Evaluation of Four Thermal Comfort Indices and Their Relationship with Physiological Variables in Feedlot Cattle

Climatic data from different years and experiments conducted in Nebraska were used to estimate four comfort thermal indices and to predict the risk of heat stress and its relationship with pen surface temperature (PST). These included the temperature–humidity index (THI), the adjusted THI (THIadj), the heat load index (HLI), and THIPST using pen surface temperature instead of air temperature. Respiration rates (RR), tympanic temperatures (TT), and panting scores (PS) were also collected in each year and from each location. During 2007, mean values of soil temperature, PST, outgoing shortwave radiation, and TT were greater than in 2008 (p \u3c 0.011). However, HLI, relative humidity, and incoming and outgoing long-wave radiation were greater during 2008 (p \u3c 0.012). The TT was positively correlated with THIPST and THIadj (0.75 and 0.70, respectively), whereas RR had a moderate correlation with THI, THIadj, and HLI (0.32, 0.27, and 0.34, respectively; p \u3c 0.001). Thermal comfort indices showed a positive correlation with TT, especially the THIPST. These relationships vary with location. However, all of the thermal indices showed weak relationships with the observed RR. This would confirm the different roles that TT and RR have as indicators of heat stress. The THIPST was the best index for predicting TT across years

Pen Surface Temperature as a Predictor of DailyWater Intake and Tympanic Temperature in Steers Finished in Feedlots

Adequate estimation of water demand in cattle production feed yards is of high importance, especially due to reduced water availability because of changes in rain precipitation patterns and amounts. The pen surface in feed yards receives and reflects solar radiation, affecting along with other factors the microclimate to which cattle are exposed. This study aimed to describe the relationship between the pen surface temperature with the daily water intake and the tympanic temperature of finishing steers. Climate variables, including solar radiation, air temperature, relative humidity, and wind speed plus pen surface temperature and soil temperature at 10.2 cm depth were recorded. Data were collected from a weather station located in the feedlot in Concord NE, whereas daily water intake was estimated from a set of experimental pens sharing a waterer in two adjacent pens. Simple and polynomial linear regressions were assessed from data collected in different experiments conducted from 2003 to 2006. Two models to predict daily water intake were developed for finishing steers using the pen surface temperature as the predictor variable. The first one included data for the period May-October (overall model) and the second one for the summer period (June-August). The best fit for the overall model was a quadratic fit (r2 = 0.86), whereas the best-fit model for the summer model was the cubic (r2 = 0.72). Subsequently, both models were validated with data from an independent experiment conducted in the summer of 2007 in the same facilities. Both models tended to slightly overestimate daily water intake when they were validated (14.6% and 12.6%, respectively). For tympanic temperature, the best-fit model was linear, explaining 76% of the observed variability. When the dataset was split into night-time and daytime the best-fit model for the night period was a quadratic one and a linear one for the daytime, both improving the explanation of the variability observed. In conclusion, pen surface temperature can be used to predict both daily water intake and tympanic temperature in feedlot steers without access to shade

Modeling Daily Water Intake in Cattle Finished in Feedlots

Simple regression and multiple regression analyses were conducted to estimate factors affecting daily water intake(DWI) of finishing cattle. Seasonal simple linear regression equations were very poor predicting DWI (r2 \u3c 0.15). Best results were obtained with the overall simple regression. The multiple regression analysis showed that daily minimum temperature (or THI), solar radiation, and dry matter intake were the most important factors affecting DWI in cattle finished in feedyards. The following prediction equation was developed: daily water intake, gal*d-1 = -0.52677+ (0.1229 *DMI, lb*d-1) + (0.01137*solar radiation, kcal*d-1) + (0.06529*daily minimum temperature, °F)

Environmental Factors Affecting Water Intake in Steers Finishing in Feedlots

Simple and multiple regression analyses were executed using records of six experiments conducted from 1999 to 2006 at the University of Nebraska Northeast Research and Extension Center. The objective of the study was to obtain the best equation to predict water intake of feedlot steers under summer and winter weather conditions. The analysis permitted regression equations to be obtained for summer, winter and both seasons (overall model). From simple regression analysis, the best predictor of water intake was minimum temperature with r2= 0.61 in the overall model. Whereas, from multiple regression analysis the overall model with the best fit had R2 = 0.70. This model included 4 factors; daily mean minimum temperature, solar radiation, dry matter intake and wind speed

A fluorescence-activatable reporter of flavivirus NS2B–NS3 protease activity enables live imaging of infection in single cells and viral plaques

The genus Flavivirus in the family Flaviviridae comprises many medically important viruses, such as dengue virus (DENV), Zika virus (ZIKV), and yellow fever virus. The quest for thera- peutic targets to combat flavivirus infections requires a better understanding of the kinetics of virus–host interactions during infections with native viral strains. However, this is precluded by limitations of current cell-based systems for monitoring flavivi- rus infection in living cells. In the present study, we report the construction of fluorescence-activatable sensors to detect the activities of flavivirus NS2B–NS3 serine proteases in living cells. The system consists of GFP-based reporters that become fluo- rescent upon cleavage by recombinant DENV-2/ZIKV proteases in vitro. A version of this sensor containing the flavivirus inter- nal NS3 cleavage site linker reported the highest fluorescence activation in stably transduced mammalian cells upon DENV-2/ ZIKV infection. Moreover, the onset of fluorescence correlated with viral protease activity. A far-red version of this flavivirus sensor had the best signal-to-noise ratio in a fluorescent Dulbec- co’s plaque assay, leading to the construction of a multireporter platform combining the flavivirus sensor with reporter dyes for detection of chromatin condensation and cell death, enabling studies of viral plaque formation with single-cell resolution. Finally, the application of this platform enabled the study of cell-population kinetics of infection and cell death by DENV-2, ZIKV, and yellow fever virus. We anticipate that future studies of viral infection kinetics with this reporter system will enable basic investigations of virus–host interactions and facilitate future applications in antiviral drug research to manage flavivi- rus infections.International Centre for Genetic Engineering and Biotechnology Grant CRP/CRI18-02.UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET)UCR::Vicerrectoría de Docencia::Salud::Facultad de Microbiologí

Age-Dependent Effects of Haptoglobin Deletion in Neurobehavioral and Anatomical Outcomes Following Traumatic Brain Injury

Cerebral hemorrhages are common features of traumatic brain injury (TBI) and their presence is associated with chronic disabilities. Recent clinical and experimental evidence suggests that haptoglobin (Hp), an endogenous hemoglobin-binding protein most abundant in blood plasma, is involved in the intrinsic molecular defensive mechanism, though its role in TBI is poorly understood. The aim of this study was to investigate the effects of Hp deletion on the anatomical and behavioral outcomes in the controlled cortical impact model using wild type (WT) C57BL/6 mice and genetically modified mice lacking the Hp gene (Hp-/-) in two age cohorts [2–4 mo old (young adult) and 7–8 mo old (older adult)]. The data obtained suggest age-dependent significant effects on the behavioral and anatomical TBI outcomes and recovery from the injury. Moreover, in the adult cohort, neurological deficits of Hp-/- mice at 24 h were significantly improved as compared to WT; whereas there were no significant differences in brain pathology between these genotypes. In contrast, in the older adult cohort, Hp-/- mice had significantly larger lesion volumes compared to WT, but neurological deficits were not significantly different. Immunohistochemistry for ionized calcium-binding adapter molecule 1 (Iba1) and glial fibrillary acidic protein (GFAP) revealed significant differences in microglial and astrocytic reactivity between Hp-/- and WT in selected brain regions of the adult but not the older adult age cohort. In conclusion, the data obtained in the study provide clarification on the age-dependent aspects of the intrinsic defensive mechanisms involving Hp that might be involved in complex pathways differentially affecting acute brain trauma outcomes

Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings

[EN] In the last years, atrial fibrillation (AF) has become one of the most remarkable health problems in the developed world. This arrhythmia is associated with an increased risk of cardiovascular events, being its early detection an unresolved challenge. To palliate this issue, long-term wearable electrocardiogram (ECG) recording systems are used, because most of AF episodes are asymptomatic and very short in their initial stages. Unfortunately, portable equipments are very susceptible to be contaminated with different kind of noises, since they work in highly dynamics and ever-changing environments. Within this scenario, the correct identification of free-noise ECG segments results critical for an accurate and robust AF detection. Hence, this work presents a deep learning-based algorithm to identify high-quality intervals in single-lead ECG recordings obtained from patients with paroxysmal AF. The obtained results have provided a remarkable ability to classify between high- and low-quality ECG segments about 92%, only misclassifying around 7% of clean AF intervals as noisy segments. These outcomes have overcome most previous ECG quality assessment algorithms also dealing with AF signals by more than 20%.This research has been supported by the grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744.Huerta, A.; Martinez-Rodrigo, A.; Arias, MA.; Langley, P.; Rieta, JJ.; Alcaraz, R. (2020). Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.367S1

Semiconductor Surface Studies

Contains an introduction, reports on two research projects and a list of publications.Joint Services Electronics Program Contract DAAL03-92-C-0001Joint Services Electronics Program Grant DAAH04-95-1-003