Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback–Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www.comp-sysbio.org/cmi2ni.

Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, "partial independence," with a new measure, "part mutual information" (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks.conditional mutual information | systems biology | network inference | conditional independence B ig data provide unprecedented information and opportunities to uncover ambiguous correlations among measured variables, but how to further infer direct associations, which means two variables are dependent given all of the remaining variables (1), quantitatively from those correlations or data remains a challenging task, in particular in science and engineering. For instance, distinguishing dependencies or direct associations between molecules is of great importance in reconstructing gene regulatory networks in biology (2-4), which can elucidate the molecular mechanisms of complex biological processes at a network level. Traditionally, correlation [e.g., the Pearson correlation coefficient (PCC)] is widely used to evaluate linear relations between the measured variables (2, 5), but it cannot distinguish indirect and direct associations due to only relying on the information of co-occurring events. Partial correlation (PC) avoids this problem by considering additional information of conditional events and can detect the direct associations. Thus, PC becomes one of the most widely used criteria to infer direct associations in various areas. As applications of PC to network reconstruction (6), recently Barzel and Barabási (7) proposed a dynamical correlation-based method to discriminate direct and indirect associations by silencing indirect effects in networks, and Feizi et al. (8) developed a network deconvolution method to distinguish direct dependencies by removing the combined effect of al...

As a commercial antibiotic, bicyclomycin (BCM) is currently the only known natural product targeting the transcription termination factor rho. It belongs to a family of highly functionalized diketopiperazine (DKP) alkaloids and bears a unique O-bridged bicyclo[4.2.2]piperazinedione ring system, a C1 triol, and terminal exo-methylene groups. We have identified and characterized the BCM biosynthetic pathway by heterologous biotransformations, in vitro biochemical assays, and one-pot enzymatic synthesis. A tRNA-dependent cyclodipeptide synthase guides the heterodimerization of leucine and isoleucine to afford the DKP precursor; subsequently, six redox enzymes, including five α-ketoglutarate/Fe -dependent dioxygenases and one cytochrome P450 monooxygenase, regio- and stereoselectively install four hydroxy groups (primary, secondary, and two tertiary), an exo-methylene moiety, and a medium-sized bridged ring through the functionalization of eight unactivated C-H bonds.

Glioblastoma (GBM) is the most common and most aggressive central nervous system tumor in adults. Due to GBM cell invasiveness and resistance to chemotherapy, current medical interventions are not satisfactory, and the prognosis for GBM is poor. It is necessary to investigate the underlying mechanism of GBM metastasis and drug resistance so that more effective treatments can be developed for GBM patients. sushi repeat-containing protein, X-linked 2 (SRPX2) is a prognostic biomarker in many different cancer cell lines and is associated with poor prognosis in cancer patients. SRPX2 overexpression promotes interactions between tumor and endothelial cells, leading to tumor progression and metastasis. We hypothesize that SRPX2 also contributes to GBM chemotherapy resistance and metastasis. Our results revealed that GBM tumor samples from 42 patients expressed higher levels of SRPX2 than the control normal brain tissue samples. High-SRPX2 expression levels are correlated with poor prognosis in those patients, as well as resistance to temozolomide in cultured GBM cells. Up-regulating SRPX2 expression in cultured GBM cell lines facilitated invasiveness and migration of GBM cells, while down-regulating SRPX2 through RNA interference was inhibitory. These results suggest that SRPX2 plays an important role in GBM metastasis. Epithelial to mesenchymal transition (EMT) is one of the processes that facilitate GBM metastasis and resistance to chemotherapy. EMT marker expression was decreased in SRPX2 down-regulated GBM cells, and MAPK signaling pathway marker expression was also decreased when SRPX2 is knocked down in GBM-cultured cells. Blocking the MAPK signaling pathway inhibited GBM metastasis but did not inhibit cell invasion and migration in SRPX2 down-regulated cells. Our results indicate that SRPX2 facilitates GBM metastasis by enhancing the EMT process via the MAPK signaling pathway.

Face Anti-spoofing gains increased attentions recently in both academic and industrial fields. With the emergence of various CNN based solutions, the multi-modal(RGB, depth and IR) methods based CNN showed better performance than single modal classifiers. However, there is a need for improving the performance and reducing the complexity. Therefore, an extreme light network architecture(FeatherNet A/B) is proposed with a streaming module which fixes the weakness of Global Average Pooling and uses less parameters. Our single FeatherNet trained by depth image only, provides a higher baseline with 0.00168 ACER, 0.35M parameters and 83M FLOPS. Furthermore, a novel fusion procedure with "ensemble + cascade" structure is presented to satisfy the performance preferred use cases. Meanwhile, the MMFD dataset is collected to provide more attacks and diversity to gain better generalization. We use the fusion method in the Face Anti-spoofing Attack Detection Challenge@CVPR2019 and got the result of 0.0013(ACER), 0.999(TPR@FPR=10e-2), 0.998(TPR@FPR=10e-3) and 0.9814(TPR@FPR=10e-4).

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Osteoarthritis (OA), a common form of degenerative joint disease, is typified by inflammatory response and the loss of cartilage matrix. Long non-coding RNAs (lncRNAs) are emerging as a new player in gene regulation and exert critical roles in diverse physiologic and pathogenic processes including OA. The lncRNA plasmacytoma variant translocation 1 (PVT1) has been implicated in cancer, diabetes and septic acute kidney injury. Recent research confirmed the elevation of PVT1 in patients with OA. However, its role in the development of OA remains poorly elucidated. In the present study, high expression of PVT1 was observed in cartilage of OA patients and IL-1β-stimulated chondrocytes. Moreover, cessation of PVT1 expression dramatically reversed the inhibition of IL-1β on collagen II and aggrecan expression, but suppressed IL-1β-induced elevation of matrix metalloproteinases (MMPs), including MMP-3, MMP-9 and MMP-13. Simultaneously, PVT1 inhibition also antagonized the production of inflammatory cytokines upon IL-1β stimulation, including prostaglandin E2 (PGE2), NO, IL-6, IL-8 and TNF-α. Further molecular mechanism analysis identified PVT1 as an endogenous sponge RNA that could directly bind to miR-149 and repress its expression and activity. More importantly, miR-149 inhibition reversed the protective roles of PVT1 cessation in attenuating IL-1β-evoked matrix aberrant catabolism and inflammation. Together, this research confirms that lowering PVT1 expression may ameliorate the progression of OA by alleviating cartilage imbalance toward catabolism and inflammatory response, thus supporting a promising therapeutic strategy against OA.

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