Linearity of cortical receptive fields measured with natural sounds

How do cortical neurons represent the acoustic environment? This question is often addressed by probing with simple stimuli such as clicks or tone pips. Such stimuli have the advantage of yielding easily interpreted answers, but have the disadvantage that they may fail to uncover complex or higher-order neuronal response properties. Here, we adopt an alternative approach, probing neuronal responses with complex acoustic stimuli, including animal vocalizations. We used in vivo whole-cell methods in the rat auditory cortex to record subthreshold membrane potential fluctuations elicited by these stimuli. Most neurons responded robustly and reliably to the complex stimuli in our ensemble. Using regularization techniques, we estimated the linear component, the spectrotemporal receptive field (STRF), of the transformation from the sound (as represented by its time-varying spectrogram) to the membrane potential of the neuron. We find that the STRF has a rich dynamical structure, including excitatory regions positioned in general accord with the prediction of the classical tuning curve. However, whereas the STRF successfully predicts the responses to some of the natural stimuli, it surprisingly fails completely to predict the responses to others; on average, only 11% of the response power could be predicted by the STRF. Therefore, most of the response of the neuron cannot be predicted by the linear component, although the response is deterministically related to the stimulus. Analysis of the systematic errors of the STRF model shows that this failure cannot be attributed to simple nonlinearities such as adaptation to mean intensity, rectification, or saturation. Rather, the highly nonlinear response properties of auditory cortical neurons must be attributable to nonlinear interactions between sound frequencies and time-varying properties of the neural encoder

VEGF released by deferoxamine preconditioned mesenchymal stem cells seeded on collagen-GAG substrates enhances neovascularization

Hypoxia preconditioning of mesenchymal stem cells (MSCs) has been shown to promote wound healing through HIF-1 alpha stabilization. Preconditioned MSCs can be applied to three-dimensional biomaterials to further enhance the regenerative properties. While environmentally induced hypoxia has proven difficult in clinical settings, this study compares the wound healing capabilities of adipose derived (Ad) MSCs seeded on a collagen-glycosaminoglycan (GAG) dermal substrate exposed to either environmental hypoxia or FDA approved deferoxamine mesylate (DFO) to stabilize HIF-1 alpha for wound healing. The release of hypoxia related reparative factors by the cells on the collagen-GAG substrate was evaluated to detect if DFO produces results comparable to environmentally induced hypoxia to facilitate optimal clinical settings. VEGF release increased in samples exposed to DFO. While the SDF-1 alpha release was lower in cells exposed to environmental hypoxia in comparison to cells cultured in DFO in vitro. The AdMSC seeded biomaterial was further evaluated in a murine model. The implants where harvested after 1 days for histological, inflammatory, and protein analysis. The application of DFO to the cells could mimic and enhance the wound healing capabilities of environmentally induced hypoxia through VEGF expression and promises a more viable option in clinical settings that is not merely restricted to the laboratory

VEGF released by deferoxamine preconditioned mesenchymal stem cells seeded on collagen-GAG substrates enhances neovascularization

Hypoxia preconditioning of mesenchymal stem cells (MSCs) has been shown to promote wound healing through HIF-1 alpha stabilization. Preconditioned MSCs can be applied to three-dimensional biomaterials to further enhance the regenerative properties. While environmentally induced hypoxia has proven difficult in clinical settings, this study compares the wound healing capabilities of adipose derived (Ad) MSCs seeded on a collagen-glycosaminoglycan (GAG) dermal substrate exposed to either environmental hypoxia or FDA approved deferoxamine mesylate (DFO) to stabilize HIF-1 alpha for wound healing. The release of hypoxia related reparative factors by the cells on the collagen-GAG substrate was evaluated to detect if DFO produces results comparable to environmentally induced hypoxia to facilitate optimal clinical settings. VEGF release increased in samples exposed to DFO. While the SDF-1 alpha release was lower in cells exposed to environmental hypoxia in comparison to cells cultured in DFO in vitro. The AdMSC seeded biomaterial was further evaluated in a murine model. The implants where harvested after 1 days for histological, inflammatory, and protein analysis. The application of DFO to the cells could mimic and enhance the wound healing capabilities of environmentally induced hypoxia through VEGF expression and promises a more viable option in clinical settings that is not merely restricted to the laboratory

Risk profile of the RET A883F germline mutation: an international collaborative study

Context: The A883F germline mutation of the REarranged during Transfection proto-oncogene causes multiple endocrine neoplasia 2B. In the revised American Thyroid Association (ATA) guidelines for the management of medullary thyroid carcinoma (MTC) the A883F mutation has been reclassified from the highest to high risk level, although no well-defined risk profile for this mutation exists. Objective: To create a risk profile for the A883F mutation for appropriate classification in the ATA risk levels. Design: Retrospective analysis. Setting: International collaboration. Patients: Included were 13 A883F carriers. Intervention: The intervention was thyroidectomy. Main Outcome Measures: Earliest age of MTC, regional lymph node metastases, distant metastases, age-related penetrance of MTC and pheochromocytoma (PHEO), overall and disease-specific survival and biochemical cure rate. Results: One and three carriers were diagnosed at age 7-9 years (median 7.5) with a normal thyroid and C-cell hyperplasia, respectively. Nine carriers had MTC diagnosed at age 10-39 years (median 19). The earliest age of MTC, regional lymph node and distant metastasis were 10, 20, 20 years, respectively. Fifty percent penetrance of MTC and PHEO was achieved by age 19 and 34 years, respectively. Five- and 10-year survival (both overall and disease-specific) were 88% and 88%, respectively. Biochemical cure for MTC at latest follow-up was achieved in 63% (5/8 carriers) with pertinent data. Conclusions: MTC of A883F carriers seems to have a more indolent natural course compared to that of M918T carriers. Our results support the classification of the A883F mutation in the ATA high risk level

Effects of Contact Network Models on Stochastic Epidemic Simulations

The importance of modeling the spread of epidemics through a population has led to the development of mathematical models for infectious disease propagation. A number of empirical studies have collected and analyzed data on contacts between individuals using a variety of sensors. Typically one uses such data to fit a probabilistic model of network contacts over which a disease may propagate. In this paper, we investigate the effects of different contact network models with varying levels of complexity on the outcomes of simulated epidemics using a stochastic Susceptible-Infectious-Recovered (SIR) model. We evaluate these network models on six datasets of contacts between people in a variety of settings. Our results demonstrate that the choice of network model can have a significant effect on how closely the outcomes of an epidemic simulation on a simulated network match the outcomes on the actual network constructed from the sensor data. In particular, preserving degrees of nodes appears to be much more important than preserving cluster structure for accurate epidemic simulations.Comment: To appear at International Conference on Social Informatics (SocInfo) 201

Automated Intelligent Monitoring and the Controlling Software System for Solar Panels

The inspection of the solar panels on a periodic basis is important to improve longevity and ensure performance of the solar system. To get the most solar potential of the photovoltaic (PV) system is possible through an intelligent monitoring & controlling system. The monitoring & controlling system has rapidly increased its popularity because of its user-friendly graphical interface for data acquisition, monitoring, controlling and measurements. In order to monitor the performance of the system especially for renewable energy source application such as solar photovoltaic (PV), data-acquisition systems had been used to collect all the data regarding the installed system. In this paper the development of a smart automated monitoring & controlling system for the solar panel is described, the core idea is based on IoT (the Internet of Things). The measurements of data are made using sensors, block management data acquisition modules, and a software system. Then, all the real-time data collection of the electrical output parameters of the PV plant such as voltage, current and generated electricity is displayed and stored in the block management. The proposed system is smart enough to make suggestions if the panel is not working properly, to display errors, to remind about maintenance of the system through email or SMS, and to rotate panels according to a sun position using the Ephemeral table that stored in the system. The advantages of the system are the performance of the solar panel system which can be monitored and analyzed

Demixed principal component analysis of neural population data

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure