Simulation study on the long term utilization of QTL for a disease trait in fish breeding programs

The aim of this study was to investigate, through stochastic simulation, the potential of using genome information and more particularly, information on the identified IPN resistance QTL in salmon breeding program in Norway. The breeding goal of the simulation was composed of two traits. The first trait was measured on the selection candidates as a growth rate whereas the second was measured only on full-sibs of the breeding candidates. The IPN resistance QTL had a very strong effect and was responsible for 83% of the genetic variation. Thus, the potential of using GAS was also tested on a QTL with a small effect only responsible for 20% of the genetic variation. Different values for genetic correlation between these two traits have been tested, 0 and -0.36. The genetic model assumed for the second trait was composed of a QTL segregating together with polygenes. Thus, two schemes were implemented, Gene-Assisted Selection (GAS) - which takes into account QTL information - and Standard Phenotypic Selection (PHE). The genetic gain from GAS and PHE obtained by combining BLUP EBVs and optimum contribution were compared at the same rate of inbreeding. The results showed that GAS led to a faster fixation of the favourable allele and achieved more gain for the second trait in short-term than the PHE. This increased gain is due to the utilization of the optimum contribution procedure. However, after the fixation time, the genetic gain was not maintained and resulted in a long-term loss compare to PHE. In previous publications, it has been showed that using optimization of the weight given to the QTL in Optimized Gene-Assisted selection (GAO) had an effect on avoiding long-term loss. Therefore, it could be interesting to implement GAO in salmon breeding program for the IPN resistance QTL

Charged particle detection performances of CMOS pixel sensors produced in a 0.18 um process with a high resistivity epitaxial layer

The apparatus of the ALICE experiment at CERN will be upgraded in 2017/18 during the second long shutdown of the LHC (LS2). A major motivation for this upgrade is to extend the physics reach for charmed and beauty particles down to low transverse momenta. This requires a substantial improvement of the spatial resolution and the data rate capability of the ALICE Inner Tracking System (ITS). To achieve this goal, the new ITS will be equipped with 50 um thin CMOS Pixel Sensors (CPS) covering either the 3 innermost layers or all the 7 layers of the detector. The CPS being developed for the ITS upgrade at IPHC (Strasbourg) is derived from the MIMOSA 28 sensor realised for the STAR-PXL at RHIC in a 0.35 um CMOS process. In order to satisfy the ITS upgrade requirements in terms of readout speed and radiation tolerance, a CMOS process with a reduced feature size and a high resistivity epitaxial layer should be exploited. In this respect, the charged particle detection performance and radiation hardness of the TowerJazz 0.18 um CMOS process were studied with the help of the first prototype chip MIMOSA 32. The beam tests performed with negative pions of 120 GeV/c at the CERN-SPS allowed to measure a signal-to-noise ratio (SNR) for the non-irradiated chip in the range between 22 and 32 depending on the pixel design. The chip irradiated with the combined dose of 1 MRad and 10^13 n_eq/cm^2 was observed to yield a SNR ranging between 11 and 23 for coolant temperatures varying from 15 C to 30 C. These SNR values were measured to result in particle detection efficiencies above 99.5% and 98% before and after irradiation respectively. These satisfactory results allow to validate the TowerJazz 0.18 um CMOS process for the ALICE ITS upgrade.Comment: (v2) Added hyper-links; (v3) A typo correcte

Development of CMOS Pixel Sensors fully adapted to the ILD Vertex Detector Requirements

CMOS Pixel Sensors are making steady progress towards the specifications of the ILD vertex detector. Recent developments are summarised, which show that these devices are close to comply with all major requirements, in particular the read-out speed needed to cope with the beam related background. This achievement is grounded on the double- sided ladder concept, which allows combining signals generated by a single particle in two different sensors, one devoted to spatial resolution and the other to time stamp, both assembled on the same mechanical support. The status of the development is overviewed as well as the plans to finalise it using an advanced CMOS process.Comment: 2011 International Workshop on Future Linear Colliders (LCWS11), Granada, Spain, 26-30 September 201

Development of CMOS pixel sensors for tracking and vertexing in high energy physics experiments

CMOS pixel sensors (CPS) represent a novel technological approach to building charged particle detectors. CMOS processes allow to integrate a sensing volume and readout electronics in a single silicon die allowing to build sensors with a small pixel pitch ($\sim 20 \mu m$) and low material budget ($\sim 0.2-0.3\% X_0$) per layer. These characteristics make CPS an attractive option for vertexing and tracking systems of high energy physics experiments. Moreover, thanks to the mass production industrial CMOS processes used for the manufacturing of CPS the fabrication construction cost can be significantly reduced in comparison to more standard semiconductor technologies. However, the attainable performance level of the CPS in terms of radiation hardness and readout speed is mostly determined by the fabrication parameters of the CMOS processes available on the market rather than by the CPS intrinsic potential. The permanent evolution of commercial CMOS processes towards smaller feature sizes and high resistivity epitaxial layers leads to the better radiation hardness and allows the implementation of accelerated readout circuits. The TowerJazz $0.18 \mu m$ CMOS process being one of the most relevant examples recently became of interest for several future detector projects. The most imminent of these project is an upgrade of the Inner Tracking System (ITS) of the ALICE detector at LHC. It will be followed by the Micro-Vertex Detector (MVD) of the CBM experiment at FAIR. Other experiments like ILD consider CPS as one of the viable options for flavour tagging and tracking sub-systems

Combining Individual Phenotypes of Feed Intake With Genomic Data to Improve Feed Efficiency in Sea Bass

Measuring individual feed intake of fish in farms is complex and precludes selective breeding for feed conversion ratio (FCR). Here, we estimated the individual FCR of 588 sea bass using individual rearing under restricted feeding. These fish were also phenotyped for their weight loss at fasting and muscle fat content that were possibly linked to FCR. The 588 fish were derived from a full factorial mating between parental lines divergently selected for high (F+) or low (F–) weight loss at fasting. The pedigree was known back to the great grand-parents. A subset of 400 offspring and their ancestors were genotyped for 1,110 SNPs which allowed to calculate the genomic heritability of traits. Individual FCR and growth rate in aquarium were both heritable (genomic h2 = 0.47 and 0.76, respectively) and strongly genetically correlated (−0.98) meaning that, under restricted feeding, faster growing fish were more efficient. FCR and growth rate in aquariums were also significantly better for fish with both parents from F– (1.38), worse for fish with two parents F+ (1.51) and intermediate for cross breed fish (F+/F– or F–/F+ at 1.46). Muscle fat content was positively genetically correlated to growth rate in aquarium and during fasting. Thus, selecting for higher growth rate in aquarium, lower weight loss during fasting and fatter fish could improve FCR in aquarium. Improving these traits would also improve FCR of fish in normal group rearing conditions, as we showed experimentally that groups composed of fish with good individual FCR were significantly more efficient. The FCR of groups was also better when the fish composing the groups had, on average, lower estimated breeding values for growth rate during fasting (losing less weight). Thus, improving FCR in aquarium and weight loss during fasting is promising to improve FCR of fish in groups but a selection response experiment needs to be done. Finally, we showed that the reliability of estimated breeding values was higher (from+10% up to +125%) with a genomic-based BLUP model than with a traditional pedigree-based BLUP, showing that genomic data would enhance the accuracy of the prediction of EBV of selection candidates

Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study

Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects. In this work, we present the first study on predicting hydration status using machine learning models from single-subject experiments, which involve 32 exercise sessions of constant moderate intensity performed with and without fluid intake. During exercise, we measured four noninvasive physiological and sweat biomarkers including heart rate, core temperature, sweat sodium concentration, and whole-body sweat rate. Sweat sodium concentration was measured from six body regions using absorbent patches. We used three machine learning models to determine the percentage of body weight loss as an indicator of dehydration with these biomarkers and compared the prediction accuracy. The results on this single subject show that these models gave similar mean absolute errors, while in general the nonlinear models slightly outperformed the linear model in most of the experiments. The prediction accuracy of using the whole-body sweat rate or heart rate was higher than using core temperature or sweat sodium concentration. In addition, the model trained on the sweat sodium concentration collected from the arms gave slightly better accuracy than from the other five body regions. This exploratory work paves the way for the use of these machine learning models to develop personalized health monitoring together with emerging, noninvasive wearable sensor devices

Spectrally-resolved measurement of concentrated light distributions for Fresnel lens concentrators

A test method that measures spectrally resolved irradiance distribution for a concentrator photovoltaic (CPV) optical system is presented. In conjunction with electrical I-V curves, it is a means to visualize and characterize the effects of chromatic aberration and nonuniform flux profiles under controllable testing conditions. The indoor characterization test bench, METHOD (Measurement of Electrical, Thermal and Optical Devices), decouples the temperatures of the primary optical element (POE) and the cell allowing their respective effects on optical and electrical performance to be analysed. In varying the temperature of the POE, the effects on electrical efficiency, focal distance, spectral sensitivity, acceptance angle and multi-junction current matching profiles can be quantified. This work presents the calibration procedures to accurately image the spectral irradiance distribution of a CPV system and a study of system behavior over lens temperature

Optimisation of CMOS pixel sensors for high performance vertexing and tracking

CMOS Pixel Sensors tend to become relevant for a growing spectrum of charged particle detection instruments. This comes mainly from their high granularity and low material budget. However, several potential applications require a higher read-out speed and radiation tolerance than those achieved with available devices based on a 0.35 micrometers feature size technology. This paper shows preliminary test results of new prototype sensors manufactured in a 0.18 micrometers process based on a high resistivity epitaxial layer of sizeable thickness. Grounded on these observed performances, we discuss a development strategy over the coming years to reach a full scale sensor matching the specifications of the upgraded version of the Inner Tracking System (ITS) of the ALICE experiment at CERN, for which a sensitive area of up to about 10 square meters may be equipped with pixel sensors.Comment: Presented at the Vienna Conference on Instrumentation 2013 4 pages, 5 figure

Real-time smart multisensing wearable platform for monitoring sweat biomarkers during exercise

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise in hot and humid conditions. Real-time noninvasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. In this work, we describe a platform that in- cludes different sweat biomonitoring prototypes of cost-effective, smart wearable devices for continuous biomonitoring of sweat during exercise. One prototype is based on conformable and disposable soft sensing patches with an integrated multi-sensor array requiring the integration of different sensors and printed sensors with their corresponding functionalization protocols on the same substrate. The second is based on silicon based sensors and paper microfluidics. Both platforms integrate a multi-sensor array for measuring sodium, potassium, and pH in sweat. We show preliminary results obtained from the multi-sensor prototypes placed on two athletes during exercise. We also show that the machine learning algorithms can predict the percentage of body weight loss during exercise from biomarkers such as heart rate and sweat sodium concentration collected over multiple subjects

Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. This work describes a wearable sweat biomonitoring patch incorporating printed electrochemical sensors into a plastic microfluidic sweat collector and data analysis that shows the real-time recorded sweat biomarkers can be used to predict a physiological biomarker. The system was placed on subjects carrying out an hour-long exercise session and results were compared to a wearable system using potentiometric robust silicon-based sensors and to commercially available HORIBA-LAQUAtwin devices. Both prototypes were applied to the real-time monitoring of sweat during cycling sessions and showed stable readings for around an hour. Analysis of the sweat biomarkers collected from the printed patch prototype shows that their real-time measurements correlate well (correlation coefficient ≥0.65 ) with other physiological biomarkers such as heart rate and regional sweat rate collected in the same session. We show for the first time, that the real-time sweat sodium and potassium concentration biomarker measurements from the printed sensors can be used to predict the core body temperature with root mean square error (RMSE) of 0.02 °C which is 71% lower compared to the use of only the physiological biomarkers. These results show that these wearable patch technologies are promising for real-time portable sweat monitoring analytical platforms, especially for athletes performing endurance exercise