Integrated Bioinformatics and Machine Learning Analysis Identify ACADL as a Potent Biomarker of Reactive Mesothelial Cells.

Mesothelial cells with reactive hyperplasia are difficult to distinguish from malignant mesothelioma cells based on cell morphology. This study aimed to identify and validate potential biomarkers that distinguish mesothelial cells from mesothelioma cells through machine learning combined with immunohistochemistry. It integrated the gene expression matrix from three Gene Expression Omnibus data sets (GSE2549, GSE12345, and GSE51024) to analyze the differently expressed genes between normal and mesothelioma tissues. Then, three machine learning algorithms, least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were used to screen and obtain four shared candidate markers, including ACADL, EMP2, GPD1L, and HMMR. The receiver operating characteristic curve analysis showed that the area under the curve for distinguishing normal mesothelial cells from mesothelioma was 0.976, 0.943, 0.962, and 0.956, respectively. The expression and diagnostic performance of these candidate genes were validated in two additional independent data sets (GSE42977 and GSE112154), indicating that the performances of ACADL, GPD1L, and HMMR were consistent between the training and validation data sets. Finally, the optimal candidate marker ACADL was verified by immunohistochemistry assay. Acyl-CoA dehydrogenase long chain (ACADL) was stained strongly in mesothelial cells, especially for reactive hyperplasic mesothelial cells, but was negative in malignant mesothelioma cells. Therefore, ACADL has the potential to be used as a specific marker of reactive hyperplasic mesothelial cells in the differential diagnosis of mesothelioma.

Personalized anti-tumor drug efficacy prediction based on clinical data.

Anti-tumor drug efficacy prediction poses an unprecedented challenge to realizing personalized medicine. This paper proposes to predict personalized anti-tumor drug efficacy based on clinical data. Specifically, we encode the clinical text as numeric vectors featured with hidden topics for patients using Latent Dirichlet Allocation model. Then, to classify patients into two classes, responsive or non-responsive to a drug, drug efficacy predictors are established by machine learning based on the Latent Dirichlet Allocation topic representation. To evaluate the proposed method, we collected and collated clinical records of lung and bowel cancer patients treated with platinum. Experimental results on the data sets show the efficacy and effectiveness of the proposed method, suggesting the potential value of clinical data in cancer precision medicine. We hope that it will promote the research of drug efficacy prediction based on clinical data.

The chemokine CCL20 can assist AFP in serological diagnosis of hepatocellular carcinoma.

The chemokine 20 (CCL20) is a member of the CC chemokine family and plays a role in tumor immunity and autoimmune disease. This work investigated the value of CCL20 as a serum diagnostic marker for primary hepatocellular carcinoma (HCC). Based on the data of hepatocellular carcinoma patients in the TCGA database, the up-regulated genes encoding secretory proteins were analyzed in each pathological stage, and the candidate marker CCL20 gene was selected. Serum concentrations of CCL20 in patients with primary HCC, benign liver disease, and healthy subjects were analyzed by enzyme-linked immunosorbent assay (ELISA). The ROC curve evaluated the efficacy of CCL20 alone or in combination with AFP in the diagnosis of HCC. It was found the expression of CCL20 in HCC patients was significantly higher than that in the benign liver disease group and healthy controls (P < 0.05); The AUC of ROC curve to distinguish HCC patients from healthy controls was 0.859, the sensitivity was 73.42%, and the specificity was 86.84%. After combination with AFP, the AUC increased to 0.968, the sensitivity was 88.16%, and the specificity was 97.37%. Although CCL20 was increased in the serum of patients with benign liver diseases, combined with AFP, the AUC to distinguish HCC patients from non-HCC cohorts (benign liver disease group and healthy control group) was 0.902, with a sensitivity of 91.67% and a specificity of 75.26%. Collectively, serum CCL20 is closely related to the occurrence of HCC, and detection of serum CCL20 can assist AFP in improving the diagnostic sensitivity of HCC.

Pan-cancer analysis reveals the associations between MMP13 high expression and carcinogenesis and its value as a serum diagnostic marker.

BACKGROUND: Matrix metalloproteinase-13 (MMP13) is a member of the endopeptidase matrix metalloproteinase family, which is involved in many normal physiological processes and even tumorigenesis. However, its co-carcinogenic signature in different cancers is not fully understood. METHODS: In this study, we first analyzed the expression of MMP13 in pan-cancer and its association with prognosis, immune infiltration, and cancer-related signaling pathways through integrated bioinformatics. Furthermore, western blotting (WB) was used to verify the expression of MMP13 and epithelial-mesenchymal transition (EMT) factors in cancer tissues. Finally, the value of MMP13 as a serum diagnostic marker was analyzed by enzyme-linked immunosorbent assay (ELISA). RESULTS: MMP13 expression is frequently upregulated in multiple cancers that always indicate an adverse prognosis. MMP13 undergoes comprehensive genetic alterations and promoter methylation reduction in various tumors. Additionally, immune correlation analysis showed that MMP13 expression was significantly associated with TMB, MSI, and tumor immune infiltration. Pathway enrichment analysis showed that MMP13 upregulation was correlated with activation of the EMT signaling pathway, which was verified by WB in lung adenocarcinoma tissues. Most importantly, ELISA results showed that serum MMP13 levels could be used for the diagnosis of multiple tumors, including BRCA, HNSC, LUAD, and LUSC, with the area under the curve (AUC) values of 0.8494, 0.9259, 0.7144, and 0.8575, respectively. CONCLUSIONS: MMP13 is often overexpressed across cancers and predicts poor prognosis, with the potential as a therapeutic target. Furthermore, the up-regulation of its expression can be effectively reflected in the serum protein level, thus serving as a valuable diagnostic marker.

A prediction model for prognosis of gastric adenocarcinoma based on six metabolism-related genes.

Background: The study of tumor metabolism is of great value to elucidate the mechanism of tumorigenesis and predict the prognosis of patients. However, the prognostic role of metabolism-related genes (MRGs) in gastric adenocarcinoma (GAD) remains poorly understood. Methods: We downloaded the gene chip dataset GSE79973 (n = 20) of GAD from the Gene Expression Omnibus (GEO) database to compare differentially expressed genes (DEGs) between normal and tumor tissues. We then extracted MRGs from these DEGs and systematically investigated the prognostic value of these differential MRGs for predicting patients' overall survival by univariable and multivariable Cox regression analysis. Six metabolic genes (ACOX3, APOE, DIO2, HSD17B4, NUAK1, and WHSC1L1) were identified as prognosis-associated hub genes, which were used to build a prognostic model in the training dataset GSE15459 (n = 200), and then validated in the dataset GSE62254 (n = 300). Results: Patients were divided into high-risk and low-risk subgroups based on the model's risk score, and it was found that patients in the high-risk subgroup had shorter overall survival than those in the low-risk subgroup, both in the training and testing datasets. In addition, for the training and testing cohorts, the area under the ROC curve of the prognostic model for one-year survival prediction was 0.723 and 0.667, respectively, indicating that the model has good predictive performance. Furthermore, we established a nomogram based on tumor stage and risk score to effectively predict the overall survival (OS) of GAD patients. The expression of 6 MRGs at the protein level was confirmed by immunohistochemistry (IHC). Kaplan-Meier survival analysis further confirmed that their expression influenced OS in GAD patients. Conclusion: Collectively, the 6 MRGs signature might be a reliable tool for assessing OS in GAD patients, with potential application value in clinical decision-making and individualized therapy.

FOLFIRI (folinic acid, fluorouracil, and irinotecan) increases not efficacy but toxicity compared with single-agent irinotecan as a second-line treatment in metastatic colorectal cancer patients: a randomized clinical trial.

BACKGROUND: FOLFIRI [irinotecan, folinic acid (CF), and fluorouracil] is considered a standard second-line chemotherapy regimen for patients with metastatic colorectal cancer (mCRC) who failed first-line XELOX/FOLFOX regimens. However, it remains unknown whether fluorouracil is still necessary in this case. This trial was designed to test the superiority of FOLFIRI over single-agent irinotecan as a second-line treatment for patients with mCRC. METHODS: This randomized clinical trial was conducted in five hospitals in China. From 4 November 2016 to 17 January 2020, patients aged 18 years or older with histologically confirmed unresectable mCRC and who had failed first-line XELOX/FOLFOX regimens were screened and enrolled. Patients were randomized to receive either FOLFIRI or irinotecan. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS), objective response rate (ORR), and toxicity. Data were analyzed on an intention-to-treat basis. RESULTS: A total of 172 patients with mCRC were randomly treated with FOLFIRI (n = 88) or irinotecan (n = 84). The median PFS was 104 and 112 days (3.5 and 3.7 months) in the FOLFIRI and irinotecan groups, respectively [hazard ratio (HR) = 1.084, 95% confidence interval (CI) = 0.7911-1.485; p = 0.6094], and there was also no significant difference in OS and ORR between the two groups. The incidence of the following adverse events (AEs) was significantly higher in the FOLFIRI group than in the irinotecan group: any grade AEs including leucopenia (73.9% versus 55.4%), neutropenia (72.7% versus 56.6%), thrombocytopenia (31.8% versus 18.1%), jaundice (18.2% versus 7.2%), mucositis (40.9% versus 14.5%), vomiting (37.5% versus 21.7%), and fever (19.3% versus 7.2%) and grade 3-4 neutropenia (47.7% versus 21.7%). CONCLUSION: This is the first head-to-head trial showing that single-agent irinotecan yielded PFS, OS, and ORR similar to FOLFIRI, with a more favorable toxicity profile; therefore, it might be a more favorable standard chemotherapy regimen for mCRC patients who failed first-line XELOX/FOLFOX regimens. TRIAL REGISTRATION: This study is registered with ClinicalTrials.gov, number NCT02935764, registered 17 October 2016, https://clinicaltrials.gov/ct2/show/NCT02935764.

An Immune-Associated Genomic Signature Effectively Predicts Pathologic Complete Response to Neoadjuvant Paclitaxel and Anthracycline-Based Chemotherapy in Breast Cancer.

Breast cancer is now the leading cause of cancer morbidity and mortality among women worldwide. Paclitaxel and anthracycline-based neoadjuvant chemotherapy is widely used for the treatment of breast cancer, but its sensitivity remains difficult to predict for clinical use. In our study, a LASSO logistic regression method was applied to develop a genomic classifier for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer. The predictive accuracy of the signature classifier was further evaluated using four other independent test sets. Also, functional enrichment analysis of genes in the signature was performed, and the correlations between the prediction score of the signature classifier and immune characteristics were explored. We found a 25-gene signature classifier through the modeling, which showed a strong ability to predict pCR to neoadjuvant chemotherapy in breast cancer. For T/FAC-based training and test sets, and a T/AC-based test set, the AUC of the signature classifier is 1.0, 0.9071, 0.9683, 0.9151, and 0.7350, respectively, indicating that it has good predictive ability for both T/FAC and T/AC schemes. The multivariate model showed that 25-gene signature was far superior to other clinical parameters as independent predictor. Functional enrichment analysis indicated that genes in the signature are mainly enriched in immune-related biological processes. The prediction score of the classifier was significantly positively correlated with the immune score. There were also significant differences in immune cell types between pCR and residual disease (RD) samples. Conclusively, we developed a 25-gene signature classifier that can effectively predict pCR to paclitaxel and anthracycline-based neoadjuvant chemotherapy in breast cancer. Our study also suggests that the immune ecosystem is actively involved in modulating clinical response to neoadjuvant chemotherapy and is beneficial to patient outcomes.

[Human papillomavirus infection in men with condyloma acuminatum: Genotype analysis of 70 cases].

OBJECTIVE: To investigate the distribution of the gene subtypes of human papillomavirus (HPV) in male patients with condyloma acuminatum (CA) and analyze the characteristics of the gene subtypes. METHODS: We extracted genomic DNA of the HPV virus from the genital tissue of 70 male CA patients, detected the DNA subtypes of HPV using the PCR-reverse dot hybridization technique, and analyzed the rates of different subtypes identified and their characteristics of distribution in different age groups. RESULTS: The male HPV-positive patients were mainly infected at the age of 20－39 years, primarily with high- and low-risk mixed infection of various subtypes, which accounted for 61.54% in the 20- to 29-year-olds and 42.86% in the 30- to 39-year-olds. Among the 70 CA patients, 22 HPV subtypes were identified, the top five subtypes including HPV 11 (21.08%), HPV 6 (19.46%), HPV 42 (6.49%), HPV 59 (6.49%) and HPV 53 (5.95%); 20 infected with a single subtype (28.57%), 19 with two subtypes (27.14%) and 31 with three or more (44.29%); and 30 infected with a low-risk single subtype (42.86%) and 40 with both high- and low-risk multiple subtypes (57.14%). CONCLUSIONS: Male patients with CA are mainly infected with HPV 11 and HPV 6, with a significantly higher rate of multi-subtype than single-subtype infection, and the multi-subtype patients chiefly with high- and low-risk mixed infection. Men aged 20－39 years old are most commonly affected by CA.

Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data.

BACKGROUND: Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. RESULTS: This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. CONCLUSIONS: Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.

High-resolution terahertz coded-aperture imaging for near-field three-dimensional target.

In this paper, we propose a high-resolution terahertz coded-aperture imaging method with fast beam scanning for near-field three-dimensional targets. This method utilizes a coded aperture to modulate incident terahertz waves randomly and drive the terahertz beam to scan the entire imaging space step by step. Theoretical analyses based on physical optics are performed, and simulation experiments are implemented to demonstrate the feasibility of the proposed method.

Matrix Metalloproteinase 11 Is a Potential Therapeutic Target in Lung Adenocarcinoma.

Lung cancer is one of the leading causes of cancer-associated death, with the etiology largely unknown. The aim of this study was to identify key driver genes with therapeutic potentials in lung adenocarcinoma (LUAD). Transcriptome microarray data from four GEO datasets (GEO: GSE7670, GSE10072, GSE68465, and GSE43458) were jointly analyzed for differentially expressed genes (DEGs). Ontologic analysis showed that most of the upregulated DEGs enriched in collagen catabolic and fibril organization processes were regulated by matrix metalloproteinases (MMPs). Matrix metalloproteinase 11 (MMP11), the highest upregulated MMP family member in LUAD-transformed cells, acted in an autocrine manner and was significantly increased in sera of LUAD patients. MMP11 depletion severely impaired LUAD cell proliferation, migration, and invasion in vitro, in line with retarded tumor growth in xenograft models. Treatment of different human LUAD cell lines with anti-MMP11 antibody significantly retarded cell growth and migration. Administration of anti-MMP11 antibody at a dose of 1 µg/g body weight significantly suppressed tumor growth in xenograft models. These findings indicate that MMP11 is a key cancer driver gene in LUAD and is an appealing target for antibody therapy.

Second-order NLO properties of bis-cyclometalated iridium(â ¢) complexes: Substituent effect and redox switch.

The iridium(III) complexes could be excellent second-order nonlinear optical (NLO) switch materials due to various advantages including abundant valence states, the diversity of coordination forms and rich electrochemical properties. In this work, the substituent effect and the multi-state switchable response of a series of novel Ir(CˆN)2ADC complexes (CˆN = cyclometalated ligands and ADC = diaminocarbene), induced by electrochemical behavior, have been calculated by density functional theory. The results show that the introducing strong electron-withdrawing groups on ADC ligands significantly enhanced the static first hyperpolarizabilities (ßtot). Moreover, a distinct improvement of the ßtot values can be found from the neutral complexes to the corresponding redox states. By this way, the remarkable multi-state NLO switch can be achieved. Remarkably, the ßtot values of the one-electron-oxidized complex 1+ and the one-electron-reduced complex 1- are ∼5.4 and ∼12.7 times larger than the corresponding neutral complex 1, respectively. The larger ßtot values are attributed to the lower transition energy and remarkable bathochromic shift of maximal absorption wavelength, which can be further illustrated by the separate distribution of ß density. We envision that these studied iridium complexes can be seen as versatile and novel second-order NLO switching materials.

Adaptively capturing the heterogeneity of expression for cancer biomarker identification.

BACKGROUND: Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells. RESULTS: In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and normal tissues. Finally, an unbiased estimator (aGRP) of GRPs is devised that can interrogate and adaptively capture the heterogeneity of gene expression. We also derived an asymptotical significance analysis procedure for the new statistic. Since no parameter needs to be preset, aGRP is easy and friendly to use for researchers without computer programming background. We evaluated the proposed method on both simulated data and real-world data and compared with previous methods. Experimental results demonstrated the superior performance of the proposed method in exploring the heterogeneity of expression for capturing subtle but consistent alterations of gene expression in cancer. CONCLUSIONS: Expression heterogeneity largely influences the performance of cancer biomarker identification from transcriptomics data. Models are needed that efficiently deal with the expression heterogeneity. The proposed method can be a standalone tool due to its capacity of adaptively capturing the sample heterogeneity and the simplicity in use. SOFTWARE AVAILABILITY: The source code of aGRP can be downloaded from https://github.com/hqwang126/aGRP .

Uranium adsorption and subsequent re-oxidation under aerobic conditions by Leifsonia sp. - Coated biochar as green trapping agent.

It has generally been assumed that the immobilization of U(VI) via polyphosphate accumulating microorganisms may present a sink for uranium, but the potential mechanisms of the process and the stability of precipitated uranium under aerobic conditions remain elusive. This study seeks to explore the mechanism, capacity, and stability of uranium precipitation under aerobic conditions by a purified indigenous bacteria isolated from acidic tailings (pH 6.5) in China. The results show that over the treatment ranges investigated, maximum removal of U(VI) from aqueous solution was 99.82% when the initial concentration of U(VI) was 42 µM, pH was 3.5, and the temperature was with 30 °C much higher than that of other reported microorganisms. The adsorption mechanism was elucidated via the use of SEM-EDS, XPS and FTIR. SEM-EDS showed two peaks of uranium on the surface. A plausible explanation for this, supported by FTIR, is that uranium precipitated on the biosorbent surfaces. XPS measurements indicated that the uranium product is most likely a mixture of 13% U(VI) and 87% U(IV). Notably, the reoxidation experiment found that the uranium precipitates were stable in the presence of Ca2+ and Mg2+, however, U(IV) is oxidized to U(VI) in the presence of NO3- and Na+ ions, resulting in rapid dissolution. It implies that the synthesized Leifsonia sp. coated biochar could be utilized as a green and effective biosorbent. However, it may not a good choice for in-situ remediation due to the subsequent re-oxidation under aerobic conditions. These observations can be of some guiding significance to the application of the bioremediation technology in surface environments.

DynSig: Modelling Dynamic Signaling Alterations along Gene Pathways for Identifying Differential Pathways.

Although a number of methods have been proposed for identifying differentially expressed pathways (DEPs), few efforts consider the dynamic components of pathway networks, i.e., gene links. We here propose a signaling dynamics detection method for identification of DEPs, DynSig, which detects the molecular signaling changes in cancerous cells along pathway topology. Specifically, DynSig relies on gene links, instead of gene nodes, in pathways, and models the dynamic behavior of pathways based on Markov chain model (MCM). By incorporating the dynamics of molecular signaling, DynSig allows for an in-depth characterization of pathway activity. To identify DEPs, a novel statistic of activity alteration of pathways was formulated as an overall signaling perturbation score between sample classes. Experimental results on both simulation and real-world datasets demonstrate the effectiveness and efficiency of the proposed method in identifying differential pathways.

Improving GRN re-construction by mining hidden regulatory signals.

Inferring gene regulatory networks (GRNs) from gene expression data is an important but challenging issue in systems biology. Here, the authors propose a dictionary learning-based approach that aims to infer GRNs by globally mining regulatory signals, known or latent. Gene expression is often regulated by various regulatory factors, some of which are observed and some of which are latent. The authors assume that all regulators are unknown for a target gene and the expression of the target gene can be mapped into a regulatory space spanned by all the regulators. Specifically, the authors modify the dictionary learning model, k-SVD, according to the sparse property of GRNs for mining the regulatory signals. The recovered regulatory signals are then used as a pool of regulatory factors to calculate a confidence score for a given transcription factor regulating a target gene. The capability of recovering hidden regulatory signals was verified on simulated data. Comparative experiments for GRN inference between the proposed algorithm (OURM) and some state-of-the-art algorithms, e.g. GENIE3 and ARACNE, on real-world data sets show the superior performance of OURM in inferring GRNs: higher area under the receiver operating characteristic curves and area under the precision-recall curves.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification.

BACKGROUND: Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal. RESULTS: This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis. CONCLUSIONS: Data heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

ctPath: Demixing pathway crosstalk effect from transcriptomics data for differential pathway identification.

Identifying differentially expressed pathways (DEPs) plays important roles in understanding tumor etiology and promoting clinical treatment of cancer or other diseases. By assuming gene expression to be a sparse non-negative linear combination of hidden pathway signals, we propose a pathway crosstalk-based transcriptomics data analysis method (ctPath) for identifying differentially expressed pathways. Biologically, pathways of different functions work in concert at the systematic level. The proposed method interrogates the crosstalks between pathways and discovers hidden pathway signals by mapping high-dimensional transcriptomics data into a low-dimensional pathway space. The resulted pathway signals reflect the activity level of pathways after removing pathway crosstalk effect and allow a robust identification of DEPs from inherently complex and noisy transcriptomics data. CtPath can also correct incomplete and inaccurate pathway annotations which frequently occur in public repositories. Experimental results on both simulation data and real-world cancer data demonstrate the superior performance of ctPath over other popular approaches. R code for ctPath is available for non-commercial use at the URL http://micblab.iim.ac.cn/Download/.

Second-Order Nonlinear Optical Properties of Carboranylated Square-Planar Pt(II) Zwitterionic Complexes: One-/Two-Dimensional Difference and Substituent Effect.

Zwitterionic complexes have been the subject of great interest in the past several decades due to their multifunctional application in supramolecular chemistry. Herein, a series of internally stable charge-compensated carboranylated square-planar Pt(II) zwitterionic complexes have been explored by density functional theory aim to assessing their structures, the first hyperpolarizabilities, first hyperpolarizability densities, and electronic absorption spectra. It is found that the first hyperpolarizabilities of two-dimensional (2D) structure complexes are much larger with respect to the one-dimensional complex. It is ascribed to the lower transition energy and more obvious charge transfer, which can be further illustrated by their large amplitude and separate distribution of first hyperpolarizability density. In addition, the first hyperpolarizabilities of 2D complexes can be further significantly modified by introducing electron-donating/withdrawing groups on the carborane cage. As a consequence, we believe that these 2D zwitterionic complexes can behave as novel second-order nonlinear optical chromophore with a promising future.

Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach.

Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)-guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l1-norm regularisation of sparse regression based on a modified version of BIC. The use of the regularisation strategy ensures the inferred GRNs to be as sparse as natural, while the modified BIC allows incorporating prior knowledge on expression regulation and thus avoids the overestimation of expression regulators as usual. Especially, the proposed method provides a clear interpretation of combinatorial regulations of gene expression by optimally extracting regulation coordination for a given target gene. Experimental results on both simulation data and real-world microarray data demonstrate the competent performance of discovering regulatory relationships in GRN reconstruction.