The XBabelPhish MAGE-ML and XML Translator

<p>Abstract</p> <p>Background</p> <p>MAGE-ML has been promoted as a standard format for describing microarray experiments and the data they produce. Two characteristics of the MAGE-ML format compromise its use as a universal standard: First, MAGE-ML files are exceptionally large – too large to be easily read by most people, and often too large to be read by most software programs. Second, the MAGE-ML standard permits many ways of representing the same information. As a result, different producers of MAGE-ML create different documents describing the same experiment and its data. Recognizing all the variants is an unwieldy software engineering task, resulting in software packages that can read and process MAGE-ML from some, but not all producers. This Tower of MAGE-ML Babel bars the unencumbered exchange of microarray experiment descriptions couched in MAGE-ML.</p> <p>Results</p> <p>We have developed XBabelPhish – an XQuery-based technology for translating one MAGE-ML variant into another. XBabelPhish's use is not restricted to translating MAGE-ML documents. It can transform XML files independent of their DTD, XML schema, or semantic content. Moreover, it is designed to work on very large (> 200 Mb.) files, which are common in the world of MAGE-ML.</p> <p>Conclusion</p> <p>XBabelPhish provides a way to inter-translate MAGE-ML variants for improved interchange of microarray experiment information. More generally, it can be used to transform most XML files, including very large ones that exceed the capacity of most XML tools.</p

Comparison of the diagnostic accuracy of three current guidelines for the evaluation of asymptomatic pancreatic cystic neoplasms.

Asymptomatic pancreatic cysts are a common clinical problem but only a minority of these cases progress to cancer. Our aim was to compare the accuracy to detect malignancy of the 2015 American Gastroenterological Association (AGA), the 2012 International Consensus/Fukuoka (Fukuoka guidelines [FG]), and the 2010 American College of Radiology (ACR) guidelines.We conducted a retrospective study at 3 referral centers for all patients who underwent resection for an asymptomatic pancreatic cyst between January 2008 and December 2013. We compared the accuracy of 3 guidelines in predicting high-grade dysplasia (HGD) or cancer in resected cysts. We performed logistic regression analyses to examine the association between cyst features and risk of HGD or cancer.A total of 269 patients met inclusion criteria. A total of 228 (84.8%) had a benign diagnosis or low-grade dysplasia on surgical pathology, and 41 patients (15.2%) had either HGD (n = 14) or invasive cancer (n = 27). Of the 41 patients with HGD or cancer on resection, only 3 patients would have met the AGA guideline\u27s indications for resection based on the preoperative cyst characteristics, whereas 30/41 patients would have met the FG criteria for resection and 22/41 patients met the ACR criteria. The sensitivity, specificity, positive predictive value, negative predictive value of HGD, and/or cancer of the AGA guidelines were 7.3%, 88.2%, 10%, and 84.1%, compared to 73.2%, 45.6%, 19.5%, and 90.4% for the FG and 53.7%, 61%, 19.8%, and 88% for the ACR guidelines. In multivariable analysis, cyst size \u3e3 cm, compared to ≤3 cm, (odds ratio [OR] = 2.08, 95% confidence interval [CI] = 1.11, 4.2) and each year increase in age (OR = 1.07, 95% CI = 1.03, 1.11) were positively associated with risk of HGD or cancer on resection.In patients with asymptomatic branch duct-intraductal papillary mucinous neoplasms or mucinous cystic neoplasms who underwent resection, the prevalence rate of HGD or cancer was 15.2%. Using the 2015 AGA criteria for resection would have missed 92.6% of patients with HGD or cancer. The more inclusive FG and ACR had a higher sensitivity for HGD or cancer but lower specificity. Given the current deficiencies of these guidelines, it will be important to determine the acceptable rate of false-positives in order to prevent a single true-positive

The Stanford Microarray Database: implementation of new analysis tools and open source release of software

The Stanford Microarray Database (SMD; ) is a research tool and archive that allows hundreds of researchers worldwide to store, annotate, analyze and share data generated by microarray technology. SMD supports most major microarray platforms, and is MIAME-supportive and can export or import MAGE-ML. The primary mission of SMD is to be a research tool that supports researchers from the point of data generation to data publication and dissemination, but it also provides unrestricted access to analysis tools and public data from 300 publications. In addition to supporting ongoing research, SMD makes its source code fully and freely available to others under an Open Source license, enabling other groups to create a local installation of SMD. In this article, we describe several data analysis tools implemented in SMD and we discuss features of our software release

Molecular characterization of a marine turtle tumor epizootic, profiling external, internal and postsurgical regrowth tumors

Sea turtle populations are under threat from an epizootic tumor disease (animal epidemic) known as fibropapillomatosis. Fibropapillomatosis continues to spread geographically, with prevalence of the disease also growing at many longer-affected sites globally. However, we do not yet understand the precise environmental, mutational and viral events driving fibropapillomatosis tumor formation and progression. Here we perform transcriptomic and immunohistochemical profiling of five fibropapillomatosis tumor types: external new, established and postsurgical regrowth tumors, and internal lung and kidney tumors. We reveal that internal tumors are molecularly distinct from the more common external tumors. However, they have a small number of conserved potentially therapeutically targetable molecular vulnerabilities in common, such as the MAPK, Wnt, TGFβ and TNF oncogenic signaling pathways. These conserved oncogenic drivers recapitulate remarkably well the core pan-cancer drivers responsible for human cancers. Fibropapillomatosis has been considered benign, but metastatic-related transcriptional signatures are strongly activated in kidney and established external tumors. Tumors in turtles with poor outcomes (died/euthanized) have genes associated with apoptosis and immune function suppressed, with these genes providing putative predictive biomarkers. Together, these results offer an improved understanding of fibropapillomatosis tumorigenesis and provide insights into the origins, inter-tumor relationships, and therapeutic treatment for this wildlife epizootic

Multicentre validation of the bedside paediatric early warning system score: a severity of illness score to detect evolving critical illness in hospitalised children

Abstract Introduction The timely provision of critical care to hospitalised patients at risk for cardiopulmonary arrest is contingent upon identification and referral by frontline providers. Current approaches require improvement. In a single-centre study, we developed the Bedside Paediatric Early Warning System (Bedside PEWS) score to identify patients at risk. The objective of this study was to validate the Bedside PEWS score in a large patient population at multiple hospitals. Methods We performed an international, multicentre, case-control study of children admitted to hospital inpatient units with no limitations on care. Case patients had experienced a clinical deterioration event involving either an immediate call to a resuscitation team or urgent admission to a paediatric intensive care unit. Control patients had no events. The scores ranged from 0 to 26 and were assessed in the 24 hours prior to the clinical deterioration event. Score performance was assessed using the area under the receiver operating characteristic (AUCROC) curve by comparison with the retrospective rating of nurses and the temporal progression of scores in case patients. Results A total of 2,074 patients were evaluated at 4 participating hospitals. The median (interquartile range) maximum Bedside PEWS scores for the 12 hours ending 1 hour before the clinical deterioration event were 8 (5 to 12) in case patients and 2 (1 to 4) in control patients (P < 0.0001). The AUCROC curve (95% confidence interval) was 0.87 (0.85 to 0.89). In case patients, mean scores were 5.3 at 20 to 24 hours and 8.4 at 0 to 4 hours before the event (P < 0.0001). The AUCROC curve (95% CI) of the retrospective nurse ratings was 0.83 (0.81 to 0.86). This was significantly lower than that of the Bedside PEWS score (P < 0.0001). Conclusions The Bedside PEWS score identified children at risk for cardiopulmonary arrest. Scores were elevated and continued to increase in the 24 hours before the clinical deterioration event. Prospective clinical evaluation is needed to determine whether this score will improve the quality of care and patient outcomes

The Stanford Microarray Database accommodates additional microarray platforms and data formats

The Stanford Microarray Database (SMD) (http://smd.stanford.edu) is a research tool for hundreds of Stanford researchers and their collaborators. In addition, SMD functions as a resource for the entire biological research community by providing unrestricted access to microarray data published by SMD users and by disseminating its source code. In addition to storing GenePix (Axon Instruments) and ScanAlyze output from spotted microarrays, SMD has recently added the ability to store, retrieve, display and analyze the complete raw data produced by several additional microarray platforms and image analysis software packages, so that we can also now accept data from Affymetrix GeneChips (MAS5/GCOS or dChip), Agilent Catalog or Custom arrays (using Agilent's Feature Extraction software) or data created by SpotReader (Niles Scientific). We have implemented software that allows us to accept MAGE-ML documents from array manufacturers and to submit MIAME-compliant data in MAGE-ML format directly to ArrayExpress and GEO, greatly increasing the ease with which data from SMD can be published adhering to accepted standards and also increasing the accessibility of published microarray data to the general public. We have introduced a new tool to facilitate data sharing among our users, so that datasets can be shared during, before or after the completion of data analysis. The latest version of the source code for the complete database package was released in November 2004 (http://smd.stanford.edu/download/), allowing researchers around the world to deploy their own installations of SMD

Owning an overweight or underweight body: distinguishing the physical, experienced and virtual body

Our bodies are the most intimately familiar objects we encounter in our perceptual environment. Virtual reality provides a unique method to allow us to experience having a very different body from our own, thereby providing a valuable method to explore the plasticity of body representation. In this paper, we show that women can experience ownership over a whole virtual body that is considerably smaller or larger than their physical body. In order to gain a better understanding of the mechanisms underlying body ownership, we use an embodiment questionnaire, and introduce two new behavioral response measures: an affordance estimation task (indirect measure of body size) and a body size estimation task (direct measure of body size). Interestingly, after viewing the virtual body from first person perspective, both the affordance and the body size estimation tasks indicate a change in the perception of the size of the participant’s experienced body. The change is biased by the size of the virtual body (overweight or underweight). Another novel aspect of our study is that we distinguish between the physical, experienced and virtual bodies, by asking participants to provide affordance and body size estimations for each of the three bodies separately. This methodological point is important for virtual reality experiments investigating body ownership of a virtual body, because it offers a better understanding of which cues (e.g. visual, proprioceptive, memory, or a combination thereof) influence body perception, and whether the impact of these cues can vary between different setups

Elucidation of The Behavioral Program and Neuronal Network Encoded by Dorsal Raphe Serotonergic Neurons

Elucidating how the brain's serotonergic network mediates diverse behavioral actions over both relatively short (minutes–hours) and long period of time (days–weeks) remains a major challenge for neuroscience. Our relative ignorance is largely due to the lack of technologies with robustness, reversibility, and spatio-temporal control. Recently, we have demonstrated that our chemogenetic approach (eg, Designer Receptors Exclusively Activated by Designer Drugs (DREADDs)) provides a reliable and robust tool for controlling genetically defined neural populations. Here we show how short- and long-term activation of dorsal raphe nucleus (DRN) serotonergic neurons induces robust behavioral responses. We found that both short- and long-term activation of DRN serotonergic neurons induce antidepressant-like behavioral responses. However, only short-term activation induces anxiogenic-like behaviors. In parallel, these behavioral phenotypes were associated with a metabolic map of whole brain network activity via a recently developed non-invasive imaging technology DREAMM (DREADD Associated Metabolic Mapping). Our findings reveal a previously unappreciated brain network elicited by selective activation of DRN serotonin neurons and illuminate potential therapeutic and adverse effects of drugs targeting DRN neurons

Patient emergency health-care use before hospital admission for COVID-19 and long-term outcomes in Scotland: a national cohort study

BackgroundIt is unclear what effect the pattern of health-care use before admission to hospital with COVID-19 (index admission) has on the long-term outcomes for patients. We sought to describe mortality and emergency readmission to hospital after discharge following the index admission (index discharge), and to assess associations between these outcomes and patterns of health-care use before such admissions.MethodsWe did a national, retrospective, complete cohort study by extracting data from several national databases and linking the databases for all adult patients admitted to hospital in Scotland with COVID-19. We used latent class trajectory modelling to identify distinct clusters of patients on the basis of their emergency admissions to hospital in the 2 years before the index admission. The primary outcomes were mortality and emergency readmission up to 1 year after index admission. We used multivariable regression models to explore associations between these outcomes and patient demographics, vaccination status, level of care received in hospital, and previous emergency hospital use.FindingsBetween March 1, 2020, and Oct 25, 2021, 33 580 patients were admitted to hospital with COVID-19 in Scotland. Overall, the Kaplan-Meier estimate of mortality within 1 year of index admission was 29·6% (95% CI 29·1-30·2). The cumulative incidence of emergency hospital readmission within 30 days of index discharge was 14·4% (95% CI 14·0-14·8), with the number increasing to 35·6% (34·9-36·3) patients at 1 year. Among the 33 580 patients, we identified four distinct patterns of previous emergency hospital use: no admissions (n=18 772 [55·9%]); minimal admissions (n=12 057 [35·9%]); recently high admissions (n=1931 [5·8%]), and persistently high admissions (n=820 [2·4%]). Patients with recently or persistently high admissions were older, more multimorbid, and more likely to have hospital-acquired COVID-19 than patients with no or minimal admissions. People in the minimal, recently high, and persistently high admissions groups had an increased risk of mortality and hospital readmission compared with those in the no admissions group. Compared with the no admissions group, mortality was highest in the recently high admissions group (post-hospital mortality HR 2·70 [95% CI 2·35-2·81]; pInterpretationLong-term mortality and readmission rates for patients hospitalised with COVID-19 were high; within 1 year, one in three patients had died and a third had been readmitted as an emergency. Patterns of hospital use before index admission were strongly predictive of mortality and readmission risk, independent of age, pre-existing comorbidities, and COVID-19 vaccination status. This increasingly precise identification of individuals at high risk of poor outcomes from COVID-19 will enable targeted support.FundingChief Scientist Office Scotland, UK National Institute for Health Research, and UK Research and Innovation