Hydro-economic modelling of the Upper Bhima Catchment, India

Water resources development has played a crucial role in the expansion of agriculture and industry in the Upper Bhima Catchment, Maharashtra, India. However, sustainable water resources management has become a challenging issue in this catchment in recent years as there is an increasing demand for renewable, yet finite water resources. Finding ways to meet this growing demand and also to achieve positive environmental and economic outcomes requires the aid of modeling tools to analyze the impact of alternative policy scenarios. Water resources management modeling at a catchment scale can provide policy makers with essential information needed to make rational resource allocation decisions..

On the Two q-Analogue Logarithmic Functions

There is a simple, multi-sheet Riemann surface associated with e_q(z)'s inverse function ln_q(w) for 0< q < 1. A principal sheet for ln_q(w) can be defined. However, the topology of the Riemann surface for ln_q(w) changes each time "q" increases above the collision point of a pair of the turning points of e_q(x). There is also a power series representation for ln_q(1+w). An infinite-product representation for e_q(z) is used to obtain the ordinary natural logarithm ln{e_q(z)} and the values of sum rules for the zeros "z_i" of e_q(z). For |z|<|z_1|, e_q(z)=exp{b(z)} where b(z) is a simple, explicit power series in terms of values of these sum rules. The values of the sum rules for the q-trigonometric functions, sin_q(z) and cos_q(z), are q-deformations of the usual Bernoulli numbers.Comment: This is the final version to appear in J.Phys.A: Math. & General. Some explict formulas added, and to update the reference

Synaptically-Competent Neurons Derived from Canine Embryonic Stem Cells by Lineage Selection with EGF and Noggin

Pluripotent stem cell lines have been generated in several domestic animal species; however, these lines traditionally show poor self-renewal and differentiation. Using canine embryonic stem cell (cESC) lines previously shown to have sufficient self-renewal capacity and potency, we generated and compared canine neural stem cell (cNSC) lines derived by lineage selection with epidermal growth factor (EGF) or Noggin along the neural default differentiation pathway, or by directed differentiation with retinoic acid (RA)-induced floating sphere assay. Lineage selection produced large populations of SOX2+ neural stem/progenitor cell populations and neuronal derivatives while directed differentiation produced few and improper neuronal derivatives. Primary canine neural lines were generated from fetal tissue and used as a positive control for differentiation and electrophysiology. Differentiation of EGF- and Noggin-directed cNSC lines in N2B27 with low-dose growth factors (BDNF/NT-3 or PDGFαα) produced phenotypes equivalent to primary canine neural cells including 3CB2+ radial progenitors, MOSP+ glia restricted precursors, VIM+/GFAP+ astrocytes, and TUBB3+/MAP2+/NFH+/SYN+ neurons. Conversely, induction with RA and neuronal differentiation produced inadequate putative neurons for further study, even though appropriate neuronal gene expression profiles were observed by RT-PCR (including Nestin, TUBB3, PSD95, STX1A, SYNPR, MAP2). Co-culture of cESC-derived neurons with primary canine fetal cells on canine astrocytes was used to test functional maturity of putative neurons. Canine ESC-derived neurons received functional GABAA- and AMPA-receptor mediated synaptic input, but only when co-cultured with primary neurons. This study presents established neural stem/progenitor cell populations and functional neural derivatives in the dog, providing the proof-of-concept required to translate stem cell transplantation strategies into a clinically relevant animal model

INTERLABORATORY COMPARISON OF SOIL PHOSPHORUS EXTRACTED BY VARIOUS SOIL TEST METHODS

The widespread use of soil phosphorus (P) data, either in the context of agronomic or environmental management, requires an explicit understanding of potential errors related to soil P testing. This study compares a variety of soil P extraction methods, each performed by 9 separate laboratories on 24 soils from across the United States. Soil clay content ranged from 0 to 47%, pH from 4.2 to 8.6, and Mehlich-3 P concentration from 2 to 205 mg kg-1. Average interlaboratory coefficients of variation (CVs) ranged from 0.11 to 0.22 for solution extracts (Bray-1 P, Fe-strip P, Mehlich-3 P, and Olsen P) and from 0.11 to 0.70 for saturated paste extracts (resin capsules and resin membranes, incubated for 2, 4, and 7 days). For soil tests based upon solution extracts, Olsen P exhibited the greatest variability among laboratories (CV = 0:22); despite its reputed suitability for a wider range of soils than Bray-1 and Mehlich-3. Soil test data were highly correlated, with the lowest correlations occurring between Olsen and Bray-1 P or Olsen and Mehlich-3 P (r = 0:77 and 0.84, respectively) and the highest correlations occurring between Olsen P and Fe-strip P or Mehlich-3 and Bray-1 P (r = 0:94 for both correlations). Results indicate that some common soil test P protocols, when carefully conducted, yield data that may be reliably compared, such as in the compilation of regional and national soil databases

Multi-Modal and Multi-Temporal Data Fusion: Outcome of the 2012 GRSS Data Fusion Contest

The 2012 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society (GRSS) aimed at investigating the potential use of very high spatial resolution (VHR) multi-modal/multi-temporal image fusion. Three different types of data sets, including spaceborne multi-spectral, spaceborne synthetic aperture radar (SAR), and airborne light detection and ranging (LiDAR) data collected over the downtown San Francisco area were distributed during the Contest. This paper highlights the three awarded research contributions which investigate (i) a new metric to assess urban density (UD) from multi-spectral and LiDAR data, (ii) simulation-based techniques to jointly use SAR and LiDAR data for image interpretation and change detection, and (iii) radiosity methods to improve surface reflectance retrievals of optical data in complex illumination environments. In particular, they demonstrate the usefulness of LiDAR data when fused with optical or SAR data. We believe these interesting investigations will stimulate further research in the related areas