Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Broussonetia papyrifera L. silage on blood biochemical parameters, growth performance, meat amino acids and fatty acids compositions in beef cattle

Objective The study was conducted to investigate the effects of Broussonetia papyrifera L. (B. papyrifera) silage on growth performance, serum biochemical parameters, meat quality, and meat amino acids and fatty acids compositions in beef cattle. Methods Sixty-four male Angus beef cattle were assigned to 4 groups with 4 pens in each group and 4 beef cattle in each pen, and fed with the total mixed ration supplemented with 0%, 5%, 10%, or 15% B. papyrifera silage for 100 days (control group, 5% group, 10% group and 15% group) separately. Results Beef cattle had significantly higher final body weight (BW) in 15% group, higher average daily gain (ADG) and dry matter intake (DMI) in 5% group, 10% group and 15% group, and higher feed conversion ratio (FCR) in 10% group and 15% group. Significantly higher blood superoxide dismutase (SOD) concentration was noted in 15% group, higher blood total antioxidant capacity (TAC) in 10% group and 15% group, lower 8-hydroxydeoxyguanosine (8-OHdG) and malondialdehyde (MDA) in 15% group. Meat had lower pH in 15% group, higher Commission International DeI’Eclairage (CIE) L* in 5% group, 10% group, and 15% group, and lower drip loss in 15% group. Greater concentration of meat polyunsaturated fatty acids (PUFA) was observed in 10% group and 15% group, and docosahexaenoic acid (DHA) in 15% group. Conclusion Diet with 15% B. papyrifera silage could improve performance and increase final BW, ADG, DMI, and FCR, enhance the antioxidant functions by decreasing blood 8-OHdG and MDA and increasing blood SOD and TAC, improve the meat quality by lowing pH and drip loss and increasing CIE L*, increase the meat PUFA and DHA concentration. Polyphenols and flavonoids might be the main components responsible for the antioxidant activity and anti-biohydrogenation in the B. papyrifera silage. And B. papyrifera silage could be used as a new feedstuff in beef cattle nutrition.
Nonlinear modeling proposal for the germination performance of Paspalum regnellii seeds: a species with forage potential in Brazil

Finding qualified standard plants is an essential requirement in the search for high-quality pasture, fact that involves knowledge about the performance of the assessed seeds. Therefore, the aim of the present study was to evaluate the influence of seed weight on the germination behavior of Paspalum regnellii seeds through the logistic growth model. The adopted values were taken from an experiment conducted in 2016 under laboratory conditions. A pneumatic separator was used to select four seed classes, depending on the weight of one thousand seeds namely: Extra light, light, middle and weighted. The logistic model was adjusted to data about the germination rate of each class, and the parameters were compared through the F test. Germination rates between classes were compared based on recorded parameters and on model critical points. The logistic model adjusted well to the values and made it possible to use the parameters and critical points of the curve to describe the germination process. In addition to have low germination rate, seeds in the light-weight batch demanded longer to complete this process. Based on estimates about the parameters and the critical points of the model, the weighted-weight seeds were superior in terms of quality than seeds in the other batches, since they recorded higher germination rate in a shorter period-of-time.
Workplace Learning of Professional Academic Advisors at Urban Universities: A Basic Interpretive Qualitative Investigation

Research suggests that high quality academic advising is central to student success. The quality of advising, however, is undoubtedly linked to the training and professional development received by advisors. Unfortunately, the current research related to advisor training and professional development is limited. In addition, while advising is provided by both faculty and professional staff, the perspectives of professional advisors is scarcely represented in the literature. The purpose of this study was to analyze the workplace learning experiences of professional advisors through the lens of adult learning. Specifically, this study sought to understand how advisors learn to perform their roles, both upon entering the profession and as they develop in their careers, by investigating the role of workplace learning as well as identifying the adult learning concepts evident in their workplace learning experiences. The basic qualitative methodology included interviews and critical incident analysis of six advisors at three Midwestern urban universities. Findings indicated that hands-on learning experiences have an impact on advisor training and professional development as do advisors’ past workplace learning experiences. Conversely, conferences and workshops are not likely to affect advisor learning. Participants also discussed their perceptions of the inherent qualities possessed by good advisors, and the notion that these qualities cannot be learned through formal or
Use of barley or corn silage when fed with barley, corn, or a blend of barley and corn on growth performance, nutrient utilization, and carcass characteristics of finishing beef cattle

Abstract The objective of this study was to evaluate the effects of the source of silage, cereal grain, and their interaction on growth performance, digestibility, and carcass characteristics of finishing beef cattle. Using a completely randomized design within an 89-d finishing study, 288 steers were randomly assigned to 1 of 24 pens (12 steers/pen) with average steer body weight (BW) within a pen of 464 kg ± 1.7 kg (mean ± SD). Diets were arranged in a 2 × 3 factorial with corn silage (CS) or barley silage (BS) included at 8% (dry matter [DM] basis). Within each silage source, diets contained dry-rolled barley grain (BG; 86% of DM), dry-rolled corn grain (CG; 85% of DM), or an equal blend of BG and CG (BCG; 85% of DM). Total tract digestibility of nutrients was estimated from fecal samples using near-infrared spectroscopy. Data were analyzed with pen as the experimental unit using the Mixed Model of SAS with the fixed effects of silage, grain, and the two-way interaction. Carcass and fecal kernel data were analyzed using GLIMMIX utilizing the same model. There were no interactions detected between silage and grain source. Feeding CG increased (P < 0.01) DM intake by 0.8 and 0.6 kg/d relative to BG and BCG, respectively. Gain-to-feed ratio was greater (P = 0.04) for BG (0.172 kg/kg) than CG (0.162 kg/kg) but did not differ from BCG (0.165 kg/kg). Furthermore, average daily gain (2.07 kg/d) and final body weight did not differ among treatments (P ≥ 0.25). Hot carcass weight (HCW) was 6.2 kg greater (372.2 vs. 366.0 kg; P < 0.01) and dressing percentage was 0.57 percentage units greater (59.53 vs. 58.96 %; P = 0.04) for steers fed CS than BS, respectively. There was no effect of dietary treatment on the severity of liver abscesses (P ≥ 0.20) with 72.0% of carcasses having clear livers, 24.4% with minor liver abscesses, and 3.6% with severe liver abscesses. Digestibility of DM, organic matter, crude protein, neutral detergent fiber, and starch were greater for BG (P < 0.01) than CG or BCG. As expected, grain source affected the appearance of grain kernels in the feces (P ≤ 0.04). Feeding CS silage increased the appearance of fractured corn kernels (P = 0.04), while feeding BS increased fiber appearance in the feces (P = 0.02). Current results indicate that when dry rolled, feeding BG resulted in improved performance and digestibility compared with CG and BCG. Even at low inclusion levels (8% of DM), CS resulted in improved carcass characteristics relative to BS.
Errors associated with compound specific δ15N analysis of amino acids in preserved fish samples purified by high pressure liquid chromatography

During the past decade compound specific nitrogen (N) isotopic analysis of amino acids (CSIA-AA) has become an increasingly used method for tracking the origin and fate of N in ecological and biogeochemical studies. CSIA-AA has the potential for resolving finer scale trophic dynamics than previously possible with bulk stable isotope analysis (SIA) and for reconstructing past food webs using historical archives of organismal samples. However, there is little information on the effects of chemical preservation used in historical archives on δ15NAA values, and conventional CSIA conducted on derivatized AAs using gas chromatography – combustion – isotope ratio mass spectrometry (GC-C-IRMS) has analytical errors in the range of what may be expected from chemical preservation. Here we present analytical errors across 11 underivatized AA standards analyzed by high pressure liquid chromatography followed by offline elemental analysis – IRMS (HPLC/EA-IRMS) an approach originally developed by Broek and McCarthy (2014). Using this method, we test the effects of ethanol and formaldehyde preservation (1½ and 27 years) on δ15NAA in Northern Anchovy (Engraulis mordax). We found minimal isotopic fractionation from the HPLC/EA-IRMS approach in 8 AAs and more than twice the precision (0.15 ± 0.08 ‰) typically reported for GC-C-IRMS. Preservation effects on δ15NAA were similar regardless of duration and type of preservative used. Although several AAs differed significantly from frozen control samples (average +1.0 ± 0.8 ‰), changes in trophic position (TP) estimates were insignificant. These results are encouraging for resolving the fine-scale natural variability expected in most low TP organisms via high precision HPLC/EA-IRMS and for the use of chemically preserved sample archives in reconstructing biogeochemical records and trophic dynamics over long time scales.
Reward-Based Improvements in Motor Control Are Driven by Multiple Error-Reducing Mechanisms

Reward has a remarkable ability to invigorate motor behavior, enabling individuals to select and execute actions with greater precision and speed. However, if reward is to be exploited in applied settings, such as rehabilitation, a thorough understanding of its underlying mechanisms is required. In a series of experiments, we first demonstrate that reward simultaneously improves the selection and execution components of a reaching movement. Specifically, reward promoted the selection of the correct action in the presence of distractors, while also improving execution through increased speed and maintenance of accuracy. These results led to a shift in the speed-accuracy functions for both selection and execution. In addition, punishment had a similar impact on action selection and execution, although it enhanced execution performance across all trials within a block, that is, its impact was noncontingent to trial value. Although the reward-driven enhancement of movement execution has been proposed to occur through enhanced feedback control, an untested possibility is that it is also driven by increased arm stiffness, an energy-consuming process that enhances limb stability. Computational analysis revealed that reward led to both an increase in feedback correction in the middle of the movement and a reduction in motor noise near the target. In line with our hypothesis, we provide novel evidence that this noise reduction is driven by a reward-dependent increase in arm stiffness. Therefore, reward drives multiple error-reduction mechanisms which enable individuals to invigorate motor performance without compromising accuracy. SIGNIFICANCE STATEMENT While reward is well-known for enhancing motor performance, how the nervous system generates these improvements is unclear. Despite recent work indicating that reward leads to enhanced feedback control, an untested possibility is that it also increases arm stiffness. We demonstrate that reward simultaneously improves the selection and execution components of a reaching movement. Furthermore, we show that punishment has a similar positive impact on performance. Importantly, by combining computational and biomechanical approaches, we show that reward leads to both improved feedback correction and an increase in stiffness. Therefore, reward drives multiple error-reduction mechanisms which enable individuals to invigorate performance without compromising accuracy. This work suggests that stiffness control plays a vital, and underappreciated, role in the reward-based imporvemenets in motor control.
Gen Y, not so Bleeding Edge: The use of information technology in university teaching

: This paper investigates the adoption of bleeding edge technology by GenY university students. This technology was in a form of interactive feedback using mobile ‘phones called ‘ClickOn’. The factors considered in regards to technology adoption are trialability, observability, complexity and compatibility. Additional issues more specifically related to student engagement and attendance were also investigated. It was found that students in this cohort were reticent to adopt the technology in their learning environment. This is in contradiction to the current literature and society’s general expectation. These findings may alert educators to possible unexpected barriers when utilising new technologies to improve student academic outcomes and as such will be of interest to university educators.
Multi-dimensional diversity patterns of the subalpine meadow on Heyeping peak, Luya Mountain, Shanxi province, China

Plant community diversity is a major research focus in community ecology. The relationship between diversity patterns and different diversity indices is important for developing and improving biodiversity protection. In order to fully understand multi-dimensional diversity patterns of the subalpine meadow on Heyeping peak of Luya Mountain, we used a systematic sampling method and set 150 1 m × 1 m plots in June of 2018. Based on an analysis of the subalpine meadow community on Heyeping peak, we measured multiple diversity indices, carried out a correlation analysis between diversity and environmental factors, and compared correlations among different diversity indices. The goal was to clarify the ecological mechanisms and variation among various diversity indices and environmental factors. The main results were as follows: (1) The species diversity distribution was uniform, the taxonomic level was narrow, functional differences were small, and different pedigree structures were present in each plot. (2) A stable correlation between pedigree diversity index (PD) and species diversity index indicated niche conservativism; the net relatedness index (NRI) of community lineage structure was significantly correlated with the nearest species taxon index (NTI), species richness, and evenness index, indicating that plant community composition in the study area is mainly affected by habitat filtration. (3) The average taxonomic distinctness index (Λ+) and the average taxonomic distinctness index (Λ+) had a stable correlation; only the functional richness index (FRic) and Patrick species richness index were closely related. (4) Among the selected environmental factors, only the forest line had a stable correlation with species diversity index and PD and showed a negative correlation change, indicating an “edge effect” distribution of species diversity in the study area. In summary, the forest line was the key factor affecting the distribution of species diversity in the study area and the species relationships within the community. This work was supported by the National Natural Science Foundation of China (31400358).
Seed dispersal as an ecosystem service by a keystone avian frugivore in New Zealand

ABSTRACT Seed dispersal service is the proportion of plant species dispersed by a given disperser. It is important since seed dispersal enables colonisation of new habitats, and because movement of seeds even short distances away from trees can reduce vulnerability of seeds and seedlings to disease, predation and density-dependent effects that occur in the immediate vicinity of parent trees. In New Zealand, indigenous large-fruited canopy tree species are susceptible to recruitment failure because Hemiphaga novaeseelandiae (kererū; Columbidae) is the only widespread avian frugivore capable of swallowing large fruit and dispersing their seeds, and its survival relies on ongoing predator control. In view of the critical nature of this mutualism, seed dispersal service was assessed by measuring the difference in composition of seeds of indigenous large-fruited canopy tree species that were dispersed by H. novaeseelandiae to under the canopies of co-fruiting large-fruited canopy tree species across the Auckland region in New Zealand. If seed dispersal were effective, seeds of all species should be found under the canopies of all tree species, i.e. low beta diversity of seed rain. Beta diversity was higher than expected at two locations, suggesting incomplete seed dispersal service; this pattern was attributed to interactions between frugivore behaviour and the local distribution of fruit. The third location had lower than expected beta diversity, reflecting dispersal of a complete array of tree species’ seeds to sampling sites, plus long-distance dispersal of nonlocal indigenous species of large seeds. Hemiphaga novaeseelandiae appears to be dispersing an array of species of large seeds between co-fruiting tree species at some Auckland locations. However, incomplete seed dispersal service at two of the locations, together with evidence of low recruitment of some species of seeds, could eventually reduce tree species diversity.
Using a Motion Sensor to Categorize Low Back Pain Patients: A Machine Learning Approach

Low back pain (LBP) remains a critical health issue impacting literally millions of people worldwide. Currently, clinical practitioners rely on subjective measures such as the STarT Back Screening Tool to categorize LBP patients, which then informs specific treatment regimens. This study sought to develop a machine learning model to classify LBP patients into different groups according to kinematic data. Specifically, an inertial measurement unit (IMU) was attached to each patient’s chest while he performed trunk flexion/extension motions at a self-selected pace. Machine learning algorithms such as support vector machine (SVM) and multi-layer perceptron (MLP) were implemented to evaluate the efficiency of the models. The results showed that the kinematic data we obtained could be used to categorize the patients into two groups: high vs. low-medium risk. We achieved accuracy levels of ~75% and 60% for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, we determined that time-scaled IMU signal resulted in the highest accuracy. Our findings support the use of body-motion measures in developing prognosis tools for healthcare applications. Our results could help overcome the need for objective clinic-based diagnosis approaches, which in turn would lead to assigning better treatment approaches and rehabilitation services for LBP sufferers.
Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI

Large scale patterns of spontaneous whole brain activity seen in resting state functional Magnetic Resonance Imaging (rsfMRI), are in part believed to arise from neural populations interacting through the structural fiber network [18]. Generative models that simulate this network activity, called Brain Network Models (BNM), are able to reproduce global averaged properties of empirical rsfMRI activity such as functional connectivity (FC) [7, 27]. However, they perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole brain data [20]. At very short timescales between measurements, it is not known how much of the variance these BNM can explain because they are not currently synchronized with the measured rsfMRI. We demonstrate that by solving for the initial conditions of BNM from an observed data point using Recurrent Neural Networks (RNN) and integrating it to predict the next time step, the trained network can explain large amounts of variance for the 5 subsequent time points of unseen future trajectory. The RNN and BNM combined system essentially models the network component of rsfMRI, and where future activity is solely based on previous neural activity propagated through the structural network. Longer instantiations of this generative model simulated over the span of minutes can reproduce average FC and the 1/f power spectrum from 0.01 to 0.3 Hz seen in fMRI. Simulated data also contain interesting resting state dynamics, such as unique repeating trajectories, called QPPs [22] that are highly correlated to the empirical trajectory which spans over 20 seconds. Moreover, it exhibits complex states and transitions as seen using k-Means analysis on windowed FC matrices [1]. This suggests that by combining BNMs with RNN to accurately predict future resting state activity at short timescales, it is learning the manifold of the network dynamics, allowing it to simulate complex resting state trajectories at longer time scales. We believe that our technique will be useful in understanding the large-scale functional organization of the brain and how different BNMs recapitulate different aspects of the system dynamics.