Functional connectivity in relation to motor performance and recovery after stroke.

Plasticity after stroke has traditionally been studied by observing changes only in the spatial distribution and laterality of focal brain activation during affected limb movement. However, neural reorganization is multifaceted and our understanding may be enhanced by examining dynamics of activity within large-scale networks involved in sensorimotor control of the limbs. Here, we review functional connectivity as a promising means of assessing the consequences of a stroke lesion on the transfer of activity within large-scale neural networks. We first provide a brief overview of techniques used to assess functional connectivity in subjects with stroke. Next, we review task-related and resting-state functional connectivity studies that demonstrate a lesion-induced disruption of neural networks, the relationship of the extent of this disruption with motor performance, and the potential for network reorganization in the presence of a stroke lesion. We conclude with suggestions for future research and theories that may enhance the interpretation of changing functional connectivity. Overall findings suggest that a network level assessment provides a useful framework to examine brain reorganization and to potentially better predict behavioral outcomes following stroke

Non-parametric statistical thresholding for sparse magnetoencephalography source reconstructions.

Uncovering brain activity from magnetoencephalography (MEG) data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources. Sparse reconstruction algorithms, such as Champagne, show great promise in that they provide focal brain activations robust to these confounds. In this paper, we address the technical considerations of statistically thresholding brain images obtained from sparse reconstruction algorithms. The source power distribution of sparse algorithms makes this class of algorithms ill-suited to "conventional" techniques. We propose two non-parametric resampling methods hypothesized to be compatible with sparse algorithms. The first adapts the maximal statistic procedure to sparse reconstruction results and the second departs from the maximal statistic, putting forth a less stringent procedure that protects against spurious peaks. Simulated MEG data and three real data sets are utilized to demonstrate the efficacy of the proposed methods. Two sparse algorithms, Champagne and generalized minimum-current estimation (G-MCE), are compared to two non-sparse algorithms, a variant of minimum-norm estimation, sLORETA, and an adaptive beamformer. The results, in general, demonstrate that the already sparse images obtained from Champagne and G-MCE are further thresholded by both proposed statistical thresholding procedures. While non-sparse algorithms are thresholded by the maximal statistic procedure, they are not made sparse. The work presented here is one of the first attempts to address the problem of statistically thresholding sparse reconstructions, and aims to improve upon this already advantageous and powerful class of algorithm

Collective action and marketing of underutilized plant species: The case of minor millets in Kolli Hills, Tamil Nadu, India

"Minor millets are examples of underutilized plant species, being locally important but rarely traded internationally with an unexploited economic potential. In the Kolli hills of Tamil Nadu, India, a genetically diverse pool of minor millet varieties are grown by the tribal farming communities to meet their subsistence food needs. Most of these minor crops were not traded outside the farming community. Despite a consumption preference among the farming communities for minor millets, in the recent past the acreage under minor millet crops have declined considerably due to the availability of substitute cash crops. As a response, the M.S. Swaminathan Research Foundation (MSSRF) based in Chennai has led targeted conservation cum commercialization intervention programs over the last 7-9 years in the Kolli Hills. In this paper we provide a first evaluation of the success of marketing development for minor millets in the Kolli Hills with a specific focus on collective action and group initiatives undertaken by the women and men self-help groups organized by the concerned non-governmental organization. We analyze the key collective actions that are taking place in the minor millet marketing chain through a series of field visits and focus group discussions with the stakeholders involved. We then compare the role of collective action in this new market with the case of marketing chains for cassava and organic pineapples, two cash crops with an expanding production in Kolli Hills. Our analysis shows the critical role of collective action and group initiative as a necessary but not sufficient condition for the successful commercialization of underutilized plant species for the benefit of the poor and the conservation of agrobiodiversity." authors' abstractCollective action, Underutilized species, Agricultural marketing, Agrobiodiversity, Markets, Small farmers,

REMOVAL OF GAUSSIAN AND IMPULSE NOISE IN THE COLOUR IMAGE PROGRESSION WITH FUZZY FILTERS

This paper is concerned with algebraic features based filtering technique, named as the adaptive statistical quality based filtering technique (ASQFT), is presented for removal of Impulse and Gaussian noise in corrupted colour images. A combination of these two filters also helps in eliminating a mixture of these two noises. One strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail. Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very useful for the processing of human knowledge where linguistic variables are used. The proposed filter is able to efficiently suppress both Gaussian noise and impulse noise, as well as mixed Gaussian impulse noise. The experiments shows that proposed method outperforms novel modern filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peaksignal- to-noise ratio (PSNR) and the normalized color difference (NCD). The expectations filter achieves a promising performance

Non-invasive genetic monitoring for the threatened valley elderberry longhorn beetle.

The valley elderberry longhorn beetle (VELB), Desmocerus californicus dimorphus (Coleoptera: Cerambycidae), is a federally threatened subspecies endemic to the Central Valley of California. The VELB range partially overlaps with that of its morphologically similar sister taxon, the California elderberry longhorn beetle (CELB), Desmocerus californicus californicus (Coleoptera: Cerambycidae). Current surveying methods are limited to visual identification of larval exit holes in the VELB/CELB host plant, elderberry (Sambucus spp.), into which larvae bore and excavate feeding galleries. Unbiased genetic approaches could provide a much-needed complementary approach that has more precision than relying on visual inspection of exit holes. In this study we developed a DNA sequencing-based method for indirect detection of VELB/CELB from frass (insect fecal matter), which can be easily and non-invasively collected from exit holes. Frass samples were collected from 37 locations and the 12S and 16S mitochondrial genes were partially sequenced using nested PCR amplification. Three frass-derived sequences showed 100% sequence identity to VELB/CELB barcode references from museum specimens sequenced for this study. Database queries of frass-derived sequences also revealed high similarity to common occupants of old VELB feeding galleries, including earwigs, flies, and other beetles. Overall, this non-invasive approach is a first step towards a genetic assay that could augment existing VELB monitoring and accurately discriminate between VELB, CELB, and other insects. Furthermore, a phylogenetic analysis of 12S and 16S data from museum specimens revealed evidence for the existence of a previously unrecognized, genetically distinct CELB subpopulation in southern California

Optimization of protease production by Bacillus licheniformis in Sugarcane bagasse using statistical experimental design

Sugarcane bagasse, the residue obtained after extracting the sugar juice from sugarcane was tested for the production of protease under solid-state fermentation (SSF) using Bacillus licheniformis. The fermentation variables were selected in accordance with Plackett-Burman design and were further optimized via response surface methodological approach. Four significant variables (K2HPO4, Beef extract, NaNO3 and Glycine) were selected for the optimization studies. The optimum values for the selected variables were; K2HPO4 -0.3464g/gds, Beef extract- 0.1039g/gds, NaNO3- 0.0334g/gds and Glycine- 0.1027g/gds. A second-order model equation was suggested and then validated experimentally.The model adequacy was very satisfactory as the coefficient of determination was 0.95. The maximum protease production was 146.28U/gd

Magnetic and Transport Properties of Ternary Indides of type R2CoIn8 (R = Ce, Pr and Dy)

We have synthesized and investigated the magnetic and transport properties of a series of compounds, R2CoIn8 (R = rare earth). Compounds form in single phase with a tetragonal structure (space group P4/mmm, no. 162). The Ce compound shows heavy fermion behavior. The magnetic susceptibility of Pr2CoIn8 shows a marked deviation from the Curie-Weiss behavior at low temperatures, which is attributed to the crystalline electric field effects. Heat capacity and magnetization measurements show that Dy2CoIn8 undergoes a magnetic transition at 17 K and a second transition near 5 K, the latter of which may be due to spin reorientation. Magnetization of this compound shows two metamagnetic transitions approximately at 3.6 T and 8.3 T.Comment: Total 7 pages of text and figure