A pervasive approach to a real-time intelligent decision support system in intensive medicine

The decision on the most appropriate procedure to provide to the patients the best healthcare possible is a critical and complex task in Intensive Care Units (ICU). Clinical Decision Support Systems (CDSS) should deal with huge amounts of data and online monitoring, analyzing numerous parameters and providing outputs in a short real-time. Although the advances attained in this area of knowledge new challenges should be taken into account in future CDSS developments, principally in ICUs environments. The next generation of CDSS will be pervasive and ubiquitous providing the doctors with the appropriate services and information in order to support decisions regardless the time or the local where they are. Consequently new requirements arise namely the privacy of data and the security in data access. This paper will present a pervasive perspective of the decision making process in the context of INTCare system, an intelligent decision support system for intensive medicine. Three scenarios are explored using data mining models continuously assessed and optimized. Some preliminary results are depicted and discussed.Fundação para a Ciência e a Tecnologia (FCT

On the temperatures developed in CFRP drilling using uncoated WC-Co tools Part I: Workpiece constituents, cutting speed and heat dissipation

Abstract This work investigated the influence of the material properties and cutting speed on the heat dissipation in the drilling of carbon fibre reinforced plastic (CFRP) composites using uncoated WC-Co tools. The first stage of the investigation compared the heat dissipation in drilling three different CFRP systems by measuring the temperatures developed at different distances around the borehole using thermocouples and an infra-red camera. The second stage studied the influence of cutting speed on the maximum temperatures developed in the workpiece in drilling a selected CFRP system in a cutting speed range from 50 to 200 m/min. The cross-linking density of the polymer matrix and the degree of crystallinity and structure of the carbon fibres exhibited a significant influence on the overall temperature and on the heat dissipation, whereas 150–200 m/min cutting speeds yielded higher concentration of heat, compared to 50–100 m/min cutting speeds

Dynamical complexity in the C.elegans neural network

We model the neuronal circuit of the C.elegans soil worm in terms of a Hindmarsh-Rose system of ordinary differential equa- tions, dividing its circuit into six communities which are determined via the Walktrap and Louvain methods. Using the numerical solution of these equations, we analyze important measures of dynamical com- plexity, namely synchronicity, the largest Lyapunov exponent, and the ?AR auto-regressive integrated information theory measure. We show that ?AR provides a useful measure of the information contained in the C.elegans brain dynamic network. Our analysis reveals that the C.elegans brain dynamic network generates more information than the sum of its constituent parts, and that attains higher levels of integrated information for couplings for which either all its communities are highly synchronized, or there is a mixed state of highly synchronized and de- synchronized communities

Author Correction: A chickpea genetic variation map based on the sequencing of 3,366 genomes

In Extended Data Fig. 1 of this Article, the labels ‘Market class’ and ‘Biological status’ were inadvertently swapped. In the corresponding figure legend, “Track 1: Biological status; Track 2: Market class;” should have been “Track 1: Market class; Track 2: Biological status;”. The original Article has been corrected online

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Diabetes mellitus: pathophysiological changes and therap

5Gs for crop genetic improvement

Here we propose a 5G breeding approach for bringing much-needed disruptive changes to crop improvement. These 5Gs are Genome assembly, Germplasm characterization, Gene function identification, Genomic breeding (GB), and Gene editing (GE). In our view, it is important to have genome assemblies available for each crop and a deep collection of germplasm characterized at sequencing and agronomic levels for identification of marker-trait associations and superior haplotypes. Systems biology and sequencing-based mapping approaches can be used to identify genes involved in pathways leading to the expression of a trait, thereby providing diagnostic markers for target traits. These genes, markers, haplotypes, and genome-wide sequencing data may be utilized in GB and GE methodologies in combination with a rapid cycle breeding strategy

Humidity sensing using PMMA-PMTGA-PMMA polymer in low coherence interferometric system

The authors describe a new optical humidity sensing system based on a multiplexed low coherence dual interferometric system allowing an absolute measurement at four different locations. The sensor is composed of a PMMA-PMTGA-PMMA polymer that combines good sensitivity and reproducibility for an overall accuracy of 1.21% relative humidity at equilibrium

Prediction of breeding values using genome wide markers for yield related traits in chickpea

Genomic selection (GS) is a modern breeding approach that predicts breeding value of lines and makes selection prior to phenotyping using genome-wide molecular marker profiling. GS can help to overcome the issues related to long selection cycles by accelerating breeding cycles so that the rate of annual genetic gain can be enhanced. In view of low productivity in chickpea, a collection of 320 elite breeding lines was selected as the “training population”. Training population was phenotyped for four yield and yield related traits at two locations for two seasons under rain-fed and irrigated conditions. Training population was also genotyped using KASPar assays (651) and DArT arrays (15,360). Genome-wide marker profiling data in combination with phenotypic data was used with six statistical methods to predict genomic estimated breeding values (GEBVs) for four yields and yield related traits. Correlation inside training (CIT) for the models tested varied from 0.138 to 0.912. Heat map analysis using genotyping data to understand the relationship within these lines suggested possibility of two different groups. As population structure can influence the accuracy in GS, analysis was re-performed by implementing population structure for calculation of GEBV. Population structure significantly affected the CIT that varied from 0.001 to 0.745 for desi group, and 0.004 to 0.727 for kabuli group. In general, Bayesian based model showed better prediction accuracy. The best prediction accuracy was obtained for 100 seed weight while prediction accuracy was low in case of seed yield

Marker-Assisted Selection for Biotic Stress Resistance in Peanut

Marker-assisted selection (MAS) in peanut has lagged behind other major crops. This is due in good part to the genetic bottleneck that occurred at tetraploidization, resulting in a limited amount of molecular variability detectable among accessions of the cultivated species. However, marker maps have been developed from wild species, and, to an increasing extent, the cultivated species using new marker types. It is expected that, with the increase in number of simple sequence repeat (SSR) markers and development of single nucleotide polymorphism (SNP)-based markers, there will be greater use of MAS in both interspecific and cultivated accession crosses. MAS has already proven itself to be useful in developing cultivars possessing resistance to the root-knot nematode, and is being used for selection for resistance to late leaf spot and rust, as well as for the high-oleic-acid trait