Mammalian Target of Rapamycin Complex 2 Controls CD8 T Cell Memory Differentiation in a Foxo1-Dependent Manner.

Upon infection, antigen-specific naive CD8 T cells are activated and differentiate into short-lived effector cells (SLECs) and memory precursor cells (MPECs). The underlying signaling pathways remain largely unresolved. We show that Rictor, the core component of mammalian target of rapamycin complex 2 (mTORC2), regulates SLEC and MPEC commitment. Rictor deficiency favors memory formation and increases IL-2 secretion capacity without dampening effector functions. Moreover, mTORC2-deficient memory T cells mount more potent recall responses. Enhanced memory formation in the absence of mTORC2 was associated with Eomes and Tcf-1 upregulation, repression of T-bet, enhanced mitochondrial spare respiratory capacity, and fatty acid oxidation. This transcriptional and metabolic reprogramming is mainly driven by nuclear stabilization of Foxo1. Silencing of Foxo1 reversed the increased MPEC differentiation and IL-2 production and led to an impaired recall response of Rictor KO memory T cells. Therefore, mTORC2 is a critical regulator of CD8 T cell differentiation and may be an important target for immunotherapy interventions.

Dismissal of the illusion of uncertainty in the assessment of a likelihood ratio

The use of the Bayes factor (BF) or likelihood ratio as a metric to assess the probative value of forensic traces is largely supported by operational standards and recommendations in different forensic disciplines. However, the progress towards more widespread consensus about foundational principles is still fragile as it raises new problems about which views differ. It is not uncommon e.g. to encounter scientists who feel the need to compute the probability distribution of a given expression of evidential value (i.e. a BF), or to place intervals or significance probabilities on such a quantity. The article here presents arguments to show that such views involve a misconception of principles and abuse of language. The conclusion of the discussion is that, in a given case at hand, forensic scientists ought to offer to a court of justice a given single value for the BF, rather than an expression based on a distribution over a range of values.

Safety of Prasugrel Loading Doses in Patients Pre-Loaded With Clopidogrel in the Setting of Primary Percutaneous Coronary Intervention: Results of a Nonrandomized Observational Study.

OBJECTIVES: The aim of this study was to assess the safety of the concurrent administration of a clopidogrel and prasugrel loading dose in patients undergoing primary percutaneous coronary intervention. BACKGROUND: Prasugrel is one of the preferred P2Y12 platelet receptor antagonists for ST-segment elevation myocardial infarction patients. The use of prasugrel was evaluated clinically in clopidogrel-naive patients. METHODS: Between September 2009 and October 2012, a total of 2,023 STEMI patients were enrolled in the COMFORTABLE (Comparison of Biomatrix Versus Gazelle in ST-Elevation Myocardial Infarction [STEMI]) and the SPUM-ACS (Inflammation and Acute Coronary Syndromes) studies. Patients receiving a prasugrel loading dose were divided into 2 groups: 1) clopidogrel and a subsequent prasugrel loading dose; and 2) a prasugrel loading dose. The primary safety endpoint was Bleeding Academic Research Consortium types 3 to 5 bleeding in hospital at 30 days. RESULTS: Of 2,023 patients undergoing primary percutaneous coronary intervention, 427 (21.1%) received clopidogrel and a subsequent prasugrel loading dose, 447 (22.1%) received a prasugrel loading dose alone, and the remaining received clopidogrel only. At 30 days, the primary safety endpoint was observed in 1.9% of those receiving clopidogrel and a subsequent prasugrel loading dose and 3.4% of those receiving a prasugrel loading dose alone (adjusted hazard ratio [HR]: 0.57; 95% confidence interval [CI]: 0.25 to 1.30, p = 0.18). The HAS-BLED (hypertension, abnormal renal/liver function, stroke, bleeding history or predisposition, labile international normalized ratio, elderly, drugs/alcohol concomitantly) bleeding score tended to be higher in prasugrel-treated patients (p = 0.076). The primary safety endpoint results, however, remained unchanged after adjustment for these differences (clopidogrel and a subsequent prasugrel loading dose vs. prasugrel only; HR: 0.54 [95% CI: 0.23 to 1.27], p = 0.16). No differences in the composite of cardiac death, myocardial infarction, or stroke were observed at 30 days (adjusted HR: 0.66, 95% CI: 0.27 to 1.62, p = 0.36). CONCLUSIONS: This observational, nonrandomized study of ST-segment elevation myocardial infarction patients suggests that the administration of a loading dose of prasugrel in patients pre-treated with a loading dose of clopidogrel is not associated with an excess of major bleeding events. (Comparison of Biomatrix Versus Gazelle in ST-Elevation Myocardial Infarction [STEMI] [COMFORTABLE]; NCT00962416; and Inflammation and Acute Coronary Syndromes [SPUM-ACS]; NCT01000701).

Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants.

One of the global targets for non-communicable diseases is to halt, by 2025, the rise in the age-standardised adult prevalence of diabetes at its 2010 levels. We aimed to estimate worldwide trends in diabetes, how likely it is for countries to achieve the global target, and how changes in prevalence, together with population growth and ageing, are affecting the number of adults with diabetes. We pooled data from population-based studies that had collected data on diabetes through measurement of its biomarkers. We used a Bayesian hierarchical model to estimate trends in diabetes prevalence-defined as fasting plasma glucose of 7.0 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs-in 200 countries and territories in 21 regions, by sex and from 1980 to 2014. We also calculated the posterior probability of meeting the global diabetes target if post-2000 trends continue. We used data from 751 studies including 4,372,000 adults from 146 of the 200 countries we make estimates for. Global age-standardised diabetes prevalence increased from 4.3% (95% credible interval 2.4-7.0) in 1980 to 9.0% (7.2-11.1) in 2014 in men, and from 5.0% (2.9-7.9) to 7.9% (6.4-9.7) in women. The number of adults with diabetes in the world increased from 108 million in 1980 to 422 million in 2014 (28.5% due to the rise in prevalence, 39.7% due to population growth and ageing, and 31.8% due to interaction of these two factors). Age-standardised adult diabetes prevalence in 2014 was lowest in northwestern Europe, and highest in Polynesia and Micronesia, at nearly 25%, followed by Melanesia and the Middle East and north Africa. Between 1980 and 2014 there was little change in age-standardised diabetes prevalence in adult women in continental western Europe, although crude prevalence rose because of ageing of the population. By contrast, age-standardised adult prevalence rose by 15 percentage points in men and women in Polynesia and Micronesia. In 2014, American Samoa had the highest national prevalence of diabetes (>30% in both sexes), with age-standardised adult prevalence also higher than 25% in some other islands in Polynesia and Micronesia. If post-2000 trends continue, the probability of meeting the global target of halting the rise in the prevalence of diabetes by 2025 at the 2010 level worldwide is lower than 1% for men and is 1% for women. Only nine countries for men and 29 countries for women, mostly in western Europe, have a 50% or higher probability of meeting the global target. Since 1980, age-standardised diabetes prevalence in adults has increased, or at best remained unchanged, in every country. Together with population growth and ageing, this rise has led to a near quadrupling of the number of adults with diabetes worldwide. The burden of diabetes, both in terms of prevalence and number of adults affected, has increased faster in low-income and middle-income countries than in high-income countries. Wellcome Trust.

Shared feature extraction for nearest neighbor face recognition

In this paper, we propose a new supervised linearfeature extraction technique for multiclass classification problemsthat is specially suited to the nearest neighbor classifier (NN).The problem of finding the optimal linear projection matrix isdefined as a classification problem and the Adaboost algorithmis used to compute it in an iterative way. This strategy allowsthe introduction of a multitask learning (MTL) criterion in themethod and results in a solution that makes no assumptions aboutthe data distribution and that is specially appropriated to solvethe small sample size problem. The performance of the methodis illustrated by an application to the face recognition problem.The experiments show that the representation obtained followingthe multitask approach improves the classic feature extractionalgorithms when using the NN classifier, especially when we havea few examples from each class

Classification of textures seen from different distances and under varying illumination direction

Changes in the angle of illumination incident upon a 3D surface texture can significantly alter its appearance, implying variations in the image texture. These texture variations produce displacements of class members in the feature space, increasing the failure rates of texture classifiers. To avoid this problem, a model-based texture recognition system which classifies textures seen from different distances and under different illumination directions is presented in this paper. The system works on the basis of a surface model obtained by means of 4-source colour photometric stereo, used to generate 2D image textures under different illumination directions. The recognition system combines coocurrence matrices for feature extraction with a Nearest Neighbour classifier. Moreover, the recognition allows one to guess the approximate direction of the illumination used to capture the test image

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Clasificación automática de textos y explotación BI

El presente proyecto tiene como objetivo desarrollar una tecnología que permita codificar grandes cantidades de texto de manera automática para posteriormente ser visualizada y analizada mediante una aplicación diseñada en Qlikview. El motor de la investigación e implementación de este proyecto se ha encontrado en la incipiente presencia de tecnologías informáticas en los procesos de codificación para ciencias políticas. De esta manera, el programa creado tiene como objetivo automatizar un proceso que se desarrolla comúnmente de manera manual y, por ende, las ventajas de introducir técnicas informáticas son notablemente valiosas. Estas automatizaciones permiten ahorrar tanto en tiempo de codificación, como en recursos económicos o humanos. Se ha elaborado una revisión teórica y metodológica que han servido como instrumentos de estudio y mejora, con el firme propósito de reducir al máximo el margen de error y ofrecer un instrumento de calidad con salida de mercado real. El método de clasificación utilizado ha sido Bayes, y se ha implementado utilizando Matlab. Los resultados de la clasificación han llegado a índices del 99.2%. En la visualización y análisis mediante Qlikview se pueden modificar los parámetros referentes a partido político, año, categoría o región, con lo que se permite analizar numerosos aspectos relacionados con la distribución de las palabras repartidas entre las diferentes categorías y en el tiempo.

Juvan kunnan jätevedenpuhdistamon ympäristöasioiden parantaminen

Diplomityö on osa Mikkelin ammattikorkeakoulun YTI:ssä vuosina 2005 - 2008 toteutettua ”Yritysten ympäristöriskit” EU-osarahoitteista hanketta. Työn tarkoituksena oli tunnistaa, arvioida ja luokittaa Etelä-Savossa, Juvalla toimivan yhdyskuntajätevedenpuhdistamon ympäristöriskit sekä antaa neuvoja löytyneiden riskien hallintaan. Jätevedenpuhdistamon toiminnasta syntyvät mahdolliset ympäristöriskitekijät tunnistettiin kirjallisuusselvitysten sekä haastattelujen avulla. Riskien tunnistamisen jälkeen riskien kartoittamiseen, arviointiin ja luokittamiseen käytettiin osia eri riskianalyysimenetelmistä. Riskit jaettiin viiteen eri riskiluokkaan; vakavasta riskistä merkityksettömään riskiin. Juvan kunnan jätevedenpuhdistamolla esiintyi neljän pienimmän riskiluokan riskejä, ja vakavia riskejä ei kartoituksessa tullut esille lainkaan. Riskejä arvioitiin niin sanotuissa normaalioloissa. Merkittävin yksittäinen riskitekijä jätevedenpuhdistamolla oli haitallisen tai poikkeavan aineen kulkeutuminen jäteveden mukana puhdistamolle. Tämä voisi heikentää puhdistamon puhdistustulosta merkittävästi. Kun laitosta ajetaan ympäristöluvassa asetettujen vaatimusten mukaisesti ja alueella sijaitsevat teollisuuslaitokset noudattavat heille annettuja määräyksiä, ei Juvan jätevedenpuhdistamolta aiheudu merkittäviä päästöjä maa-perään tai pohjavesiin.

A Survey and Classification of Software Testing Tools

Software testing is one of the essential parts in software engineering process. The objective of the study was to describe software testing tools and the corresponding use. The thesis contains examples of software testing tools usage. The study was conducted as a literature study, with focus on current software testing practices and quality assurance standards. In the paper a tool classifier was employed, and testing tools presented in study were classified according to it. We found that it is difficult to distinguish current available tools by certain testing activities as many of them contain functionality that exceeds scopes of a single testing type.

Oliopohjainen konenäkösovelluskehys

Monet teollisuuden konenäkö- ja hahmontunnistusongelmat ovat hyvin samantapaisia, jolloin prototyyppisovelluksia suunniteltaessa voitaisiin hyödyntää pitkälti samoja komponentteja. Oliopohjaiset sovelluskehykset tarjoavat erinomaisen tavan nopeuttaa ohjelmistokehitystä uudelleenkäytettävyyttä parantamalla. Näin voidaan sekä mahdollistaa konenäkösovellusten laajempi käyttö että säästää kustannuksissa. Tässä työssä esitellään konenäkösovelluskehys, joka on perusarkkitehtuuriltaan liukuhihnamainen. Ylätason rakenne koostuu sensorista, datankäsittelyoperaatioista, piirreirrottimesta sekä luokittimesta. Itse sovelluskehyksen lisäksi on toteutettu joukko kuvankäsittely- ja hahmontunnistusoperaatioita. Sovelluskehys nopeuttaa selvästi ohjelmointityötä ja helpottaa uusien kuvankäsittelyoperaatioiden lisää mistä.

Mapping of use and occupation of the soil and irrigation water quality in the city of Salto do Lontra-Paraná, Brazil

The objective of this study consisted on mapping the use and soil occupation and evaluation of the quality of irrigation water used in Salto do Lontra, in the state of Paraná, Brazil. Images of the satellite SPOT-5 were used to perform the supervised classification of the Maximum Likelihood algorithm - MAXVER, and the water quality parameters analyzed were pH, EC, HCO3-, Cl-, PO4(3-), NO3-, turbidity, temperature and thermotolerant coliforms in two distinct rainfall periods. The water quality data were subjected to statistical analysis by the techniques of PCA and FA, to identify the most relevant variables in assessing the quality of irrigation water. The characterization of soil use and occupation by the classifier MAXVER allowed the identification of the following classes: crops, bare soil/stubble, forests and urban area. The PCA technique applied to irrigation water quality data explained 53.27% of the variation in water quality among the sampled points. Nitrate, thermotolerant coliforms, temperature, electrical conductivity and bicarbonate were the parameters that best explained the spatial variation of water quality.

Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.

Preventive diagnosis of dairy cow lameness

This research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified Modelling Language (UML). Data were collected in a commercial dairy herd using two different subgroups (H1 and H2), in order to validate the Fuzzy inference functions. The numbers of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) responses were used to build the classifier system up, after an established gold standard comparison. A Lesion Incidence Possibility (LIP) developed function indicates the chances of a cow becoming lame. The obtained lameness percentage in H1 and H2 was 8.40% and 1.77%, respectively. The system estimated a Lesion Incidence Possibility (LIP) of 5.00% and 2.00% in H1 and H2, respectively. The system simulation presented 3.40% difference from real cattle lameness data for H1, while for H2, it was 0.23%; indicating the system efficiency in decision-making.