Content-Based Exploration of Archival Images Using Neural Networks

We present DAIRE (Deep Archival Image Retrieval Engine), an image exploration tool based on latent representations derived from neural networks, which allows scholars to "query" using an image of interest to rapidly find related images within a web archive. This work represents one part of our broader effort to move away from text-centric analyses of web archives and scholarly tools that are direct reflections of methods for accessing the live web. This short piece describes the implementation of our system and a case study on a subset of the GeoCities web archive.This research was supported in part by the Andrew W. Mellon Foundation and the Social Sciences and Humanities Research Council of Canada

A uniform proteomics MS/MS analysis platform utilizing open XML file formats

The analysis of tandem mass (MS/MS) data to identify and quantify proteins is hampered by the heterogeneity of file formats at the raw spectral data, peptide identification, and protein identification levels. Different mass spectrometers output their raw spectral data in a variety of proprietary formats, and alternative methods that assign peptides to MS/MS spectra and infer protein identifications from those peptide assignments each write their results in different formats. Here we describe an MS/MS analysis platform, the Trans-Proteomic Pipeline, which makes use of open XML file formats for storage of data at the raw spectral data, peptide, and protein levels. This platform enables uniform analysis and exchange of MS/MS data generated from a variety of different instruments, and assigned peptides using a variety of different database search programs. We demonstrate this by applying the pipeline to data sets generated by ThermoFinnigan LCQ, ABI 4700 MALDI-TOF/TOF, and Waters Q-TOF instruments, and searched in turn using SEQUEST, Mascot, and COMET

Improving Precancerous Case Characterization via Transformer-based Ensemble Learning

The application of natural language processing (NLP) to cancer pathology reports has been focused on detecting cancer cases, largely ignoring precancerous cases. Improving the characterization of precancerous adenomas assists in developing diagnostic tests for early cancer detection and prevention, especially for colorectal cancer (CRC). Here we developed transformer-based deep neural network NLP models to perform the CRC phenotyping, with the goal of extracting precancerous lesion attributes and distinguishing cancer and precancerous cases. We achieved 0.914 macro-F1 scores for classifying patients into negative, non-advanced adenoma, advanced adenoma and CRC. We further improved the performance to 0.923 using an ensemble of classifiers for cancer status classification and lesion size named entity recognition (NER). Our results demonstrated the potential of using NLP to leverage real-world health record data to facilitate the development of diagnostic tests for early cancer prevention

Characterization and specificity of the linear epitope of the enterovirus 71 VP2 protein

10.1186/1743-422X-9-55Virology Journal95

System-based proteomic analysis of the interferon response in human liver cells

BACKGROUND: Interferons (IFNs) play a critical role in the host antiviral defense and are an essential component of current therapies against hepatitis C virus (HCV), a major cause of liver disease worldwide. To examine liver-specific responses to IFN and begin to elucidate the mechanisms of IFN inhibition of virus replication, we performed a global quantitative proteomic analysis in a human hepatoma cell line (Huh7) in the presence and absence of IFN treatment using the isotope-coded affinity tag (ICAT) method and tandem mass spectrometry (MS/MS). RESULTS: In three subcellular fractions from the Huh7 cells treated with IFN (400 IU/ml, 16 h) or mock-treated, we identified more than 1,364 proteins at a threshold that corresponds to less than 5% false-positive error rate. Among these, 54 were induced by IFN and 24 were repressed by more than two-fold, respectively. These IFN-regulated proteins represented multiple cellular functions including antiviral defense, immune response, cell metabolism, signal transduction, cell growth and cellular organization. To analyze this proteomics dataset, we utilized several systems-biology data-mining tools, including Gene Ontology via the GoMiner program and the Cytoscape bioinformatics platform. CONCLUSIONS: Integration of the quantitative proteomics with global protein interaction data using the Cytoscape platform led to the identification of several novel and liver-specific key regulatory components of the IFN response, which may be important in regulating the interplay between HCV, interferon and the host response to virus infection

Natural zeolite characterization for adsorptive coagulation flocculation (ACF) removal of ammonium in drinking water treatment process

The naturally occurring zeolite (N Z01) was characterized and used as an adsorbent for the removal of ammonium (N-NH4+) from water. The characterization results show that the NZ01 is mainly composed of clinoptilolite, quartz and plagioclase and has the cation-exchange capacity (CEC) of 64 cmol/kg. Batch adsorption results show that the best ammonia removal was at pH close to that of the natural water (= pH 7). The increase in initial ammonium nitrogen concentration from 5 to 50 ppm resulted in an increase of the adsorption capacity from 0.64 to 15.1 mg NH4+-N/g. The Jar test experiments indicate the introduction of the NZ01 enhanced the ammonium removal efficiency. All these results demonstrate that the NZ01 is potential to be used for the removal of ammonium in drinking water treatm ent process

Reducing Implicit Racial Preferences: II Intervention Effectiveness Across Time

Implicit preferences are malleable, but does that change last? We tested 9 interventions (8 real and 1 sham) to reduce implicit racial preferences over time. In 2 studies with a total of 6,321 participants, all 9 interventions immediately reduced implicit preferences. However, none were effective after a delay of several hours to several days. We also found that these interventions did not change explicit racial preferences and were not reliably moderated by motivations to respond without prejudice. Short-term malleability in implicit preferences does not necessarily lead to long-term change, raising new questions about the flexibility and stability of implicit preferences. (PsycINFO Database Recor

Getting the invite list right : a discussion of sepsis severity scoring systems in severe complicated intra-abdominal sepsis and randomized trial inclusion criteria

Background: Severe complicated intra-abdominal sepsis (SCIAS) is a worldwide challenge with increasing incidence. Open abdomen management with enhanced clearance of fluid and biomediators from the peritoneum is a potential therapy requiring prospective evaluation. Given the complexity of powering multi-center trials, it is essential to recruit an inception cohort sick enough to benefit from the intervention; otherwise, no effect of a potentially beneficial therapy may be apparent An evaluation of abilities of recognized predictive systems to recognize SCIAS patients was conducted using an existing intra-abdominal sepsis (IAS) database. Methods: All consecutive adult patients with a diffuse secondary peritonitis between 2012 and 2013 were collected from a quaternary care hospital in Finland, excluding appendicitis/cholecystitis. From this retrospectively collected database, a target population (93) of those with either ICU admission or mortality were selected. The performance metrics of the Third Consensus Definitions for Sepsis and Septic Shock based on both SOFA and quick SOFA, the World Society of Emergency Surgery Sepsis Severity Score (WSESSSS), the APACHE II score, Manheim Peritonitis Index (MPI), and the Calgary Predisposition, Infection, Response, and Organ dysfunction (CPIRO) score were all tested for their discriminant ability to identify this subgroup with SCIAS and to predict mortality. Results: Predictive systems with an area under-the-receiving-operating characteristic (AUQ curve >= 0.8 included SOFA, Sepsis-3 definitions, APACHE II, WSESSSS, and CPIRO scores with the overall best for CPIRO. The highest identification rates were SOFA score >= 2 (78.4%), followed by the WSESSSS score >= 8 (73.1%), SOFA >= 3 (752%), and APACHE II >= 14 (68.8%) identification. Combining the Sepsis-3 septic-shock definition and WSESSS >= 8 increased detection to 80%. Including CPIRO score >= 3 increased this to 82.8% (Sensitivity-SN; 83% Specificity-SP; 74%. Comparatively, SOFA >= 4 and WSESSSS >= 8 with or without septic-shock had 83.9% detection (SN; 84%, SP; 75%, 25% mortality). Conclusions: No one scoring system behaves perfectly, and all are largely dominated by organ dysfunction. Utilizing combinations of SOFA, CPIRO, and WSESSSS scores in addition to the Sepsis-3 septic shock definition appears to offer the widest "inclusion-criteria" to recognize patients with a high chance of mortality and ICU admission.Peer reviewe