Characterization and mechanism study of aqueous cationic polymerization of p-methylstyrene.

Aqueous cationic polymerization has attracted considerable interest as a novel polymerization technique, because it conforms to the "green chemistry" trend and challenges the concept of traditional cationic polymerization. In this paper, a CumOH/B(C6F5)3/Et2O system was used to initiate the aqueous polymerization of p-methylstyrene through suspension and emulsion methods. Several types of surfactants were used, including the cationic surfactant CTAB, non-ionic surfactant NP-40, and anionic surfactant SDBS, and the influences of initiator concentration and temperature on polymerization were investigated. Consistent with previous literature, initiator activity was positively correlated with temperature unlike in traditional cationic polymerization. Gaussian 09W simulation software was used to calculate and optimize changes in the bond lengths and angles of B(C6F5)3 after ether was added to the system. The addition of ether increased the polarity of B(C6F5)3, rendering it soluble in water. 1H-NMR was used in identifying the main chain and terminal structures of the polymer, and the mechanism of p-methylstyrene aqueous phase cationic polymerization was proposed.

NEDD9 promotes invasion and migration of colorectal cancer cell line HCT116 via JNK/EMT.

Neural precursor cell-expressed, developmentally-downregulated 9 (NEDD9) is a multi-domain skeleton protein that serves an important role in the cell signaling process via modulating invasion, metastasis, proliferation and apoptosis of tumor cells. The present study identified that the expression levels of NEDD9 in colorectal cancer were elevated. Therefore, the effect of downregulating the expression of NEDD9 in terms of invasion and migration of colorectal cancer cells was investigated and the role of the JNK pathway in these processes was also investigated. The data revealed that downregulation of NEDD9 and JNK inhibitors suppressed invasion and migration, decreased expression levels of phosphorylated JNK, increased the expression levels of E-cadherin and decreased the expression levels of vimentin. In summary, NEDD9 promotes invasion and migration of colorectal cancer cells via the JNK pathway.

Spiroorthoester group-containing pregnane glycosides from the root barks of Periploca chrysantha and their inhibitory activities against the proliferation of B and T lymphocytes.

Phytochemical investigation of the root barks of Periploca chrysantha D. S. Yao, X. C. Chen et J. W. Ren (Asclepiadaceae) led to the elucidation of four new spiroorthoester group-containing pregnane glycosides (1-4), named periplosides W-Y and 3-O-formyl-periploside F. Their structures were elucidated on the basis of extensive spectroscopic analysis. The four new pregnane glycosides (1-4) were found to exhibit significantly inhibitory activities against the proliferation of B and T lymphocytes and favorable selective index comparable to those of cyclosporin A.

[Tissue culture of callus and establishment of regeneration system of Tussilago farfara petiole].

Young petiole of Tussilago farfara was used as the material to investigate the plant growth regulators which could influence in vitro culture and plant regeneration and to establish rapid propagation technique. The ideal sterilization method was that young petiole of T. farfara was sterilized with 75% ethanol for 30 s, and then transferred to saturated bleaching power supernatant for 15 min. The suitable medium for callus induction was MS+6-BA 3.0 mg•L⁻¹+2,4-D 2.0 mg•L⁻¹ with 96.2% induction rate. The seedlings had better differentiation with 91% differentiation rate and 8.26 buds on the medium containing MS+ZT 2.0 mg•L⁻¹+NAA 0.3 mg•L⁻¹. The preferred enrichment medium of adventitious bud was MS+KT 1.0 mg•L⁻¹+IBA 0.3 mg•L⁻¹ with 11.81 enrichment times and 4.9 cm seedling height. The rooting medium included 1/2MS+IBA 0.2 mg•L⁻¹ with the average number of rooting was 5.86 and the rooting rate was above 95.22%. The container seedlings can grow well and the survival rate was more than 90% when they were transplanted on the medium added with river sand and organic fertilizer with the ratio of 3∶1. The field experiments indicated that significant differences in increment and yield of pollen grains among the tissue-culture, cultivation and wild type of T. farfara under the same cultivation conditions. The cultivated plants were relatively high on the increment and yield of pollen grains. The active ingredient content of the tissue culture and the wild materials was basically the same.

Machine learning methods to predict child posttraumatic stress: a proof of concept study.

BACKGROUND: The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD. METHODS: ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. RESULTS: Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. CONCLUSIONS: In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.

Breast Cancer Prognostics Using Multi-Omics Data.

Breast cancer affects one in eight women in America and is a leading cause of death from cancer worldwide. In the current study, four types of Omics data including copy number variation, gene expression, proteome and phosphoproteome were collected from seventy-seven breast cancer patients. Individual types of Omics data were used to separately construct predictive models to predict ten-year survival, an important clinical hallmark. The predictive models constructed with proteome data achieved decent predictivity (mean AUC = 0.725) and outperforms the models constructed with other types of Omics data. This indicates that high quality, large scale protein data is more effective for survival prediction compared to other types of omics data. Further, we experimented with ten different data fusion techniques (generic and Multi-kernel learning based) to test whether combining multi-Omics data can result in improved predictive performance. None of the data fusion techniques tested in the current study outperforms the predictive models built with the proteome data.

A Complex Systems Approach to Causal Discovery in Psychiatry.

Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach--the Complex Systems-Causal Network (CS-CN) method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry.