Validation of a context analysis method for microRNA data

We have previously presented a data-oriented approach to the study of microRNA-gene interactions, a hot topic of research in molecular biology, building heavily on methods from the document analysis field. This paper evaluates the performances of the method by means of a cross-validation approach. Latent information can be effectively exploited to suggest directions for laboratory experiments, an important topic in microRNA research, since these experiments are costly in both resources and time

Layered ensemble model for short-term traffic flow forecasting with outlier detection

YesReal time traffic flow forecasting is a necessary requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. This paper focuses on short-term traffic flow forecasting by taking into consideration both spatial (road links) and temporal (lag or past traffic flow values) information. We propose a Layered Ensemble Model (LEM) which combines Artificial Neural Networks and Graded Possibilistic Clustering obtaining an accurate forecast of the traffic flow rates with outlier detection. Experimentation has been carried out on two different data sets. The former was obtained from real UK motorway and the later was obtained from simulated traffic flow on a street network in Genoa (Italy). The proposed LEM model for short-term traffic forecasting provides promising results and given the ability for outlier detection, accuracy, robustness of the proposed approach, it can be fruitful integrated in traffic flow management systems

Tracking time evolving data streams for short-term traffic forecasting

YesData streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems

Graded possibilistic clustering of non-stationary data streams

YesMultidimensional data streams are a major paradigm in data science. This work focuses on possibilistic clustering algorithms as means to perform clustering of multidimensional streaming data. The proposed approach exploits fuzzy outlier analysis to provide good learning and tracking abilities in both concept shift and concept drift

Development and analytical performance of an automated screening method for cannabinoids on the Dimension clinical chemistry system

A fully automated, random access method for the determination of cannabinoids (UTHC) was developed for the Dimension AR and XL clinical chemistry systems. The method utilizes Abuscreen ONLINE reagents and a multianalyte liquid calibrator containing 11-nor-Δ9-THC-9-carboxylic acid. Within-run and total reproducibility, determined using NCCLS protocol EP5- T2, was less than 0.6% and 1.6% CV, respectively, at all concentrations. Calibration stability was retained for at least 30 days. An extensive evaluation of non-structurally related drugs and various physiological substances indicated lack of interference in the method. No sample carry-over was observed following a specimen containing 1886 ng/ml 11-nor-Δ9-THC-9-carboxylic acid. A 99.1% agreement (N = 445 samples) was found between an EMIT based method on the aca discrete clinical analyser and the Dimension UTHC method

Structural characterization of the Xi class glutathione transferase from the haloalkaliphilic archaeon Natrialba magadii

Xi class glutathione transferases (GSTs) are a recently identified group, within this large superfamily of enzymes, specifically endowed with glutathione-dependent reductase activity on glutathionyl-hydroquinone. Enzymes belonging to this group are widely distributed in bacteria, fungi, and plants but not in higher eukaryotes. Xi class GSTs are also frequently found in archaea and here we focus on the enzyme produced by the extreme haloalkaliphilic archaeon Natrialba magadii (NmGHR). We investigated its function and stability and determined its 3D structure in the apo form by X-ray crystallography. NmGHR displays the same fold of its mesophilic counterparts, is enriched in negatively charged residues, which are evenly distributed along the surface of the protein, and is characterized by a peculiar distribution of hydrophobic residues. A distinctive feature of haloalkaliphilic archaea is their preference for γ-glutamyl-cysteine over glutathione as a reducing thiol. Indeed we found that the N. magadii genome lacks a gene coding for glutathione synthase. Analysis of NmGHR structure suggests that the thiol binding site (G-site) of the enzyme is well suited for hosting γ-glutamyl-cysteine

Dietary potassium intake and risk of diabetes : a systematic review and meta-analysis of prospective studies

(1) Background: Dietary potassium intake is positively associated with reduction of cardiovascular risk. Several data are available on the relationship between dietary potassium intake, diabetes risk and glucose metabolism, but with inconsistent results. Therefore, we performed a meta-analysis of the prospective studies that explored the effect of dietary potassium intake on the risk of diabetes to overcome these limitations. (2) Methods: A random-effects dose–response meta-analysis was carried out for prospective studies. A potential non-linear relation was investigated using restricted cubic splines. (3) Results: A total of seven prospective studies met the inclusion criteria. Dose–response analysis detected a non-linear relationship between dietary potassium intake and diabetes risk, with significant inverse association starting from 2900 mg/day by questionnaire and between 2000 and 5000 mg/day by urinary excretion. There was high heterogeneity among studies, but no evidence of publication bias was found. (4) Conclusions: The results of this meta-analysis indicate that habitual dietary potassium consumption is associated with risk of diabetes by a non-linear dose–response relationship. The beneficial threshold found supports the campaigns in favour of an increase in dietary potassium intake to reduce the risk of morbidity and mortality. Further studies should be carried out to explore this topic