A two-step Bayesian network approach to identify key SNPs associated to multiple phenotypic traits in four purebred laying hen lines.

When purebred laying hen chicks hatch, they remain at a rearing farm until approximately 17 weeks of age, after which they are transferred to a laying farm. Chicks or pullets are removed from the flocks during these 17 weeks if they display any rearing abnormality. The aim of this study was to investigate associations between single nucleotide polymorphisms (SNPs) and rearing success of 4 purebred White Leghorns layer lines by implementing a Bayesian network approach. Phenotypic traits and SNPs of four purebred genetic White Leghorn layer lines were available for 23,000 rearing batches obtained between 2010 and 2020. Associations between incubation traits (clutch size, embryo mortality), rearing traits (genetic line, first week mortality, rearing abnormalities, natural death, rearing success, pullet flock age, and season) and SNPs were analyzed, using a two-step Bayesian Network (BN) approach. Furthermore, the SNPs were connected to their corresponding genes, which were further explored in bioinformatics databases. BN analysis revealed a total of 28 SNPs associated with some of the traits: ten SNPs were associated with clutch size, another 10 with rearing abnormalities, a single SNP with natural death, and seven SNPs with first week mortality. Exploration via bioinformatics databases showed that one of the SNPs (ENAH) had a protein predicted network composed of 11 other proteins. The major hub of this SNP was CDC42 protein, which has a role in egg production and reproduction. The results highlight the power of BNs in knowledge discovery and how their application in complex biological systems can help getting a deeper understanding of functionality underlying genetic variation of rearing success in laying hens. Improved welfare and production might result from the identified SNPs. Selecting for these SNPs through breeding could reduce stress and increase livability during rearing.

Longitudinal individual predictions from irregular repeated measurements data.

Intensive longitudinal data can be used to explore important associations and patterns between various types of inputs and outcomes. Nonlinear relations and irregular measurement occasions can pose problems to develop an accurate model for these kinds of data. This paper focuses on the development, fitting and evaluation of a prediction model with irregular intensive longitudinal data. A three-step process for developing a prediction tool for (daily) monitoring and prediction is outlined and illustrated for intensive weight measurements in piglets. Step 1 addresses a nonlinear relation in the data by developing and applying a normalizing transformation. Step 2 addresses the intermittent nature of the time points by aligning the measurement times to a common time grid with a broken-stick model. Step 3 addresses the prediction problem by selecting and evaluating inputs and covariates in the model to obtain accurate predictions. The final model predicts future outcomes accurately, while allowing for nonlinearities between input and output and for different measurement histories of individuals. The methodology described can be used to develop a tool to deal with intensive irregular longitudinal data that uses the available information in an optimal way. The resulting tool demonstrated to perform well for piglet weight prediction and can be adapted to many different applications.

Automated Step Detection in Inertial Measurement Unit Data From Turkeys.

Locomotion is an important welfare and health trait in turkey production. Current breeding values for locomotion are often based on subjective scoring. Sensor technologies could be applied to obtain objective evaluation of turkey gait. Inertial measurement units (IMUs) measure acceleration and rotational velocity, which makes them attractive devices for gait analysis. The aim of this study was to compare three different methods for step detection from IMU data from turkeys. This is an essential step for future feature extraction for the evaluation of turkey locomotion. Data from turkeys walking through a corridor with IMUs attached to each upper leg were annotated manually. We evaluated change point detection, local extrema approach, and gradient boosting machine in terms of step detection and precision of start and end point of the steps. All three methods were successful in step detection, but local extrema approach showed more false detections. In terms of precision of start and end point of steps, change point detection performed poorly due to significant irregular delay, while gradient boosting machine was most precise. For the allowed distance to the annotated steps of 0.2 s, the precision of gradient boosting machine was 0.81 and the recall was 0.84, which is much better in comparison to the other two methods (<0.61). At an allowed distance of 1 s, performance of the three models was similar. Gradient boosting machine was identified as the most accurate for signal segmentation with a final goal to extract information about turkey gait; however, it requires an annotated training dataset.

Machine learning to further improve the decision which boar ejaculates to process into artificial insemination doses.

Current artificial insemination (AI) laboratory practices assess semen quality of each boar ejaculate to decide which ones to process into AI doses. This decision is aided with two, world-wide used, motility parameters that come available through computer assisted semen analysis (CASA). This decision process, however, still results in AI doses with variable and sometimes suboptimal fertility outcomes (e.g., small litter size). The hypothesis was that the decision which ejaculates to process into AI doses can be improved by adding more data from CASA systems, and data from other sources, in combination with a data-driven model. Available data consisted of ejaculates that passed the initial decision, and thus, were processed into AI doses and used to inseminate sows. Data were divided into a training set (6793 records) and a validation set (1191 records) for model development, and an independent test set (1434 records) for performance assessment. Gradient Boosting Machine (GBM) models were developed to predict four fertility phenotypes of interest (gestation length, total number born, number born alive, and number of stillborn piglets). Each fertility phenotype was considered as a numeric and as a binary outcome parameter, totaling to eight different fertility phenotypes. Data used to further improve the decision process originated from four sources: 1) CASA information, 2) boar ejaculate information, 3) breeding value estimations, and 4) weather information. These data were used to create seven prediction sets, where each new set added parameters to the ones included in the previous set. The GBM models predicted fertility phenotypes with low correlations (for numeric phenotypes) and area under the curve values (for binary phenotypes) on the test data. Hence, results demonstrated that a combination of more data and GBM did not enable further improvement of the AI dose quality checks, resulting in the rejection of our hypothesis. However, our study revealed parameters affecting boar ejaculate fertility which were not used in today's decision process. These parameters (listed in the top 10 in at least four GBM models) included one parameter associated with boar ejaculate information, two with breeding value estimations, five with CASA information, and one with weather information. These parameters, therefore, should be further investigated for their potential value when assessing the quality of boar ejaculates in daily routine AI doses processing.

Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking.

Damaging behaviors, like feather pecking (FP), have large economic and welfare consequences in the commercial laying hen industry. Selective breeding can be used to obtain animals that are less likely to perform damaging behavior on their pen-mates. However, with the growing tendency to keep birds in large groups, identifying specific birds that are performing or receiving FP is difficult. With current developments in sensor technologies, it may now be possible to identify laying hens in large groups that show less FP behavior and select them for breeding. We propose using a combination of sensor technology and genomic methods to identify feather peckers and victims in groups. In this review, we will describe the use of "-omics" approaches to understand FP and give an overview of sensor technologies that can be used for animal monitoring, such as ultra-wideband, radio frequency identification, and computer vision. We will then discuss the identification of indicator traits from both sensor technologies and genomics approaches that can be used to select animals for breeding against damaging behavior.

MAVIS: an integrated system for live microscopy and vitrification.

Cryo-electron microscopy of vitrified biological samples can provide three-dimensional reconstructions of macromolecules and organelles within bacteria and cells at nanometer scale resolution, even in native conditions. Localization of specific structures and imaging of cellular dynamics in cellular cryo-electron microscopy is limited by (i) the use of cryo-fixation to preserve cellular structures, (ii) the restricted availability of electron dense markers to label molecules inside cells and (iii) the inherent low contrast of cryo electron microscopy. These limitations can be mitigated to a large extend by correlative light and electron microscopy, where the sample is imaged by both light and electron microscopy. Here we present a Microscopy and Vitrification Integrated System (MAVIS) that combines a light microscope with a plunger to vitrify thin specimens. MAVIS provides the capability for fluorescence light microscopic imaging of living cells and bacteria that are adhered to an electron microscopy grid and subsequent vitrification within a time frame of seconds. The instrument allows targeting of dynamic biological events in time and space by fluorescence microscopy for subsequent cryo light and electron microscopy. Here we describe the design and performance of the MAVIS, illustrated with biological examples.

Standardized and automated solid-phase extraction procedures for high-throughput proteomics of body fluids.

In order to balance the speed of analytical sample preparation procedures with mass spectrometry (MS)-based clinical proteomics the application of high-throughput robotic systems for body fluid workup is essential. In this paper we describe the implementation of various solid-phase extraction (SPE) sample preparation protocols on two different platforms, namely: 1) Magnetic bead-based SPE of peptides and proteins from body fluids on a Hamilton liquid handling workstation; 2) Cartridge-based SPE on a SPARK Symbiosis system. All SPE protocols were optimized for MS-based proteomics and compared with respect to obtained peptide- and protein profiles. Throughput numbers that were achieved in a 24 hour time frame for the sample workup procedures were more than 700 samples for the magnetic bead-based method and over 1000 samples for the cartridge-based method.

Comparison of CMT1A and CMT2: similarities and differences.

To evaluate the clinical and electrophysiological similarities and differences between two large groups of patients with Charcot-Marie-Tooth disease, i.e. CMT1A and CMT2, we performed a post hoc comparison of clinical and electrophysiological data. Most CMT1A and CMT2 patients had the classical CMT phenotype. Age of onset was significantly later in CMT2. Total areflexia was present in approximately half of the CMT1A patients whereas it was rare in CMT2. Foot deformities and weakness of knee extensor and foot dorsal flexor muscles were more frequent in CMT1A. Median nerve motor nerve conduction velocities (MNCV) were always less than 38 m/s in CMT1A patients, whereas this was also the case in 16% of the CMT2 patients. Sensory nerve conduction velocities showed less overlap. In both CMT1A and CMT2 CMAP and SNAP amplitudes were often reduced or not obtainable in the legs. In CMT1A, SNAP amplitude was more reduced and SNAP duration more prolonged than in CMT2. We conclude that there are no robust clinical signs or symptoms that differentiate between CMT1A and CMT2 patients. Electrodiagnostical studies show a length-dependent motor and sensory axonal dysfunction in both CMT-types. Additional SNAP and SNCV evaluation may be helpful in focusing molecular genetic analysis in the occasional case of CMT2 showing slow motor nerve conduction velocities overlapping with CMT1A values. The reduction of CMAP and SNAP amplitudes in CMT1A is probably a combined effect of demyelination and axonal dysfunction.

Eyelid movements: behavioral studies of blinking in humans under different stimulus conditions.

The kinematics and neurophysiological aspects of eyelid movements were examined during spontaneous, voluntary, air puff, and electrically induced blinking in healthy human subjects, using the direct magnetic search coil technique simultaneously with electromyographic recording of the orbicularis oculi muscles (OO-EMG). For OO-EMG recordings, surface electrodes were attached to the lower eyelids. To measure the vertical lid displacement, a search coil with a diameter of 3 mm was placed 1 mm from the rim on the upper eyelid on a marked position. Blink registrations were performed from the zero position and from 28 randomly chosen positions. Blinks elicited by electrical stimulation of the supraorbital nerve had shortest duration and were least variable. In contrast, spontaneous blinks had longer duration and greater variability. Blinks induced by air puff had a slightly longer duration and similar variability as electrically induced blinks. There was a correlation between the maximal down phase amplitude and the integrated OO-EMG. Blink duration and maximal down phase amplitude were affected by eye position. Eyes positioned 30 degrees above horizontal displayed the shortest down phase duration and the largest maximal down phase amplitude and velocity. At 30 degrees below horizontal, blinks had the longest total duration, the longest down phase duration, and the lowest maximal down phase amplitude and velocity. The simultaneously recorded integrated OO-EMG was largest in the 30 degrees downward position. In four subjects, the average blinking data showed a linear relation between eye position and OO-EMG, maximal down phase amplitude, and maximal downward velocity.