Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.

Mycobacterium tuberculosis, the bacillus that causes tuberculosis (TB), infects 2 billion people across the globe, and results in 8-9 million new TB cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. We investigated the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using clinical indicators of disease, granuloma histopathological features, and immune response traits identified five new loci on mouse chromosomes 1, 2, 4, 16 and three previously identified loci on chromosomes 3 and 17. Quantitative trait loci (QTLs) on chromosomes 1, 16, and 17, associated with multiple correlated traits and had similar patterns of allele effects, suggesting these QTLs contain important genetic regulators of responses to M. tuberculosis. To narrow the list of candidate genes in QTLs, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks, generating functional scores. The scores were then used to rank candidates for each mapped trait in each locus, resulting in 11 candidates: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Importantly, all 11 candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling. Further, all candidates contain single nucleotide polymorphisms (SNPs), and some but not all SNPs were predicted to have deleterious consequences on protein functions. Multiple methods were used for validation including (i) a statistical method that showed Diversity Outbred mice carrying PWH/PhJ alleles on chromosome 17 QTL have shorter survival; (ii) quantification of S100A8 protein levels, confirming predicted allele effects; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and new functionally relevant gene candidates that may be major regulators of granuloma necrosis and acute inflammation in pulmonary TB.

Investigación del Rendimiento de los Juegos de Esports con Análisis Envolvente de Datos / Investigação do Desempenho dos Jogos Esports com Análise de Envelopamento de Dados / Investigation of the Performance of e-sports Games with Data Envelopment Analysis

Hoy en día, los torneos de deportes electrónicos como el Juego Cibernético Mundial, se organizan el Campeonato de las Naciones Europeas, la serie Internacional de Premiership y la Serie ESL Pro. Estos torneos incluyen juegos populares como contraataque, Dota 2, League of Legends, Starcraft 2, Super Smash Bros y Street Fighter. En este estudio, las actuaciones de los juegos de eSports se analizarán con el Análisis Envolvente de Datos (DEA). En consecuencia, está dirigido a ayudar a las empresas de eSports a que sean efectivas determinando las eficiencias relativas de los Juegos de ESPort, unidades de toma de decisiones efectivas e ineficaces (unidades de producción de DMU que producen resultados similares utilizando insumos similares). Para este propósito determinando los juegos efectivos y comparando los otros juegos según ellos permite la optimización de los valores de las variables de salida según la variable de entrada.La DEA es un método no paramétrico utilizado para estudiar la efectividad relativa de las Unidades de Toma de Decisiones cuando hay múltiples entradas y múltiples salidas. El Análisis de la Envolvente de Datos (DEA) es un método no paramétrico utilizado para examinar la eficiencia relativa de las Unidades de Toma de Decisiones (unidades de producción de DMUs que producen salidas similares utilizando entradas similares) cuando hay entradas múltiples y múltiples salidas. La medición de la eficiencia de las empresas y conocer la fuente de su ineficiencia es un tema importante para los ESPorts como en todos los sectores. El análisis de envolvimiento de datos se usa frecuentemente para medir la eficiencia de DMU. (AU)

Today, electronic sports tournaments such as the World Cyber Game, European Nations Championship, InternationalPremiership Series and Esl Pro Series are organized. These tournaments include popular games such as Counter-Strike, Dota 2, League of Legends, StarCraft 2, Super Smash Bros and Street Fighter. In this study, the performances of eSports games will be analyzed with Data Envelopment Analysis (DEA). Accordingly, it is aimed to help eSports companies to be effective by determining the relative efficiencies of eSport games, effective and ineffective Decision-Making Units (DMUs production units that produce similar outputs using similar inputs). For this purpose, determining the effective games and comparing the other games according to them enables the optimization of the values of the output variables according to the input variable. DEA is a non-parametric method used to study the relative effectiveness of Decision Making Units when there are multiple inputs and multiple outputs. Data Envelopment Analysis (DEA) is a non-parametric method used to examine the relative efficiency of Decision Making Units (DMUs production units that produce similar outputs using similar inputs) when there are multiple inputs and multiple outputs. Measuring the efficiency of businesses and knowing the source of their inefficiency is an important issue for eSports as in every sector.Data Envelopment Analysis is frequently used for measuring the efficiency of DMU. (AU)

Hoje em dia, os torneios esportivos eletrônicos, como o World Cyber Game, são organizados pelo Campeonato Europeu das Nações, a série internacional de Premiership e a série ESL Pro. Esses torneios incluem jogos populares como contra-ataque, DotA 2, League of Legends, Starcraft 2, Super Smash Bros e Street Fighter. Neste estudo, os desempenhos dos jogos eSports serão analisados com análise de envelope de dados (DEA). Consequentemente, visa ajudar as empresas de espantar a serem eficazes determinando as eficiências relativas de jogos de esportes, unidades de decisão eficazes e ineficazes (unidades de produção DMU que produzem resultados semelhantes com insumos semelhantes). Para este propósito determinar os jogos eficazes e comparando os outros jogos de acordo com eles permite a optimização dos valores das variáveis de saida de acordo com a variável de entrada. O DEA é um método não paramétrico usado para estudar a eficácia relativa das unidades de decisão quando existem várias entradas e várias saídas. A análise do envelope de dados (DEA) é um método não paramétrico usado para examinar a eficiência relativa das unidades de decisão (unidadesde produção DMU que produzem saídas semelhantes usando entradas semelhantes) quando várias entradas e várias saídas. A medição da eficiência das empresas e conhecer a fonte de sua ineficiência é uma questão importante para o eSports como em todos os setores. A análise de envolvimento de dados é freqüentemente usada para medir a eficiência do DMU. (AU)

Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer.

In this paper, we propose a pathological image compression framework to address the needs of Big Data image analysis in digital pathology. Big Data image analytics require analysis of large databases of high-resolution images using distributed storage and computing resources along with transmission of large amounts of data between the storage and computing nodes that can create a major processing bottleneck. The proposed image compression framework is based on the JPEG2000 Interactive Protocol and aims to minimize the amount of data transfer between the storage and computing nodes as well as to considerably reduce the computational demands of the decompression engine. The proposed framework was integrated into hotspot detection from images of breast biopsies, yielding considerable reduction of data and computing requirements.

FOXP3-stained image analysis for follicular lymphoma: Optimal adaptive thresholding with maximal nucleus coverage.

Immunohistochemical detection of FOXP3 antigen is a usable marker for detection of regulatory T lymphocytes (TR) in formalin fixed and paraffin embedded sections of different types of tumor tissue. TR plays a major role in homeostasis of normal immune systems where they prevent auto reactivity of the immune system towards the host. This beneficial effect of TR is frequently "hijacked" by malignant cells where tumor-infiltrating regulatory T cells are recruited by the malignant nuclei to inhibit the beneficial immune response of the host against the tumor cells. In the majority of human solid tumors, an increased number of tumor-infiltrating FOXP3 positive TR is associated with worse outcome. However, in follicular lymphoma (FL) the impact of the number and distribution of TR on the outcome still remains controversial. In this study, we present a novel method to detect and enumerate nuclei from FOXP3 stained images of FL biopsies. The proposed method defines a new adaptive thresholding procedure, namely the optimal adaptive thresholding (OAT) method, which aims to minimize under-segmented and over-segmented nuclei for coarse segmentation. Next, we integrate a parameter free elliptical arc and line segment detector (ELSD) as additional information to refine segmentation results and to split most of the merged nuclei. Finally, we utilize a state-of-the-art super-pixel method, Simple Linear Iterative Clustering (SLIC) to split the rest of the merged nuclei. Our dataset consists of 13 region-of-interest images containing 769 negative and 88 positive nuclei. Three expert pathologists evaluated the method and reported sensitivity values in detecting negative and positive nuclei ranging from 83-100% and 90-95%, and precision values of 98-100% and 99-100%, respectively. The proposed solution can be used to investigate the impact of FOXP3 positive nuclei on the outcome and prognosis in FL.

Computerized detection and segmentation of mitochondria on electron microscope images.

Mitochondrial function plays an important role in the regulation of cellular life and death, including disease states. Disturbance in mitochondrial function and distribution can be accompanied by significant morphological alterations. Electron microscopy tomography (EMT) is a powerful technique to study the 3D structure of mitochondria, but the automatic detection and segmentation of mitochondria in EMT volumes has been challenging due to the presence of subcellular structures and imaging artifacts. Therefore, the interpretation, measurement and analysis of mitochondrial distribution and features have been time consuming, and development of specialized software tools is very important for high-throughput analyses needed to expedite the myriad studies on cellular events. Typically, mitochondrial EMT volumes are segmented manually using special software tools. Automatic contour extraction on large images with multiple mitochondria and many other subcellular structures is still an unaddressed problem. The purpose of this work is to develop computer algorithms to detect and segment both fully and partially seen mitochondria on electron microscopy images. The detection method relies on mitochondria's approximately elliptical shape and double membrane boundary. Initial detection results are first refined using active contours. Then, our seed point selection method automatically selects reliable seed points along the contour, and segmentation is finalized by automatically incorporating a live-wire graph search algorithm between these seed points. In our evaluations on four images containing multiple mitochondria, 52 ellipses are detected among which 42 are true and 10 are false detections. After false ellipses are eliminated manually, 14 out of 15 fully seen mitochondria and 4 out of 7 partially seen mitochondria are successfully detected. When compared with the segmentation of a trained reader, 91% Dice similarity coefficient was achieved with an average 4.9 nm boundary error.

Automatic detection of follicular regions in H&E images using iterative shape index.

Follicular Lymphoma (FL) accounts for 20-25% of non-Hodgkin lymphomas in the United States. The first step in grading FL is identifying follicles. Our paper discusses a novel technique to segment follicular regions in H&E stained images. The method is based on three successive steps: (1) region-based segmentation, (2) iterative shape index (concavity index) calculation, (3) and recursive watershed. A novel aspect of this method is the use of iterative Concavity Index (CI) to control the follicular splitting process in recursive watershed. CI takes into consideration the convex hull of the object and the closest area surrounding it. The mean Zijbendos similarity index (ZSI) final segmentation score on fifteen cases was 78.33%, with a standard deviation of 2.83.

Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees.

OBJECTIVE: The goal of this study was to develop an algorithm to semi-automatically segment the meniscus in a series of magnetic resonance (MR) images to use for normal knees and those with moderate osteoarthritis (OA). METHOD: The segmentation method was developed then evaluated on 10 baseline MR images obtained from subjects with no evidence, symptoms, or risk factors of knee (OA), and 14 from subjects with established knee OA enrolled in the Osteoarthritis Initiative (OAI). After manually choosing a seed point within the meniscus, a threshold level was calculated through a Gaussian fit model. Under anatomical, intensity, and range constraints, a threshold operation was completed followed by conditional dilation and post-processing. The post-processing operation reevaluates the pixels included and excluded in the area surrounding the meniscus to improve accuracy. The developed method was evaluated for both normal and degenerative menisci by comparing the segmentation algorithm results with manual segmentations from five human readers. RESULTS: The semi-automated segmentation method produces results similar to those of trained observers, with an average similarity index over 0.80 for normal participants and 0.75, 0.67, and 0.64 for participants with established knee OA with Osteoarthritis Research Society International (OARSI) joint space narrowing (JSN) scores of 0, one, and two respectively. CONCLUSION: The semi-automatic segmentation method produced accurate and consistent segmentations of the meniscus when compared to manual segmentations in the assessment of normal menisci in mild to moderate OA. Future studies will examine the change in volume, thickness, and intensity characteristics at different stages of OA.

Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation.

Neuroblastoma (NB) is one of the most frequently occurring cancerous tumors in children. The current grading evaluations for patients with this disease require pathologists to identify certain morphological characteristics with microscopic examinations of tumor tissues. Thanks to the advent of modern digital scanners, it is now feasible to scan cross-section tissue specimens and acquire whole-slide digital images. As a result, computerized analysis of these images can generate key quantifiable parameters and assist pathologists with grading evaluations. In this study, image analysis techniques are applied to histological images of haematoxylin and eosin (H&E) stained slides for identifying image regions associated with different pathological components. Texture features derived from segmented components of tissues are extracted and processed by an automated classifier group trained with sample images with different grades of neuroblastic differentiation in a multi-resolution framework. The trained classification system is tested on 33 whole-slide tumor images. The resulting whole-slide classification accuracy produced by the computerized system is 87.88%. Therefore, the developed system is a promising tool to facilitate grading whole-slide images of NB biopsies with high throughput.

Computer-aided Prognosis of Neuroblastoma on Whole-slide Images: Classification of Stromal Development.

We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB. According to the International Neuroblastoma Pathology Classification (the Shimada system), NB patients are classified into favorable and unfavorable histology based on the tissue morphology. In this study, we propose an image analysis system that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. Our statistical framework performs the classification based on texture features extracted using co-occurrence statistics and local binary patterns. Due to the high resolution of digitized whole-slide images, we propose a multi-resolution approach that mimics the evaluation of a pathologist such that the image analysis starts from the lowest resolution and switches to higher resolutions when necessary. We employ an offine feature selection step, which determines the most discriminative features at each resolution level during the training step. A modified k-nearest neighbor classifier is used to determine the confidence level of the classification to make the decision at a particular resolution level. The proposed approach was independently tested on 43 whole-slide samples and provided an overall classification accuracy of 88.4%.

Selection of an optimal neural network architecture for computer-aided detection of microcalcifications--comparison of automated optimization techniques.

Many computer-aided diagnosis (CAD) systems use neural networks (NNs) for either detection or classification of abnormalities. Currently, most NNs are "optimized" by manual search in a very limited parameter space. In this work, we evaluated the use of automated optimization methods for selecting an optimal convolution neural network (CNN) architecture. Three automated methods, the steepest descent (SD), the simulated annealing (SA), and the genetic algorithm (GA), were compared. We used as an example the CNN that classifies true and false microcalcifications detected on digitized mammograms by a prescreening algorithm. Four parameters of the CNN architecture were considered for optimization, the numbers of node groups and the filter kernel sizes in the first and second hidden layers, resulting in a search space of 432 possible architectures. The area Az under the receiver operating characteristic (ROC) curve was used to design a cost function. The SA experiments were conducted with four different annealing schedules. Three different parent selection methods were compared for the GA experiments. An available data set was split into two groups with approximately equal number of samples. By using the two groups alternately for training and testing, two different cost surfaces were evaluated. For the first cost surface, the SD method was trapped in a local minimum 91% (392/432) of the time. The SA using the Boltzman schedule selected the best architecture after evaluating, on average, 167 architectures. The GA achieved its best performance with linearly scaled roulette-wheel parent selection; however, it evaluated 391 different architectures, on average, to find the best one. The second cost surface contained no local minimum. For this surface, a simple SD algorithm could quickly find the global minimum, but the SA with the very fast reannealing schedule was still the most efficient. The same SA scheme, however, was trapped in a local minimum on the first cost surface. Our CNN study demonstrated that, if optimization is to be performed on a cost surface whose characteristics are not known a priori, it is advisable that a moderately fast algorithm such as a SA using a Boltzman cooling schedule be used to conduct an efficient and thorough search, which may offer a better chance of reaching the global minimum.

Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.

Mass segmentation is used as the first step in many computer-aided diagnosis (CAD) systems for classification of breast masses as malignant or benign. The goal of this paper was to study the accuracy of an automated mass segmentation method developed in our laboratory, and to investigate the effect of the segmentation stage on the overall classification accuracy. The automated segmentation method was quantitatively compared with manual segmentation by two expert radiologists (R1 and R2) using three similarity or distance measures on a data set of 100 masses. The area overlap measures between R1 and R2, the computer and R1, and the computer and R2 were 0.76 +/- 0.13, 0.74 +/- 0.11, and 0.74 +/- 0.13, respectively. The interobserver difference in these measures between the two radiologists was compared with the corresponding differences between the computer and the radiologists. Using three similarity measures and data from two radiologists, a total of six statistical tests were performed. The difference between the computer and the radiologist segmentation was significantly larger than the interobserver variability in only one test. Two sets of texture, morphological, and spiculation features, one based on the computer segmentation, and the other based on radiologist segmentation, were extracted from a data set of 249 films from 102 patients. A classifier based on stepwise feature selection and linear discriminant analysis was trained and tested using the two feature sets. The leave-one-case-out method was used for data sampling. For case-based classification, the area Az under the receiver operating characteristic (ROC) curve was 0.89 and 0.88 for the feature sets based on the radiologist segmentation and computer segmentation, respectively. The difference between the two ROC curves was not statistically significant.

Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses.

A new classification scheme was developed to classify mammographic masses as malignant and benign by using interval change information. The masses on both the current and the prior mammograms were automatically segmented using an active contour method. From each mass, 20 run length statistics (RLS) texture features, 3 speculation features, and 12 morphological features were extracted. Additionally, 20 difference RLS features were obtained by subtracting the prior RLS features from the corresponding current RLS features. The feature space consisted of the current RLS features, the difference RLS features, the current and prior speculation features, and the current and prior mass sizes. Stepwise feature selection and linear discriminant analysis classification were used to select and merge the most useful features. A leave-one-case-out resampling scheme was used to train and test the classifier using 140 temporal image pairs (85 malignant, 55 benign) obtained from 57 biopsy-proven masses (33 malignant, 24 benign) in 56 patients. An average of 10 features were selected from the 56 training subsets: 4 difference RLS features, 4 RLS features, and 1 speculation feature from the current image, and 1 speculation feature from the prior, were most often chosen. The classifier achieved an average training Az of 0.92 and a test Az of 0.88. For comparison, a classifier was trained and tested using features extracted from the 120 current single images. This classifier achieved an average training Az of 0.90 and a test Az of 0.82. The information on the prior image significantly (p = 0.015) improved the accuracy for classification of the masses.

Identification of relative protein bands in polyacrylamide gel electrophoresis (PAGE) using a multi-resolution snake algorithm.

In polyacrylamide gel electrophoresis (PAGE) image analysis, it is important to determine the percentage of the protein of interest of a protein mixture. This study presents reliable computer software to determine this percentage. The region of interest containing the protein band is detected using the snake algorithm. The iterative snake algorithm is implemented in a multi-resolutional framework. The snake is initialized on a low-resolution image. Then, the final position of the snake at the low resolution is used as the initial position in the higher-resolution image. Finally, the area of the protein is estimated as the area enclosed by the final position of the snake.