The Crystal Structure of N-(2-Hydroxyethyl)taurine, HOCH2CH2NHCH2CH2SOJH

The crystals of N-(2-hydroxyethyl)taur~ne are orthorhombic; a= 9.666 (4), b = 11.681 (6), c = 12.754 (8) A; space group is Pbca with eight formula units in the unit cell. A three-dimensional X-ray crystal structure analysis has shown that the compound crystallizes as zwitterion, formula HOCH2CH2NH2•CH2CH2S03-. Dihedral aingle S- C- C- N = 175.60, and N- C- C-0 = - 59.8°. Zwitterions are connected by hydrogen bonds into a three-dimensional network

The Crystal Structure of N-(2-Hydroxyethyl)taurine, HOCH2CH2NHCH2CH2SOJH

The crystals of N-(2-hydroxyethyl)taur~ne are orthorhombic; a= 9.666 (4), b = 11.681 (6), c = 12.754 (8) A; space group is Pbca with eight formula units in the unit cell. A three-dimensional X-ray crystal structure analysis has shown that the compound crystallizes as zwitterion, formula HOCH2CH2NH2•CH2CH2S03-. Dihedral aingle S- C- C- N = 175.60, and N- C- C-0 = - 59.8°. Zwitterions are connected by hydrogen bonds into a three-dimensional network

Classification of integrable discrete Klein-Gordon models

The Lie algebraic integrability test is applied to the problem of classification of integrable Klein-Gordon type equations on quad-graphs. The list of equations passing the test is presented containing several well-known integrable models. A new integrable example is found, its higher symmetry is presented.Comment: 12 pages, submitted to Physica Script

The phosphorylated prodrug FTY720 is a histone deacetylase inhibitor that reactivates ERα expression and enhances hormonal therapy for breast cancer

Estrogen receptor-α (ERα)-negative breast cancer is clinically aggressive and does not respond to conventional hormonal therapies. Strategies that lead to re-expression of ERα could sensitize ERα-negative breast cancers to selective ER modulators. FTY720 (fingolimod, Gilenya), a sphingosine analog, is the Food and Drug Administration (FDA)-approved prodrug for treatment of multiple sclerosis that also has anticancer actions that are not yet well understood. We found that FTY720 is phosphorylated in breast cancer cells by nuclear sphingosine kinase 2 and accumulates there. Nuclear FTY720-P is a potent inhibitor of class I histone deacetylases (HDACs) that enhances histone acetylations and regulates expression of a restricted set of genes independently of its known effects on canonical signaling through sphingosine-1-phosphate receptors. High-fat diet (HFD) and obesity, which is now endemic, increase breast cancer risk and have been associated with worse prognosis. HFD accelerated the onset of tumors with more advanced lesions and increased triple-negative spontaneous breast tumors and HDAC activity in MMTV-PyMT transgenic mice. Oral administration of clinically relevant doses of FTY720 suppressed development, progression and aggressiveness of spontaneous breast tumors in these mice, reduced HDAC activity and strikingly reversed HFD-induced loss of estrogen and progesterone receptors in advanced carcinoma. In ERα-negative human and murine breast cancer cells, FTY720 reactivated expression of silenced ERα and sensitized them to tamoxifen. Moreover, treatment with FTY720 also re-expressed ERα and increased therapeutic sensitivity of ERα-negative syngeneic breast tumors to tamoxifen in vivo more potently than a known HDAC inhibitor. Our work suggests that a multipronged attack with FTY720 is a novel combination approach for effective treatment of both conventional hormonal therapy-resistant breast cancer and triple-negative breast cancer

Geometric Approach to Pontryagin's Maximum Principle

Since the second half of the 20th century, Pontryagin's Maximum Principle has been widely discussed and used as a method to solve optimal control problems in medicine, robotics, finance, engineering, astronomy. Here, we focus on the proof and on the understanding of this Principle, using as much geometric ideas and geometric tools as possible. This approach provides a better and clearer understanding of the Principle and, in particular, of the role of the abnormal extremals. These extremals are interesting because they do not depend on the cost function, but only on the control system. Moreover, they were discarded as solutions until the nineties, when examples of strict abnormal optimal curves were found. In order to give a detailed exposition of the proof, the paper is mostly self\textendash{}contained, which forces us to consider different areas in mathematics such as algebra, analysis, geometry.Comment: Final version. Minors changes have been made. 56 page

Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach

International audienceBackground: Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field

Mining clinical relationships from patient narratives

Background The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. One part of this system is the identification of relationships between clinically important entities in the text. Typical approaches to relationship extraction in this domain have used full parses, domain-specific grammars, and large knowledge bases encoding domain knowledge. In other areas of biomedical NLP, statistical machine learning (ML) approaches are now routinely applied to relationship extraction. We report on the novel application of these statistical techniques to the extraction of clinical relationships. Results We have designed and implemented an ML-based system for relation extraction, using support vector machines, and trained and tested it on a corpus of oncology narratives hand-annotated with clinically important relationships. Over a class of seven relation types, the system achieves an average F1 score of 72%, only slightly behind an indicative measure of human inter annotator agreement on the same task. We investigate the effectiveness of different features for this task, how extraction performance varies between inter- and intra-sentential relationships, and examine the amount of training data needed to learn various relationships. Conclusion We have shown that it is possible to extract important clinical relationships from text, using supervised statistical ML techniques, at levels of accuracy approaching those of human annotators. Given the importance of relation extraction as an enabling technology for text mining and given also the ready adaptability of systems based on our supervised learning approach to other clinical relationship extraction tasks, this result has significance for clinical text mining more generally, though further work to confirm our encouraging results should be carried out on a larger sample of narratives and relationship types

The first small-molecule inhibitors of members of the ribonuclease E family

The Escherichia coli endoribonuclease RNase E is central to the processing and degradation of all types of RNA and as such is a pleotropic regulator of gene expression. It is essential for growth and was one of the first examples of an endonuclease that can recognise the 5′-monophosphorylated ends of RNA thereby increasing the efficiency of many cleavages. Homologues of RNase E can be found in many bacterial families including important pathogens, but no homologues have been identified in humans or animals. RNase E represents a potential target for the development of new antibiotics to combat the growing number of bacteria that are resistant to antibiotics in use currently. Potent small molecule inhibitors that bind the active site of essential enzymes are proving to be a source of potential drug leads and tools to dissect function through chemical genetics. Here we report the use of virtual high-throughput screening to obtain small molecules predicted to bind at sites in the N-terminal catalytic half of RNase E. We show that these compounds are able to bind with specificity and inhibit catalysis of Escherichia coli and Mycobacterium tuberculosis RNase E and also inhibit the activity of RNase G, a paralogue of RNase E

A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods