Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a prevalent and heterogeneous disease with significant genomic variations between the early and advanced stages. The identification of key genes and pathways driving NSCLC tumor progression is critical for improving the diagnosis and treatment outcomes of this disease. METHODS: In this study, we conducted single-cell transcriptome analysis on 93,406 cells from 22 NSCLC patients to characterize malignant NSCLC cancer cells. Utilizing cNMF, we classified these cells into distinct modules, thus identifying the diverse molecular profiles within NSCLC. Through pseudotime analysis, we delineated temporal gene expression changes during NSCLC evolution, thus demonstrating genes associated with disease progression. Using the XGBoost model, we assessed the significance of these genes in the pseudotime trajectory. Our findings were validated by using transcriptome sequencing data from The Cancer Genome Atlas (TCGA), supplemented via LASSO regression to refine the selection of characteristic genes. Subsequently, we established a risk score model based on these genes, thus providing a potential tool for cancer risk assessment and personalized treatment strategies. RESULTS: We used cNMF to classify malignant NSCLC cells into three functional modules, including the metabolic reprogramming module, cell cycle module, and cell stemness module, which can be used for the functional classification of malignant tumor cells in NSCLC. These findings also indicate that metabolism, the cell cycle, and tumor stemness play important driving roles in the malignant evolution of NSCLC. We integrated cNMF and XGBoost to select marker genes that are indicative of both early and advanced NSCLC stages. The expression of genes such as CHCHD2, GAPDH, and CD24 was strongly correlated with the malignant evolution of NSCLC at the single-cell data level. These genes have been validated via histological data. The risk score model that we established (represented by eight genes) was ultimately validated with GEO data. CONCLUSION: In summary, our study contributes to the identification of temporal heterogeneous biomarkers in NSCLC, thus offering insights into disease progression mechanisms and potential therapeutic targets. The developed workflow demonstrates promise for future applications in clinical practice.

Predicting drug synergy using a network propagation inspired machine learning framework.

Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability. Here, we proposed a novel network propagation-based machine learning framework to predict synergistic drug combinations. Based on the topological information of a comprehensive drug-drug association network, we innovatively introduced an affinity score between drug pairs as one of the features to train machine learning models. We applied network-based strategy to evaluate their therapeutic potential to different cancer types. Finally, we identified 17 specific-, 21 general- and 40 broad-spectrum antitumor drug combinations, in which 69% drug combinations were validated by vitro cellular experiments, 83% drug combinations were validated by literature reports and 100% drug combinations were validated by biological function analyses. By quantifying the network relationships between drug targets and cancer-related driver genes in the human protein-protein interactome, we show the existence of four distinct patterns of drug-drug-disease relationships. We also revealed that 32 biological pathways were correlated with the synergistic mechanism of broad-spectrum antitumor drug combinations. Overall, our model offers a powerful scalable screening framework for cancer treatments.

DAPredict: a database for drug action phenotype prediction.

The phenotypes of drug action, including therapeutic actions and adverse drug reactions (ADRs), are important indicators for evaluating the druggability of new drugs and repositioning the approved drugs. Here, we provide a user-friendly database, DAPredict (http://bio-bigdata.hrbmu.edu.cn/DAPredict), in which our novel original drug action phenotypes prediction algorithm (Yang,J., Zhang,D., Liu,L. et al. (2021) Computational drug repositioning based on the relationships between substructure-indication. Brief. Bioinformatics, 22, bbaa348) was embedded. Our algorithm integrates characteristics of chemical genomics and pharmacogenomics, breaking through the limitations that traditional drug development process based on phenotype cannot analyze the mechanism of drug action. Predicting phenotypes of drug action based on the local active structures of drugs and proteins can achieve more innovative drug discovery across drug categories and simultaneously evaluate drug efficacy and safety, rather than traditional one-by-one evaluation. DAPredict contains 305 981 predicted relationships between 1748 approved drugs and 454 ADRs, 83 117 predicted relationships between 1478 approved drugs and 178 Anatomical Therapeutic Chemicals (ATC). More importantly, DAPredict provides an online prediction tool, which researchers can use to predict the action phenotypic spectrum of more than 110 000 000 compounds (including about 168 000 natural products) and corresponding proteins to analyze their potential effect mechanisms. DAPredict can also help researchers obtain the phenotype-corresponding active structures for structural optimization of new drug candidates, making it easier to evaluate the druggability of new drug candidates and develop more innovative drugs across drug categories. Database URL:  http://bio-bigdata.hrbmu.edu.cn/DAPredict/.

Multi-level characteristics recognition of cancer core therapeutic targets and drug screening for a broader patient population.

Introduction: Target therapy for cancer cell mutation has brought attention to several challenges in clinical applications, including limited therapeutic targets, less patient benefits, and susceptibility to acquired due to their clear biological mechanisms and high specificity in targeting cancers with specific mutations. However, the identification of truly lethal synthetic lethal therapeutic targets for cancer cells remains uncommon, primarily due to compensatory mechanisms. Methods: In our pursuit of core therapeutic targets (CTTs) that exhibit extensive synthetic lethality in cancer and the corresponding potential drugs, we have developed a machine-learning model that utilizes multiple levels and dimensions of cancer characterization. This is achieved through the consideration of the transcriptional and post-transcriptional regulation of cancer-specific genes and the construction of a model that integrates statistics and machine learning. The model incorporates statistics such as Wilcoxon and Pearson, as well as random forest. Through WGCNA and network analysis, we identify hub genes in the SL network that serve as CTTs. Additionally, we establish regulatory networks for non-coding RNA (ncRNA) and drug-target interactions. Results: Our model has uncovered 7277 potential SL interactions, while WGCNA has identified 13 gene modules. Through network analysis, we have identified 30 CTTs with the highest degree in these modules. Based on these CTTs, we have constructed networks for ncRNA regulation and drug targets. Furthermore, by applying the same process to lung cancer and renal cell carcinoma, we have identified corresponding CTTs and potential therapeutic drugs. We have also analyzed common therapeutic targets among all three cancers. Discussion: The results of our study have broad applicability across various dimensions and histological data, as our model identifies potential therapeutic targets by learning multidimensional complex features from known synthetic lethal gene pairs. The incorporation of statistical screening and network analysis further enhances the confidence in these potential targets. Our approach provides novel theoretical insights and methodological support for the identification of CTTs and drugs in diverse types of cancer.

[Effects of Biochar Application on Soil Organic Nitrogen Component and Active Nitrogen in Eucalyptus Plantations After Five Years in Northern Guangxi].

The objective of this study was to research the characteristics of fractions of organic nitrogen and active nitrogen and their relationship under different biochar applications and to provide a basis for the preparation and practical application of biochar from Eucalyptus forest wastes. In a long-term positioning test of biochar application from 2017, six different treatments were selected:0(CK), 0.5%(T1), 1%(T2), 2%(T3), 4%(T4), and 6%(T5). The contents of soil organic nitrogen components, total nitrogen(TN), dissolved organic nitrogen(DON), and microbial biomass nitrogen(MBN) following the different treatments were measured. The results showed that:① compared with that of the control, with the increase in biochar application, the contents of soil TN, acidolysis of total organic nitrogen(AHON), ammonia nitrogen(AN), amino acid nitrogen(AAN), MBN, DON, and nitrogen storage(NS) increased significantly by 45.48%-156.32%, 44.31%-171.31%, 38.06%-223.37%, 39.42%-163.32%, 36.72%-109%, 23.27%-113.51%, and 29.45%-62.37%, respectively. The contents of soil hydrolyzable unknown nitrogen(HUN) and non-hydrolyzable nitrogen(NHN) also increased significantly by 88.41%-158.71% and 50.24%-139.01%, respectively. The contents of soil amino sugar nitrogen(ASN) decreased by 7.72%-32.73%. The contents of different forms of organic nitrogen fractions in all treatments displayed an order of AN > AAN > NHN > HUN > ASN. Compared with the no biochar treatment, each biochar treatment increased the contents and proportion of AHON in the TN. ② With the exception of HUN, the contents of other soil organic nitrogen components and active nitrogen content decreased with the increase in soil depth. ③ There were significantly positive correlations between TN, MBN, and DON and AHON, NHN, and NS contents. The principal component analysis showed that bulk density and ASN and TN and HUN, AAN, DON, and AHON were closely related, respectively. In conclusion, the application of forestry waste biochar for five years could significantly increase the content of soil organic nitrogen component and active nitrogen, thereby improving the capacity of the soil to supply nitrogen. AHON, AN, and AAN were the main factors contributing to soil active nitrogen content.

Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs.

Background: Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study. Methods: In this study, the R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) project, and univariate Cox regression analysis was performed to find prognostic immune-related lncRNAs. A total of 117 combinations based on 10 algorithms were integrated to determine the immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERSORT algorithm, the R package maftools, pRRophetic, and clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, drug sensitivity analysis, and pathway enrichment score. Results: A total of 321 immune-related lncRNAs were found, and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the risk score (RS) developed by the 18-lncRNA signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high- and low-risk groups according to the median RS, and the low-risk group had a better prognosis, tumor immune microenvironment, and tumor signature enrichment score and a higher metabolism, frequency of genomic mutations, proportion of immune cell infiltration, and antitumor drug resistance. Furthermore, 86 differentially expressed genes (DEGs) between high- and low-risk groups were mainly enriched in immune-related pathways. Conclusion: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high- and low-risk groups according to the RS.

Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations.

Identifying drug phenotypic effects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug candidates (NDCs). However, current computational methods for predicting phenotypic effects of NDCs are mainly based on the overall structure of an NDC or a related target. These approaches often lead to inconsistencies between the structures and functions and limit the prediction space of NDCs. In this study, first, we constructed quantitative associations of substructure-domain, domain-ADR, and domain-ATC (Anatomical Therapeutic Chemical Classification System code) through L1LOG and L1SVM machine learning models. These associations represent relationships between phenotypes (ADRs and ATCs) and local structures of drugs and proteins. Then, based on these established associations, substructure-phenotype relationships were constructed which were utilized to quantify drug-phenotype relationships. Thus, this approach could achieve high-throughput and effective evaluations of the druggability of NDCs by referring to the established substructure-phenotype relationships and structural information of NDCs without additional prior knowledge. Using this computational pipeline, 83,205 drug-ATC relationships (including 1,479 drugs and 178 ATCs) and 306,421 drug-ADR relationships (including 1,752 drugs and 454 ADRs) were predicted in total. The prediction results were validated at four levels: five-fold cross validation, public databases, literature, and molecular docking. Furthermore, three case studies demonstrated the feasibility of our method. 79 ATCs and 269 ADRs were predicted to be related to Maraviroc, an approved drug, including the existing antiviral effect in clinical use. Additionally, we also found risk substructures of severe ADRs, for example, SUB215 (>= 1, saturated or only aromatic carbon ring size 7) can result in shock. And we analyzed the mechanism of action (MOA) of interested drugs based on the established drug-substructure-domain-protein associations. In a word, this approach through establishing drug-substructure-phenotype relationships can achieve quantitative prediction of phenotypes for a given NDC or drug without any prior knowledge except its structure information. Using that way, we can directly obtain the relationships between substructure and phenotype of a compound, which is more convenient to analyze the phenotypic mechanism of drugs and accelerate the process of rational drug design.

Exploring Precise Medication Strategies for OSCC Based on Single-Cell Transcriptome Analysis from a Dynamic Perspective.

At present, most patients with oral squamous cell carcinoma (OSCC) are in the middle or advanced stages at the time of diagnosis. Advanced OSCC patients have a poor prognosis after traditional therapy, and the complex heterogeneity of OSCC has been proven to be one of the main reasons. Single-cell sequencing technology provides a powerful tool for dissecting the heterogeneity of cancer. However, most of the current studies at the single-cell level are static, while the development of cancer is a dynamic process. Thus, understanding the development of cancer from a dynamic perspective and formulating corresponding therapeutic measures for achieving precise treatment are highly necessary, and this is also one of the main study directions in the field of oncology. In this study, we combined the static and dynamic analysis methods based on single-cell RNA-Seq data to comprehensively dissect the complex heterogeneity and evolutionary process of OSCC. Subsequently, for clinical practice, we revealed the association between cancer heterogeneity and the prognosis of patients. More importantly, we pioneered the concept of pseudo-time score of patients, and we quantified the levels of heterogeneity based on the dynamic development process to evaluate the relationship between the score and the survival status at the same stage, finding that it is closely related to the prognostic status. The pseudo-time score of patients could not only reflect the tumor status of patients but also be used as an indicator of the effects of drugs on the patients so that the medication strategy can be adjusted on time. Finally, we identified candidate drugs and proposed precision medication strategies to control the condition of OSCC in two respects: treatment and blocking.

Precision treatment exploration of breast cancer based on heterogeneity analysis of lncRNAs at the single-cell level.

BACKGROUND: Breast cancer (BC) is a complex disease with high heterogeneity, which often leads to great differences in treatment results. Current common molecular typing method is PAM50, which shows positive results for precision medicine; however, room for improvement still remains because of the different prognoses of subtypes. Therefore, in this article, we used lncRNAs, which are more tissue-specific and developmental stage-specific than other RNAs, as typing markers and combined single-cell expression profiles to retype BC, to provide a new method for BC classification and explore new precise therapeutic strategies based on this method. METHODS: Based on lncRNA expression profiles of 317 single cells from 11 BC patients, SC3 was used to retype BC, and differential expression analysis and enrichment analysis were performed to identify biological characteristics of new subtypes. The results were validated for survival analysis using data from TCGA. Then, the downstream regulatory genes of lncRNA markers of each subtype were searched by expression correlation analysis, and these genes were used as targets to screen therapeutic drugs, thus proposing new precision treatment strategies according to the different subtype compositions of patients. RESULTS: Seven lncRNA subtypes and their specific biological characteristics are obtained. Then, 57 targets and 210 drugs of 7 subtypes were acquired. New precision medicine strategies were proposed according to the different compositions of patient subtypes. CONCLUSIONS: For patients with different subtype compositions, we propose a strategy to select different drugs for different patients, which means using drugs targeting multi subtype or combinations of drugs targeting a single subtype to simultaneously kill different cancer cells by personalized treatment, thus reducing the possibility of drug resistance and even recurrence.

Computational drug repositioning based on the relationships between substructure-indication.

At present, computational methods for drug repositioning are mainly based on the whole structures of drugs, which limits the discovery of new functions due to the similarities between local structures of drugs. In this article, we, for the first time, integrated the features of chemical-genomics (substructure-domain) and pharmaco-genomics (domain-indication) based on the assumption that drug-target interactions are mediated by the substructures of drugs and the domains of proteins to identify the relationships between substructure-indication and establish a drug-substructure-indication network for predicting all therapeutic effects of tested drugs through only information on the substructures of drugs. In total, 83 205 drug-indication relationships with different correlation scores were obtained. We used three different verification methods to indicate the accuracy of the method and the reliability of the scoring system. We predicted all indications of olaparib using our method, including the known antitumor effect and unknown antiviral effect verified by literature, and we also discovered the inhibitory mechanism of olaparib toward DNA repair through its specific sub494 (o = C-C: C), as it participates in the low synthesis of the poly subfunction of the apoptosis pathway (hsa04210) by inhibiting the Inositol 1,4,5-trisphosphate receptor(s) (ITPRs) and hydrolyzing poly (ADP ribose) polymerases. ElectroCardioGrams of four drugs (quinidine, amiodarone, milrinone and fosinopril) demonstrated the effect of anti-arrhythmia. Unlike previous studies focusing on the overall structures of drugs, our research has great potential in the search for more therapeutic effects of drugs and in predicting all potential effects and mechanisms of a drug from the local structural similarity.

[Effects of Eucalyptus Branches Biochar Application on Soil Physicochemical Properties of Acidified Soil in a Eucalyptus plantation in Northern Guangxi].

This study aims to explore the effects of different biochar applications on soil physical and chemical properties in a Eucalyptus plantation in Northern Guangxi, find the best biochar application amount, and provide scientific guidance for the efficient utilization of forest residue and soil improvement. The soil of a four-year Eucalyptus plantation at the Huangmian forest farm in Northern Guangxi was selected as the study area, and six treatments including 0 (CK), 0.5% (T1), 1.0% (T2), 2% (T3), 4% (T4), and 6% (T5) were set through a field-positioning experiment to analyze the changes in soil physical and chemical properties under different application rates. Compared with the 0-30 cm soil layer of the control treatment, biochar application decreased the mean soil bulk by 3.82%-33.55%, while it increased the soil natural water content, capillary porosity, and total capillary porosity by 7.67%-31.75%, 8.95%-33.19%, and 9.28%-35.86%, respectively. The contents of exchangeable acid, exchangeable aluminum, exchangeable hydrogen, and exchangeable sodium in the soil decreased by 8.28%-70.03%, 5.55%-70.34%, 5.10%-21.78%, and 12.81%-49.27%. Biochar application increased the cation exchange capacity, electrical conductivity, exchangeable magnesium, and exchangeable calcium by 27.08%-160.39%, 117.00%-546.64%, 17.10%-66.14%, and 17.38%-71.38%, respectively. Soil pH increased by 0.17-1.29 after biochar addition. Similarly, the contents of soil organic carbon, total phosphorus, total potassium, available nitrogen, available phosphorus, and available potassium increased by 10.94%-51.37%, 14.29%-59.45%, 6.48%-59.57%, 6.28%-29.41%, 4.79%-19.81%, and 7.72%-75.87%. There was a positive correlation among the main physical and chemical factors. The physical and chemical properties reached their maximum values in the T4 or T5 treatment (4% or 6%). Biochar application provided considerable relief from soil acidification in the Eucalyptus plantation and had a positive effect on soil physicochemical properties. The addition 4%-6% of ripe Eucalyptus biochar produced the optimum results. The results show that biochar can improve the physical and chemical properties of soil, increase soil fertility, and enhance the soil's ability to retain water and fertilizer after twelve months. The findings of this study can be used as a reference in practical applications for soil improvement and sustainable management of Eucalyptus plantations.

Identification of Early Warning Signals at the Critical Transition Point of Colorectal Cancer Based on Dynamic Network Analysis.

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Due to the lack of early diagnosis methods and warning signals of CRC and its strong heterogeneity, the determination of accurate treatments for CRC and the identification of specific early warning signals are still urgent problems for researchers. In this study, the expression profiles of cancer tissues and the expression profiles of tumor-adjacent tissues in 28 CRC patients were combined into a human protein-protein interaction (PPI) network to construct a specific network for each patient. A network propagation method was used to obtain a mutant giant cluster (GC) containing more than 90% of the mutation information of one patient. Next, mutation selection rules were applied to the GC to mine the mutation sequence of driver genes in each CRC patient. The mutation sequences from patients with the same type CRC were integrated to obtain the mutation sequences of driver genes of different types of CRC, which provide a reference for the diagnosis of clinical CRC disease progression. Finally, dynamic network analysis was used to mine dynamic network biomarkers (DNBs) in CRC patients. These DNBs were verified by clinical staging data to identify the critical transition point between the pre-disease state and the disease state in tumor progression. Twelve known drug targets were found in the DNBs, and 6 of them have been used as targets for anticancer drugs for clinical treatment. This study provides important information for the prognosis, diagnosis and treatment of CRC, especially for pre-emptive treatments. It is of great significance for reducing the incidence and mortality of CRC.

CHG: A Systematically Integrated Database of Cancer Hallmark Genes.

BACKGROUND: The analysis of cancer diversity based on a logical framework of hallmarks has greatly improved our understanding of the occurrence, development and metastasis of various cancers. METHODS: We designed Cancer Hallmark Genes (CHG) database which focuses on integrating hallmark genes in a systematic, standard way and annotates the potential roles of the hallmark genes in cancer processes. Following the conceptual criteria description of hallmark function the keywords for each hallmark were manually selected from the literature. Candidate hallmark genes collected were derived from 301 pathways of KEGG database by Lucene and manually corrected. RESULTS: Based on the variation data, we finally identified the hallmark genes of various types of cancer and constructed CHG. And we also analyzed the relationships among hallmarks and potential characteristics and relationships of hallmark genes based on the topological structures of their networks. We manually confirm the hallmark gene identified by CHG based on literature and database. We also predicted the prognosis of breast cancer, glioblastoma multiforme and kidney papillary cell carcinoma patients based on CHG data. CONCLUSIONS: In summary, CHG, which was constructed based on a hallmark feature set, provides a new perspective for analyzing the diversity and development of cancers.

[Soil Organic Carbon Distribution and Components in Different Plant Communities Along a Water Table Gradient in the Huixian Karst Wetland in Guilin].

In order to reveal the effect of vegetation type and soil physicochemical properties on the distribution of soil organic carbon and its components, a field survey was carried out on nine different plant communities along a water table gradient in the Huixian wetland with samples of soil at 0-10 cm, 10-20 cm, and 20-30 cm in depth. The soil organic carbon (SOC), light fraction organic carbon (LFOC), heavy fraction organic carbon (HFOC), easily oxidized organic carbon (EOC), dissolved organic carbon (DOC), particulate organic carbon (POC), and microbial biomass carbon (MBC) were measured. The correlations among soil organic carbon components and soil physicochemical properties were also examined. The results showed that:① The average proportion of LFOC and HFOC to SOC at 0-30 cm soil depth was 11.10% and 88.90%, respectively. The distribution ratio of the heavy component was much higher than of the light component in soils. ② The content of SOC, DOC, EOC, POC, and MBC (except in the Panicum repens community) and the values of DOC/SOC, EOC/SOC, and POC/SOC all decreased with increase of the soil depth. ③ Among the nine different plant communities, the contents of SOC, LFOC, HFOC, MBC, DOC, EOC, and POC of Cladium chinense were significantly higher than for other communities in same soil layers. ④ There were significantly positive correlations among soil organic carbon components (SOC) and soil total nitrogen (TN). LFOC, HFOC, DOC, and POC were also positively correlated with soil pH. The soil bulk density was significantly negative correlated with LFOC, HFOC, DOC, EOC, and POC, and the content of clay was also negatively correlated with LFOC, HFOC, DOC, POC, and MBC. ⑤ Path analysis showed that TN, soil pH, soil sand content, and soil water content (SWC) has indirect effects on HFOC by influencing other soil factors. Soil TN had strong positive effects on EOC, DOC, and POC, and SWC also has the largest direct negative effect on MBC. This showed that there were close interactions between soil physicochemical properties and soil organic carbon components. This study may provide a reference base for sustainable development and scientific predictions regarding the Huixian Karst wetland.

Ratiometric and Turn-On Luminescence Detection of Water in Organic Solvents Using a Responsive Europium-Organic Framework.

The development of simple, rapid-response sensors for water detection in organic solvents is highly desirable in the chemical industry. Here we demonstrate a unique luminescence water sensor based on a dual-emitting europium-organic framework (Eu-MOF), which is assembled from a purposely selected 2-aminoterephthalic acid ligand with responsive fluorescence inherent in its intramolecular charge transfer (ICT) process. This ICT process can be rapidly switched-on in the presence of water owing to its ability to boost and stabilize the ICT state. In contrast, the Eu3+ emission within the framework is insensitive to water and can serve as a reference, thus enabling highly sensitive water detection in a turn-on and ratiometric way. In addition, the significant ratiometric luminescence response induced by water makes Eu-MOF undergo a distinct change of emitting color from red to blue, which is favorable for visual analysis with the naked eye. Sensitive determination of water content (0.05-10% v/v) in various organic solvents is achieved in multiple readouts including ratiometric emission intensity, emission color, or the Commission Internationale de l'Eclairage (CIE) chromaticity coordinate. The present Eu-MOF sensor featuring high sensitivity and reusability, self-calibration, simple fabrication and operation, and capability for real-time and in situ detection is expected to have practical applications in water analysis for industrial processes.

[Effects of Reclamation on Soil Nutrients and Microbial Activities in the Huixian Karst Wetland in Guilin].

To investigate the effect of reclamation on soil quality in the Huixian Karst Wetland, samples from different soil levels were collected from marsh wetland, reclaimed paddy field, and reclaimed dry farmland, for analyzing soil nutrient (carbon, nitrogen, phosphorous, and potassium) contents, microbial biomass carbon/nitrogen (MBC/MBN), and microbial activity indicators[i.e. basal respiration (BR), potential respiration (PR), microbial quotient (qMB), and metabolic quotient (qCO2)]. The correlations between the soil nutrient contents and soil microbial activity indictors were examined. The results showed that:①Artificial reclamation led to the trend of slight acidity in the soil and a marked loss in soil nutrients, while, the pH value, soil water content (SWC), and the contents of soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), available phosphorus (AP), total potassium (TK), and available potassium (AK) decreased with reclamation. ②Among all the microbes, bacteria were the most numerous, followed by actinomycetes, and fungi were the least numerous. The microbial quantity decreased with the increase in the soil depth on the whole. The proportion of bacteria and actinomycetes were much higher in the paddy field, and that of fungi was the highest in the dry farmland. ③ In total, protease, sucrase, urease, catalase, and polyphenol oxidase activities decreased with the increasing of soil depths. Soil reclamation reduced the soil enzyme activities. ④qCO2 decreased after an initial increase in the marsh wetland, while it rose gradually in the reclaimed paddy field and reclaimed dry farmland. The contents of MBC, MBN, BR, PR, and qMB were the highest in the marsh wetland, followed by those in the reclaimed paddy field, with the lowest contents occurring in the reclaimed dry farmland. The trend of qCO2 contents in the 0-10 cm and 10-20 cm soil layers followed the order of marsh wetland > paddy field > dry farmland, but in the 20-30 cm and 30-40 cm soil layers, it showed the order dry farmland > paddy field > marsh wetland. The continuation of reclamation resulted in the decrease in soil microbial activity, and soil quality as well, especially in the dry farmland. Meanwhile, we should reduce the areas of paddy fields and dry farmlands under reclamation during the process of wetland ecological restoration in future. Conversion of farmlands to wetlands or lakes, to improve and increase the size of wetland ecosystems of nearby lands, should be done gradually.

Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network.

Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-based and function-based screening) was proposed to identify the SL gene pairs by mining 697 cancer genes and the human signaling network, which had 6306 proteins and 62937 protein-protein interactions. The network-based screening was composed of a stability score constructed using a network information centrality measure (the average shortest path length) and the distance-based screening between the cancer gene and the non-cancer gene. Then, the non-cancer genes were extracted and annotated using drug-target interaction and drug description information to obtain potential anticancer drug targets. Finally, the human SL data in SynLethDB, the existing drug sensitivity data and text-mining were utilized for target validation. We successfully identified 2555 SL gene pairs and 57 potential anticancer drug targets. Among them, CDK1, CDK2, PLK1 and WEE1 were verified by all three aspects and could be preferentially used in specific targeted therapy in the future.

Predicting hepatocellular carcinoma through cross-talk genes identified by risk pathways.

Hepatocellular carcinoma (HCC) is the most frequent type of liver cancer with poor survival rate and high mortality. Despite efforts on the mechanism of HCC, new molecular markers are needed for exact diagnosis, evaluation and treatment. Here, we combined transcriptome of HCC with networks and pathways to identify reliable molecular markers. Through integrating 249 differentially expressed genes with syncretic protein interaction networks, we constructed a HCC-specific network, from which we further extracted 480 pivotal genes. Based on the cross-talk between the enriched pathways of the pivotal genes, we finally identified a HCC signature of 45 genes, which could accurately distinguish HCC patients with normal individuals and reveal the prognosis of HCC patients. Among these 45 genes, 15 showed dysregulated expression patterns and a part have been reported to be associated with HCC and/or other cancers. These findings suggested that our identified 45 gene signature could be potential and valuable molecular markers for diagnosis and evaluation of HCC.

A luminescent Lanthanide-free MOF nanohybrid for highly sensitive ratiometric temperature sensing in physiological range.

Luminescent MOF materials with tunable emissions and energy/charge transfer processes have been extensively explored as ratiometric temperature sensors. However, most of the ratiometric MOF thermometers reported thus far are based on the MOFs containing photoactive lanthanides, which are potentially facing cost issue and serious supply shortage. Here, we present a ratiometric luminescent thermometer based on a dual-emitting lanthanide-free MOF hybrid, which is developed by encapsulation of a fluorescent dye into a robust nanocrystalline zirconium-based MOF through a one-pot synthesis approach. The structure and morphology of the hybrid product was characterized by Powder X-ray diffraction (PXRD), N2 adsorption-desorption measurement and Scanning electron microscopy (SEM). The pore confinement effect well isolates the guest dye molecules and therefore suppresses the nonradiative energy transfer process between dye molecules. The incorporated dye emission is mainly sensitized by the organic linkers within MOF through fluorescence resonance energy transfer. The ratiometric luminescence of the MOF hybrid shows a significant response to temperature due to the thermal-related back energy transfer process from dye molecules and organic linkers, thus can be exploited for self-calibrated temperature sensing. The maximum thermometric sensitivity is 1.19% °C-1 in the physiological temperature range, which is among the highest for the ratiomtric MOF thermometers that operating in 25-45°C. The temperature resolution is better than 0.1°C over the entire operative range (20-60°C). By integrating the advantages of excellent stability, nanoscale nature, and high sensitivity and precision in the physiological temperature range, this dye@MOF hybrid might have potential application in biomedical diagnosis. What' more, this work has expanded the possibility of non-lanthanide luminescent MOF materials for the development of ratiometric temperature sensors.

Biological evaluation and energetic analyses of novel GSK-3ß inhibitors.

Glycogen synthase kinase-3 beta (GSK-3ß) is involved in multiple signaling pathways. Consistent with its critical roles in normal cells, abnormalities in GSK-3ß activity have been implicated in diabetes, heart disease, Parkinson disease, and Alzheimer's disease. In this study, a series of new scaffolds of small molecule inhibitors of GSK-3ß were identified by virtual screening and bioassay. Candidates that adhere to drug-like criteria from a virtual library of compounds were tested using computational docking studies. Twenty selected compounds were tested, which led to the discovery of two hits. Compound 14 (IC50 = 8.48 µM) and compound 19 (IC50 = 2.19 µM) were identified with high affinity. Molecular dynamics (MD) simulations, in conjunction with molecular mechanics/Poisson-Boltzmann surface area binding free-energy analysis, were employed to gain insight into the binding modes and energetics of GSK-3ß inhibitors. The detailed analysis of molecular dynamics results shows that Ile62, Val70, Tyr134, and Leu188 in GSK-3ß are key residues responsible to the binding of compound 14 and compound 19. Importantly, our results also validated this combined virtual screening and biophysical technique approach to discovery kinase inhibitors, which may be applied for future inhibitor discovery work for GSK-3ß.