OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.

MOTIVATION: Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. RESULTS: We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r2) on 27/33 datasets, with an average improvement of 7.2%-22.9% against the state-of-the-art bioactivity prediction methods. AVAILABILITY AND IMPLEMENTATION: The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC.

AFSE: towards improving model generalization of deep graph learning of ligand bioactivities targeting GPCR proteins.

Ligand molecules naturally constitute a graph structure. Recently, many excellent deep graph learning (DGL) methods have been proposed and used to model ligand bioactivities, which is critical for the virtual screening of drug hits from compound databases in interest. However, pharmacists can find that these well-trained DGL models usually are hard to achieve satisfying performance in real scenarios for virtual screening of drug candidates. The main challenges involve that the datasets for training models were small-sized and biased, and the inner active cliff cases would worsen model performance. These challenges would cause predictors to overfit the training data and have poor generalization in real virtual screening scenarios. Thus, we proposed a novel algorithm named adversarial feature subspace enhancement (AFSE). AFSE dynamically generates abundant representations in new feature subspace via bi-directional adversarial learning, and then minimizes the maximum loss of molecular divergence and bioactivity to ensure local smoothness of model outputs and significantly enhance the generalization of DGL models in predicting ligand bioactivities. Benchmark tests were implemented on seven state-of-the-art open-source DGL models with the potential of modeling ligand bioactivities, and precisely evaluated by multiple criteria. The results indicate that, on almost all 33 GPCRs datasets and seven DGL models, AFSE greatly improved their enhancement factor (top-10%, 20% and 30%), which is the most important evaluation in virtual screening of hits from compound databases, while ensuring the superior performance on RMSE and $r^2$. The web server of AFSE is freely available at http://noveldelta.com/AFSE for academic purposes.

Does settlement land expansion necessarily induce the decrease of cultivated land? Differences in national scale and local counties of China.

With the extensive industrialization and urbanization taking place in China during the recent decades, land use throughout the country has experienced profound changes influenced not only by the demand for population growth and living standard improvement but also by the constraints of series of land use policies. However, whether the conflict between the expansion of settlement land (SL) and the loss of cultivated land (CL) have been resolved at the national scale or transferred between the local regions remains unclear. Based on yearly ESA CCI land use and land cover products from 1992 to 2020, the long-term trends of quantity and spatial pattern of SL expansion and CL change in China from national and local views were investigated using trend statistic methods, and finally a comprehensive zoning framework was proposed to recognize the trade-off and synergies relationships between SL expansion and CL change. There are a continuous expansion of SL with global linear trends showing three breakpoints in 2000, 2005, and 2012, and a fluctuation decline of CL presented with four breakpoints in 1997, 2002, 2006, and 2013. Aggregation and dispersion tendencies with linear characteristics of SL expansion and CL change were found with breakpoints in 2001, 2008, 2012, and 2016 and breakpoints in 2001 and 2010, respectively. A spotty spatial pattern of SL was shown spatially coincident with urban agglomerations in China while the planar continuous characteristic was found for CL. Local counties were classified into five tradeoff and synergies zones (TSZs), where general synergies (G-S) and decoupling (D) of SL expansion and CL change were rare cases and the different change in quantity and trend of SL expansion and CL change in local counties was concealed by the national trend. A few scattered counties were belonging to G-S and D TSZs, while most of the counties in the central-east and western China were in General-Tradeoff (G-T) and Superior-Tradeoff (S-T) TSZs. Counties in south and north China with higher percentages of CL were more prevalent in Superior-Synergy (S-S) TSZ. Our findings explicated the complex relationships between SL expansion and CL change of China at the national scale and in local counties, which pointed out the differences of unified land use management activities across scales and could provide insights for future policy-making and management measures of land use to both ensure the national food security and promote regional sustainable development more synchronously.

Single-cell analysis of localized prostate cancer patients links high Gleason score with an immunosuppressive profile.

BACKGROUND: Evading immune surveillance is a hallmark for the development of multiple cancer types. Whether immune evasion contributes to the pathogenesis of high-grade prostate cancer (HGPCa) remains an area of active inquiry. METHODS: Through single-cell RNA sequencing and multicolor flow cytometry of freshly isolated prostatectomy specimens and matched peripheral blood, we aimed to characterize the tumor immune microenvironment (TME) of localized prostate cancer (PCa), including HGPCa and low-grade prostate cancer (LGPCa). RESULTS: HGPCa are highly infiltrated by exhausted CD8+ T cells, myeloid cells, and regulatory T cells (TRegs). These HGPCa-infiltrating CD8+ T cells expressed high levels of exhaustion markers including TIM3, TOX, TCF7, PD-1, CTLA4, TIGIT, and CXCL13. By contrast, a high ratio of activated CD8+  effector T cells relative to TRegs and myeloid cells infiltrate the TME of LGPCa. HGPCa CD8+  tumor-infiltrating lymphocytes (TILs) expressed more androgen receptor and prostate-specific membran antigen yet less prostate-specific antigen than the LGPCa CD8+  TILs. The PCa TME was infiltrated by macrophages but these did not clearly cluster by M1 and M2 markers. CONCLUSIONS: Our study reveals a suppressive TME with high levels of CD8+ T cell exhaustion in localized PCa, a finding enriched in HGPCa relative to LGPCa. These studies suggest a possible link between the clinical-pathologic risk of PCa and the associated TME. Our results have implications for our understanding of the immunologic mechanisms of PCa pathogenesis and the implementation of immunotherapy for localized PCa.

Vina-GPU 2.0: Further Accelerating AutoDock Vina and Its Derivatives with Graphics Processing Units.

Modern drug discovery typically faces large virtual screens from huge compound databases where multiple docking tools are involved for meeting various real scenes or improving the precision of virtual screens. Among these tools, AutoDock Vina and its numerous derivatives are the most popular and have become the standard pipeline for molecular docking in modern drug discovery. Our recent Vina-GPU method realized 14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX 3090 GPU in one virtual screening case. Further speedup of AutoDock Vina and its derivatives with graphics processing units (GPUs) is beneficial to systematically push their popularization in large-scale virtual screens due to their high benefit-cost ratio and easy operation for users. Thus, we proposed the Vina-GPU 2.0 method to further accelerate AutoDock Vina and the most common derivatives with new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused by the discrepancy in their docking algorithms, our Vina-GPU 2.0 adopts different GPU acceleration strategies. In virtual screening for two hot protein kinase targets, RIPK1 and RIPK3, from the DrugBank database, our Vina-GPU 2.0 reaches an average of 65.6-fold, 1.4-fold, and 3.6-fold docking acceleration against the original AutoDock Vina, QuickVina 2, and QuickVina-W while ensuring their comparable docking accuracy. In addition, we develop a friendly and installation-free graphical user interface tool for their convenient usage. The codes and tools of Vina-GPU 2.0 are freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU-2.0, coupled with explicit instructions and examples.

Genome-wide association study identifies kallikrein 5 in type 2 inflammation-low asthma.

BACKGROUND: Clinical studies of type 2 (T2) cytokine-related neutralizing antibodies in asthma have identified a substantial subset of patients with low levels of T2 inflammation who do not benefit from T2 cytokine neutralizing antibody treatment. Non-T2 mechanisms are poorly understood in asthma but represent a redefined unmet medical need. OBJECTIVE: We sought to gain a better understanding of genetic contributions to T2-low asthma. METHODS: We utilized an unbiased genome-wide association study of patients with moderate to severe asthma stratified by T2 serum biomarker periostin. We also performed additional expression and biological analysis for the top genetic hits. RESULTS: We identified a novel protective single nucleotide polymorphism at chr19q13.41, which is selectively associated with T2-low asthma and establishes Kallikrein-related peptidase 5 (KLK5) as the causal gene mediating this association. Heterozygous carriers of the single nucleotide polymorphisms have reduced KLK5 expression. KLK5 is secreted by human bronchial epithelial cells and elevated in asthma bronchial alveolar lavage. T2 cytokines IL-4 and IL-13 downregulate KLK5 in human bronchial epithelial cells. KLK5, dependent on its catalytic function, induces epithelial chemokine/cytokine expression. Finally, overexpression of KLK5 in airway or lack of an endogenous KLK5 inhibitor, SPINK5, leads to spontaneous airway neutrophilic inflammation. CONCLUSION: Our data identify KLK5 to be the causal gene at a novel locus at chr19q13.41 associated with T2-low asthma.

Multivalley Superconductivity in Monolayer Transition Metal Dichalcogenides.

In transition metal dichalcogenides (TMDs), Ising superconductivity with an antisymmetric spin texture on the Fermi surface has attracted wide interest due to the exotic pairing and topological properties. However, it is not clear whether the Q valley with a giant spin splitting is involved in the superconductivity of heavily doped semiconducting 2H-TMDs. Here by taking advantage of a high-quality monolayer WS2 on hexagonal boron nitride flakes, we report an ionic-gating induced superconducting dome with a record high critical temperature of ∼6 K, accompanied by an emergent nonlinear Hall effect. The nonlinearity indicates the development of an additional high-mobility channel, which (corroborated by first principle calculations) can be ascribed to the population of Q valleys. Thus, multivalley population at K and Q is suggested to be a prerequisite for developing superconductivity. The involvement of Q valleys also provides insights to the spin textured Fermi surface of Ising superconductivity in the large family of transition metal dichalcogenides.

Nomogram for prediction of prolonged postoperative ileus after colorectal resection.

BACKGROUND: Prolonged postoperative ileus (PPOI) is a major complication in patients undergoing colorectal resection. The aim of this study was to analyze the risk factors contributing to PPOI, and to develop an effective nomogram to determine the risks of this population. METHODS: A total of 1,254 patients with colorectal cancer who underwent radical colorectal resection at Fujian Cancer Hospital from March 2016 to August 2021 were enrolled as a training cohort in this study. Univariate analysis and multivariate logistic regressions were performed to determine the correlation between PPOI and clinicopathological characteristics. A nomogram predicting the incidence of PPOI was constructed. The cohort of 153 patients from Fujian Provincial Hospital were enrolled as a validation cohort. Internal and external validations were used to evaluate the prediction ability by area under the receiver operating characteristic curve (AUC) and a calibration plot. RESULTS: In the training cohort, 128 patients (10.2%) had PPOI after colorectal resection. The independent predictive factors of PPOI were identified, and included gender, age, surgical approach and intraoperative fluid overload. The AUC of nomogram were 0.779 (95% CI: 0.736-0.822) and 0.791 (95%CI: 0.677-0.905) in the training and validation cohort, respectively. The two cohorts of calibration plots showed a good consistency between nomogram prediction and actual observation. CONCLUSIONS: A highly accurate nomogram was developed and validated in this study, which can be used to provide individual prediction of PPOI in patients after colorectal resection, and this predictive power can potentially assist surgeons to make the optimal treatment decisions.

Rapidly accelerated development of neutralizing COVID-19 antibodies by reducing cell line and CMC development timelines.

Since the Coronavirus Disease 2019 (COVID-19) outbreak, unconventional cell line development (CLD) strategies have been taken to enable development of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-neutralizing antibodies at expedited speed. We previously reported a novel chemistry, manufacturing, and control (CMC) workflow and demonstrated a much-shortened timeline of 3-6 months from DNA to investigational new drug (IND) application. Hereafter, we have incorporated this CMC strategy for many SARS-CoV-2-neutralizing antibody programs at WuXi Biologics. In this paper, we summarize the accelerated development of a total of seven antibody programs, some of which have received emergency use authorization  approval in less than 2 years. Stable pools generated under good manufacturing practice (GMP) conditions consistently exhibited similar productivity and product quality at different scales and batches, enabling rapid initiation of phase I clinical trials. Clones with comparable product quality as parental pools were subsequently screened and selected for late-stage development and manufacturing. Moreover, a preliminary stability study plan was devised to greatly reduce the time required for final clone determination and next-generation sequencing-based viral testing was implemented to support rapid conditional release of the master cell bank for GMP production. The successful execution of these COVID-19 programs relies on our robust, fit for purpose, and continuously improving CLD platform. The speed achieved for pandemic-related biologics development may innovate typical biologics development timelines and become a new standard in the industry.

Smaller particular matter, larger risk of female lung cancer incidence? Evidence from 436 Chinese counties.

BACKGROUND: Many studies have reported the effects of PM2.5 and PM10 on human health, however, it remains unclear whether particular matter with finer particle size has a greater effect. OBJECTIVES: This work aims to examine the varying associations of the incidence rate of female lung cancer with PM1, PM2.5 and PM10 in 436 Chinese cancer registries between 2014 and 2016. METHODS: The effects of PM1, PM2.5 and PM10 were estimated through three regression models, respectively. Mode l only included particular matter, while Model 2 and Model 3 further controlled for time and location factors, and socioeconomic covariates, respectively. Moreover, two sensitivity analyses were performed to investigate the robustness of three particular matte effects. Then, we examined the modifying role of urban-rural division on the effects of PM1, PM2.5 and PM10, respectively. RESULTS: The change in the incidence rate of female lung cancer relative to its mean was 5.98% (95% CI: 3.40, 8.56%) for PM1, which was larger than the values of PM2.5 and PM10 at 3.75% (95% CI: 2.33, 5.17%) and 1.57% (95% CI: 0.73, 2.41%), respectively. The effects of three particular matters were not sensitive in the two sensitivity analyses. Moreover, urban-rural division positively modified the associations of the incidence rate of female lung cancer with PM1, PM2.5 and PM10. CONCLUSIONS: The effect on the incidence rate of female lung cancer was greater for PM1, followed by PM2.5 and PM10. There were positive modifying roles of urban-rural division on the effects of three particular matters. The finding supports the argument that finer particular matters are more harmful to human health, and also highlights the great significance to develop guidelines for PM1 control and prevention in Chinese setting.

Disruption of IRE1α through its kinase domain attenuates multiple myeloma.

Multiple myeloma (MM) arises from malignant immunoglobulin (Ig)-secreting plasma cells and remains an incurable, often lethal disease despite therapeutic advances. The unfolded-protein response sensor IRE1α supports protein secretion by deploying a kinase-endoribonuclease module to activate the transcription factor XBP1s. MM cells may co-opt the IRE1α-XBP1s pathway; however, the validity of IRE1α as a potential MM therapeutic target is controversial. Genetic disruption of IRE1α or XBP1s, or pharmacologic IRE1α kinase inhibition, attenuated subcutaneous or orthometastatic growth of MM tumors in mice and augmented efficacy of two established frontline antimyeloma agents, bortezomib and lenalidomide. Mechanistically, IRE1α perturbation inhibited expression of key components of the endoplasmic reticulum-associated degradation machinery, as well as secretion of Ig light chains and of cytokines and chemokines known to promote MM growth. Selective IRE1α kinase inhibition reduced viability of CD138+ plasma cells while sparing CD138- cells derived from bone marrows of newly diagnosed or posttreatment-relapsed MM patients, in both US- and European Union-based cohorts. Effective IRE1α inhibition preserved glucose-induced insulin secretion by pancreatic microislets and viability of primary hepatocytes in vitro, as well as normal tissue homeostasis in mice. These results establish a strong rationale for developing kinase-directed inhibitors of IRE1α for MM therapy.

Spatiotemporal association of carbon dioxide emissions in China's urban agglomerations.

The reduction of carbon dioxide (CO2) emissions and sustainable development in low-carbon ways are of great significance to urban agglomerations. However, few studies are exploring the relationship between CO2 emissions and socioeconomic development at city levels from the perspective of clusters of regions. Based on the open data of inventory for anthropogenic CO2 emissions, nighttime light data, and population dataset as a proxy for the socioeconomic development levels of urban agglomerations, we used Mann-Kendall trend test, Tapio decoupling analysis, and spatial autocorrelation analysis to explore the spatiotemporal association of CO2 emissions and the impact of socioeconomic development on emissions in the nineteen urban agglomerations in China. Findings showed that the growth of CO2 emissions in China was primarily concentrated in urban agglomerations. The CO2 emissions in eastern coastal and northern urban agglomerations were much higher than those in other areas, while the emissions in western urban agglomerations were the lowest. The periodic characteristics of CO2 emissions were consistent with China's five-year development plan. Urban agglomerations in the early stage from 2000 to 2002 or with developed and stable industrial structures tended to achieve decoupling. High-high (HH) clusters of socioeconomic development with CO2 emissions were mainly distributed in urban agglomerations of the Beijing-Tianjin-Hebei region (BTH), the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), Yangtze River Delta (YRD), Huhhot-Baotou-Ordos-Yulin (HBOY), Shandong Peninsula (SP), and Central Shanxi (CS). Most of the clusters except those in HBOY shrunk from 2000 to 2010 and remained relatively stable from 2010 to 2019. These urban agglomerations should promote synergistic emission reduction. High-low (HL) clusters mostly appeared in central cities with a high socioeconomic level and surrounding cities with low CO2 emissions s, i.e., in urban agglomerations of Chengdu-Chongqing region (CC), the Beibu Gulf (BG), and Lanzhou-Xining (LX). These urban agglomerations with prominent polarization phenomena should adhere to regional overall coordination and thus minimize total regional costs of CO2 emission reduction. The results could provide references for the synergistic reduction of CO2 emissions and the coordinated development in urban agglomerations.

Quantification of ecosystem services supply-demand and the impact of demographic change on cultural services in Shenzhen, China.

Previous studies on the supply and demand of ecosystem services (ES) mainly focused on inter-annual changes, and no studies have explored the impact of demographic change on the ES supply and demand on fine-grained time scales. Thus, taking Shenzhen as an example, the status of ES supply and demand, as well as diurnal population changes and their impacts on cultural services were analyzed at different time periods using mobile phone signaling data, ecological supply-demand ratio (ESDR), Geo-Informatic Tupu, InVEST model and buffer zone. The results showed that the population declines successively on workdays, weekends and holidays, and that the daytime population is greater than the nighttime. Water yield services can basically meet the demand in terms of quantity and spatial distribution, however, carbon sequestration and cultural services showed the opposite results. The main type of ESDR changes in cultural services are the mutual conversion of deficit and balance, and these are concentrated in areas with high forest coverage and small populations, but frequent population changes. In addition, when the fixed population is too large, the use of time-varying population data will conceal the impact of demographic changes on ES supply and demand, and other data are needed for auxiliary analysis. Overall, this study provides a new research perspective for the ES supply and demand and can provide a theoretical basis for refined sustainable urban management.

Accelerating AutoDock Vina with GPUs.

AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.

RealVS: Toward Enhancing the Precision of Top Hits in Ligand-Based Virtual Screening of Drug Leads from Large Compound Databases.

Accurate modeling of compound bioactivities is essential for the virtual screening of drug leads. In real-world scenarios, pharmacists tend to choose from the top-k hit compounds ranked by predicted bioactivities from a large database with interest to continue wet experiments for drug discovery. Significant improvement of the precision of the top hits in ligand-based virtual screening of drug leads is more valuable than conventional schemes for accurately predicting the bioactivities of all compounds from a large database. Here, we proposed a new method, RealVS, to significantly improve the top hits' precision and learn interpretable key substructures associated with compound bioactivities. The features of RealVS involve the following points. (1) Abundant transferable information from the source domain was introduced for alleviating the insufficiency of inactive ligands associated with drug targets. (2) The adversarial domain alignment was adopted to fit the distribution of generated features of compounds from the training data set and that from the screening database for greater model generalization ability. (3) A novel objective function was proposed to simultaneously optimize the classification loss, regression loss, and adversarial loss, where most inactive ligands tend to be screened out before activity regression prediction. (4) Graph attention networks were adopted for learning key substructures associated with ligand bioactivities for better model interpretability. The results on a large number of benchmark data sets show that our method has significantly improved the precision of top hits under various k values in ligand-based virtual screening of drug leads from large compound databases, which is of great value in real-world scenarios. The web server of RealVS is freely available at noveldelta.com/RealVS for academic purposes, where virtual screening of hits from large compound databases is accessible.

Caspase-11 cleaves gasdermin D for non-canonical inflammasome signalling.

Intracellular lipopolysaccharide from Gram-negative bacteria including Escherichia coli, Salmonella typhimurium, Shigella flexneri, and Burkholderia thailandensis activates mouse caspase-11, causing pyroptotic cell death, interleukin-1ß processing, and lethal septic shock. How caspase-11 executes these downstream signalling events is largely unknown. Here we show that gasdermin D is essential for caspase-11-dependent pyroptosis and interleukin-1ß maturation. A forward genetic screen with ethyl-N-nitrosourea-mutagenized mice links Gsdmd to the intracellular lipopolysaccharide response. Macrophages from Gsdmd(-/-) mice generated by gene targeting also exhibit defective pyroptosis and interleukin-1ß secretion induced by cytoplasmic lipopolysaccharide or Gram-negative bacteria. In addition, Gsdmd(-/-) mice are protected from a lethal dose of lipopolysaccharide. Mechanistically, caspase-11 cleaves gasdermin D, and the resulting amino-terminal fragment promotes both pyroptosis and NLRP3-dependent activation of caspase-1 in a cell-intrinsic manner. Our data identify gasdermin D as a critical target of caspase-11 and a key mediator of the host response against Gram-negative bacteria.

Electrical contacts to few-layer MoS2 with phase-engineering and metal intercalation for tuning the contact performance.

Due to Fermi-level pinning in metal-two-dimensional MoS2 junctions, improving the performance of MoS2-based electrical devices is still under extensive study. The device performance of few-layer MoS2 depends strongly on the number of layers. In this work, via density-functional theory calculations, a comprehensive understanding from the atomistic view was reached for the interlayer interaction between metal and few-layer MoS2 with phase-engineering and intercalation doping, which are helpful for improving the contact performance. These two methods are probed to tune the performance of few-layer MoS2-based field-effect transistors, and both of them can tune the Schottky barrier height. Phase-engineering, which means that the MoS2 layer in contact with metal is converted to the T phase, can transform the Schottky barrier from n- to p-type. Intercalation doping, which takes advantage of annealing and results in metal atom interaction in between MoS2 layers, makes the MoS2 layers become quasi-freestanding and converts the indirect bandgap into direct bandgap. Our atomistic insights help improve the performance of few-layer MoS2-based electronic devices.

Presence of Metabolic Syndrome and Thyroid Nodules in Subjects with Colorectal Polyps.

BACKGROUND Thyroid nodules (TNs) and metabolic syndrome (MS) have been individually associated with colorectal polyps. However, the potential joint relationship between them in relation to colorectal polyps has not been fully evaluated. This study aimed to validate the association of TNs/MS and colorectal polyps/adenomas and to determine the risk of colonic polyps in patients with TNs/MS. MATERIAL AND METHODS A retrospective study was conducted on patients undergoing routine health checks in the First Affiliated Hospital of Wenzhou Medical University from July 2014 to August 2017. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for colorectal polyps/adenomas after adjusting for confounding factors. Then patients were divided into 4 groups according to whether they had TNs or MS. Relative excess risks of interaction, attributable proportion, and synergy index were used to determine the additive interaction of TNs and MS on colorectal polyps/adenomas. RESULTS A total of 4514 eligible patients were included in this study. TNs and MS were confirmed to be independent risk factors for colorectal polyps/adenomas. Compared with the group of TNs(-)/MS(-), the odds ratios of TNs(+)/MS(+) in colorectal polyps (odds ratio [OR]: 3.031, 95% confidence interval [CI]: 2.262-4.062, P<0.05) or adenomas (OR: 2.894, 95% CI: 2.099-3.990, P<0.05) were significantly increased, and there was an interactive additive effect between TNs and MS. CONCLUSIONS TNs and MS have an associative and superimposing effect on the increased occurrence of colorectal adenomas. Colonoscopy screening should be advocated for patients with both of these diseases.

Climatic modification effects on the association between PM1 and lung cancer incidence in China.

BACKGROUND: Nationwide studies that examine climatic modification effects on the association between air pollution and health outcome are limited in developing countries. Moreover, few studies focus on PM1 pollution despite its greater health effect. OBJECTIVES: This study aims to determine the modification effects of climatic factors on the associations between PM1 and the incidence rates of lung cancer for males and females in China. METHODS: We conducted a nationwide analysis in 345 Chinese counties (districts) from 2014 to 2015. Mean air temperature and relative humidity over the study period were used as the proxies of climatic conditions. In terms of the multivariable linear regression model, we examined climatic modification effects in the stratified and combined datasets according to the three-category and binary divisions of climatic factors. Moreover, we performed three sensitivity analyses to test the robustness of climatic modification effects. RESULTS: We found a stronger association between PM1 and the incidence rate of male lung cancer in counties with high levels of air temperature or relative humidity. If there is a 10 µg/m3 shift in PM1, then the change in male incidence rate relative to its mean was higher by 4.39% (95% CI: 2.19, 6.58%) and 8.37% (95% CI: 5.18, 11.56%) in the middle and high temperature groups than in the low temperature group, respectively. The findings of climatic modification effects were robust in the three sensitivity analyses. No significant modification effect was discovered for female incidence rate. CONCLUSIONS: Male residents in high temperature or humidity counties suffer from a larger effect of PM1 on the incidence rate of lung cancer in China. Future research on air pollution-related health impact assessment should consider the differential air pollution effects across different climatic conditions.

Modifiers versus Channels: Creating Shape-Selective Catalysis of Metal Nanoparticles/Porous Nanomaterials.

Shape-selective catalysis plays a key role in chemical synthesis. Porous nanomaterials with uniform pore structures are ideal supports for metal nanoparticles (MNPs) to generate efficient shape-selective catalysis. However, many commercial irregular porous nanomaterials face the challenge to realize satisfactory shape selectivity due to the lack of molecular sieving structures. Herein, we report a concept of creating shape selectivity in MNPs/porous nanomaterials through intentionally poisoning certain MNPs using suitable modifiers. The remaining MNPs within the substrates can cooperate with the channels to generate selectivity. Such a strategy not only applies to regular porous nanomaterials (such as MOFs, zeolites) but also extended to irregular porous nanomaterials (such as active carbon, P25). Potentially, the matching among different MNPs, corresponding modifiers, and porous nanomaterials makes our strategy promising in selective catalytic systems.