Effects of a High-Protein Corn Product on Nutrient Digestibility and Production Responses in Mid-Lactation Dairy Cows

An experiment was conducted to assess the effects of a high-protein corn product (56% crude protein; CP) relative to other sources of protein on the lactation performance of dairy cows. Twenty-four Holstein cows (1,367 ± 105 lb of body weight, 111 ± 34 days in milk, 2.28 ± 0.46 lactations; mean ± standard deviation) were randomly assigned to treatment sequence in a replicated 4 × 4 Latin square design balanced for carryover effects. Cows were individually fed one of four diets with a different protein concentrate source during each 28-day period, including: soybean meal (SBM), high-protein corn product (HPCP), soybean meal with rumen-bypass soy protein (SBMBP), and canola meal with rumen-bypass soy protein (CANBP). Diets were formulated for equal concentrations of CP and balanced to meet lysine and methionine requirements. The SBM diet was formulated to provide 5.7% rumen-undegradable protein (RUP), while SBMBP and CANBP diets were formulated for 6.8% RUP to match HPCP. The CANBP diet increased dry matter intake compared with SBM and HPCP. Treatment affected milk yield, as SBMBP and CANBP increased yield compared with SBM, but HPCP decreased milk yield compared to all treatments. HPCP reduced CP intake as a percent of total intake and increased the CP content of feed refusals, indicative of selection against HPCP. HPCP decreased apparent total tract CP digestibility, leading to less urine nitrogen excretion and greater fecal nitrogen output. SBMBP and CANBP performed equally in nearly every variable measured, except SBMBP increased milk urea nitrogen concentration. In conclusion, the HPCP diet reduced milk yield, milk component yields, urine nitrogen excretion, and increased fecal nitrogen excretion due to lesser total tract CP digestibility

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page

Need Polynomial Systems Be Doubly-Exponential?

Polynomial Systems, or at least their algorithms, have the reputation of being doubly-exponential in the number of variables [Mayr and Mayer, 1982], [Davenport and Heintz, 1988]. Nevertheless, the Bezout bound tells us that that number of zeros of a zero-dimensional system is singly-exponential in the number of variables. How should this contradiction be reconciled? We first note that [Mayr and Ritscher, 2013] shows that the doubly exponential nature of Gr\"{o}bner bases is with respect to the dimension of the ideal, not the number of variables. This inspires us to consider what can be done for Cylindrical Algebraic Decomposition which produces a doubly-exponential number of polynomials of doubly-exponential degree. We review work from ISSAC 2015 which showed the number of polynomials could be restricted to doubly-exponential in the (complex) dimension using McCallum's theory of reduced projection in the presence of equational constraints. We then discuss preliminary results showing the same for the degree of those polynomials. The results are under primitivity assumptions whose importance we illustrate.Comment: Extended Abstract for ICMS 2016 Presentation. arXiv admin note: text overlap with arXiv:1605.0249

Adolescent Experiences with Self-Asphyxial Behaviors and Problematic Drinking in Emerging Adulthood

Self-asphyxial behavior to achieve a euphoric high (The Choking Game; TCG), occurs most often during early adolescence. Participants in TCG often engage in other risky behaviors. This study investigated the relationship between prior experience with TCG and problematic drinking behaviors in emerging adulthood. Emerging adults, 18 to 25 years old (N = 1248), 56% female, and 78% Caucasian completed an online survey regarding knowledge of and prior engagement in TCG and current drinking behaviors. Participants who personally engaged in TCG during childhood/adolescence or were familiar with TCG reported significantly more problematic drinking behaviors during emerging adulthood. Those present when others engaged in TCG but resisted participation themselves reported significantly less current problematic drinking behaviors than those who participated, but significantly more current problematic drinking behaviors than those never present. Emerging adults with increased social familiarity with TCG during adolescence endorsed greater problematic drinking behaviors. Results suggest resistance skills may generalize across time/activities

Bidirectional associations between body dissatisfaction and depressive symptoms from adolescence through early adulthood

Body dissatisfaction and depressive symptoms are commonly experienced during adolescence and increase the risk of adverse health outcomes, especially eating disorders. However, the dominant temporal associations between these two experiences (i.e., whether one is a risk factor for the other or the two are mutually reinforcing) has yet to be fully explored. We examined the associations between body dissatisfaction and depressive symptoms assessed at baseline and 5- and 10-year follow-up in younger (M age = 12.9 years at baseline, 56% female, n = 577) and older (M age = 15.9 years at baseline, 57% female, n = 1,325) adolescent cohorts assessed as part of Project Eating Among Teens and Young Adults. Associations between body dissatisfaction and depressive symptoms were examined using cross-lagged models. For females, the dominant directionality was for body dissatisfaction predicting later depressive symptoms. For males, the picture was more complex, with developmentally sensitive associations in which depressive symptoms predicted later body dissatisfaction in early adolescence and early adulthood, but the reverse association was dominant during middle adolescence. These findings suggest that interventions should be tailored to dynamic risk profiles that shift over adolescence and early adulthood, and that targeting body dissatisfaction at key periods during development may have downstream impacts on depressive symptoms

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes

The influence of soil communities on the temperature sensitivity of soil respiration

Soil respiration represents a major carbon flux between terrestrial ecosystems and the atmosphere, and is expected to accelerate under climate warming. Despite its importance in climate change forecasts, however, our understanding of the effects of temperature on soil respiration (RS) is incomplete. Using a metabolic ecology approach we link soil biota metabolism, community composition and heterotrophic activity, to predict RS rates across five biomes. We find that accounting for the ecological mechanisms underpinning decomposition processes predicts climatological RS variations observed in an independent dataset (n = 312). The importance of community composition is evident because without it RS is substantially underestimated. With increasing temperature, we predict a latitudinal increase in RS temperature sensitivity, with Q10 values ranging between 2.33 ±0.01 in tropical forests to 2.72 ±0.03 in tundra. This global trend has been widely observed, but has not previously been linked to soil communities

ALMA reveals a chemically evolved submillimeter galaxy at z=4.76

The chemical properties of high-z galaxies provide important information to constrain galaxy evolutionary scenarios. However, widely-used metallicity diagnostics based on rest-frame optical emission lines are not usable for heavily dust-enshrouded galaxies (such as Sub-Millimeter Galaxies; SMGs), especially at z>3. Here we focus on the flux ratio of the far-infrared fine-structure emission lines [NII]205um and [CII]158um to assess the metallicity of high-z SMGs. Through ALMA cycle 0 observations, we have detected the [NII]205um emission in a strongly [CII]-emitting SMG, LESS J033229.4-275619 at z=4.76. The velocity-integrated [NII]/[CII] flux ratio is 0.043 +/- 0.008. This is the first measurement of the [NII]/[CII] flux ratio in high-z galaxies, and the inferred flux ratio is similar to the ratio observed in the nearby universe (~0.02-0.07). The velocity-integrated flux ratio and photoionization models suggest that the metallicity in this SMG is consistent with solar, implying the chemical evolution has progressed very rapidly in this system at z=4.76. We also obtain a tight upper limit on the CO(12-11) transition, which translates into CO(12-11)/CO(2-1) <3.8 (3 sigma). This suggests that the molecular gas clouds in LESS J033229.4-275619 are not affected significantly by the radiation field emitted by the AGN in this system.Comment: 5 pages, 3 figures, accepted for publication in Astronomy and Astrophysics Letter

Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition

There has been recent interest in the use of machine learning (ML) approaches within mathematical software to make choices that impact on the computing performance without affecting the mathematical correctness of the result. We address the problem of selecting the variable ordering for cylindrical algebraic decomposition (CAD), an important algorithm in Symbolic Computation. Prior work to apply ML on this problem implemented a Support Vector Machine (SVM) to select between three existing human-made heuristics, which did better than anyone heuristic alone. The present work extends to have ML select the variable ordering directly, and to try a wider variety of ML techniques. We experimented with the NLSAT dataset and the Regular Chains Library CAD function for Maple 2018. For each problem, the variable ordering leading to the shortest computing time was selected as the target class for ML. Features were generated from the polynomial input and used to train the following ML models: k-nearest neighbours (KNN) classifier, multi-layer perceptron (MLP), decision tree (DT) and SVM, as implemented in the Python scikit-learn package. We also compared these with the two leading human constructed heuristics for the problem: Brown's heuristic and sotd. On this dataset all of the ML approaches outperformed the human made heuristics, some by a large margin.Comment: Accepted into CICM 201

Productivity of a Triticale and Crimson Clover Winter Cover Crop for Dairies

The potential for a winter cover crop to align with agronomic objectives and to support milk production was evaluated at the Kansas State University Dairy Teaching and Research Center, Manhattan, KS. August planting of a triticale and crimson clover blend following corn silage harvest resulted in production of more than 3.5 tons of dry matter prior to subsequent corn planting. After ensiling, the impact of triticale/crimson clover silage (TCS) on milk production was evaluated in 48 mid- to late-lactation Holstein cows. Cows were blocked by parity (1 and 2+) and milk production, then randomly assigned within block to treatment sequence and pen. The crossover design consisted of two 21-day periods, with 17 days of diet adaptation and 4 days of sampling. Treatments were a diet which included TCS at 15% of diet dry matter (DM) and a control ration in which TCS was primarily replaced by alfalfa and grass hays. The TCS diet included additional bypass soybean meal in an attempt to balance metabolizable protein supply across diets. Samples of rations, feed refusals, and milk were obtained daily, and milk yield was recorded. The TCS diet decreased dry matter intake (48.4 vs. 55.9 ± 3.4 lb/d; P = 0.02), but did not alter milk yield (P = 0.97); therefore, feed efficiency was greater for the TCS diet (P = 0.04). Milk fat concentration tended to increase on the TCS diet (P \u3c 0.10) whereas milk lactose yield tended to be lesser for TCS (P = 0.09), but other milk components analyzed (milk protein, MUN, SCC) did not differ between diets (P \u3e 0.15). Utilization of TCS also impacted the dairy’s nutrient management plan, as the winter forage harvest removed 40 and 340 lb/a of phosphorus and potassium, respectively. Overall, the blend of triticale and crimson clover as a winter cover crop produced good quality silage that maintained high milk production while also removing key nutrients from the soil to benefit nutrient management planning