Dissociation Between Visual Attention and Visual Mental Imagery

Visual mental imagery (which involves generating and transforming visual mental representations, i.e., seeing with the mind's eye) and visual attention appear to be distinct processes. However, some researchers have claimed that imagery effects can be explained by appeal to attention (and thus, that imagery is nothing more than a form of attention). In this study, we used a size manipulation to demonstrate that imagery and attention are distinct processes. We reasoned that if participants are asked to perform each function (imagery and attention) using stimuli of two different sizes (large and small), and that stimulus size affects the two functions differently, then we could conclude that imagery and attention are distinct cognitive processes. Our analyses showed that participants performed the imagery task with greater facility at a large size, whereas attention was performed more easily using smaller stimuli. This finding demonstrates that imagery and attention are distinct cognitive processes.Psycholog

Fast and flexible simulation and parameter estimation for synthetic biology using bioscrape

In systems and synthetic biology, it is common to build chemical reaction network (CRN) models of biochemical circuits and networks. Although automation and other high-throughput techniques have led to an abundance of data enabling data-driven quantitative modeling and parameter estimation, the intense amount of simulation needed for these methods still frequently results in a computational bottleneck. Here we present bioscrape (Bio-circuit Stochastic Single-cell Reaction Analysis and Parameter Estimation) - a Python package for fast and flexible modeling and simulation of highly customizable chemical reaction networks. Specifically, bioscrape supports deterministic and stochastic simulations, which can incorporate delay, cell growth, and cell division. All functionalities - reaction models, simulation algorithms, cell growth models, and partioning models - are implemented as interfaces in an easily extensible and modular object-oriented framework. Models can be constructed via Systems Biology Markup Language (SBML), a simple internal XML language, or specified programmatically via a Python API. Simulation run times obtained with the package are comparable to those obtained using C code - this is particularly advantageous for computationally expensive applications such as Bayesian inference or simulation of cell lineages. We first show the package's simulation capabilities on a variety of example simulations of stochastic gene expression. We then further demonstrate the package by using it to do parameter inference on a model of integrase enzyme-mediated DNA recombination dynamics with experimental data. The bioscrape package is publicly available online (https://github.com/ananswam/bioscrape) along with more detailed documentation and examples

Evaluation of genomic island predictors using a comparative genomics approach

<p>Abstract</p> <p>Background</p> <p>Genomic islands (GIs) are clusters of genes in prokaryotic genomes of probable horizontal origin. GIs are disproportionately associated with microbial adaptations of medical or environmental interest. Recently, multiple programs for automated detection of GIs have been developed that utilize sequence composition characteristics, such as G+C ratio and dinucleotide bias. To robustly evaluate the accuracy of such methods, we propose that a dataset of GIs be constructed using criteria that are independent of sequence composition-based analysis approaches.</p> <p>Results</p> <p>We developed a comparative genomics approach (IslandPick) that identifies both very probable islands and non-island regions. The approach involves 1) flexible, automated selection of comparative genomes for each query genome, using a distance function that picks appropriate genomes for identification of GIs, 2) identification of regions unique to the query genome, compared with the chosen genomes (positive dataset) and 3) identification of regions conserved across all genomes (negative dataset). Using our constructed datasets, we investigated the accuracy of several sequence composition-based GI prediction tools.</p> <p>Conclusion</p> <p>Our results indicate that AlienHunter has the highest recall, but the lowest measured precision, while SIGI-HMM is the most precise method. SIGI-HMM and IslandPath/DIMOB have comparable overall highest accuracy. Our comparative genomics approach, IslandPick, was the most accurate, compared with a curated list of GIs, indicating that we have constructed suitable datasets. This represents the first evaluation, using diverse and, independent datasets that were not artificially constructed, of the accuracy of several sequence composition-based GI predictors. The caveats associated with this analysis and proposals for optimal island prediction are discussed.</p

Effects of nerve-sparing procedures on surgical margins after robot-assisted radical prostatectomy

BACKGROUND: Nerve-sparing (NS) techniques could potentially increase positive surgical margins (PSM) after robot-assisted radical prostatectomy (RARP). Nevertheless, the available studies have revealed ambiguous results among distinct groups. This study purposed to clarify the details of NS techniques to accurately estimate their influence on margin status. METHODS: We studied RARPs performed by one surgeon from 2010 to 2018. Surgical margins were evaluated by the laterality and levels of NS techniques in site-specific prostate lobes. The multivariable analysis evaluated the effects of nerve-sparing procedures, combined with other covariate factors, on margin status. RESULTS: Overall, four hundred nineteen RARPs involving 838 prostate lobes were analyzed. Notably, 181 patients (43.4%) had pT2-stage, and 236 (56.6%) had pT3-stage cancer. The PSM rates for patients who underwent unilateral, bilateral, and non NS procedures were 30.3%, 28.8%, and 50%, respectively (p = 0.233) or in stratification by pT2 (p = 0.584) and pT3 (p = 0.116) stage. The posterolateral PSM rates among site-specific prostate lobes were 10.9%, 22.4%, and 18.9% for complete, partial, and non NS techniques, respectively (p = 0.001). The partial NS group revealed a significant increase in PSM rate compared with the complete NS (OR 2.187, 95% CI 1.19-4.03) and non NS (OR 2.237, 95% CI 1.01-4.93) groups in site-specific prostate lobes. CONCLUSION: Partial NS procedures have a potential risk of increasing the PSM rate than complete and non NS procedures do. Therefore, correct case selection is required before performing partial NS techniques

The Distribution of Pairwise Genetic Distances: A Tool for Investigating Disease Transmission

Whole-genome sequencing of pathogens has recently been used to investigate disease outbreaks and is likely to play a growing role in real-time epidemiological studies. Methods to analyze high-resolution genomic data in this context are still lacking, and inferring transmission dynamics from such data typically requires many assumptions. While recent studies have proposed methods to infer who infected whom based on genetic distance between isolates from different individuals, the link between epidemiological relationship and genetic distance is still not well understood. In this study, we investigated the distribution of pairwise genetic distances between samples taken from infected hosts during an outbreak. We proposed an analytically tractable approximation to this distribution, which provides a framework to evaluate the likelihood of particular transmission routes. Our method accounts for the transmission of a genetically diverse inoculum, a possibility overlooked in most analyses. We demonstrated that our approximation can provide a robust estimation of the posterior probability of transmission routes in an outbreak and may be used to rule out transmission events at a particular probability threshold. We applied our method to data collected during an outbreak of methicillin-resistant Staphylococcus aureus, ruling out several potential transmission links. Our study sheds light on the accumulation of mutations in a pathogen during an epidemic and provides tools to investigate transmission dynamics, avoiding the intensive computation necessary in many existing methods

Text mining processing pipeline for semi structured data D3.3

Unstructured and semi-structured cohort data contain relevant information about the health condition of a patient, e.g., free text describing disease diagnoses, drugs, medication reasons, which are often not available in structured formats. One of the challenges posed by medical free texts is that there can be several ways of mentioning a concept. Therefore, encoding free text into unambiguous descriptors allows us to leverage the value of the cohort data, in particular, by facilitating its findability and interoperability across cohorts in the project.Named entity recognition and normalization enable the automatic conversion of free text into standard medical concepts. Given the volume of available data shared in the CINECA project, the WP3 text mining working group has developed named entity normalization techniques to obtain standard concepts from unstructured and semi-structured fields available in the cohorts. In this deliverable, we present the methodology used to develop the different text mining tools created by the dedicated SFU, UMCG, EBI, and HES-SO/SIB groups for specific CINECA cohorts

High-throughput enzyme engineering for commercial-scale production of natural products

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High-throughput enzyme engineering for commercial-scale production of natural products

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