Mesenchymal stem cell-based therapy for ischemic stroke

Ischemic stroke represents a major, worldwide health burden with increasing incidence. Patients affected by ischemic strokes currently have few clinically approved treatment options available. Most currently approved treatments for ischemic stroke have narrow therapeutic windows, severely limiting the number of patients able to be treated. Mesenchymal stem cells represent a promising novel treatment for ischemic stroke. Numerous studies have demonstrated that mesenchymal stem cells functionally improve outcomes in rodent models of ischemic stroke. Recent studies have also shown that exosomes secreted by mesenchymal stem cells mediate much of this effect. In the present review, we summarize the current literature on the use of mesenchymal stem cells to treat ischemic stroke. Further studies investigating the mechanisms underlying mesenchymal stem cells tissue healing effects are warranted and would be of benefit to the field

Asymmetric interlimb transfer of concurrent adaptation to opposing dynamic forces

Interlimb transfer of a novel dynamic force has been well documented. It has also been shown that unimanual adaptation to opposing novel environments is possible if they are associated with different workspaces. The main aim of this study was to test if adaptation to opposing velocity dependent viscous forces with one arm could improve the initial performance of the other arm. The study also examined whether this interlimb transfer occurred across an extrinsic, spatial, coordinative system or an intrinsic, joint based, coordinative system. Subjects initially adapted to opposing viscous forces separated by target location. Our measure of performance was the correlation between the speed profiles of each movement within a force condition and an ‘average’ trajectory within null force conditions. Adaptation to the opposing forces was seen during initial acquisition with a significantly improved coefficient in epoch eight compared to epoch one. We then tested interlimb transfer from the dominant to non-dominant arm (D → ND) and vice-versa (ND → D) across either an extrinsic or intrinsic coordinative system. Interlimb transfer was only seen from the dominant to the non-dominant limb across an intrinsic coordinative system. These results support previous studies involving adaptation to a single dynamic force but also indicate that interlimb transfer of multiple opposing states is possible. This suggests that the information available at the level of representation allowing interlimb transfer can be more intricate than a general movement goal or a single perceived directional error

A direct examination of the dynamics of dipolarization fronts using MMS

Energy conversion on the dipolarization fronts (DFs) has attracted much research attention through the suggestion that intense current densities associated with DFs can modify the more global magnetotail current system. The current structures associated with a DF are at the scale of one to a few ion gyroradii, and their duration is comparable to a spacecraft's spin period. Hence, it is crucial to understand the physical mechanisms of DFs with measurements at a timescale shorter than a spin period. We present a case study whereby we use measurements from the Magnetospheric Multiscale (MMS) Mission, which provides full 3-D particle distributions with a cadence much shorter than a spin period. We provide a cross validation amongst the current density calculations and examine the assumptions that have been adopted in previous literature using the advantages of MMS mission (i.e., small-scale tetrahedron and high temporal resolution). We also provide a cross validation on the terms in the generalized Ohm's law using these advantageous measurements. Our results clearly show that the majority of the currents on the DF are contributed by both ion and electron diamagnetic drifts. Our analysis also implies that the ion frozen-in condition does not hold on the DF, while electron frozen-in condition likely holds. The new experimental capabilities allow us to accurately calculate Joule heating within the DF, which shows that plasma energy is being converted to magnetic energy in our event

Intelligent Monitoring System for Bird Behavior Study

Until now, the best way to obtain relevant information about the behaviour of animals is capturing them. However, the procedure to capture individuals cause them stress and introduces an effect on the measurement that can affect the behaviour of the animals. To solve this problems this paper describes a novel intelligent motoring system for birds breeding in nest boxes. This system is based in a network of smart-nest boxes that allows access to the acquired data all over the world through internet. A prototype of the proposed system has been implemented for the evaluation of a lesser kestrel breeding colony in Southern Spain. This prototype has offered in a short time more valuable information that several years of manual captures. This prototype has demonstrated that the proposed system allows short and log time animal behaviour evaluation without interferences or causing stress.Junta de Andalucía P06-RNM-01712Junta de Andalucía P06-RNM-04588Junta de Andalucía P07-TIC-02476Junta de Andalucía TIC-570

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Ultrasonication effects on thermal and rheological properties of carbon nanotube suspensions

The preparation of nanofluids is very important to their thermophysical properties. Nanofluids with the same nanoparticles and base fluids can behave differently due to different nanofluid preparation methods. The agglomerate sizes in nanofluids can significantly impact the thermal conductivity and viscosity of nanofluids and lead to a different heat transfer performance. Ultrasonication is a common way to break up agglomerates and promote dispersion of nanoparticles into base fluids. However, research reports of sonication effects on nanofluid properties are limited in the open literature. In this work, sonication effects on thermal conductivity and viscosity of carbon nanotubes (0.5 wt%) in an ethylene glycol-based nanofluid are investigated. The corresponding effects on the agglomerate sizes and the carbon nanotube lengths are observed. It is found that with an increased sonication time/energy, the thermal conductivity of the nanofluids increases nonlinearly, with the maximum enhancement of 23% at sonication time of 1,355 min. However, the viscosity of nanofluids increases to the maximum at sonication time of 40 min, then decreases, finally approaching the viscosity of the pure base fluid at a sonication time of 1,355 min. It is also observed that the sonication process not only reduces the agglomerate sizes but also decreases the length of carbon nanotubes. Over the current experimental range, the reduction in agglomerate size is more significant than the reduction of the carbon nanotube length. Hence, the maximum thermal conductivity enhancement and minimum viscosity increase are obtained using a lengthy sonication, which may have implications on application

Extraordinarily high biomass benthic community on Southern Ocean seamounts

We describe a previously unknown assemblage of seamount-associated megabenthos that has by far the highest peak biomass reported in the deep-sea outside of vent communities. The assemblage was found at depths of 2–2.5 km on rocky geomorphic features off the southeast coast of Australia, in an area near the Sub-Antarctic Zone characterised by high rates of surface productivity and carbon export to the deep-ocean. These conditions, and the taxa in the assemblage, are widely distributed around the Southern mid-latitudes, suggesting the high-biomass assemblage is also likely to be widespread. The role of this assemblage in regional ecosystem and carbon dynamics and its sensitivities to anthropogenic impacts are unknown. The discovery highlights the lack of information on deep-sea biota worldwide and the potential for unanticipated impacts of deep-sea exploitation

Surface Modification and Planar Defects of Calcium Carbonates by Magnetic Water Treatment

Powdery calcium carbonates, predominantly calcite and aragonite, with planar defects and cation–anion mixed surfaces as deposited on low-carbon steel by magnetic water treatment (MWT) were characterized by X-ray diffraction, electron microscopy, and vibration spectroscopy. Calcite were found to form faceted nanoparticles having 3x () commensurate superstructure and with well-developed {} and {} surfaces to exhibit preferred orientations. Aragonite occurred as laths having 3x () commensurate superstructure and with well-developed () surface extending along [100] direction up to micrometers in length. The (hkil)-specific coalescence of calcite and rapid lath growth of aragonite under the combined effects of Lorentz force and a precondensation event account for a beneficial larger particulate/colony size for the removal of the carbonate scale from the steel substrate. The coexisting magnetite particles have well-developed {011} surfaces regardless of MWT

Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: A multi-resolution graph analysis

In addition to the development of beta amyloid plaques and neurofibrillary tangles, Alzheimer's disease (AD) involves the loss of connecting structures including degeneration of myelinated axons and synaptic connections. However, the extent to which white matter tracts change longitudinally, particularly in the asymptomatic, preclinical stage of AD, remains poorly characterized. In this study we used a novel graph wavelet algorithm to determine the extent to which microstructural brain changes evolve in concert with the development of AD neuropathology as observed using CSF biomarkers. A total of 118 participants with at least two diffusion tensor imaging (DTI) scans and one lumbar puncture for CSF were selected from two observational and longitudinally followed cohorts. CSF was assayed for pathology specific to AD (Aβ42 and phosphorylated-tau), neurodegeneration (total-tau), axonal degeneration (neurofilament light chain protein; NFL), and synaptic degeneration (neurogranin). Tractography was performed on DTI scans to obtain structural connectivity networks with 160 nodes where the nodes correspond to specific brain regions of interest (ROIs) and their connections were defined by DTI metrics (i.e., fractional anisotropy (FA) and mean diffusivity (MD)). For the analysis, we adopted a multi-resolution graph wavelet technique called Wavelet Connectivity Signature (WaCS) which derives higher order representations from DTI metrics at each brain connection. Our statistical analysis showed interactions between the CSF measures and the MRI time interval, such that elevated CSF biomarkers and longer time were associated with greater longitudinal changes in white matter microstructure (decreasing FA and increasing MD). Specifically, we detected a total of 17 fiber tracts whose WaCS representations showed an association between longitudinal decline in white matter microstructure and both CSF p-tau and neurogranin. While development of neurofibrillary tangles and synaptic degeneration are cortical phenomena, the results show that they are also associated with degeneration of underlying white matter tracts, a process which may eventually play a role in the development of cognitive decline and dementia

Histone deacetylases as new therapy targets for platinum-resistant epithelial ovarian cancer

Introduction: In developed countries, ovarian cancer is the fourth most common cancer in women. Due to the nonspecific symptomatology associated with the disease many patients with ovarian cancer are diagnosed late, which leads to significantly poorer prognosis. Apart from surgery and radiotherapy, a substantial number of ovarian cancer patients will undergo chemotherapy and platinum based agents are the mainstream first-line therapy for this disease. Despite the initial efficacy of these therapies, many women relapse; therefore, strategies for second-line therapies are required. Regulation of DNA transcription is crucial for tumour progression, metastasis and chemoresistance which offers potential for novel drug targets. Methods: We have reviewed the existing literature on the role of histone deacetylases, nuclear enzymes regulating gene transcription. Results and conclusion: Analysis of available data suggests that a signifant proportion of drug resistance stems from abberant gene expression, therefore HDAC inhibitors are amongst the most promising therapeutic targets for cancer treatment. Together with genetic testing, they may have a potential to serve as base for patient-adapted therapies