Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Influence of ventilation type in volatile organic compounds exposure: poultry case

Agricultural workers especially poultry farmers are at increased risk of occupational respiratory diseases. Epidemiological studies showed increased prevalence of respiratory symptoms and adverse changes in pulmonary function parameters in poultry workers. In poultry production volatile organic compounds (VOCs) presence can be due to some compounds produced by molds that are volatile and are released directly into the air. These are known as microbial volatile organic compounds (MVOCs). Because these compounds often have strong and/or unpleasant odors, they can be the source of odors associated with molds. MVOC's are products of the microorganisms primary and secondary metabolism and are composed of low molecular weight alcohols, aldehydes, amines, ketones, terpenes, aromatic and chlorinated hydrocarbons, and sulfur-based compounds, all of which are variations of carbon-based molecules.

Exposure to volatile organic compounds in a composting plant

Some previous studies have suggested that some of the volatile organic compounds (VOCs) found in composting plants may have a toxic effect that can influence, besides surroundings populations, workers from the composting plants. Impact of waste management to the environment and workers is already recognised as an environment and occupational health concerns. Several studies regarding the VOCs and bioaerosols emissions from composting have been conducted all over Europe and also in Asia. However, in Portugal the studies developed are scarce and normally VOCs are not studied and recognized as a risk factor present in this occupational setting. Consudering this, a study was developed in a Portuguese composting plant aiming to clarify if there was VOCs presence in the workplaces.

Exposure to volatile organic compounds, particulate matter and fungi in a composting plant

Composting is an important process of solid waste management and it can be used for treatment of a variety of different wastes (green waste, household waste, sewage sludge and more). This process aims to: 1. Reduce the volumes of waste and; 2. Create a valuable product which can be recycled as a soil amendment in agriculture and gardening. A natural self-heating process involving the biological degradation of organic matter under aerobic conditions. The handling of waste and compost is responsible for the release of airborne microorganisms and their compounds in the air. Possible contaminants: a) Dust; b) Mesophilic and thermophilic microorganisms; c) Volatile organic compounds; d) Endotoxins and mycotoxins…. Aim: assess exposure/contamination to: a) Volatile organic compounds (VOCs); b) Particulate matter (PM); c) Fungi. In a composting plant located in Lisbon. An additional goal was to identify the workplace with higher level of contamination. In a totally indoor composting plant. The composting operations consisted: 1º Waste already sorted is unloaded in a reception area; 2º Pretreatment - remove undesirable materials from the process (glass, rocks, plastics, metals…); 3º Anaerobic digestion; 4º Dehydration; 5º Open composting with forced aeration. All the process takes thirteen weeks.

Copper-organic frameworks assembled from in situ generated 5-(4-pyridyl) tetrazole building blocks: synthesis, structural features, topological analysis and catalytic oxidation of alcohols

Two new metal- organic compounds {[Cu-3(mu(3)-4-(p)tz)(4)(mu(2)-N-3)(2)(DMF)(2)](DMF)(2)}(n) (1) and {[Cu(4ptz) (2)(H2O)(2)]}(n) (2) {4-ptz = 5-(4-pyridyl)tetrazolate} with 3D and 2D coordination networks, respectively, have been synthesized while studying the effect of reaction conditions on the coordination modes of 4-pytz by employing the [2 + 3] cycloaddition as a tool for generating in situ the 5-substituted tetrazole ligands from 4-pyridinecarbonitrile and NaN3 in the presence of a copper(II) salt. The obtained compounds have been structurally characterized and the topological analysis of 1 discloses a topologically unique trinodal 3,5,6-connected 3D network which, upon further simplification, results in a uninodal 8-connected underlying net with the bcu (body centred cubic) topology driven by the [Cu-3(mu(2)-N-3)(2)] cluster nodes and mu(3)-4-ptz linkers. In contrast, the 2D metal-organic network in 2 has been classified as a uninodal 4-connected underlying net with the sql [Shubnikov tetragonal plane net] topology assembled from the Cu nodes and mu(2)-4-ptz linkers. The catalytic investigations disclosed that 1 and 2 act as active catalyst precursors towards the microwave-assisted homogeneous oxidation of secondary alcohols (1-phenylethanol, cyclohexanol, 2-hexanol, 3-hexanol, 2-octanol and 3-octanol) with tert-butylhydroperoxide, leading to the yields of the corresponding ketones up to 86% (TOF = 430 h(-1)) and 58% (TOF = 290 h(-1)) in the oxidation of 1-phenylethanol and cyclohexanol, respectively, after 1 h under low power ( 10 W) microwave irradiation, and in the absence of any added solvent or additive.

ECG-based biometrics: A real time classification approach

Behavioral biometrics is one of the areas with growing interest within the biosignal research community. A recent trend in the field is ECG-based biometrics, where electrocardiographic (ECG) signals are used as input to the biometric system. Previous work has shown this to be a promising trait, with the potential to serve as a good complement to other existing, and already more established modalities, due to its intrinsic characteristics. In this paper, we propose a system for ECG biometrics centered on signals acquired at the subject's hand. Our work is based on a previously developed custom, non-intrusive sensing apparatus for data acquisition at the hands, and involved the pre-processing of the ECG signals, and evaluation of two classification approaches targeted at real-time or near real-time applications. Preliminary results show that this system leads to competitive results both for authentication and identification, and further validate the potential of ECG signals as a complementary modality in the toolbox of the biometric system designer.

Study of the interactions in a three-way crossed classification model

Crossed classification models are applied in many investigations taking in consideration the existence of interaction between all factors or, in alternative, excluding all interactions, and in this case only the effects and the error term are considered. In this work we use commutative Jordan algebras in the study of the algebraic structure of these designs and we use them to obtain similar designs where only some of the interactions are considered. We finish presenting the expressions of the variance componentes estimators.

Clinical classification versus semiquantification with adapted reference values for 123I-FP-CIT SPECT in a nuclear medicine department

Semi quantification (SQ) in DaTScan® studies is broadly used in clinic daily basis, however there is a suspicious about its discriminative capability, and concordance with the diagnostic classification performed by the physician. Aim: Evaluate the discriminate capability of an adapted database and reference's values of healthy controls for the Dopamine Transporters (DAT) with 123I–FP-IT named DBRV adapted to Nuclear Medicine Department's protocol and population of Infanta Cristina's Hospital, and its concordance with the physician classification.

Greener Selective Cycloalkane Oxidations with Hydrogen Peroxide Catalyzed by Copper-5-(4-pyridyl)tetrazolate Metal-Organic Frameworks

Microwave assisted synthesis of the Cu(I) compound [Cu(µ4-4-ptz)]n [1, 4-ptz = 5-(4-pyridyl)tetrazolate] has been performed by employing a relatively easy method and within a shorter period of time compared to its sister compounds. The syntheses of the Cu(II) compounds [Cu3(µ3-4-ptz)4(µ2-N3)2(DMF)2]n∙(DMF)2n (2) and [Cu(µ2-4-ptz)2(H2O)2]n (3) using a similar method were reported previously by us. MOFs 1-3 revealed high catalytic activity toward oxidation of cyclic alkanes (cyclopentane, -hexane and -octane) with aqueous hydrogen peroxide, under very mild conditions (at room temperature), without any added solvent or additive. The most efficient system (2/H2O2) showed, for the oxidation of cyclohexane, a turnover number (TON) of 396 (TOF of 40 h−1), with an overall product yield (cyclohexanol and cyclohexanone) of 40% relative to the substrate. Moreover, the heterogeneous catalytic systems 1–3 allowed an easy catalyst recovery and reuse, at least for four consecutive cycles, maintaining ca. 90% of the initial high activity and concomitant high selectivity.

Partial classification of Lorenz knots: Syllable permutations of torus knots words

We define families of aperiodic words associated to Lorenz knots that arise naturally as syllable permutations of symbolic words corresponding to torus knots. An algorithm to construct symbolic words of satellite Lorenz knots is defined. We prove, subject to the validity of a previous conjecture, that Lorenz knots coded by some of these families of words are hyperbolic, by showing that they are neither satellites nor torus knots and making use of Thurston's theorem. Infinite families of hyperbolic Lorenz knots are generated in this way, to our knowledge, for the first time. The techniques used can be generalized to study other families of Lorenz knots.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.