The present study describes the development of a neurocognitive paradigm: “Assessing Neurocognition via Gamified Experimental Logic” (ANGEL), for performing the parametric evaluation of multiple neurocognitive functions simultaneously. ANGEL employs an audiovisual sensory motor design for the acquisition of multiple event related potentials (ERPs)—the C1, P50, MMN, N1, N170, P2, N2pc, LRP, P300, and ERN. The ANGEL paradigm allows assessment of 10 neurocognitive variables over the course of three “game” levels of increasing complexity ranging from simple passive observation to complex discrimination and response in the presence of multiple distractors. The paradigm allows assessment of several levels of rapid decision making: speeded up response vs. response-inhibition; responses to easy vs. difficult tasks; responses based on gestalt perception of clear vs. ambiguous stimuli; and finally, responses with set shifting during challenging tasks. The paradigm has been tested using 18 healthy participants from both sexes and the possibilities of varied data analyses have been presented in this paper. The ANGEL approach provides an ecologically valid assessment (as compared to existing tools) that quickly yields a very rich dataset and helps to assess multiple ERPs that can be studied extensively to assess cognitive functions in health and disease conditions.
We present Topology-based Geolocation (TBG), a novel approach to estimating the geographic location of arbitrary Internet hosts. We motivate our work by showing that 1) existing approaches, based on end-to-end delay measurements from a set of landmarks, fail to outperform much simpler techniques, and 2) the error of these approaches is strongly determined by the distance to the nearest landmark, even when triangulation is used to combine estimates from different landmarks. Our approach improves on these earlier techniques by leveraging network topology, along with measurements of network delay, to constrain host position. We convert topology and delay data into a set of constraints, then solve for router and host locations simultaneously. This approach improves the consistency of location estimates, reducing the error substantially for structured networks in our experiments on Abilene and Sprint. For networks with insufficient structural constraints, our techniques integrate external hints that are validated using measurements before being trusted. Together, these techniques lower the median estimation error for our university-based dataset to 67 km vs. 228 km for the best previous approach.
We computed Higuchi's fractal dimension (FD) of resting, eyes closed EEG recorded from 30 scalp locations in 18 male neuroleptic-naïve, recent-onset schizophrenia (NRS) subjects and 15 male healthy control (HC) subjects, who were group-matched for age. Schizophrenia patients showed a diffuse reduction of FD except in the bilateral temporal and occipital regions, with the reduction being most prominent bifrontally. The positive symptom (PS) schizophrenia subjects showed FD values similar to or even higher than HC in the bilateral temporo-occipital regions, along with a co-existent bifrontal FD reduction as noted in the overall sample of NRS. In contrast, this increase in FD values in the bilateral temporo-occipital region was absent in the negative symptom (NS) subgroup. The regional differences in complexity suggested by these findings may reflect the aberrant brain dynamics underlying the pathophysiology of schizophrenia and its symptom dimensions. Higuchi's method of measuring FD directly in the time domain provides an alternative for the more computationally intensive nonlinear methods of estimating EEG complexity.
Distributed hash tables (DHTs) provide scalable, key-based lookup of objects in dynamic network environments. Although DHTs have been studied extensively from an analytical perspective, only recently have wide deployments enabled empirical examination. This paper reports measurements of the Azureus BitTorrent client's DHT, which is in active use by more than 1 million nodes on a daily basis. The Azureus DHT operates on untrusted, unreliable endhosts, offering a glimpse into the implementation challenges associated with making structured overlays work in practice. Our measurements provide characterizations of churn, overhead, and performance in this environment. We leverage these measurements to drive the design of a modified DHT lookup algorithm that reduces median DHT lookup time by an order of magnitude for a nominal increase in overhead.
Regenerative medicine is an evolving interdisciplinary topic of research involving numerous technological methods that utilize stem cells to repair damaged tissues. Particularly, mesenchymal stem cells (MSCs) are a great tool in regenerative medicine because of their lack of tumorogenicity, immunogenicity and ability to perform immunomodulatory as well as anti-inflammatory functions. Numerous studies have investigated the role of MSCs in tissue repair and modulation of allogeneic immune responses. MSCs derived from different sources hold unique regenerative potential as they are self-renewing and can differentiate into chondrocytes, osteoblasts, adipocytes, cardiomyocytes, hepatocytes, endothelial and neuronal cells, among which neuronal-like cells have gained special interest. MSCs also have the ability to secrete multiple bioactive molecules capable of stimulating recovery of injured cells and inhibiting inflammation. In this review we focus on neural differentiation potential of MSCs isolated from different sources and how certain growth factors/small molecules can be used to derive neuronal phenotypes from MSCs. We also discuss the efficacy of MSCs when transplanted in vivo and how they can generate certain neurons and lead to relief or recovery of the diseased condition. Furthermore, we have tried to evaluate the appropriatemerits of different sources ofMSCs with respect to their propensity towards neurological differentiation as well as their effectiveness in preclinical studies.
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