Insights on trends in the management of metrological analysis of devices in blood and transfusion medicine services: Verification based on their fitness for purpose and suitability for their respective applications.

In the field of blood and transfusion medicine, European Directorate for the Quality of Medicines & HealthCare recommends practices for metrological devices. Monitoring and measurement resources, as well as their permitted metrological limits, must be considered in this regard. We must understand variables such as metrological domains, the International System of Units, metrological traceability, and metrological requirements and controls in the field of transfusions in order to understand these metrological devices. An approach to metrological analysis and subsequent actions was constructed using flowcharts. As part of this methodology, a metrological device was selected, along with regulatory and normative requirements. A calibration and a test were conducted to demonstrate the application of the figure based on current recommendations. Current recommendations are consistent with the suggested ROC approach. The metrological devices must be verified based on their suitability for their respective applications (fitness-for-purpose).

Intraoperative Angle Measurement of Anatomical Structures: A Systematic Review.

Ensuring precise angle measurement during surgical correction of orientation-related deformities is crucial for optimal postoperative outcomes, yet there is a lack of an ideal commercial solution. Current measurement sensors and instrumentation have limitations that make their use context-specific, demanding a methodical evaluation of the field. A systematic review was carried out in March 2023. Studies reporting technologies and validation methods for intraoperative angular measurement of anatomical structures were analyzed. A total of 32 studies were included, 17 focused on image-based technologies (6 fluoroscopy, 4 camera-based tracking, and 7 CT-based), while 15 explored non-image-based technologies (6 manual instruments and 9 inertial sensor-based instruments). Image-based technologies offer better accuracy and 3D capabilities but pose challenges like additional equipment, increased radiation exposure, time, and cost. Non-image-based technologies are cost-effective but may be influenced by the surgeon's perception and require careful calibration. Nevertheless, the choice of the proper technology should take into consideration the influence of the expected error in the surgery, surgery type, and radiation dose limit. This comprehensive review serves as a valuable guide for surgeons seeking precise angle measurements intraoperatively. It not only explores the performance and application of existing technologies but also aids in the future development of innovative solutions.

Retinoic Acid-Mediated Control of Energy Metabolism Is Essential for Lung Branching Morphogenesis.

Lung branching morphogenesis relies on intricate epithelial-mesenchymal interactions and signaling networks. Still, the interplay between signaling and energy metabolism in shaping embryonic lung development remains unexplored. Retinoic acid (RA) signaling influences lung proximal-distal patterning and branching morphogenesis, but its role as a metabolic modulator is unknown. Hence, this study investigates how RA signaling affects the metabolic profile of lung branching. We performed ex vivo lung explant culture of embryonic chicken lungs treated with DMSO, 1 µM RA, or 10 µM BMS493. Extracellular metabolite consumption/production was evaluated by using 1H-NMR spectroscopy. Mitochondrial respiration and biogenesis were also analyzed. Proliferation was assessed using an EdU-based assay. The expression of crucial metabolic/signaling components was examined through Western blot, qPCR, and in situ hybridization. RA signaling stimulation redirects glucose towards pyruvate and succinate production rather than to alanine or lactate. Inhibition of RA signaling reduces lung branching, resulting in a cystic-like phenotype while promoting mitochondrial function. Here, RA signaling emerges as a regulator of tissue proliferation and lactate dehydrogenase expression. Furthermore, RA governs fatty acid metabolism through an AMPK-dependent mechanism. These findings underscore RA's pivotal role in shaping lung metabolism during branching morphogenesis, contributing to our understanding of lung development and cystic-related lung disorders.

Oxidative responses in small juveniles of Colossoma macropomum anesthetized and sedated with Ocimum gratissimum L. essential oil.

This study evaluated the use of essential oil of Ocimum gratissimum (EOOG) for anesthesia and in transport of Colossoma macropomum. Experiment 1, Test 1, anesthesia induction and recovery times were determined using different EOOG concentrations (0, 20, 50, 100, 200, 300 mg L-1), with two size classes: Juveniles I (0.86 g) and Juveniles II (11.46 g) (independent tests in a completely randomized design). Based on the results of Test 1, in Test 2 Juveniles II were exposed to EOOG concentrations: 0, 20, 100 mg L-1. Tissue samples were collected immediately after induction and 1 h post-recovery, to assess oxidative status variables. Experiment 2, Juveniles I (0.91 g) and Juveniles II (14.76 g) were submitted to transport in water with different concentrations of EOOG (0, 5, 10 mg L-1) (independent tests in a completely randomized design). The effects on oxidative status variables were evaluated. Concentrations between 50 and 200 mg L-1 EOOG can be indicated for Juveniles I, while concentrations between 50 and 100 mg L-1 EOOG for Juveniles II. The concentration of 100 mg L-1 EOOG was able to prevent oxidative damage in the liver. In Experiment 2, the concentrations of 5 and 10 mg L-1 EOOG added to the transport water caused sedation for both studied size classes of juveniles and did not cause oscillations in water quality variables nor any mortality. The concentration of 10 mg L-1 EOOG improved the oxidative status. It can be concluded that EOOG can be used for anesthesia and transport of C. macropomum.

Analyzing soccer match sprint distances: A comparison of GPS-based absolute and relative thresholds.

This study compared the most common absolute sprint threshold (> 25.2 km/h) with relative and individualized thresholds (> 70%, > 75%, > 80%, > 85% and > 90% of peak match speed). Twenty elite soccer players, competing in the first division of the Portuguese League, were monitored using GNSS equipment during thirty-four official matches. Peak match speed was retrieved as the individual maximal speed reached during the full season. Distances were registered when speed overcame the absolute and the relative thresholds. Mean ± SD of peak speeds and distances covered were calculated, and Pearson correlation (r) and mean paired differences were performed to analyze relationships and differences between thresholds. The peak match speed was 32.9 ± 1.4 km/h. Correlations between distances covered using the absolute and relative thresholds varied from very strong (> 70%: r = 0.84, p < .001; > 75%: r = 0.89, p < .001; and > 80%: r = 0.88, p < .001), strong (> 85%: r = 0.79, p < .001), to moderate (> 90%: r = 0.59, p < .001). Overall, the > 75% (ES: 0.23 [95% CI: 0.16, 0.31]) and the > 90% (ES: -1.65 [95%CI: -1.85, -1.48]) relative thresholds presented the smallest and largest differences, respectively, with the absolute threshold. Differences were also found when considering the playing positions. While the distances covered by central midfielders were similar between the absolute and > 80% thresholds (-0.03 [-0.16, 0.10]), fullbacks covered largely more distance -1.88 [-2.42 -1.50]) in the absolute threshold than in the > 80% threshold. The distances covered by players varied based on the selected threshold, affecting the distances covered by different playing positions. Being the highest speed threshold within displacements thresholds, the absolute sprint threshold showed greater similarity to lower rather than higher relative thresholds.

A novel approach to automatic seizure detection using computer vision and independent component analysis.

OBJECTIVE: Epilepsy is a neurological disease that affects ~50 million people worldwide, 30% of which have refractory epilepsy and recurring seizures, which may contribute to higher anxiety levels and poorer quality of life. Seizure detection may contribute to addressing some of the challenges associated with this condition, by providing information to health professionals regarding seizure frequency, type, and/or location in the brain, thereby improving diagnostic accuracy and medication adjustment, and alerting caregivers or emergency services of dangerous seizure episodes. The main focus of this work was the development of an accurate video-based seizure-detection method that ensured unobtrusiveness and privacy preservation, and provided novel approaches to reduce confounds and increase reliability. METHODS: The proposed approach is a video-based seizure-detection method based on optical flow, principal component analysis, independent component analysis, and machine learning classification. This method was tested on a set of 21 tonic-clonic seizure videos (5-30 min each, total of 4 h and 36 min of recordings) from 12 patients using leave-one-subject-out cross-validation. RESULTS: High accuracy levels were observed, namely a sensitivity and specificity of 99.06% ± 1.65% at the equal error rate and an average latency of 37.45 ± 1.31 s. When compared to annotations by health care professionals, the beginning and ending of seizures was detected with an average offset of 9.69 ± 0.97 s. SIGNIFICANCE: The video-based seizure-detection method described herein is highly accurate. Moreover, it is intrinsically privacy preserving, due to the use of optical flow motion quantification. In addition, given our novel independence-based approach, this method is robust to different lighting conditions, partial occlusions of the patient, and other movements in the video frame, thereby setting the base for accurate and unobtrusive seizure detection.

Stress response of Rhamdia quelen to the interaction stocking density - Feeding regimen.

This study aimed to verify the effect of different feeding and stocking conditions during 14 days on the gene expression of several hormones and enzymes related to the stress cascade and metabolic parameters in silver catfish Rhamdia quelen under the following experimental conditions: 1) fed at low stocking density (2.5 kg m-3, LSD-F); 2) fed at high stocking density (32 kg m-3, HSD-F); 3) food-deprived at LSD (LSD-FD); and 4) food-deprived at HSD (HSD-FD). Fish from LSD-F and HSD-F groups were fed daily (1 % of their body mass), while fish from food-deprived groups (LSD-FD and HSD-FD) were not fed during the experimental time. Plasma metabolic parameters (glucose, lactate, triglycerides, and proteins) and hepatosomatic index (HSI) were evaluated. In addition, mRNA expression of genes related to the stress axis (crh, pomca, pomcb, nr3c2, star, hsd11b2 and hsd20b), heat shock protein family (hsp90 and hspa12a), sodium-dependent noradrenaline transporter (slc6a2), and growth axis (gh and igf1) were also assessed. Specific growth rate and HSI decreased in food-deprived fish regardless of stocking density. The HSD-FD group showed weight loss compared to the HSD-F, LSD-F, and LSD-FD groups. Plasma glucose and triglycerides were reduced in food-deprived groups, while lactate and protein levels did not change. The expression of key players of the stress response (crh, pomca, pomcb, hsd11b2, nr3c2, and hsp90b) and growth (gh and igf1) pathways were differently regulated depending on the experimental condition, whereas no statistical difference between treatments was found for hsd20b, scl6a2, hspa12a, and star mRNAs expression. This study suggests that LSD acts as a stressor affecting negatively the physiological status of fed fish, as demonstrated by the reduction in growth rates, altered metabolic orchestration, and a higher crh mRNA expression. In addition, food deprivation also increased mRNA expression of other assessed genes (nr3c2, hsp90b, pomca, and pomcb) in fish from the HSD group, indicating higher responsiveness to stress in this stocking density when combined with food deprivation.

Validity and reliability of Eforto®, a system to (self-)monitor grip strength and muscle fatigability in older persons.

INTRODUCTION: We developed Eforto®, an innovative system for (self-)monitoring of grip strength (GS) and muscle fatigability (Fatigue Resistance (FR = time until GS decreased to 50% of maximum during sustained contraction) and grip work (GW = area under the strength-time curve)). The Eforto® system consists of a rubber bulb that is wirelessly connected to a smartphone-based application, and a telemonitoring platform. The aim was to evaluate the validity and reliability of Eforto® to measure muscle fatigability. METHODS: Community-dwelling older persons (n = 61), geriatric inpatients (n = 26) and hip fracture patients (n = 25) were evaluated for GS and muscle fatigability. In community dwellers fatigability was tested twice in the clinic (once with Eforto®, once with Martin Vigorimeter (MV), standard analog handgrip system) and for six consecutive days as a self-assessment at home with Eforto®. In hospitalized participants, fatigability was tested twice using Eforto®, once by a researcher and once by a health professional. RESULTS: Criterion validity was supported by good to excellent correlations between Eforto® and MV for GS (r = 0.95) and muscle fatigability (FR r = 0.81 and GW r = 0.73), and no significant differences in measurements between both systems. Inter-rater and intra-rater reliability for GW were moderate to excellent (intra-class correlation: 0.59-0.94). The standard error of measurement for GW was small for geriatric inpatients and hip fracture patients (224.5 and 386.5 kPa*s) and higher for community-dwellers (661.5 kPa*s). DISCUSSION/CONCLUSION: We established the criterion validity and reliability of Eforto® in older community-dwelling persons and hospitalized patients, supporting the implementation of Eforto® for (self-)monitoring of muscle fatigability.

A Novel Smart Chair System for Posture Classification and Invisible ECG Monitoring.

In recent years, employment in sedentary occupations has continuously risen. Office workers are more prone to prolonged static sitting, spending 65−80% of work hours sitting, increasing risks for multiple health problems, including cardiovascular diseases and musculoskeletal disorders. These adverse health effects lead to decreased productivity, increased absenteeism and health care costs. However, lack of regulation targeting these issues has oftentimes left them unattended. This article proposes a smart chair system, with posture and electrocardiography (ECG) monitoring modules, using an "invisible" sensing approach, to optimize working conditions, without hindering everyday tasks. For posture classification, machine learning models were trained and tested with datasets composed by center of mass coordinates in the seat plane, computed from the weight measured by load cells fixed under the seat. Models were trained and evaluated in the classification of five and seven sitting positions, achieving high accuracy results for all five-class models (>97.4%), and good results for some seven-class models, particularly the best performing k-NN model (87.5%). For ECG monitoring, signals were acquired at the armrests covered with conductive nappa, connected to a single-lead sensor. Following signal filtering and segmentation, several outlier detection methods were applied to remove extremely noisy segments with mislabeled R-peaks, but only DBSCAN showed satisfactory results for the ECG segmentation performance (88.21%) and accuracy (90.50%).

Characterization and Validation of Flexible Dry Electrodes for Wearable Integration.

When long-term biosignal monitoring is required via surface electrodes, the use of conventional silver/silver chloride (Ag/AgCl) gelled electrodes may not be the best solution, as the gel in the electrodes tends to dry out over time. In this work, the electrical behaviour and performance of dry electrodes for biopotential monitoring was assessed. Three materials were investigated and compared against the gold-standard Ag/AgCl gelled electrodes. To characterize their electrical behaviour, the impedance response over the frequency was evaluated, as well as its signal to noise ratio. The electrodes' performance was evaluated by integrating them in a proven electrocardiogram (ECG) acquisition setup where an ECG signal was acquired simultaneously with a set of dry electrodes and a set of standard Ag/AgCl gelled electrodes as reference. The obtained results were morphologically compared using the Normalised Root Mean Squared Error (nRMSE) and the Cosine Similarity (CS). The findings of this work suggest that the use of dry electrodes for biopotential monitoring is a suitable replacement for the conventional Ag/AgCl gelled electrodes. The signal obtained with dry electrodes is comparable to the one obtained with the gold standard, with the advantage that these do not require the use of gel and can be easily integrated into fabric to facilitate their use in long-term monitoring scenarios.

Morphological Autoencoders for Beat-by-Beat Atrial Fibrillation Detection Using Single-Lead ECG.

Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. Autoencoders (AEs) can be used as an automatic feature extraction tool, tailoring the resulting features to a specific classification task. By coupling an encoder to a classifier, it is possible to reduce the dimension of the Electrocardiogram (ECG) heartbeat waveforms and classify them. In this work we show that morphological features extracted using a Sparse AE are sufficient to distinguish AFib from Normal Sinus Rhythm (NSR) beats. In addition to the morphological features, rhythm information was included in the model using a proposed short-term feature called Local Change of Successive Differences (LCSD). Using single-lead ECG recordings from two referenced public databases, and with features from the AE, the model was able to achieve an F1-score of 88.8%. These results show that morphological features appear to be a distinct and sufficient factor for detecting AFib in ECG recordings, especially when designed for patient-specific applications. This is an advantage over state-of-the-art algorithms that need longer acquisition times to extract engineered rhythm features, which also requires careful preprocessing steps. To the best of our knowledge, this is the first work that presents a near real-time morphological approach for AFib detection under naturalistic ECG acquisition with a mobile device.

Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor.

Wearable devices have been shown to play an important role in disease prevention and health management, through the multimodal acquisition of peripheral biosignals. However, many of these wearables are exposed, limiting their long-term acceptability by some user groups. To overcome this, a wearable smart sock integrating a PPG sensor and an EDA sensor with textile electrodes was developed. Using the smart sock, EDA and PPG measurements at the foot/ankle were performed in test populations of 19 and 15 subjects, respectively. Both measurements were validated by simultaneously recording the same signals with a standard device at the hand. For the EDA measurements, Pearson correlations of up to 0.95 were obtained for the SCL component, and a mean consensus of 69% for peaks detected in the two locations was obtained. As for the PPG measurements, after fine-tuning the automatic detection of systolic peaks, the index finger and ankle, accuracies of 99.46% and 87.85% were obtained, respectively. Moreover, an HR estimation error of 17.40±14.80 Beats-Per-Minute (BPM) was obtained. Overall, the results support the feasibility of this wearable form factor for unobtrusive EDA and PPG monitoring.

Benchmarking of Sensor Configurations and Measurement Sites for Out-of-the-Lab Photoplethysmography.

Photoplethysmography (PPG) is used for heart-rate monitoring in a variety of contexts and applications due to its versatility and simplicity. These applications, namely studies involving PPG data acquisition during day-to-day activities, require reliable and continuous measurements, which are often performed at the index finger or wrist. However, some PPG sensors are susceptible to saturation, motion artifacts, and discomfort upon their use. In this paper, an off-the-shelf PPG sensor was benchmarked and modified to improve signal saturation. Moreover, this paper explores the feasibility of using an optimized sensor in the lower limb as an alternative measurement site. Data were collected from 28 subjects with ages ranging from 18 to 59 years. To validate the sensors' performance, signal saturation and quality, wave morphology, performance of automatic systolic peak detection, and heart-rate estimation, were compared. For the upper and lower limb locations, the index finger and the first toe were used as reference locations, respectively. Lowering the amplification stage of the PPG sensor resulted in a significant reduction in signal saturation, from 18% to 0.5%. Systolic peak detection at rest using an automatic algorithm showed a sensitivity and precision of 0.99 each. The posterior wrist and upper arm showed pulse wave morphology correlations of 0.93 and 0.92, respectively. For these locations, peak detection sensitivity and precision were 0.95, 0.94 and 0.89, 0.89, respectively. Overall, the adjusted PPG sensors are a good alternative for obtaining high-quality signals at the fingertips, and for new measurement sites, the posterior pulse and the upper arm allow for high-quality signal extraction.

Feasibility of 3D Body Tracking from Monocular 2D Video Feeds in Musculoskeletal Telerehabilitation.

Musculoskeletal conditions affect millions of people globally; however, conventional treatments pose challenges concerning price, accessibility, and convenience. Many telerehabilitation solutions offer an engaging alternative but rely on complex hardware for body tracking. This work explores the feasibility of a model for 3D Human Pose Estimation (HPE) from monocular 2D videos (MediaPipe Pose) in a physiotherapy context, by comparing its performance to ground truth measurements. MediaPipe Pose was investigated in eight exercises typically performed in musculoskeletal physiotherapy sessions, where the Range of Motion (ROM) of the human joints was the evaluated parameter. This model showed the best performance for shoulder abduction, shoulder press, elbow flexion, and squat exercises. Results have shown a MAPE ranging between 14.9% and 25.0%, Pearson's coefficient ranging between 0.963 and 0.996, and cosine similarity ranging between 0.987 and 0.999. Some exercises (e.g., seated knee extension and shoulder flexion) posed challenges due to unusual poses, occlusions, and depth ambiguities, possibly related to a lack of training data. This study demonstrates the potential of HPE from monocular 2D videos, as a markerless, affordable, and accessible solution for musculoskeletal telerehabilitation approaches. Future work should focus on exploring variations of the 3D HPE models trained on physiotherapy-related datasets, such as the Fit3D dataset, and post-preprocessing techniques to enhance the model's performance.

Searching for causal relationships among latent variables concerning performance, carcass, and meat quality traits in broilers.

In causal relationship studies, the latent variables may summarize the phenotypes in theoretical traits according to their phenotypic correlations, improving the understanding of causal relationships between broilers phenotypes. In this study, we aimed to investigate potential causal relationships among latent variables in broilers using a structural equation model in the context of genetic analysis. The data used in this study comprised 14 traits in broilers with 2,017 records each, and 104,154 animals in pedigree. Four latent variables (WEIGHT, LOSSES, COLOUR, and VISCERA) were defined and validated using Bayesian Confirmatory Factor Analysis. Subsequently, a search for causal linkage structures was performed, obtaining a single causal link structure between the latent variables. Then, this information was used to fit the structural equation model (SEM). The results from the SEM indicated positive causal effects of the variables WEIGHT and LOSSES on the variables VISCERA and COLOUR, respectively, with structural coefficient estimates of 1.006 and 0.040, respectively. On the other hand, an antagonist causal effect of the variable WEIGHT on the variable LOSSES was verified, with a structural coefficient estimate of -4.333. These results highlight the causal relationship between performance and meat quality traits, which may be associated with the natural processes involved in the conversion of muscle into meat and the structural changes in muscle tissues due to intense selection for high growth rates in broilers.

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning.

Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain-Computer Interfaces (BCI) allows for unobtrusively monitoring one's cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human-computer interaction variables.

Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography.

This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the creation of a proof-of-concept system integrated into a toilet seat. In this work, a biometrics pipeline was devised, which tested four different classifiers, varying the population from 2 to 17 subjects and simulating a residential environment. However, for this approach to be industrially viable, further optimization is required, particularly regarding electrode materials that are compatible with industrial processes. As such, we also explore the use of a conductive silicone material as electrodes, aiming at the industrial-scale production of a toilet seat capable of recording ECG data, without the need for body-worn devices. A desirable aspect when using such a system is matching the recorded data with the monitored user, ideally using a minimal sensor set, further reinforcing the relevance of user identification through ECG signals collected at the thighs. Our approach was evaluated against a reference device for a population of 17 healthy and pathological individuals, covering a wide age range (24-70 years). With the silicone composite, we were able to acquire signals in 100% of the sessions, with a mean heart rate deviation between a reference system and our experimental device of 2.82 ± 1.99 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.91 ± 0.06. For biometric detection, the best classifier was the Binary Convolutional Neural Network (BCNN), with an accuracy of 100% for a population of up to four individuals.

System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics.

Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easily "fooled" by artificial replicas, among other caveats. This has lead researchers to explore other modalities, in particular based on physiological signals. Electrocardiography (ECG) has seen a growing interest, and many ECG-enabled security identification devices have been proposed in recent years, as electrocardiography signals are, in particular, a very appealing solution for today's demanding security systems-mainly due to the intrinsic aliveness detection advantages. These Electrocardiography (ECG)-enabled devices often need to meet small size, low throughput, and power constraints (e.g., battery-powered), thus needing to be both resource and energy-efficient. However, to date little attention has been given to the computational performance, in particular targeting the deployment with edge processing in limited resource devices. As such, this work proposes an implementation of an Artificial Intelligence (AI)-enabled ECG-based identification embedded system, composed of a RISC-V based System-on-a-Chip (SoC). A Binary Convolutional Neural Network (BCNN) was implemented in our SoC's hardware accelerator that, when compared to a software implementation of a conventional, non-binarized, Convolutional Neural Network (CNN) version of our network, achieves a 176,270× speedup, arguably outperforming all the current state-of-the-art CNN-based ECG identification methods.

Estimation of genetic parameters and inbreeding depression in Piau pig breed.

This study is aimed at estimating genetic parameters, effective population size, inbreeding, and inbreeding depression for birth weight, weaning weight, and average pre-weaning daily weight gain (ADG) in Piau pigs. We used information from 3841 Piau pigs, and four linear models were fitted in single-trait analyses, including or excluding maternal genetic effect, common litter effect, or a combination. The adjustments of the models were compared using the likelihood ratio test, in which the model that presented the best fit for each trait was used to estimate the (co)variance components. The inbreeding depression effect was evaluated using a linear model that included the fixed effects of sex, parity order, contemporary group, and inbreeding coefficient as a fixed covariate. The weights at birth and weaning showed low direct heritabilities (0.08 and 0.05, respectively), while the ADG showed moderate heritability (0.20). The weight at birth showed high genetic correlations with the weight at weaning (0.90) and the ADG (0.82). The weight at weaning and the ADG also showed a high genetic correlation (0.99). There was an inbreeding increase over the generations and a reduction in the effective population size. In the last generation evaluated, all the animals were inbred, the average inbreeding coefficient was 0.07, and the effective population size was 20.8. A significant inbreeding effect on ADG was observed, where an increase of 1% in the inbreeding coefficient resulted in a decrease of 0.005 g in the ADG. Thus, increasing effective population size is mandatory for controlling inbreeding and reducing the loss of variability in this Piau pig population.

Autoregressive model for genetic evaluation of longitudinal reproductive traits in Brazilian Holstein cattle.

Reproductive efficiency is major determinant of the dairy herd profitability. Thus, reproductive traits have been widely used as selection objectives in the current dairy cattle breeding programs. We aimed to evaluate strategies to model days open (DO), calving interval (CI) and daughter pregnancy rate (DPR) in Brazilian Holstein cattle. These reproductive traits were analysed by the autoregressive (AR) model and compared with classical repeatability (REP) model using 127,280, 173,092 and 127,280 phenotypic records, respectively. The first three calving orders of cows from 1,469 Holstein herds were used here. The AR model reported lower values for Akaike Information Criteria and Mean Square Errors, as well as larger model probabilities, for all evaluated traits. Similarly, larger additive genetic and lower residual variances were estimated from AR model. Heritability and repeatability estimates were similar for both models. Heritabilities for DO, CI and DPR were 0.04, 0.07 and 0.04; and 0.05, 0.06 and 0.04 for AR and REP models, respectively. Individual EBV reliabilities estimated from AR for DO, CI and DPR were, in average, 0.29, 0.30 and 0.29 units higher than those obtained from REP model. Rank correlation between EBVs obtained from AR and REP models considering the top 10 bulls ranged from 0.72 to 0.76; and increased from 0.98 to 0.99 for the top 100 bulls. The percentage of coincidence between selected bulls from both methods increased over the number of bulls included in the top groups. Overall, the results of model-fitting criteria, genetic parameters estimates and EBV predictions were favourable to the AR model, indicating that it may be applied for genetic evaluation of longitudinal reproductive traits in Brazilian Holstein cattle.