Evaluation of Pasture Systems and Grain Levels for Growing Beef Cattle with Prediction Equations for Estimating Animal Performance

A three-year grazing study evaluated native, native-interseeded range and a tame grass pasture production, quality and animal performance. Animal production and performance were evaluated with yearling steers fed corn daily at zero, 0.5 and 1.0 percent of body weight. Interseeding alfalfa into native rangelands enhanced sod-forming over bunch grasses and allowed alfalfa to become the dominant species. Alfalfa was not selectively graved in the interseeded range and in 1978 contributed 35 percent of the dry matter yield during the spring and summer. Forage quality declined with maturity; IVDMD and CP decreased while ADF, NDF, lignin and silica all increased with the grazing season. Alfalfa greatly increased forage quality in the interseeded range with tame pastures having the best quality season-long. Five methods were evaluated to estimate dry matter intake for grazing steers. Methods using forage quality along with animal weights and gains gave higher predictions than using body weight alone. Average daily gains of steers increased with increasing levels of grain supplementation; however, no differences were found between pasture systems. Interseeded and tame pastures increased gains per ha and carrying capacity by 60 and 54 percent, respectively, over the native range. Grain supplementation at 0.5 and 1.0 percent of body weight increased carrying capacity by 9 and 21 percent, respectively over the zero grain level. Native and interseeded range steers consistently suffered weight loss in the late fall grazing periods at zero and 0.5 percent grain levels, while the 1.0 percent grain level allowed animals to maintain body weights. Native range required 32 percent more acreage per animal unit than interseeded or tame pastures. Four equations to predict mean average daily gain were recommended on the basis of research or producer use. Coefficients of determination ranged from 48.8 to 52.6 percent and standard deviations from 0.281 to 0.291. The most practical equations require estimates of dry matter intake, IVDMD (or TDN) ADF, NDF, animal body weight and period of the grazing season

Determination of static contraction times to exhaustion for given percentages of a 1 Rm in the bench press, leg press, and pulldown exercises

The purpose of this study was to determine the time to exhaustion based on percentages of the 1 RM in the bench press, leg press, and pulldown exercises. Eleven healthy males, age 27.7 +/- 5.5 years, volunteered for the study to compare the strength differences between their maximum isotonic strength and isometric strength (a static contraction hold) in the pulldown, bench press, and leg press exercises. Five randomized static contractions sets were performed 5 cm below the lockout position in the bench press and leg press exercises. The static contraction position for the pulldown was when the forearm was at a 90° angle relative to the upper arm. The percentages of the static contraction sets were based on the time to exhaustion normally associated with weight/strength training. The results indicate that greater loads can be used for longer contraction times during the static contraction sets than conventional, full range movements

A method to correct differential nonlinearities in subranging analog-to-digital converters used for digital gamma-ray spectroscopy

The influence on $\gamma$-ray spectra of differential nonlinearities (DNL) in subranging, pipelined analog-to-digital converts (ADCs) used for digital $\gamma$-ray spectroscopy was investigated. The influence of the DNL error on the $\gamma$-ray spectra, depending on the input count-rate and the dynamic range has been investigated systematically. It turned out, that the DNL becomes more significant in $\gamma$-ray spectra with larger dynamic range of the spectroscopy system. An event-by-event offline correction algorithm was developed and tested extensively. This correction algorithm works especially well for high dynamic ranges

Functional consequences of sphingomyelinase-induced changes in erythrocyte membrane structure.

Inflammation enhances the secretion of sphingomyelinases (SMases). SMases catalyze the hydrolysis of sphingomyelin into phosphocholine and ceramide. In erythrocytes, ceramide formation leads to exposure of the removal signal phosphatidylserine (PS), creating a potential link between SMase activity and anemia of inflammation. Therefore, we studied the effects of SMase on various pathophysiologically relevant parameters of erythrocyte homeostasis. Time-lapse confocal microscopy revealed a SMase-induced transition from the discoid to a spherical shape, followed by PS exposure, and finally loss of cytoplasmic content. Also, SMase treatment resulted in ceramide-associated alterations in membrane-cytoskeleton interactions and membrane organization, including microdomain formation. Furthermore, we observed increases in membrane fragility, vesiculation and invagination, and large protein clusters. These changes were associated with enhanced erythrocyte retention in a spleen-mimicking model. Erythrocyte storage under blood bank conditions and during physiological aging increased the sensitivity to SMase. A low SMase activity already induced morphological and structural changes, demonstrating the potential of SMase to disturb erythrocyte homeostasis. Our analyses provide a comprehensive picture in which ceramide-induced changes in membrane microdomain organization disrupt the membrane-cytoskeleton interaction and membrane integrity, leading to vesiculation, reduced deformability, and finally loss of erythrocyte content. Understanding these processes is highly relevant for understanding anemia during chronic inflammation, especially in critically ill patients receiving blood transfusions

Comparative Performance of Deep Learning and Radiologists for the Diagnosis and Localization of Clinically Significant Prostate Cancer at MRI:A Systematic Review

BACKGROUND: Deep learning (DL)-based models have demonstrated an ability to automatically diagnose clinically significant prostate cancer (PCa) on MRI scans and are regularly reported to approach expert performance. The aim of this work was to systematically review the literature comparing deep learning (DL) systems to radiologists in order to evaluate the comparative performance of current state-of-the-art deep learning models and radiologists. METHODS: This systematic review was conducted in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Studies investigating DL models for diagnosing clinically significant (cs) PCa on MRI were included. The quality and risk of bias of each study were assessed using the checklist for AI in medical imaging (CLAIM) and QUADAS-2, respectively. Patient level and lesion-based diagnostic performance were separately evaluated by comparing the sensitivity achieved by DL and radiologists at an identical specificity and the false positives per patient, respectively. RESULTS: The final selection consisted of eight studies with a combined 7337 patients. The median study quality with CLAIM was 74.1% (IQR: 70.6-77.6). DL achieved an identical patient-level performance to the radiologists for PI-RADS ≥ 3 (both 97.7%, SD = 2.1%). DL had a lower sensitivity for PI-RADS ≥ 4 (84.2% vs. 88.8%, p = 0.43). The sensitivity of DL for lesion localization was also between 2% and 12.5% lower than that of the radiologists. CONCLUSIONS: DL models for the diagnosis of csPCa on MRI appear to approach the performance of experts but currently have a lower sensitivity compared to experienced radiologists. There is a need for studies with larger datasets and for validation on external data