EE, 6.21% scored high on DP, and 33.99% were at high risk of PA. GPs who were unmarried, had lower levels of job satisfaction, and had been exposed to workplace violence experienced higher levels of burnout. Intriguingly, no statistically significant associations were found between burnout and the duration of GP practice, age, sex, income, practice setting, and professional level. Conclusion This is the first study of occupational burnout in Chinese general practice. Burnout is prevalent among GPs in Hubei, China. Interventions aimed at increasing job satisfaction, improving doctor-patient relationships and providing safer workplace environments may be promising strategies to reduce burnout among GPs in Hubei, China.

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Ever-increasing amounts of high-throughput data provide us with opportunities to detect essential proteins from protein interaction networks (PINs). Existing network-based approaches are limited by the poor quality of the underlying PIN data, which exhibits high rates of false positive and false negative results. To overcome this problem, researchers have focused on the prediction of essential proteins by combining PINs with other biological data, which has led to the emergence of various interactions between proteins. It remains challenging, however, to use aggregated multiplex interactions within a single analysis framework to identify essential proteins. In this study, we created a multiplex biological network (MON) by initially integrating PINs, protein domains, and gene expression profiles. Next, we proposed a new approach to discover essential proteins by extending the random walk with restart algorithm to the tensor, which provides a data model representation of the MON. In contrast to existing approaches, the proposed MON approach considers for the importance of nodes and the different types of interactions between proteins during the iteration. MON was implemented to identify essential proteins within two yeast PINs. Our comprehensive experimental results demonstrated that MON outperformed 11 other state-of-the-art approaches in terms of precision-recall curve, jackknife curve, and other criteria.

Abstract Background Gatekeeper policy, requiring a patient to visit a primary care provider first, and the patient needs to get his or her primary care provider’s referral before seeing a specialist or going to a hospital, has been implemented in China for about ten years, and it is necessary to assess the patients’ acceptance of gatekeeper policy and to explore the factors influencing patients’ acceptance. Methods A cross-sectional study with 1162 respondents was conducted between July and September 2015 at four community health centers (CHCs) in Wuhan, China. Face-to-face interview was used to collect information on demographics, acceptance of the gate keeper policy and satisfaction with community health services. Patients’ satisfaction with community health service was evaluated using the European Patients Evaluate General/Family Practice scale and binary logistic regression model was used to examine the factors influencing patients’ acceptance of community health services as gate keepers. Results A total of 512 (43.06%) patients accepted gatekeeper policy. Mandatory reimbursement provision (OR: 1.63, 95% CI: 1.23–2.15), patient satisfaction with the aspects of medical care (OR: 1.92, 95% CI: 1.12–3.29) and organization of care (OR: 1.66, 95% CI: 1.05–2.62) were associated with acceptance of gatekeeper policy, after adjusting for potential confounders. Moreover, young people (OR: 0.35, 95%CI: 0.22–0.56) seemed to be more reluctant to accept the policy, when compared with the elder. Conclusions Our study suggests that mandatory reimbursement provision greatly affects patients’ acceptance of gatekeeper policy, therefore, the policy-maker should pay attention to the negative effect of its mandatory reimbursement provision on patients’ acceptance of the policy. However, improving the aspects of medical care and organization of care will contribute to implementation of gatekeeper policy.

Background: Cancer patients who have undergone radiotherapy may have an increased risk of subsequent stroke. A clear and detailed understanding of this risk has not been established.Methods: A search for research articles published from January 1990 to November 2017 in the English language was conducted. Subsequent stroke risk in cancer survivors was compared using relative risk (RR) and 95% confidence intervals (CI) according to whether or not radiotherapy was given.Results: A total of 12 eligible studies were identified including 57,881 total patients. All studies were retrospective, as no prospective studies were identified. The meta-analysis revealed a higher overall risk of subsequent stroke in cancer survivors/patients given radiotherapy compared to those not given radiotherapy (RR: 2.09, 95% CI: 1.45, 3.16). In addition, compared to patients not given radiotherapy, there was an increased risk of subsequent stroke for radiotherapy treated patients with Hodgkin's lymphoma (RR: 2.81, 95% CI: 0.69, 4.93) or head/neck/brain/nasopharyngeal cancer (RR: 2.16, 95% CI: 1.16, 3.16), for patients younger than 40 years (RR: 3.53, 95% CI: 2.51, 4.97) or aged 40–49 years (RR: 1.23, 95% CI: 1.09, 1.45) and for patients treated in Asia (RR: 1.88, 95% CI: 1.48, 2.29), the United States (RR: 1.62, 95% CI: 1.01, 2.23), or in Europe (RR: 4.11, 95% CI 2.62, 6.45).Conclusions: The available literature indicates an approximate overall doubling of the subsequent stroke risk in cancer patients given radiotherapy. The elevated risk was generally statistically significant according to cancer type, baseline patient age and region or country where treatment was given. Caution is required in interpreting these findings due to the heterogeneity of populations represented and lack of standardization and completeness across published studies. Further, if real, we cannot conclude the extent to which patient, treatment and/or investigational factors are responsible for this apparent elevated risk. An objective and more detailed understanding of the risks of radiotherapy, and how to prevent them, is urgently required. It is the responsibility of all who provide cancer services to ensure that the experience of all their patients is documented and analyzed using quality registries.

This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value < 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson’s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0–500 m and 2000–2500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and/or replace coarse resolution satellite imagery in the estimation of RSM.

Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI0 was used for computing TVDI, and both NDVIATI and NDVITVDI for dividing the entire CLP) were firstly identified with the best validation results (R¯) of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum R¯ of 0.82 ± 0.007 for Criterion 2 and of 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 104 km2 and 667.44 × 104 km2 with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management.

Urbanization is a key determinant of fine particulate matter (PM2.5) pollution variability. However, there is a limited understanding of different urbanization factors’ roles in PM2.5 pollution. Using satellite-derived PM2.5 data from 2002 to 2017, we investigated the spatiotemporal evolution and the spatial autocorrelation of PM2.5 pollution in the Yangtze River Delta (YRD) region. Afterwards, the impacts of three urbanization factors (population urbanization, land urbanization and economic urbanization) on PM2.5 pollution were estimated by a spatial Durbin panel data model (SDM). Obtained results showed that: (i) PM2.5 pollution was larger in the north than in the south of YRD; (ii) Lianyungang and Yancheng cities had significant increasing trends in PM2.5 pollution from 2002 to 2017; (iii) the regional median center of PM2.5 pollution was observed in the Nanjing city, with gradual shifting to the northwest during the 16-year period; (iv) PM2.5 pollution showed significant and positive spatial autocorrelation and spillover effect; (v) population urbanization contributed more to the increase in PM2.5 pollution than land urbanization, while economic urbanization had no significant impact. The present study highlights the impacts of three urbanization factors on PM2.5 pollution which represent valuable and relevant information for air pollution control and urban planning.

Timely and effective estimation and monitoring of soil moisture (SM) provides not only an understanding of regional SM status for agricultural management or potential drought but also a basis for characterizing water and energy exchange. The apparent thermal inertia (ATI) and Temperature Vegetation Dryness Index (TVDI) are two widely used indices to reflect SM from remote sensing data. While the ATI-based model is routinely used to estimate the SM of bare soil and sparsely vegetated areas, the TVDI-based model is more suitable for areas with dense vegetation coverage. In this study, we present an iteration procedure that allows us to identify optimal Normalized Difference Vegetation Index (NDVI) thresholds for subregions and estimate their relative soil moisture (RSM) using three models (the ATI-based model, the TVDI-based model, and the ATI/TVDI joint model) from 1 January to 31 December 2017, in the Chinese Loess Plateau. The initial NDVI (NDVI0) was first introduced to obtain TVDI value and two other thresholds of NDVIATI and NDVITVDI were designed for dividing the whole area into three subregions (the ATI subregion, the TVDI subregion, and the ATI/TVDI subregion). The NDVI values corresponding to maximum R-values (correlation coefficient) between estimated RSM and in situ RSM measurements were chosen as optimal NDVI thresholds after performing as high as 48,620 iterations with 10 rounds of 10-fold cross-calibration and validation for each period. An RSM map of the whole study area was produced by merging the RSM of each of the three subregions. The spatiotemporal and comparative analysis further indicated that the ATI/TVDI joint model has higher applicability (accounting for 36/38 periods) and accuracy than the ATI-based and TVDI-based models. The highest average R-value between the estimated RSM and in situ RSM measurements was 0.73 ± 0.011 (RMSE—root mean square error, 3.43 ± 0.071% and MAE—mean absolute error, 0.05 ± 0.025) on the 137th day of 2017 (DOY—day of the year, 137). Although there is potential for improved mapping of RSM for the entire Chinese Loess Plateau, the iteration procedure of identifying optimal thresholds determination offers a promising method for achieving finer-resolution and robust RSM estimation in large heterogeneous areas.

Based on remote sensing image interpretation data of land use/cover change (LUCC) for six periods from 1995 to 2020, we used the equivalent factor method to assess the value of ecosystem services (ESV) in Jiangsu Province and investigated its spatial and temporal evolution characteristics on the grid scale in combination with spatial autocorrelation analysis. The results showed that total value of ecosystem services in Jiangsu Province is on the rise, with an increase of 3.15 billion USD compared with that in 1995. Water area has the largest proportion of ESV and hydrological regulation makes the greatest contribution among ecosystem services, reaching 32.99 billion USD in 2020. The value of ecosystem services measured by the coefficient of sensitivity index of various land use types is as follows: water area>cropland>forestland>grassland>unused Land. Ecosystem service value is positively correlated to spatial distribution, but the correlation is gradually weakening, and the clustering relationship is dominated by high–high and low–low aggregation. The study’s findings will serve as useful guidelines for creating policies in Jiangsu Province pertaining to land use planning and eco-environmental protection. [ABSTRACT FROM AUTHOR]