Effect of Prices, Traits and Market Structure on Corn Seeding Density

Recent agronomic research finds that economically optimal seeding densities have likely increased for many Midwestern corn farmers as a result of genetic improvements including new GM traits such as Bt corn and herbicide tolerance. We derive a per acre demand model for hybrid seed corn to examine the determinants of corn seeding densities and estimate the model using a large data set of individual farmer seed corn purchases. Current results identify factors other than prices affecting farmer corn seeding densities. Among these factors are the GM trait of the seed corn, measures of the local seed corn market structure, seed purchase source and intended end use. We interpret these effects in terms of information effects—farmers with more/better access to the latest agronomic research indicating that recommended seeding densities should be increased tend to plant corn at higher densities.hybrid seed corn, Bt corn, herbicide tolerance, Herfindahl index, corn borer, rootworm, hyperbolic yield model, Agribusiness, Crop Production/Industries, Farm Management, Industrial Organization, Production Economics, D2, D21, Q1, Q12,

Enhanced Characterization of Drug Metabolism and the Influence of the Intestinal Microbiome: A Pharmacokinetic, Microbiome, and Untargeted Metabolomics Study.

Determining factors that contribute to interindividual and intra-individual variability in pharmacokinetics (PKs) and drug metabolism is essential for the optimal use of drugs in humans. Intestinal microbes are important contributors to variability; however, such gut microbe-drug interactions and the clinical significance of these interactions are still being elucidated. Traditional PKs can be complemented by untargeted mass spectrometry coupled with molecular networking to study the intricacies of drug metabolism. To show the utility of molecular networking on metabolism we investigated the impact of a 7-day course of cefprozil on cytochrome P450 (CYP) activity using a modified Cooperstown cocktail and assessed plasma, urine, and fecal data by targeted and untargeted metabolomics and molecular networking in healthy volunteers. This prospective study revealed that cefprozil decreased the activities of CYP1A2, CYP2C19, and CYP3A, decreased alpha diversity and increased interindividual microbiome variability. We further demonstrate a relationship between the loss of microbiome alpha diversity caused by cefprozil and increased drug and metabolite formation in fecal samples. Untargeted metabolomics/molecular networking revealed several omeprazole metabolites that we hypothesize may be metabolized by both CYP2C19 and bacteria from the gut microbiome. Our observations are consistent with the hypothesis that factors that perturb the gut microbiome, such as antibiotics, alter drug metabolism and ultimately drug efficacy and toxicity but that these effects are most strongly revealed on a per individual basis

Clustering protein sequences with a novel metric transformed from sequence similarity scores and sequence alignments with neural networks

BACKGROUND: The sequencing of the human genome has enabled us to access a comprehensive list of genes (both experimental and predicted) for further analysis. While a majority of the approximately 30000 known and predicted human coding genes are characterized and have been assigned at least one function, there remains a fair number of genes (about 12000) for which no annotation has been made. The recent sequencing of other genomes has provided us with a huge amount of auxiliary sequence data which could help in the characterization of the human genes. Clustering these sequences into families is one of the first steps to perform comparative studies across several genomes. RESULTS: Here we report a novel clustering algorithm (CLUGEN) that has been used to cluster sequences of experimentally verified and predicted proteins from all sequenced genomes using a novel distance metric which is a neural network score between a pair of protein sequences. This distance metric is based on the pairwise sequence similarity score and the similarity between their domain structures. The distance metric is the probability that a pair of protein sequences are of the same Interpro family/domain, which facilitates the modelling of transitive homology closure to detect remote homologues. The hierarchical average clustering method is applied with the new distance metric. CONCLUSION: Benchmarking studies of our algorithm versus those reported in the literature shows that our algorithm provides clustering results with lower false positive and false negative rates. The clustering algorithm is applied to cluster several eukaryotic genomes and several dozens of prokaryotic genomes

Circadian Timing of Food Intake Contributes to Weight Gain

Studies of body weight regulation have focused almost entirely on caloric intake and energy expenditure. However, a number of recent studies in animals linking energy regulation and the circadian clock at the molecular, physiological, and behavioral levels raise the possibility that the timing of food intake itself may play a significant role in weight gain. The present study focused on the role of the circadian phase of food consumption in weight gain. We provide evidence that nocturnal mice fed a high‐fat diet only during the 12‐h light phase gain significantly more weight than mice fed only during the 12‐h dark phase. A better understanding of the role of the circadian system for weight gain could have important implications for developing new therapeutic strategies for combating the obesity epidemic facing the human population today

A novel FRET-based screen in high-throughput format to identify inhibitors of malarial and human glucose transporters

The glucose transporter PfHT is essential to the survival of the malaria parasite Plasmodium falciparum and has been shown to be a druggable target with high potential for pharmacological intervention. Identification of compounds against novel drug targets is crucial to combating resistance against current therapeutics. Here, we describe the development of a cell-based assay system readily adaptable to high-throughput screening that directly measures compound effects on PfHT-mediated glucose transport. Intracellular glucose concentrations are detected using a genetically encoded fluorescence resonance energy transfer (FRET)-based glucose sensor. This allows assessment of the ability of small molecules to inhibit glucose uptake with high accuracy (Z′ factor of >0.8), thereby eliminating the need for radiolabeled substrates. Furthermore, we have adapted this assay to counterscreen PfHT hits against the human orthologues GLUT1, -2, -3, and -4. We report the identification of several hits after screening the Medicines for Malaria Venture (MMV) Malaria Box, a library of 400 compounds known to inhibit erythrocytic development of P. falciparum. Hit compounds were characterized by determining the half-maximal inhibitory concentration (IC(50)) for the uptake of radiolabeled glucose into isolated P. falciparum parasites. One of our hits, compound MMV009085, shows high potency and orthologue selectivity, thereby successfully validating our assay for antimalarial screening

Advancing Pharmacist Collaborative Care within Academic Health Systems.

INTRODUCTION:The scope of pharmacy practice has evolved over the last few decades to focus on the optimization of medication therapy. Despite this positive impact, the lack of reimbursement remains a significant barrier to the implementation of innovative pharmacist practice models. SUMMARY:We describe the successful development, implementation and outcomes of three types of pharmacist collaborative care models: (1) a pharmacist with physician oversight, (2) pharmacist-interprofessional teams and (3) physician-pharmacist teams. The outcome measurement of these pharmacist care models varied from the design phase to patient volume measurement and to comprehensive quality dashboards. All of these practice models have been successfully funded by affiliated health systems or grants. CONCLUSIONS:The expansion of pharmacist services delivered by clinical faculty has several benefits to affiliated health systems: (1) significant improvements in patient care quality, (2) access to experts in specialty areas, and (3) the dissemination of outcomes with national and international recognition, increasing the visibility of the health system

Design and Implementation of an Interdisciplinary Elective Course in Drug Discovery, Development, and Commercialization

Objective: To describe the design and implementation of an elective course in drug discovery, development, and commercialization for pharmacy, medical, biomedical graduate, business, and law students. Case Study: This course included didactic lectures, student group discussions, a longitudinal assignment, and a question and answer panel session. A 9-item instrument using a 5-point response scale was used for course evaluation. The longitudinal assignment was the creation and presentation of a product lifecycle strategic plan (PLSP). Respondents rated 'agree' and 'strongly agree' in the course providing useful information on drug discovery (39% and 53%), drug development (39% and 60%), and drug commercialization (33% and 60%). The majority of student-reported overall understanding of the drug discovery and drug development process was rated 'very good' (49% and 46%), while the drug commercialization process was rated 'good' (46%). Conclusions: An elective course on drug discovery, development, and commercialization included enrollment of students with diverse educational training. The course provided useful information and improved overall student understanding.   Type: Case Stud

Identifying dynamical modules from genetic regulatory systems: applications to the segment polarity network

BACKGROUND It is widely accepted that genetic regulatory systems are 'modular', in that the whole system is made up of smaller 'subsystems' corresponding to specific biological functions. Most attempts to identify modules in genetic regulatory systems have relied on the topology of the underlying network. However, it is the temporal activity (dynamics) of genes and proteins that corresponds to biological functions, and hence it is dynamics that we focus on here for identifying subsystems. RESULTS Using Boolean network models as an exemplar, we present a new technique to identify subsystems, based on their dynamical properties. The main part of the method depends only on the stable dynamics (attractors) of the system, thus requiring no prior knowledge of the underlying network. However, knowledge of the logical relationships between the network components can be used to describe how each subsystem is regulated. To demonstrate its applicability to genetic regulatory systems, we apply the method to a model of the Drosophila segment polarity network, providing a detailed breakdown of the system. CONCLUSION We have designed a technique for decomposing any set of discrete-state, discrete-time attractors into subsystems. Having a suitable mathematical model also allows us to describe how each subsystem is regulated and how robust each subsystem is against perturbations. However, since the subsystems are found directly from the attractors, a mathematical model or underlying network topology is not necessarily required to identify them, potentially allowing the method to be applied directly to experimental expression data