A Survey of Flow Cytometry Data Analysis Methods

Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation, making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined

Aortoesophageal Fistula Due to Caustic Ingestion

PurposeCaustic agent ingestion may produce corrosive lesions that can extend beyond adjacent organs. We report three cases of aortoesophageal fistulas (AEF) after caustic ingestion that were diagnosed by autopsy.ResultsAEF is a fatal complication that should be suspected in any patient with caustic ingestion who presents with gastrointestinal bleeding. A high index of suspicion, early recognition by gastrointestinal endoscopy, computed tomography scan, and aortography are important to improve the outcome

Merging Mixture Components for Cell Population Identification in Flow Cytometry

We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated flow cytometry data distributions. Our framework allows the automated selection of the number of distinct cell subpopulations and we are able to identify cases where the algorithm fails, thus making it suitable for application in a high throughput FCM analysis pipeline. Furthermore, we demonstrate a method for summarizing complex merged cell subpopulations in a simple manner that integrates with the existing flowClust framework and enables downstream data analysis. We demonstrate the performance of our framework on simulated and real FCM data. The software is available in the flowMerge package through the Bioconductor project

Kronos: a workflow assembler for genome analytics and informatics.

BackgroundThe field of next-generation sequencing informatics has matured to a point where algorithmic advances in sequence alignment and individual feature detection methods have stabilized. Practical and robust implementation of complex analytical workflows (where such tools are structured into "best practices" for automated analysis of next-generation sequencing datasets) still requires significant programming investment and expertise.ResultsWe present Kronos, a software platform for facilitating the development and execution of modular, auditable, and distributable bioinformatics workflows. Kronos obviates the need for explicit coding of workflows by compiling a text configuration file into executable Python applications. Making analysis modules would still require programming. The framework of each workflow includes a run manager to execute the encoded workflows locally (or on a cluster or cloud), parallelize tasks, and log all runtime events. The resulting workflows are highly modular and configurable by construction, facilitating flexible and extensible meta-applications that can be modified easily through configuration file editing. The workflows are fully encoded for ease of distribution and can be instantiated on external systems, a step toward reproducible research and comparative analyses. We introduce a framework for building Kronos components that function as shareable, modular nodes in Kronos workflows.ConclusionsThe Kronos platform provides a standard framework for developers to implement custom tools, reuse existing tools, and contribute to the community at large. Kronos is shipped with both Docker and Amazon Web Services Machine Images. It is free, open source, and available through the Python Package Index and at https://github.com/jtaghiyar/kronos

Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images

Graph convolutional neural networks have shown significant potential in natural and histopathology images. However, their use has only been studied in a single magnification or multi-magnification with late fusion. In order to leverage the multi-magnification information and early fusion with graph convolutional networks, we handle different embedding spaces at each magnification by introducing the Multi-Scale Relational Graph Convolutional Network (MS-RGCN) as a multiple instance learning method. We model histopathology image patches and their relation with neighboring patches and patches at other scales (i.e., magnifications) as a graph. To pass the information between different magnification embedding spaces, we define separate message-passing neural networks based on the node and edge type. We experiment on prostate cancer histopathology images to predict the grade groups based on the extracted features from patches. We also compare our MS-RGCN with multiple state-of-the-art methods with evaluations on several source and held-out datasets. Our method outperforms the state-of-the-art on all of the datasets and image types consisting of tissue microarrays, whole-mount slide regions, and whole-slide images. Through an ablation study, we test and show the value of the pertinent design features of the MS-RGCN

Towards Development of a 3-State Self-Paced Brain-Computer Interface

Most existing brain-computer interfaces (BCIs) detect specific mental activity in a so-called synchronous paradigm. Unlike synchronous systems which are operational at specific system-defined periods, self-paced (asynchronous) interfaces have the advantage of being operational at all times. The low-frequency asynchronous switch design (LF-ASD) is a 2-state self-paced BCI that detects the presence of a specific finger movement in the ongoing EEG. Recent evaluations of the 2-state LF-ASD show an average true positive rate of 41% at the fixed false positive rate of 1%. This paper proposes two designs for a 3-state self-paced BCI that is capable of handling idle brain state. The two proposed designs aim at detecting right- and left-hand extensions from the ongoing EEG. They are formed of two consecutive detectors. The first detects the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement). It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1%) in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1%) in the context of a 2-state self-paced BCI

Fast Mode Assignment for Quality Scalable Extension of the High Efficiency Video Coding (HEVC) Standard: A Bayesian Approach

ABSTRACT The new compression standard, known as the High Efficiency Video Coding (HEVC), aims at significantly improving the compression efficiency compared to previous standards. There has been significant interest in developing a scalable version of this standard. As expected, the HEVC scalable video version, which is called SHVC, increases the complexity of the codec compared to the non-scalable counterpart. In this paper, we propose an adaptive fast mode assigning method based on a Bayesian classifier that reduces SHVC's coding complexity by up to 68.55%, while maintaining the overall quality and bit-rates

Studies of salivary pepsin in patients with gastro‐oesophageal reflux disease

Background Gastro‐oesophageal reflux disease (GERD) is difficult to diagnose without invasive testing. Peptest (RD Biomed, Hull, UK) is a recently marketed diagnostic tool which aims to quantify salivary pepsin as a marker of reflux, providing a rapid alternative to invasive procedures. Aim To evaluate optimal timing for sampling, and to evaluate the accuracy of Peptest against an independent measure. Methods Thirty diagnosed GERD patients (12 female, mean age 49 [range 20‐72]) and 20 asymptomatic subjects (14 female, mean age 56 [range 21‐56]) were subject to diurnal saliva sampling, with additional samples for 60 minutes following self‐reported reflux symptoms and triggering of a proximal reflux alarm. Saliva samples were split and were analysed by both Peptest and ELISA with operators for each blinded to sample identity. Results Salivary pepsin was detectable in most patients and most volunteers. Peptest scores were significantly lower for patients than controls (P < 0.005). ELISA scores showed no difference between patients and controls. There was no effect of diurnal sampling time (P = 0.75) or time after symptoms (P = 0.76) on Peptest readout. There was no correlation between Peptest and Pepsin ELISA (P = 0.55); Bland‐Altman analysis suggested no agreement between the tests (P = 0.414). Receiver‐operator curve suggests that neither Peptest (P = 0.3328) nor pepsin (P = 0.4476) is useful for predicting GERD. Conclusion Salivary pepsin is not a reliable tool for the diagnosis of GERD

The driver landscape of sporadic chordoma.

Chordoma is a malignant, often incurable bone tumour showing notochordal differentiation. Here, we defined the somatic driver landscape of 104 cases of sporadic chordoma. We reveal somatic duplications of the notochordal transcription factor brachyury (T) in up to 27% of cases. These variants recapitulate the rearrangement architecture of the pathogenic germline duplications of T that underlie familial chordoma. In addition, we find potentially clinically actionable PI3K signalling mutations in 16% of cases. Intriguingly, one of the most frequently altered genes, mutated exclusively by inactivating mutation, was LYST (10%), which may represent a novel cancer gene in chordoma.Chordoma is a rare often incurable malignant bone tumour. Here, the authors investigate driver mutations of sporadic chordoma in 104 cases, revealing duplications in notochordal transcription factor brachyury (T), PI3K signalling mutations, and mutations in LYST, a potential novel cancer gene in chordoma