Preoperative systemic inflammation predicts postoperative infectious complications in patients undergoing curative resection for colorectal cancer

The presence of systemic inflammation before surgery, as evidenced by the glasgow prognostic score (mGPS), predicts poor long-term survival in colorectal cancer. The aim was to examine the relationship between the preoperative mGPS and the development of postoperative complications in patients undergoing potentially curative resection for colorectal cancer. Patients (n=455) who underwent potentially curative resections between 2003 and 2007 were assessed consecutively, and details were recorded in a database. The majority of patients presented for elective surgery (85%) were over the age of 65 years (70%), were male (58%), were deprived (53%), and had TNM stage I/II disease (61%), had preoperative haemoglobin (56%), white cell count (87%) and mGPS 0 (58%) in the normal range. After surgery, 86 (19%) patients developed a postoperative complication; 70 (81%) of which were infectious complications. On multivariate analysis, peritoneal soiling (P<0.01), elevated preoperative white cell count (P<0.05) and mGPS (P<0.01) were independently associated with increased risk of developing a postoperative infection. In elective patients, only the mGPS (OR=1.75, 95% CI=1.17-2.63, P=0.007) was significantly associated with increased risk of developing a postoperative infection. Preoperative elevated mGPS predicts increased postoperative infectious complications in patients undergoing potentially curative resection for colorectal cancer

Comparison of figure-of-8 and circular coils for threshold tracking transcranial magnetic stimulation measurements

OBJECTIVES: The transcranial magnetic stimulation (TMS) technique of threshold-tracking short-interval intracortical inhibition (T-SICI) has been proposed as a diagnostic tool for amyotrophic lateral sclerosis (ALS). Most of these studies have used a circular coil, whereas a figure-of-8 coil is usually recommended for paired-pulse TMS measurements. The aim of this study was to compare figure-of-8 and circular coils for T-SICI in the upper limb, with special attention to reproducibility, and the pain or discomfort experienced by the subjects. METHODS: Twenty healthy subjects (aged: 45.5 ± 6.7, mean ± SD, 9 females, 11 males) underwent two examinations with each coil, in morning and afternoon sessions on the same day, with T-SICI measured at interstimulus intervals (ISIs) from 1-7 ms. After each examination the subjects rated degree of pain/discomfort from 0 to 10 using a numerical rating scale (NRS). RESULTS: Mean T-SICI was higher for the figure-of-8 than for the circular coil at ISI of 2 ms (p < 0.05) but did not differ at other ISIs. Intra-subject variability did not differ between coils, but mean inhibition from 1-3.5 ms was less variable between subjects with the figure-of-8 coil (SD 7.2% vs. 11.2% RMT, p < 0.05), and no such recordings were without inhibition (vs. 6 with the circular coil). The subjects experienced less pain/discomfort with the figure-of-8 coil (mean NRS: 1.9 ± 1.28 vs 2.8 ± 1.60, p < 0.005). DISCUSSION: The figure-of-8 coil may have better applicability in patients, due to the lower incidence of lack of inhibition in healthy subjects, and the lower experience of pain or discomfort

AI Researchers, Video Games Are Your Friends!

If you are an artificial intelligence researcher, you should look to video games as ideal testbeds for the work you do. If you are a video game developer, you should look to AI for the technology that makes completely new types of games possible. This chapter lays out the case for both of these propositions. It asks the question "what can video games do for AI", and discusses how in particular general video game playing is the ideal testbed for artificial general intelligence research. It then asks the question "what can AI do for video games", and lays out a vision for what video games might look like if we had significantly more advanced AI at our disposal. The chapter is based on my keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad audience.Comment: in Studies in Computational Intelligence Studies in Computational Intelligence, Volume 669 2017. Springe

High-throughput, quantitative analyses of genetic interactions in E. coli.

Large-scale genetic interaction studies provide the basis for defining gene function and pathway architecture. Recent advances in the ability to generate double mutants en masse in Saccharomyces cerevisiae have dramatically accelerated the acquisition of genetic interaction information and the biological inferences that follow. Here we describe a method based on F factor-driven conjugation, which allows for high-throughput generation of double mutants in Escherichia coli. This method, termed genetic interaction analysis technology for E. coli (GIANT-coli), permits us to systematically generate and array double-mutant cells on solid media in high-density arrays. We show that colony size provides a robust and quantitative output of cellular fitness and that GIANT-coli can recapitulate known synthetic interactions and identify previously unidentified negative (synthetic sickness or lethality) and positive (suppressive or epistatic) relationships. Finally, we describe a complementary strategy for genome-wide suppressor-mutant identification. Together, these methods permit rapid, large-scale genetic interaction studies in E. coli

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

Background: Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies. Results: In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT. Conclusion: Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html webcite

Manuscript Architect: a Web application for scientific writing in virtual interdisciplinary groups

BACKGROUND: Although scientific writing plays a central role in the communication of clinical research findings and consumes a significant amount of time from clinical researchers, few Web applications have been designed to systematically improve the writing process. This application had as its main objective the separation of the multiple tasks associated with scientific writing into smaller components. It was also aimed at providing a mechanism where sections of the manuscript (text blocks) could be assigned to different specialists. Manuscript Architect was built using Java language in conjunction with the classic lifecycle development method. The interface was designed for simplicity and economy of movements. Manuscripts are divided into multiple text blocks that can be assigned to different co-authors by the first author. Each text block contains notes to guide co-authors regarding the central focus of each text block, previous examples, and an additional field for translation when the initial text is written in a language different from the one used by the target journal. Usability was evaluated using formal usability tests and field observations. RESULTS: The application presented excellent usability and integration with the regular writing habits of experienced researchers. Workshops were developed to train novice researchers, presenting an accelerated learning curve. The application has been used in over 20 different scientific articles and grant proposals. CONCLUSION: The current version of Manuscript Architect has proven to be very useful in the writing of multiple scientific texts, suggesting that virtual writing by interdisciplinary groups is an effective manner of scientific writing when interdisciplinary work is required

A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01

Affinity and stability of peptides bound by major histocompatibility complex (MHC) class I molecules are important factors in presentation of peptides to cytotoxic T lymphocytes (CTLs). In silico prediction methods of peptide-MHC binding followed by experimental analysis of peptide-MHC interactions constitute an attractive protocol to select target peptides from the vast pool of viral proteome peptides. We have earlier reported the peptide binding motif of the porcine MHC-I molecules SLA-1*04:01 and SLA-2*04:01, identified by an ELISA affinity-based positional scanning combinatorial peptide library (PSCPL) approach. Here, we report the peptide binding motif of SLA-3*04:01 and combine two prediction methods and analysis of both peptide binding affinity and stability of peptide-MHC complexes to improve rational peptide selection. Using a peptide prediction strategy combining PSCPL binding matrices and in silico prediction algorithms (NetMHCpan), peptide ligands from a repository of 8900 peptides were predicted for binding to SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01 and validated by affinity and stability assays. From the pool of predicted peptides for SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01, a total of 71, 28, and 38 % were binders with affinities below 500 nM, respectively. Comparison of peptide-SLA binding affinity and complex stability showed that peptides of high affinity generally, but not always, produce complexes of high stability. In conclusion, we demonstrate how state-of-the-art prediction and in vitro immunology tools in combination can be used for accurate selection of peptides for MHC class I binding, hence providing an expansion of the field of peptide-MHC analysis also to include pigs as a livestock experimental model.Fil: Pedersen, Lasse Eggers. Technical University of Denmark; DinamarcaFil: Rasmussen, Michael. Universidad de Copenhagen; DinamarcaFil: Harndahl, Mikkel. Universidad de Copenhagen; DinamarcaFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas (subsede Chascomús) | Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas (subsede Chascomús); ArgentinaFil: Buus, Søren. Universidad de Copenhagen; DinamarcaFil: Jungersen, Gregers. Technical University of Denmark; Dinamarc