FOCUSED-Short-Term Wind Speed Forecast Correction Algorithm Based on Successive NWP Forecasts for Use in Traffic Control Decision Support Systems.

In this paper, we propose a new algorithm, called FOCUSED (FOrecast Correction Using Successive prEDictions), for forecast correction of short-term wind speed predictions. We developed FOCUSED with the aim of improving the forecast of bora gusts, which frequently result in high-speed wind situations dangerous for traffic. The motivation arises from occasionally ambiguous results of the currently deployed decision support system, which aids traffic management in strong and gusty wind conditions at the coast of Croatia. The proposed correction algorithm uses characteristics of numerical weather prediction models to iteratively forecast the wind speed multiple times for the same future window. We use these iterative predictions as input features of the FOCUSED algorithm and get the corrected predictions as the output. We compared the proposed algorithm with artificial neural networks, random forests, support vector machines, and linear regression to demonstrate the superiority of the algorithm's performance on a data set comprising five years of real data measurements at the Croatian bridge "Krk" and complementary historical forecasts by ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) numerical weather prediction model.

Reconstructing dynamical networks via feature ranking.

Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the structure of networks from time-resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Here, we use the insights from machine learning to design a new method of network reconstruction that essentially makes no such assumptions. Specifically, we interpret the available trajectories (data) as "features" and use two independent feature ranking approaches-Random Forest and RReliefF-to rank the importance of each node for predicting the value of each other node, which yields the reconstructed adjacency matrix. We show that our method is fairly robust to coupling strength, system size, trajectory length, and noise. We also find that the reconstruction quality strongly depends on the dynamical regime.

Homogeneous clusters of Alzheimer's disease patient population.

BACKGROUND: Identification of biomarkers for the Alzheimer's disease (AD) is a challenge and a very difficult task both for medical research and data analysis. METHODS: We applied a novel clustering tool with the goal to identify subpopulations of the AD patients that are homogeneous in respect of available clinical as well as in respect of biological descriptors. RESULTS: The main result is identification of three clusters of patients with significant problems with dementia. The evaluation of properties of these clusters demonstrates that brain atrophy is the main driving force of dementia. The unexpected result is that the largest subpopulation that has very significant problems with dementia has besides mild signs of brain atrophy also large ventricular, intracerebral and whole brain volumes. Due to the fact that ventricular enlargement may be a consequence of brain injuries and that a large majority of patients in this subpopulation are males, a potential hypothesis is that such medical status is a consequence of a combination of previous traumatic events and degenerative processes. CONCLUSIONS: The results may have substantial consequences for medical research and clinical trial design. The clustering methodology used in this study may be interesting also for other medical and biological domains.

Machine-learning ready data on the thermal power consumption of the Mars Express Spacecraft.

We present six datasets containing telemetry data of the Mars Express Spacecraft (MEX), a spacecraft orbiting Mars operated by the European Space Agency. The data consisting of context data and thermal power consumption measurements, capture the status of the spacecraft over three Martian years, sampled at six different time resolutions that range from 1 min to 60 min. From a data analysis point-of-view, these data are challenging even for the more sophisticated state-of-the-art artificial intelligence methods. In particular, given the heterogeneity, complexity, and magnitude of the data, they can be employed in a variety of scenarios and analyzed through the prism of different machine learning tasks, such as multi-target regression, learning from data streams, anomaly detection, clustering, etc. Analyzing MEX's telemetry data is critical for aiding very important decisions regarding the spacecraft's status and operation, extracting novel knowledge, and monitoring the spacecraft's health, but the data can also be used to benchmark artificial intelligence methods designed for a variety of tasks.

Authentication of key aroma compounds in apple using stable isotope approach.

Gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) for the analysis of key volatile compounds sampled using headspace solid phase microextraction (HS-SPME) is an appropriate tool for authenticity assessment of apple aromas. The current research characterises 18 laboratory produced and 15 commercial apple recovery aroma samples, establishes a database of δ13C values of 16 aroma compounds with respect to their origin (synthetic and natural), and assesses the authenticity of commercially available aroma compounds. Analysis of so-called natural aroma products, revealed δ13C values that were within the expected authentic range although the data did reveal possible falsifications. The sensitivity of the method was evaluated through simple isotope mass balance calculation. Falsification identification is possible for most aromatic substances when the amount of added synthetic compound is in tens of percent.

Personality traits in miners with past occupational elemental mercury exposure.

In this study, we evaluated the impact of long-term occupational exposure to elemental mercury vapor (Hg0) on the personality traits of ex-mercury miners. Study groups included 53 ex-miners previously exposed to Hg0 and 53 age-matched controls. Miners and controls completed the self-reporting Eysenck Personality Questionnaire and the Emotional States Questionnaire. The relationship between the indices of past occupational exposure and the observed personality traits was evaluated using Pearson's correlation coefficient and on a subgroup level by machine learning methods (regression trees). The ex-mercury miners were intermittently exposed to Hg0 for a period of 7-31 years. The means of exposure-cycle urine mercury (U-Hg) concentrations ranged from 20 to 120 microg/L. The results obtained indicate that ex-miners tend to be more introverted and sincere, more depressive, more rigid in expressing their emotions and are likely to have more negative self-concepts than controls, but no correlations were found with the indices of past occupational exposure. Despite certain limitations, results obtained by the regression tree suggest that higher alcohol consumption per se and long-term intermittent, moderate exposure to Hg0 (exposure cycle mean U-Hg concentrations > 38.7 < 53.5 microg/L) in interaction with alcohol remain a plausible explanation for the depression associated with negative self-concept found in subgroups of ex-mercury miners. This could be one of the reason for the higher risk of suicide among miners of the Idrija Mercury Mine in the last 45 years.

Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

This paper presents homogeneous clusters of patients, identified in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological descriptors. Properties of the constructed clusters clearly demonstrate the differences between female and male Alzheimer's disease patient groups. The major difference is the existence of two male subpopulations with unexpected values of intracerebral and whole brain volumes.

Candida and Fusarium species known as opportunistic human pathogens from customer-accessible parts of residential washing machines.

Energy constraints have altered consumer practice regarding the use of household washing machines. Washing machines were developed that use lower washing temperatures, smaller amounts of water and biodegradable detergents. These conditions may favour the enrichment of opportunistic human pathogenic fungi. We focused on the isolation of fungi from two user-accessible parts of washing machines that often contain microbial biofilms: drawers for detergents and rubber door seals. Out of 70 residential washing machines sampled in Slovenia, 79% were positive for fungi. In total, 72 strains belonging to 12 genera and 26 species were isolated. Among these, members of the Fusarium oxysporum and Fusarium solani species complexes, Candida parapsilosis and Exophiala phaeomuriformis represented 44% of fungi detected. These species are known as opportunistic human pathogens and can cause skin, nail or eye infections also in healthy humans. A machine learning analysis revealed that presence of detergents and softeners followed by washing temperature, represent most critical factors for fungal colonization. Three washing machines with persisting malodour that resulted in bad smelling laundry were analysed for the presence of fungi and bacteria. In these cases, fungi were isolated in low numbers (7.5 %), while bacteria Micrococcus luteus, Pseudomonas aeruginosa, and Sphingomonas species prevailed.

Changes of poultry faecal microbiota associated with Clostridium difficile colonisation.

Bacterial, fungal and archaeal microbiota was analysed in 143 chicken faecal samples from a single poultry farm. After DHPLC (denaturing high performance liquid chromatography) 15 bacterial groups, 10 fungal groups and a single archaeal species were differentiated. Samples were grouped into two clusters with significantly different frequencies of C. difficile positive and negative samples in each cluster. Acidaminococcus intestini, described here for the first time as a part of poultry faecal microbiota, was significantly more likely present in C. difficile negative samples, while presence/absence of some other microorganisms (Enterococcus cecorum, Lactobacillus galinarum, Moniliella sp. and Trichosporon asahii) was close to significance. Two other groups not reported previously for poultry, Coprobacillus sp. and Turicibacter sp. did not differ significantly between C. difficile positive and negative samples. Differences in microbiota diversity depend on animal age, but not on the presence of C. difficile. With machine learning (WEKA J48) we have defined specific combinations of microbial groups predictive for C. difficile colonisation. Microbial groups associated with C. difficile colonisation in poultry are different than those reported for humans and include bacteria as well as fungi. Also with this approach A. intestini was found to be most strongly related to C. difficile negative samples.

Gut microbiota patterns associated with colonization of different Clostridium difficile ribotypes.

C. difficile infection is associated with disturbed gut microbiota and changes in relative frequencies and abundance of individual bacterial taxons have been described. In this study we have analysed bacterial, fungal and archaeal microbiota by denaturing high pressure liquid chromatography (DHPLC) and with machine learning methods in 208 faecal samples from healthy volunteers and in routine samples with requested C. difficile testing. The latter were further divided according to stool consistency, C. difficile presence or absence and C. difficile ribotype (027 or non-027). Lower microbiota diversity was a common trait of all routine samples and not necessarily connected only to C. difficile colonisation. Differences between the healthy donors and C. difficile positive routine samples were detected in bacterial, fungal and archaeal components. Bifidobacterium longum was the single most important species associated with C. difficile negative samples. However, by machine learning approaches we have identified patterns of microbiota composition predictive for C. difficile colonization. Those patterns also differed between samples with C. difficile ribotype 027 and other C. difficile ribotypes. The results indicate that not only the presence of a single species/group is important but that certain combinations of gut microbes are associated with C. difficile carriage and that some ribotypes (027) might be associated with more disturbed microbiota than the others.

Neuroblastoma tumorigenesis is regulated through the Nm23-H1/h-Prune C-terminal interaction.

Nm23-H1 is one of the most interesting candidate genes for a relevant role in Neuroblastoma pathogenesis. H-Prune is the most characterized Nm23-H1 binding partner, and its overexpression has been shown in different human cancers. Our study focuses on the role of the Nm23-H1/h-Prune protein complex in Neuroblastoma. Using NMR spectroscopy, we performed a conformational analysis of the h-Prune C-terminal to identify the amino acids involved in the interaction with Nm23-H1. We developed a competitive permeable peptide (CPP) to impair the formation of the Nm23-H1/h-Prune complex and demonstrated that CPP causes impairment of cell motility, substantial impairment of tumor growth and metastases formation. Meta-analysis performed on three Neuroblastoma cohorts showed Nm23-H1 as the gene highly associated to Neuroblastoma aggressiveness. We also identified two other proteins (PTPRA and TRIM22) with expression levels significantly affected by CPP. These data suggest a new avenue for potential clinical application of CPP in Neuroblastoma treatment.