EFFECTS OF HIGH AND LOW MANAGEMENT INTENSITY ON PROFITABILITY FOR THREE WATERMELON GENOTYPES

A replicated, small plot study on watermelon [Citrullus lanatus (Thunberg) Matsumura and Nakai] in 1997, 1999, and 2000 revealed that production management intensity affected yields and profitability of watermelon, in Oklahoma. Management intensity was based on a combination of cultural practices and levels of use of production methods. Low intensity management (LM) consisted of use of soil fertilization and weed control. High intensity management (HM) included the same weed control and fertilization as LM but also included use of plastic mulch, drip irrigation, insect pest control, and plant disease control. Cost and return analyses were based on the range of actual prices during the cropping season and the range of yields during the three years. Yields from the seedless triploid genotype 'Gem Dandy' consistently resulted in greater positive net revenue under HM than the diploid open pollinated 'Allsweet' or the hybrid diploid 'Sangria'. Under LM, yields from the seedless triploid also resulted in greater net revenues when conditions were favorable or lost less money than the open pollinated 'Allsweet' or the hybrid diploid 'Sangria' when conditions were unfavorable.Crop Production/Industries,

PAX6 does not regulate Nfia and Nfib expression during neocortical development

The Nuclear factor I (NFI) family of transcription factors regulates proliferation and differentiation throughout the developing central nervous system. In the developing telencephalon of humans and mice, reduced Nfi expression is associated with agenesis of the corpus callosum and other neurodevelopmental defects. Currently, little is known about how Nfi expression is regulated during early telencephalic development. PAX6, a transcription factor important for telencephalic development, has been proposed as an upstream regulator of Nfi expression in the neocortex. Here we demonstrate that, in the developing neocortex of mice, NFIA and NFIB are endogenously expressed in gradients with high caudo-medial to low rostro-lateral expression and are most highly expressed in the cortical plate. We found that this expression pattern deviates from that of PAX6, suggesting that PAX6 does not drive Nfi expression. This is supported by in vitro reporter assays showing that PAX6 overexpression does not regulate Nfi promoter activity. Similarly, we also found that in the Pax6 Small Eye mutant, no changes in Nfi mRNA or protein expression are observed in the neocortical ventricular zone where PAX6 and the NFIs are expressed. Together these data demonstrate that in mice, PAX6 is not a transcriptional activator of Nfi expression during neocortical development

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Inhibition of NF-κB improves the stress resistance and myogenic differentiation of MDSPCs isolated from naturally aged mice

A decline in the regenerative capacity of adult stem cells with aging is well documented. As a result of this decline, the efficacy of autologous stem cell therapies is likely to decline with increasing donor age. In these cases, strategies to restore the function of aged stem cells would have clinical utility. Globally, the transcription factor NF-κB is up-regulated in aged tissues. Given the negative role that NF-κB plays in myogenesis, we investigated whether the age-related decline in the function of muscle-derived stem/progenitor cells (MDSPCs) could be improved by inhibition of NF-κB. Herein, we demonstrate that pharmacologic or genetic inhibition of NF-κB activation increases myogenic differentiation and improves resistance to oxidative stress. Our results suggest that MDSPC “aging” may be reversible, and that pharmacologic targeting of pathways such as NF-κB may enhance the efficacy of cell-based therapies

Shared Representational Geometry Across Neural Networks

Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights. Is there some correspondence between these neural network solutions? For linear networks, it has been shown that different instances of the same network architecture encode the same representational similarity matrix, and their neural activity patterns are connected by orthogonal transformations. However, it is unclear if this holds for non-linear networks. Using a shared response model, we show that different neural networks encode the same input examples as different orthogonal transformations of an underlying shared representation. We test this claim using both standard convolutional neural networks and residual networks on CIFAR10 and CIFAR100.Comment: Integration of Deep Learning Theories workshop, NeurIPS 201

The influence of constitutive law choice used to characterise atherosclerotic tissue material properties on computing stress values in human carotid plaques.

Calculating high stress concentration within carotid atherosclerotic plaques has been shown to be complementary to anatomical features in assessing vulnerability. Reliability of stress calculation may depend on the constitutive laws/strain energy density functions (SEDFs) used to characterize tissue material properties. Different SEDFs, including neo-Hookean, one-/two-term Ogden, Yeoh, 5-parameter Mooney-Rivlin, Demiray and modified Mooney-Rivlin, have been used to describe atherosclerotic tissue behavior. However, the capacity of SEDFs to fit experimental data and the difference in the stress calculation remains unexplored. In this study, seven SEDFs were used to fit the stress-stretch data points of media, fibrous cap, lipid and intraplaque hemorrhage/thrombus obtained from 21 human carotid plaques. Semi-analytic solution, 2D structure-only and 3D fully coupled fluid-structure interaction (FSI) analyses were used to quantify stress using different SEDFs and the related material stability examined. Results show that, except for neo-Hookean, all other six SEDFs fitted the experimental points well, with vessel stress distribution in the circumferential and radial directions being similar. 2D structural-only analysis was successful for all seven SEDFs, but 3D FSI were only possible with neo-Hookean, Demiray and modified Mooney-Rivlin models. Stresses calculated using Demiray and modified Mooney-Rivlin models were nearly identical. Further analyses indicated that the energy contours of one-/two-term Ogden and 5-parameter Mooney-Rivlin models were not strictly convex and the material stability indictors under homogeneous deformations were not always positive. In conclusion, considering the capacity in characterizing material properties and stabilities, Demiray and modified Mooney-Rivlin SEDF appear practical choices for mechanical analyses to predict the critical mechanical conditions within carotid atherosclerotic plaques.This research is supported by BHF PG/11/74/29100, HRUK RG2638/14/16, the NIHR Cambridge Biomedical Research Centre, and National Natural Science Foundation of China (81170291).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.jbiomech.2015.09.02

A Review on Mechanics and Mechanical Properties of 2D Materials - Graphene and Beyond

Since the first successful synthesis of graphene just over a decade ago, a variety of two-dimensional (2D) materials (e.g., transition metal-dichalcogenides, hexagonal boron-nitride, etc.) have been discovered. Among the many unique and attractive properties of 2D materials, mechanical properties play important roles in manufacturing, integration and performance for their potential applications. Mechanics is indispensable in the study of mechanical properties, both experimentally and theoretically. The coupling between the mechanical and other physical properties (thermal, electronic, optical) is also of great interest in exploring novel applications, where mechanics has to be combined with condensed matter physics to establish a scalable theoretical framework. Moreover, mechanical interactions between 2D materials and various substrate materials are essential for integrated device applications of 2D materials, for which the mechanics of interfaces (adhesion and friction) has to be developed for the 2D materials. Here we review recent theoretical and experimental works related to mechanics and mechanical properties of 2D materials. While graphene is the most studied 2D material to date, we expect continual growth of interest in the mechanics of other 2D materials beyond graphene