Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the U.S. Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, environment, and piglet parameters on PWM. Then, different models (beta-regression and machine learning model: a random forest [RF]) were evaluated. Finally, the RF model was used to predict PWM and overlays for all listed contributing factors. On average, the mean birth weight was 1.44 kg, and the mean mortality was 16.1% where 5.55% was for stillbirths and 6.20% was contributed by overlays. No significant effect was found for seasonal and location variations on PWM. Significant differences were observed in the effects of litter lines on PWM (P \u3c 0.05). Landrace-sired litters had a PWM of 16.26% (±0.13), whereas Yorkshire-sired litters had 15.91% (±0.13). PWM increased with higher parity orders (P \u3c 0.05) due to larger litter sizes. The RF model provided the best fit for PWM prediction with a root mean squared errors of 2.28 and a correlation coefficient (r) of 0.89 between observed and predicted values. Features’ importance from the RF model indicated that, PWM increased with the increase of litter size (mean decrease accuracy (MDA) = 93.17), decrease in mean birth weight (MDA = 22.72), increase in health diagnosis (MDA = 15.34), longer gestation length (MDA = 11.77), and at older parity (MDA = 10.86). However, in this study, the location of the farrowing crate, seasonal differences, and litter line turned out to be the least important predictors for PWM. For overlays, parity order was the highest importance predictor (MDA = 7.68) followed by litter size and mean birth weight. Considering the challenges to reducing the PWM in the larger litters produced in modern swine industry and the limited studies exploring multiple major contributing factors, this study provides valuable insights for breeding and production management, as well as further investigations on postural transitions and behavior analysis of sows during the lactation period

Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the U.S. Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, environment, and piglet parameters on PWM. Then, different models (beta-regression and machine learning model: a random forest [RF]) were evaluated. Finally, the RF model was used to predict PWM and overlays for all listed contributing factors. On average, the mean birth weight was 1.44 kg, and the mean mortality was 16.1% where 5.55% was for stillbirths and 6.20% was contributed by overlays. No significant effect was found for seasonal and location variations on PWM. Significant differences were observed in the effects of litter lines on PWM (P \u3c 0.05). Landrace-sired litters had a PWM of 16.26% (±0.13), whereas Yorkshire-sired litters had 15.91% (±0.13). PWM increased with higher parity orders (P \u3c 0.05) due to larger litter sizes. The RF model provided the best fit for PWM prediction with a root mean squared errors of 2.28 and a correlation coefficient (r) of 0.89 between observed and predicted values. Features’ importance from the RF model indicated that, PWM increased with the increase of litter size (mean decrease accuracy (MDA) = 93.17), decrease in mean birth weight (MDA = 22.72), increase in health diagnosis (MDA = 15.34), longer gestation length (MDA = 11.77), and at older parity (MDA = 10.86). However, in this study, the location of the farrowing crate, seasonal differences, and litter line turned out to be the least important predictors for PWM. For overlays, parity order was the highest importance predictor (MDA = 7.68) followed by litter size and mean birth weight. Considering the challenges to reducing the PWM in the larger litters produced in modern swine industry and the limited studies exploring multiple major contributing factors, this study provides valuable insights for breeding and production management, as well as further investigations on postural transitions and behavior analysis of sows during the lactation period

Statistical and Machine Learning Approaches to Describe Factors affecting Preweaning Mortality of Piglets

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the United States Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, environment, and piglet parameters on PWM. Then, different models (beta-regression and machine learning model: a random forest [RF]) were evaluated. Finally, the RF model was used to predict PWM and overlays for all listed contributing factors. On average, the mean birth weight was 1.44 kg, and the mean mortality was 16.1% where 5.55% was for stillbirths and 6.20% was contributed by overlays. No significant effect was found for seasonal and location variations on PWM. Significant differences were observed in the effects of litter lines on PWM (P \u3c 0.05). Landrace-sired litters had a PWM of 16.26% (± 0.13), whereas Yorkshire-sired litters had 15.91% (± 0.13). PWM increased with higher parity orders (P \u3c 0.05) due to larger litter sizes. The RF model provided the best fit for PWM prediction with a root mean squared errors of 2.28 and a correlation coefficient (r) of 0.89 between observed and predicted values. Features’ importance from the RF model indicated that, PWM increased with the increase of litter size (mean decrease accuracy (MDA) = 93.17), decrease in mean birth weight (MDA = 22.72), increase in health diagnosis (MDA = 15.34), longer gestation length (MDA = 11.77), and at older parity (MDA = 10.86). However, in this study, the location of the farrowing crate, seasonal differences, and litter line turned out to be the least important predictors for PWM. For overlays, parity order was the highest importance predictor (MDA = 7.68) followed by litter size and mean birth weight. Considering the challenges to reducing the PWM in the larger litters produced in modern swine industry and the limited studies exploring multiple major contributing factors, this study provides valuable insights for breeding and production management, as well as further investigations on postural transitions and behavior analysis of sows during the lactation period

Psychosocial risk factors for obesity among women in a family planning clinic

BACKGROUND: The epidemiology of obesity in primary care populations has not been thoroughly explored. This study contributes to filling this gap by investigating the relationship between obesity and different sources of personal stress, mental health, exercise, and demographic characteristics. METHODS: A cross-sectional survey using a convenience sample. Five hundred women who attended family planning clinics were surveyed and 274 provided completed answers to all of the questions analyzed in this study. Exercise, self-rated mental health, stress, social support, and demographic variables were included in the survey. Multiple logistic regression analysis was performed. RESULTS: After adjusting for mental health, exercise, and demographic characteristics of subjects, analysis of the data indicated that that being having a large family and receiving no support from parents were related to obesity in this relatively young low-income primary care sample, but self-reported stress and most types of social support were not significant. CONCLUSION: Obesity control programs in primary care centers directed at low-income women should target women who have large families and who are not receiving support from their parents

Signaling from β1- and β2-adrenergic receptors is defined by differential interactions with PDE4

β1- and β2-adrenergic receptors (βARs) are highly homologous, yet they play clearly distinct roles in cardiac physiology and pathology. Myocyte contraction, for instance, is readily stimulated by β1AR but not β2AR signaling, and chronic stimulation of the two receptors has opposing effects on myocyte apoptosis and cell survival. Differences in the assembly of macromolecular signaling complexes may explain the distinct biological outcomes. Here, we demonstrate that β1AR forms a signaling complex with a cAMP-specific phosphodiesterase (PDE) in a manner inherently different from a β2AR/β-arrestin/PDE complex reported previously. The β1AR binds a PDE variant, PDE4D8, in a direct manner, and occupancy of the receptor by an agonist causes dissociation of this complex. Conversely, agonist binding to the β2AR is a prerequisite for the recruitment of a complex consisting of β-arrestin and the PDE4D variant, PDE4D5, to the receptor. We propose that the distinct modes of interaction with PDEs result in divergent cAMP signals in the vicinity of the two receptors, thus, providing an additional layer of complexity to enforce the specificity of β1- and β2-adrenoceptor signaling

Dobutamine stress cardiovascular magnetic resonance at 3 Tesla

<p>Abstract</p> <p>Purpose</p> <p>The assessment of inducible wall motion abnormalities during high-dose dobutamine-stress cardiovascular magnetic resonance (DCMR) is well established for the identification of myocardial ischemia at 1.5 Tesla. Its feasibility at higher field strengths has not been reported. The present study was performed to prospectively determine the feasibility and diagnostic accuracy of DCMR at 3 Tesla for depicting hemodynamically significant coronary artery stenosis (≥ 50% diameter stenosis) in patients with suspected or known coronary artery disease (CAD).</p> <p>Materials and methods</p> <p>Thirty consecutive patients (6 women) (66 ± 9.3 years) were scheduled for DCMR between January and May 2007 for detection of coronary artery disease. Patients were examined with a Philips Achieva 3 Tesla system (Philips Healthcare, Best, The Netherlands), using a spoiled gradient echo cine sequence. Technical parameters were: spatial resolution 2 × 2 × 8 mm³, 30 heart phases, spoiled gradient echo TR/TE: 4.5/2.6 msec, flip angle 15°. Images were acquired at rest and stress in accordance with a standardized high-dose dobutamine-atropine protocol during short breath-holds in three short and three long-axis views. Dobutamine was administered using a standard protocol (10 μg increments every 3 minutes up to 40 μg dobutamine/kg body weight/minute plus atropine if required to reach target heart rate). The study protocol included administration of 0.1 mmol/kg/body weight Gd-DTPA before the cine images at rest were acquired to improve the image quality. The examination was terminated if new or worsening wall-motion abnormalities or chest pain occurred or when > 85% of age-predicted maximum heart rate was reached. Myocardial ischemia was defined as new onset of wall-motion abnormality in at least one segment. In addition, late gadolinium enhancement (LGE) was performed. Images were evaluated by two blinded readers. Diagnostic accuracy was determined with coronary angiography as the reference standard. Image quality and wall-motion at rest and maximum stress level were evaluated using a four-point scale.</p> <p>Results</p> <p>In 27 patients DCMR was performed successfully, no patient had to be excluded due to insufficient image quality. Twenty-two patients were examined by coronary angiography, which depicted significant stenosis in 68.2% of the patients. Patient-based sensitivity and specificity were 80.0% and 85.7% respectively and accuracy was 81.8%. Interobserver variability for assessment of wall motion abnormalities was 88% (κ = 0.760; p < 0.0001). Negative and positive predictive values were 66.7% and 92.3%, respectively. No significant differences in average image quality at rest versus stress for short or long-axis cine images were found.</p> <p>Conclusion</p> <p>High-dose DCMR at 3T is feasible and an accurate method to depict significant coronary artery stenosis in patients with suspected or known CAD.</p

Characterization of High-Value Bioactives in Some Selected Varieties of Pakistani Rice (Oryza sativa L.)

The present study reports the composition and variation of fatty acids, sterols, tocopherols and γ-oryzanol among selected varieties namely Basmati Super, Basmati 515, Basmati 198, Basmati 385, Basmati 2000, Basmati 370, Basmati Pak, KSK-139, KS-282 and Irri-6 of Pakistani rice (Oryza sativa L). Oil content extracted with n-hexane from different varieties of brown rice seed (unpolished rice) ranged from 1.92% to 2.72%. Total fatty acid contents among rice varieties tested varied between 18240 and 25840 mg/kg brown rice seed. The rice tested mainly contained oleic (6841–10952 mg/kg) linoleic (5453–7874 mg/kg) and palmitic acid (3613–5489 mg/kg). The amounts of total phytosterols (GC and GC-MS analysis), with main contribution from β-sitosterol (445–656 mg/kg), campesterol (116–242 mg/kg), Δ5-avenasterol (89–178 mg/kg) and stigmasterol (75–180 mg/kg) were established to be 739.4 to 1330.4 mg/kg rice seed. The content of α-, γ- and δ-tocopherols as analyzed by HPLC varied from 39.0–76.1, 21.6–28.1 and 6.5–16.5 mg/kg rice seed, respectively. The amounts of different γ-oryzanol components (HPLC data), identified as cycloartenyl ferulate, 24-methylene cycloartanyl ferulate, campesteryl ferulate and β-sitosteryl ferulate, were in the range of 65.5–103.6, 140.2–183.1, 29.8–45.5 and 8.6–10.4 mg/kg rice seed, respectively. Overall, the concentration of these bioactives was higher in the Basmati rice cultivars showing their functional food superiority. In conclusion, the tested varieties of Pakistani rice, especially the Basmati cultivars, can provide best ingredients for functional foods

Comparative mapping of expressed sequence tags containing microsatellites in rainbow trout (Oncorhynchus mykiss)

BACKGROUND: Comparative genomics, through the integration of genetic maps from species of interest with whole genome sequences of other species, will facilitate the identification of genes affecting phenotypes of interest. The development of microsatellite markers from expressed sequence tags will serve to increase marker densities on current salmonid genetic maps and initiate in silico comparative maps with species whose genomes have been fully sequenced. RESULTS: Eighty-nine polymorphic microsatellite markers were generated for rainbow trout of which at least 74 amplify in other salmonids. Fifty-five have been associated with functional annotation and 30 were mapped on existing genetic maps. Homologous sequences were identified for 20 of the EST containing microsatellites to identify comparative assignments within the tetraodon, mouse, and/or human genomes. CONCLUSION: The addition of microsatellite markers constructed from expressed sequence tag data will facilitate the development of high-density genetic maps for rainbow trout and comparative maps with other salmonids and better studied species

Quantitative analysis of late gadolinium enhancement in hypertrophic cardiomyopathy: comparison of diagnostic performance in myocardial fibrosis between gadobutrol and gadopentetate dimeglumine

The purpose of this study was to compare different semi-automated late gadolinium enhancement (LGE) quantification techniques using gadobutrol and gadopentetate dimeglumine contrast agents with regard to the diagnosis of fibrotic myocardium in patients with hypertrophic cardiomyopathy (HCM). Thirty patients with HCM underwent two cardiac MRI protocols with use of gadobutrol and gadopentetate dimeglumine. Contrast-tonoise ratio (CNR) between LGE area and remote myocardium (CNRremote), between LGE area and left ventricular blood pool (CNRpool), and signal-to-noise ratio (SNR) in LGE were compared. The presence and quantity of LGE were determined by visual assessment. With signal threshold versus reference mean (STRM) based thresholds of 2 SD, 5 SD, and 6 SD above the mean signal intensity (SI) of reference myocardium, the full-width at half-maximum (FWHM) technique was used. The volume and segments of the LGE area were compared between the two types of contrast agents. LGE was present in 26 of 30 (86.6%) patients in both protocols. The CNRremote of fibrotic myocardium in gadobutrol and gadopentetate dimeglumine agents was 26.82 ± 14.24 and 21.46 ± 10.59, respectively (P &lt; 0.05). The CNRpool was significantly higher in gadobutrol (9.32 ± 7.64 vs. 6.39 ± 6.11, P &lt; 0.05). The SNR was higher in gadobutrol (33.36 ± 14.35 vs. 27.53 ± 10.91, P &lt; 0.05). The volume of scar size in MR images acquired with gadobutrol were significantly higher than those with gadopentetate dimeglumine (P &lt; 0.05), and the STRM of 5 SD technique showed the greatest agreement with visual assessment (ICC = 0.99) in both examinations. There was no significant difference in fibrotic segments of the fibrotic myocardium in the LGE area (P &lt; 0.05). This study proved that the Gadobutrol was an effective contrast agent for LGE imaging with superior delineation of fibrotic myocardium as compared to gadopentetate dimeglumine. The 5 SD technique yields the closest approximation of the extent of LGE identified by visual assessment