Nutritional Support for Small Ruminant Development Based on Oil Palm By-products

Biomass by-products or plant residues from the plantation system would play a crucial role in animal production since the utilization of forages from the underneath tree crops would be less or minimal when the palm oil crop mature. By-products generated from the palm oil system vary, but in relation to the animal production they could be generally categorized into the fibrous by-products and the non-fibrous (concentrate) by-products. Palm oil mill effluent (POME) and palm kernel cake (PKC) are concentrate by-products produced during the processing of palm oil extraction which have great potency to support sheep and goat production, although limiting factors such as contamination of shell and high copper level in PKC need to be considered in their utilization as feed. The fibrous palm oil by-products include oil palm fronds (OPF) and oil palm trunk (OPT) generated from the palm crop trees and oil palm empty fruit bunch (OPEFB) and palm pressed fiber (PPF) generated from processing of fresh fruits to yield crude oil. These fibrous by-products cannot meet the metabolisable energy required for high growth rate and for lactation of sheep and goats due to low DM digestibility, low crude protein content, low fermentable carbohydrate and low level of intake. Limited inclusion level in ration should be applied for those by-products to yield an acceptable production level of sheep and goats. Pretreatments (physical, chemical, and biological) gave some improvement in their nutritional qualities, however additional cost of pretreatments need to be considered. In the future, there would be a great challenge for the utilization of those fibrous by-products as animal feed since bioconversion of lignocellulosic materials to products such as chemicals (bioethanol, sugar, and bioplastic), fuels, and organic fertilizers are receiving greater interest. Some comparative advantages of these natural wastes are their relatively low cost, renewable and widespread in nature for used in an industrial operation

Composition and concentration anomalies for structure and dynamics of Gaussian-core mixtures

We report molecular dynamics simulation results for two-component fluid mixtures of Gaussian-core particles, focusing on how tracer diffusivities and static pair correlations depend on temperature, particle concentration, and composition. At low particle concentrations, these systems behave like simple atomic mixtures. However, for intermediate concentrations, the single-particle dynamics of the two species largely decouple, giving rise to the following anomalous trends. Increasing either the concentration of the fluid (at fixed composition) or the mole fraction of the larger particles (at fixed particle concentration) enhances the tracer diffusivity of the larger particles, but decreases that of the smaller particles. In fact, at sufficiently high particle concentrations, the larger particles exhibit higher mobility than the smaller particles. Each of these dynamic behaviors is accompanied by a corresponding structural trend that characterizes how either concentration or composition affects the strength of the static pair correlations. Specifically, the dynamic trends observed here are consistent with a single empirical scaling law that relates an appropriately normalized tracer diffusivity to its pair-correlation contribution to the excess entropy.Comment: 5 pages, 4 figure

Generalized Rosenfeld scalings for tracer diffusivities in not-so-simple fluids: Mixtures and soft particles

Rosenfeld [Phys. Rev. A 15, 2545 (1977)] noticed that casting transport coefficients of simple monatomic, equilibrium fluids in specific dimensionless forms makes them approximately single-valued functions of excess entropy. This has predictive value because, while the transport coefficients of dense fluids are difficult to estimate from first principles, excess entropy can often be accurately predicted from liquid-state theory. Here, we use molecular simulations to investigate whether Rosenfeld's observation is a special case of a more general scaling law relating mobility of particles in mixtures to excess entropy. Specifically, we study tracer diffusivities, static structure, and thermodynamic properties of a variety of one- and two-component model fluid systems with either additive or non-additive interactions of the hard-sphere or Gaussian-core form. The results of the simulations demonstrate that the effects of mixture concentration and composition, particle-size asymmetry and additivity, and strength of the interparticle interactions in these fluids are consistent with an empirical scaling law relating the excess entropy to a new dimensionless (generalized Rosenfeld) form of tracer diffusivity, which we introduce here. The dimensionless form of the tracer diffusivity follows from knowledge of the intermolecular potential and the transport / thermodynamic behavior of fluids in the dilute limit. The generalized Rosenfeld scaling requires less information, and provides more accurate predictions, than either Enskog theory or scalings based on the pair-correlation contribution to the excess entropy. As we show, however, it also suffers from some limitations, especially for systems that exhibit significant decoupling of individual component tracer diffusivities.Comment: 15 pages, 10 figure

Correlation of baseline biomarkers with clinical outcomes and response to fulvestrant with vandetanib or placebo in patients with bone predominant metastatic breast cancer: An OCOG ZAMBONEY sub-study

AbstractBackgroundBone metastases are common in women with breast cancer and often result in skeletal related events (SREs). As the angiogenic factor vascular endothelial growth factor (VEGF) regulates osteoclast activity and is associated with more extensive bone metastases and SRE risk in metastatic breast cancer, we hypothesized that blockade of VEGF signaling could be a therapeutic strategy for inhibiting bone metastases progression and possibly prolonging overall (OS) or progression-free survival (PFS). The Zamboney trial was a randomized placebo-controlled study designed to assess whether patients with bone predominant metastatic breast cancer benefited from addition of the VEGF receptor (VEGFR) targeting agent, vandetanib, to endocrine therapy with fulvestrant. As a companion study, evaluation of biomarkers and their potential association with response to vandetanib or SRE risk was performed.MethodsBaseline overnight fasted serum from enrolled patients was analyzed for levels of various putative biomarkers including; VEGF-A, soluble (s)VEGFR2, sVEGFR3, transforming growth factor (TGF)-β1 and activinA by ELISA. Spearman correlation coefficients and Wilcoxon rank sum tests were used to investigate potential relationships between biomarker values and baseline clinical parameters. Prognostic and predictive ability of each marker was investigated using Cox proportional hazards regression with adjustments for treatment and baseline strata of serum CTx (<400 versus ≥400ng/L).ResultsOf 129 enrolled patients, serum was available for analysis in 101; 51 in vandetanib and 50 in placebo arm. Mean age amongst consenting patients was 59.8 years. Clinical characteristics were not significantly different between patients with or without serum biomarker data and serum markers were similar for patients by treatment arm. Baseline sVEGFR2 was prognostic for OS (HR=0.77, 95% CI=0.61–0.96, p=0.020), and although a modest association was observed, it was not significant for PFS (HR=0.90, 95% CI=0.80–1.01, p=0.085) nor time to first SRE (HR=0.82, 95% CI=0.66–1.02, p=0.079). When interaction terms were evaluated, sVEGFR2 was not found to be predictive of response to vandetanib, although a modest association remained with respect to PFS (interaction p=0.085). No other marker showed any significant prognostic or predictive ability with any measured outcome.ConclusionsIn this clinical trial, sVEGFR2 appeared prognostic for OS, hence validation of sVEGFR2 should be conducted. Moreover, the role of sVEGFR2 in breast cancer bone metastasis progression should be elucidated

Mechanical Testing of 3D Printed Prosthetics

The Rapid Orthotics for CURE Kenya team as a whole aims to empower the orthopedic technicians in the CURE Kenya hospital by creating, optimizing, and testing 3D printed prosthetics and orthotics. Our team started in 2016 by creating a 3D printing process for below the knee prosthetic sockets. Since then, we had adapted to the hospital\u27s needs over the years, expanding the capabilities of the system itself. Presently, a section of our team has worked specifically with these leg sockets to ensure the safety and functionality for patients. They have done testing to make sure the sockets are strong enough and to make sure the silicone liners are safe for use in developing countries. In addition to safety testing, over the years we have created ankle-foot orthotics and prosthetic hands. The design part of our team works to create new 3D printed devices to help our clients reach more patients. By 2024 we hope to fully integrate our expanded system in the orthopedic workshop in Kijabe, Kenya.https://mosaic.messiah.edu/engr2020/1018/thumbnail.jp

Modeling HIV-1 Drug Resistance as Episodic Directional Selection

The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance

HIV-Specific Probabilistic Models of Protein Evolution

Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1) genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1–the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic error. We argue that our model derivation procedure is immediately applicable to other organisms with extensive sequence data available, such as Hepatitis C and Influenza A viruses

CodonTest: Modeling Amino Acid Substitution Preferences in Coding Sequences

Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes