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

Genetic Variability Study in a Wide Germplasm of Domesticated Peach Through High Throughput

Peach (Prunus persica (L.) Batsch) is one of the most economically important fruit crops in temperate areas. Classical fruit tree breeding is generally slow and inefficient. Molecular markers could improve its efficiency but, although nowadays many Mendelian traits are mapped in peach and SSR markers have been found to be linked to some of the key major genes, its use in breeding programs is still limited. Main reasons for that are insufficient linkage between the markers and the genes and the lack of markers suitable for medium-high degree of multiplexing. To address this limitation, about 1,300 peach cultivars were genotyped with the 9K peach SNP chip (Verde et al. 2012) in the frame of FruitBreedomics project. This germplasm was chosen to be representative of the genetic diversity present in five germplasm collection in Europe and in China. Out of the 8144SNPs present in the chip, about 4300 were positively genotyped and used for the further analysis. The average number of heterozygous loci in the genotyped accessions was 1186 (spanning from 13 to 2775). The preliminary results of the population structure reveal three main subpopulations and the presence of high number of admixed individuals. LD seems to decay at distance longer than ca. 1 Mb. These results will be instrumental for implementing LD-based mapping of QTLs and genes in peach

SK2 Channels Associate With mGlu1α Receptors and CaV2.1 Channels in Purkinje Cells

The small-conductance, Ca2+-activated K+ (SK) channel subtype SK2 regulates the spike rate and firing frequency, as well as Ca2+ transients in Purkinje cells (PCs). To understand the molecular basis by which SK2 channels mediate these functions, we analyzed the exact location and densities of SK2 channels along the neuronal surface of the mouse cerebellar PCs using SDS-digested freeze-fracture replica labeling (SDS-FRL) of high sensitivity combined with quantitative analyses. Immunogold particles for SK2 were observed on post- and pre-synaptic compartments showing both scattered and clustered distribution patterns. We found an axo-somato-dendritic gradient of the SK2 particle density increasing 12-fold from soma to dendritic spines. Using two different immunogold approaches, we also found that SK2 immunoparticles were frequently adjacent to, but never overlap with, the postsynaptic density of excitatory synapses in PC spines. Co-immunoprecipitation analysis demonstrated that SK2 channels form macromolecular complexes with two types of proteins that mobilize Ca2+: CaV2.1 channels and mGlu1α receptors in the cerebellum. Freeze-fracture replica double-labeling showed significant co-clustering of particles for SK2 with those for CaV2.1 channels and mGlu1α receptors. SK2 channels were also detected at presynaptic sites, mostly at the presynaptic active zone (AZ), where they are close to CaV2.1 channels, though they are not significantly co-clustered. These data demonstrate that SK2 channels located in different neuronal compartments can associate with distinct proteins mobilizing Ca2+, and suggest that the ultrastructural association of SK2 with CaV2.1 and mGlu1α provides the mechanism that ensures voltage (excitability) regulation by distinct intracellular Ca2+ transients in PCs

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

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

Satellite-Based Carbon Estimation in Scotland: AGB and SOC

The majority of state-of-the-art research employs remote sensing on AGB (Above Ground Biomass) and SOC (Soil Organic Carbon) separately, although some studies indicate a positive correlation between the two. We intend to combine the two domains in our research to improve state-of-the-art total carbon estimation. We begin by establishing a baseline model in our study area in Scotland, using state-of-the-art methodologies in the SOC and AGB domains. The effects of feature engineering techniques such as variance inflation factor and feature selection on machine learning models are then investigated. This is extended by combining predictor variables from the two domains. Finally, we leverage the possible correlation between AGB and SOC to establish a relationship between the two and propose novel models in an attempt to outperform the state-of-the-art results. We compared three machine learning techniques, boosted regression tree, random forest, and xgboost. These techniques have been demonstrated to be the most effective in both domains. This research makes three contributions: (i) Including Digital Elevation Map (DEM) as a predictor variable in the AGB model improves the model result by 13.5 % on average across the three machine learning techniques experimented, implying that DEM should be considered for AGB estimation as well, despite the fact that it has previously been used exclusively for SOC estimation. (ii) Using SOC and SOC Density improves the prediction of the AGB model by a significant 14.2% on average compared to the state-of-the-art baseline (When comparing the R2 value across all three modeling techniques in Model B and Model H, there is an increase from 0.5016 to 0.5604 for BRT, 0.4958 to 0.5925 for RF and 0.5161 to 0.5750 for XGB), which strengthens our experiment results and suggests a future research direction of combining AGB and SOC as a joint study domain. (iii) Including AGB as a predictor variable for SOC improves model performance for Random Forest, but reduced performance for Boosted Regression tree and XG Boost, indicating that the results are specific to ML models and more research is required on the feature space and modeling techniques. Additionally, we propose a method for estimating total carbon using data from Sentinel 1, Sentinel 2, Landsat 8, Digital Elevation, and the Forest Inventory