Using machine learning to predict gene expression and discover sequence motifs

Recently, large amounts of experimental data for complex biological systems have become available. We use tools and algorithms from machine learning to build data-driven predictive models. We first present a novel algorithm to discover gene sequence motifs associated with temporal expression patterns of genes. Our algorithm, which is based on partial least squares (PLS) regression, is able to directly model the flow of information, from gene sequence to gene expression, to learn cis regulatory motifs and characterize associated gene expression patterns. Our algorithm outperforms traditional computational methods e.g. clustering in motif discovery. We then present a study of extending a machine learning model for transcriptional regulation predictive of genetic regulatory response to Caenorhabditis elegans. We show meaningful results both in terms of prediction accuracy on the test experiments and biological information extracted from the regulatory program. The model discovers DNA binding sites ab intio. We also present a case study where we detect a signal of lineage-specific regulation. Finally we present a comparative study on learning predictive models for motif discovery, based on different boosting algorithms: Adaptive Boosting (AdaBoost), Linear Programming Boosting (LPBoost) and Totally Corrective Boosting (TotalBoost). We evaluate and compare the performance of the three boosting algorithms via both statistical and biological validation, for hypoxia response in Saccharomyces cerevisiae

Highly efficient CO2 capture with simultaneous iron and CaO recycling for the iron and steel industry

An efficient CO2 capture process has been developed by integrating calcium looping (CaL) and waste recycling technologies into iron and steel production. A key advantage of such a process is that CO2 capture is accompanied by simultaneous iron and CaO recycling from waste steel slag. High-purity CaO-based CO2 sorbents, with CaO content as high as 90 wt%, were prepared easily via acid extraction of steel slag using acetic acid. The steel slag-derived CO2 sorbents exhibited better CO2 reactivity and slower (linear) deactivation than commercial CaO during calcium looping cycles. Importantly, the recycling efficiency of iron from steel slag with an acid extraction is improved significantly due to a simultaneous increase in the recovery of iron-rich materials and the iron content of the materials recovered. High-quality iron ore with iron content of 55.1–70.6% has been recovered from waste slag in this study. Although costing nearly six times as much as naturally derived CaO in the purchase of feedstock, the final cost of the steel slag-derived, CaO-based sorbent developed is compensated by the byproducts recovered, i.e., high-purity CaO, high-quality iron ore, and acetone. This could reduce the cost of the steel slag-derived CO2 sorbent to 57.7 € t−1, appreciably lower than that of the naturally derived CaO. The proposed integrated CO2 capture process using steel slag-derived, CaO-based CO2 sorbents developed appears to be cost-effective and promising for CO2 abatement from the iron and steel industry

The Influence of Expectation on Nondeceptive Placebo and Nocebo Effects

Nondeceptive placebo has demonstrated its efficiency in clinical practice. Although the underlying mechanisms are still unclear, nondeceptive placebo effect and nondeceptive nocebo effect may be mediated by expectation. To examine the extent to which expectation influences these effects, the present study compared nondeceptive placebo and nocebo effects with different expectation levels. Seventy-two healthy female participants underwent a standard conditioning procedure to establish placebo and nocebo effects. Sequentially, participants were randomized to one of the four experimental groups-baseline (BL), no expectation intervention (NoEI), expectation increasing (EI), and expectation decreasing (ED) groups, to receive either no intervention or interventions through different verbal suggestions that modulated their expectation. Placebo and nocebo effects were established in all four groups after the conditioning phase. However, after disclosing the placebo and nocebo, the analgesic and the hyperalgesic effects only persisted in the EI group, when compared with the BL group. Our results provide evidence highlighting the critical role of increased expectation in nondeceptive placebo and nocebo effects. The finding suggests that open-label placebo or nocebo per se might be insufficient to induce strong analgesic or hyperalgesic response and sheds insights into administrating open-label placebo and avoiding open-label nocebo in clinical practice.</p

Protective effect of astragalus injection against myocardial injury in septic young rats via inhibition of JAK/STAT signal pathway and regulation of inflammation

Purpose: To investigate the protective effect of astragalus injection against myocardial injury in septic young rats, and the underlying mechanism of action. Methods: Seventy-two healthy Sprague Dawley (SD) rats were randomly selected and used to establish a young rat model of sepsis. The young rats were randomly divided into 3 groups: sham, model and astragalus injection groups. Each group had 24 young rats. Serum cardiac troponin I (cTnI), IL-10, IL-6, JAK2 and STAT3 were measured after op. Results: Compared with sham group, serum cTnI level in the model group was significantly higher, while serum cTnI level of the drug group was significantly lower than that of the model group (p &lt; 0.05). Compared with model group, the level of IL-10 in the myocardial tissue of the drug group was significantly elevated, while IL-6 level was lower (p &lt; 0.05). Relative to sham rats, myocardial JAK2 and STAT3 protein levels in model rats were high. However, myocardial JAK2 and STAT3 proteins in the drug-treated rats were significantly downregulated, relative to model rats (p &lt; 0.05). Conclusion: Astragalus injection upregulates IL-10 and IL-6 in rats by inhibiting the activation of JAK/STAT signal pathway, and via maintenance of pro-inflammation/anti-inflammation balance. Thus, astragalus exerts protective effect against myocardial injury in sepsis, and can potentially be developed for use as such in clinical practice. Keywords: Astragalus injection, JAK/STAT signal pathway, Pro-inflammatory/anti-inflammatory imbalance, Sepsis, Myocardial injur

Glycerol Carbonate: A Novel Biosolvent with Strong Ionizing and Dissociating Powers

The activity of biocatalysts in nonaqueous solvents is related to the interaction of organic solvents with cells or enzymes. The behavior of proteins is strongly dependent on the protonation state of their ionizable groups, which ionization constants are greatly affected by the solvent. Due to the weak ionizing and dissociating powers of common organic solvents, the charge of the protein will change significantly when the protein is transferred from water to common organic solvents, resulting in protein denaturation. In this work, glycerol carbonate (GC) was synthesized, which ionizing and dissociating abilities were very close to those of water. Transesterification activities of Candida antarctica lipase B (CALB) in GC were comparable to those in water and remained constant during 4-week storage. Bacillus subtilis and Saccharomyecs cerevisiae were cultured in liquid media containing GC with test tubes. In the medium containing low GC concentration, Bacillus subtilis and Saccharomyecs cerevisiae grew well as in a medium containing no organic solvent, but, in the medium containing high GC concentration, the growth of Bacillus subtilis and Saccharomyecs cerevisiae was suppressed. The results suggested that GC is a potential biosolvent, which has great significance to biocatalysis in nonaqueous solvents

Moxibustion has a positive effect on pulmonary fibrosis: an alternative approach

Background: An increasing number of people suffered idiopathic fibrosis (IPF) and the current treatment was far from clinical satisfaction. Moxibustion, another effective and safe unconventional therapy, had been introduced to treat this refractory disease. The study aimed to investigate the effect of moxibustion on a bleomycin A5-induced pulmonary fibrosis model.Materials and Methods: Sprague-dawley (SD) rats were randomly allocated to the blank group, model group, moxibustion group, and prednisone group, for which they received no treatment, modeling, moxibustion treatment and prednisone treatment. After four-week treatment, the rats were euthanized for Hematoxylin and Eosin (H.E.) staining, and TGF-β1 and IFN-γ protein and mRNA detection in lungs.Results: In the model group, TGF-β1 was significantly increased and IFN-γ was significantly decreased at both protein and mRNA levels in comparison to the blank group. In the moxibustion and prednisone group, however, TGF- β1 was decreased and IFN-γ was increased at both protein and mRNA levels in comparison to the model groups. Compared with prednisone, moxibustion showed comparable effect in lowing TGF-β1 (P&gt;0.05) and better effect in up-regulating IFN-γ(P&gt;0.05).Conclusion: The study concludes moxibustion protected pulmonary fibrosis by downregulating TGF-β1 and upregulating IFN-γ cytokines at both mRNA and protein levels, and the effect was comparable to prednisone. Moxibustion could be used as a therapeutic alternative treatment for pulmonary fibrosis.Keywords: moxibustion; pulmonary fibrosis; TGF-β1; IFN-γ; rat

Inundation resilience analysis of metro-network from a complex system perspective using the grid hydrodynamic model and FBWM approach : a case study of Wuhan

The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional response solutions merged multiple sources of data and knowledge to support decision-making. An obvious drawback is that original data sources for evaluations are often stationary, inaccurate, and subjective, owing to the complexity and uncertainty of the metro station’s actual physical environment. Meanwhile, the flood propagation path inside the whole metro station network was prone to be neglected. This paper presents a comprehensive approach to analyzing the resilience of metro networks to solve these problems. Firstly, we designed a simplified weighted and directed metro network module containing six characteristics by a topological approach while considering the slope direction between sites. Subsequently, to estimate the devastating effects and details of the flood hazard on the metro system, a 100-year rainfall–flood scenario simulation was conducted using high-precision DEM and a grid hydrodynamic model to identify the initially above-ground inundated stations (nodes). We developed a dynamic node breakdown algorithm to calculate the inundation sequence of the nodes in the weighted and directed network of the metro. Finally, we analyzed the resilience of the metro network in terms of toughness strength and organization recovery capacity, respectively. The fuzzy best–worst method (FBWM) was developed to obtain the weight of each assessment metric and determine the toughness strength of each node and the entire network. The results were as follows. (1) A simplified three-dimensional metro network based on a complex system perspective was established through a topological approach to explore the resilience of urban subways. (2) A grid hydrodynamic model was developed to accurately and efficiently identify the initially flooded nodes, and a dynamic breakdown algorithm realistically performed the flooding process of the subway network. (3) The node toughness strength was obtained automatically by a nonlinear FBWM method under the constraint of the minimum error to sustain the resilience assessment of the metro network. The research has considerable implications for managing underground flooding and enhancing the resilience of the metro network

Learning ‘‘graph-mer’’ Motifs that Predict Gene Expression Trajectories in Development

A key problem in understanding transcriptional regulatory networks is deciphering what cis regulatory logic is encoded in gene promoter sequences and how this sequence information maps to expression. A typical computational approach to this problem involves clustering genes by their expression profiles and then searching for overrepresented motifs in the promoter sequences of genes in a cluster. However, genes with similar expression profiles may be controlled by distinct regulatory programs. Moreover, if many gene expression profiles in a data set are highly correlated, as in the case of whole organism developmental time series, it may be difficult to resolve fine-grained clusters in the first place. We present a predictive framework for modeling the natural flow of information, from promoter sequence to expression, to learn cis regulatory motifs and characterize gene expression patterns in developmental time courses. We introduce a cluster-free algorithm based on a graph-regularized version of partial least squares (PLS) regression to learn sequence patterns—represented by graphs of k-mers, or “graph-mers”—that predict gene expression trajectories. Applying the approach to wildtype germline development in Caenorhabditis elegans, we found that the first and second latent PLS factors mapped to expression profiles for oocyte and sperm genes, respectively. We extracted both known and novel motifs from the graph-mers associated to these germline-specific patterns, including novel CG-rich motifs specific to oocyte genes. We found evidence supporting the functional relevance of these putative regulatory elements through analysis of positional bias, motif conservation and in situ gene expression. This study demonstrates that our regression model can learn biologically meaningful latent structure and identify potentially functional motifs from subtle developmental time course expression data

DDNet: Dual-path Decoder Network for Occlusion Relationship Reasoning

Occlusion relationship reasoning based on convolution neural networks consists of two subtasks: occlusion boundary extraction and occlusion orientation inference. Due to the essential differences between the two subtasks in the feature expression at the higher and lower stages, it is challenging to carry on them simultaneously in one network. To address this issue, we propose a novel Dual-path Decoder Network, which uniformly extracts occlusion information at higher stages and separates into two paths to recover boundary and occlusion orientation respectively in lower stages. Besides, considering the restriction of occlusion orientation presentation to occlusion orientation learning, we design a new orthogonal representation for occlusion orientation and proposed the Orthogonal Orientation Regression loss which can get rid of the unfitness between occlusion representation and learning and further prompt the occlusion orientation learning. Finally, we apply a multi-scale loss together with our proposed orientation regression loss to guide the boundary and orientation path learning respectively. Experiments demonstrate that our proposed method achieves state-of-the-art results on PIOD and BSDS ownership datasets