Development and Evaluation of a 9K SNP Addition to the Peach Ipsc 9K SNP Array v1

The IPSC 9K peach SNP array released by the international community has been a valuable tool in research and application. Even though majority of SNPs (84%) were polymorphic in the evaluation panels there were many genomic regions with low coverage, including those important for breeding. The existing peach array has been updated with 9K additional SNPs covering previously identified gaps and including recently identified SNPs important for breeding. SNPs (1,808,996) identified by sequencing 49 genomes of additional peach accessions were used as the main source of additional SNPs. Focal point strategy was used to select 8,971 SNPs within 40kb window from the 2,821 focal points distributed across the genome. Additional 129 SNPs were chosen to saturate either regions important for breeding or close the gaps larger than 100kb. The array was validated with 1,770 peach and 26 Prunus accessions (almond, plum, apricot, wild relatives). The add-on contained 7,862 SNPs evenly spread across 8 peach pseudo-molecules with only one SNP positioned on scaffold 13 covering 224.99Mbp of peach genome. The 9K add-on improved the 9K peach array by increasing the total number of usable SNPs by 7,206. The number of SNPs per chromosome increased on average by 50% with only on average 0.18% increase in total physical coverage. Number of gaps larger than 0.3 Mbp was reduced to 2 one on each chromosome 3 and 8. Overall genotyping efficiency in all material was >90% except in almond, 82%. Number of informative markers, assessed by ASSIsT software, were highest in peach 64% and lowest in almond 10%, with 61% of markers being informative in wild Prunus (12) and 35% in apricot (4) and 2 - 33% in Japanese and European plum, respectively. Among 36.2% discarded markers 33% were monomorphic and 30% shifted homozygous in material used. Those markers could be informative in different background raising total number of informative markers. Ann addition of new SNPs to array improved the density and usefulness of the array in Prunus species. The practical applications of new 16K Illumina SNP peach array will be discussed. Specified Source(s) of Funding: USDA-NIFA-SCRI-Ros- BREED (2014-51181-22378

Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization

Despite the success of automated pattern recognition methods in problems of human brain tumor diagnostic classification, limited attention has been paid to the issue of automated data quality assessment in the field of MRS for neuro-oncology. Beyond some early attempts to address this issue, the current standard in practice is MRS quality control through human (expert-based) assessment. One aspect of automatic quality control is the problem of detecting artefacts in MRS data. Artefacts, whose variety has already been reviewed in some detail and some of which may even escape human quality control, have a negative influence in pattern recognition methods attempting to assist tumor characterization. The automatic detection of MRS artefacts should be beneficial for radiology as it guarantees more reliable tumor characterizations, as well as the development of more robust pattern recognition-based tumor classifiers and more trustable MRS data processing and analysis pipelines. Feature extraction methods have previously been used to help distinguishing between good and bad quality spectra to apply subsequent supervised pattern recognition techniques. In this study, we apply feature extraction differently and use a variant of a method for blind source separation, namely Convex Non-Negative Matrix Factorization, to unveil MRS signal sources in a completely unsupervised way. We hypothesize that, while most sources will correspond to the different tumor patterns, some of them will reflect signal artefacts. The experimental work reported in this paper, analyzing a combined short and long echo time 1H-MRS database of more than 2000 spectra acquired at 1.5T and corresponding to different tumor types and other anomalous masses, provides a first proof of concept that points to the possible validity of this approach

On the Design of a Web-Based Decision Support System for Brain Tumour Diagnosis Using Distributed Agents

This paper introduces HealthAgents, an EC-funded research project to improve the classification of brain tumours through multi-agent decision support over a distributed network of local databases or Data Marts. HealthAgents will not only develop new pattern recognition methods for a distributed classification and analysis of in vivo MRS and ex vivo/in vitro HRMAS and DNA data, but also define a method to assess the quality and usability of a new candidate local database containing a set of new cases, based on a compatibility score

Functional Role of Glutamine 28 and Arginine 39 in Double Stranded RNA Cleavage by Human Pancreatic Ribonuclease

Human pancreatic ribonuclease (HPR), a member of RNase A superfamily, has a high activity on double stranded (ds) RNA. By virtue of this activity HPR appears to be involved in the host-defense against pathogenic viruses. To delineate the mechanism of dsRNA cleavage by HPR, we have investigated the role of glutamine 28 and arginine 39 of HPR in its activity on dsRNA. A non-basic residue glycine 38, earlier shown to be important for dsRNA cleavage by HPR was also included in the study in the context of glutamine 28 and arginine 39. Nine variants of HPR respectively containing Q28A, Q28L, R39A, G38D, Q28A/R39A, Q28L/R39A, Q28A/G38D, R39A/G38D and Q28A/G38D/R39A mutations were generated and functionally characterized. The far-UV CD-spectral analysis revealed all variants, except R39A, to have structures similar to that of HPR. The catalytic activity of all HPR variants on single stranded RNA substrate was similar to that of HPR, whereas on dsRNA, the catalytic efficiency of all single residue variants, except for the Q28L, was significantly reduced. The dsRNA cleavage activity of R39A/G38D and Q28A/G38D/R39A variants was most drastically reduced to 4% of that of HPR. The variants having reduced dsRNA cleavage activity also had reduction in their dsDNA melting activity and thermal stability. Our results indicate that in HPR both glutamine 28 and arginine 39 are important for the cleavage of dsRNA. Although these residues are not directly involved in catalysis, both arginine 39 and glutamine 28 appear to be facilitating a productive substrate-enzyme interaction during the dsRNA cleavage by HPR

A statistical analysis protocol for the time-differentiated target temperature management after out-of-hospital cardiac arrest (TTH48) clinical trial

Background: The TTH48 trial aims to determine whether prolonged duration (48 hours) of targeted temperature management (TTM) at 33 (+/- 1) degrees C results in better neurological outcomes compared to standard duration (24 hours) after six months in comatose out-of-hospital cardiac arrest (OHCA) patients.Methods: TTH48 is an investigator-initiated, multicentre, assessor-blinded, randomised, controlled superiority trial of 24 and 48 hours of TTM at 33 (+/- 1) degrees C performed in 355 comatose OHCA patients aged 18 to 80 years who were admitted to ten intensive care units (ICUs) in six Northern European countries.The primary outcome of the study is the Cerebral Performance Category (CPC) score observed at six months after cardiac arrest. CPC scores of 1 and 2 are defined as good neurological outcomes, and CPC scores of 3, 4 and 5 are defined as poor neurological outcomes. The secondary outcomes are as follows: mortality within six months after cardiac arrest, CPC at hospital discharge, Glasgow Coma Scale (GCS) score on day 4, length of stay in ICU and at hospital and the presence of any adverse events such as cerebral, circulatory, respiratory, gastrointestinal, renal, metabolic measures, infection or bleeding.With the planned sample size, we have 80% power to detect a 15% improvement in good neurological outcomes at a two-sided statistical significance level of 5%.Discussion: We present a detailed statistical analysis protocol (SAP) that specifies how primary and secondary outcomes should be evaluated. We also predetermine covariates for adjusted analyses and pre-specify sub-groups for sensitivity analyses. This pre-planned SAP will reduce analysis bias and add validity to the findings of this trial on the effect of length of TTM on important clinical outcomes after cardiac arrest

An analysis of the three-dimensional kinetics and kinematics of maximal effort punches among amateur boxers.

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Performance Analysis in Sport on 27-9-18, available online: https://doi.org/10.1080/24748668.2018.1525651The purpose of this study was to quantify the 3D kinetics and kinematics of six punch types among amateur boxers. Fifteen males (age: 24.9 ± 4.2 years; stature: 1.78 ± 0.1 m; body mass: 75.3 ± 13.4 kg; boxing experience: 6.3 ± 2.8 years) performed maximal effort punches against a suspended punch bag during which upper body kinematics were assessed via a 3D motion capture system, and ground reaction forces (GRF) of the lead and rear legs via two force plates. For all variables except elbowjoint angular velocity, analysis revealed significant (P < 0.05) differences between straight, hook and uppercut punches. The lead hook exhibited the greatest peak fist velocity (11.95 ± 1.84 m/s), the jab the shortest delivery time (405 ± 0.15 ms), the rear uppercut the greatest shoulder-joint angular velocity (1069.8 ± 104.5°/s), and the lead uppercut the greatest elbow angular velocity (651.0 ± 357.5°/s). Peak resultant GRF differed significantly (P < 0.05) between rear and lead legs for the jab punch only. Whilst these findings provide novel descriptive data for coaches and boxers, future research should examine if physical and physiological capabilities relate to the key biomechanical qualities associated with maximal punching performance

QTL mapping for brown rot (Monilinia fructigena) resistance in an intraspecific peach (Prunus persica L. Batsch) F1 progeny

Brown rot (BR) caused by Monilinia spp. leads to significant post-harvest losses in stone fruit production, especially peach. Previous genetic analyses in peach progenies suggested that BR resistance segregates as a quantitative trait. In order to uncover genomic regions associated with this trait and identify molecular markers for assisted selection (MAS) in peach, an F1 progeny from the cross "Contender" (C, resistant) 7 "Elegant Lady" (EL, susceptible) was chosen for quantitative trait loci (QTL) analysis. Over two phenotyping seasons, skin (SK) and flesh (FL) artificial infections were performed on fruits using a Monilinia fructigena isolate. For each treatment, infection frequency (if) and average rot diameter (rd) were scored. Significant seasonal and intertrait correlations were found. Maturity date (MD) was significantly correlated with disease impact. Sixty-three simple sequence repeats (SSRs) plus 26 single-nucleotide polymorphism (SNP) markers were used to genotype the C 7 EL population and to construct a linkage map. C 7 EL map included the eight Prunus linkage groups (LG), spanning 572.92 cM, with an average interval distance of 6.9 cM, covering 78.73 % of the peach genome (V1.0). Multiple QTL mapping analysis including MD trait as covariate uncovered three genomic regions associated with BR resistance in the two phenotyping seasons: one containing QTLs for SK resistance traits near M1a (LG C 7 EL-2, R2 = 13.1-31.5 %) and EPPISF032 (LG C 7 EL-4, R2 = 11-14 %) and the others containing QTLs for FL resistance, near markers SNP_IGA_320761 and SNP_IGA_321601 (LG3, R2 = 3.0-11.0 %). These results suggest that in the C 7 EL F1 progeny, skin resistance to fungal penetration and flesh resistance to rot spread are distinguishable mechanisms constituting BR resistance trait, associated with different genomic regions. Discovered QTLs and their associated markers could assist selection of new cultivars with enhanced resistance to Monilinia spp. in fruit

Agent-Based Distributed Decision Support System for Brain Tumour Diagnosis and Prognosis

Brain tumours remain an important cause of morbidity and mortality in Europe. Diagnosis using Magnetic Resonance Imaging (MRI) is non-invasive, but only achieves 60-90% accuracy depending on the tumour type and grade. The current gold standard classification of brain tumours by biopsy and histopathological analysis involves invasive surgical procedure and incurs a risk. Nowadays the diagnosis and treatment of brain tumours is typically based on clinical symptoms, radiological appearance and often a histopathological diagnosis of a biopsy. However, treatment response of histologically or radiologically-similar tumours can vary widely, particularly in children. Magnetic Resonance Spectroscopy (MRS) is a non-invasive technique for determining the tissue biochemical composition (metabolomic profile) of a tumour. Additionally, the genomic profile, determined using DNA microarrays, facilitates the classification of tumour grades and types not trivially distinguished by morphologic appearance. Thus, we propose the definition of a decision support system (DSS) which employs MRS and genomic profiles. This DSS will deploy an ad hoc agent-based architecture in order to negotiate a distributed diagnostic tool for brain tumours, implement data mining techniques, transfer clinical data and extract information. The distributed nature of our approach will help the users to observe local centre policies for sharing information whilst allowing them to benefit from the use of distributed data warehouse (d-DWH). Moreover, it will permit the design of local classifiers targeting a specific patient population. We argue that this new information for classifying tumours along with clinical data, should be securely and easy accessible in order to improve the diagnosis and prognosis of tumours. All data will be stored anonymously, and securely through a network of data marts based on all this information acquired and stored at centres throughout Europe. This network will grant bona-fide access to an organisation in return for its contribution of clinical data to a d-DWH/Decision Support System (d-DSS). This rest of this paper is structured as follows. First, we provide some background on the underlying technologies for this project: brain tumour detection and agent technology. Then we provide the architectural specification. Finally, we conclude with our future work