Signal Reconstruction via H-infinity Sampled-Data Control Theory: Beyond the Shannon Paradigm

This paper presents a new method for signal reconstruction by leveraging sampled-data control theory. We formulate the signal reconstruction problem in terms of an analog performance optimization problem using a stable discrete-time filter. The proposed H-infinity performance criterion naturally takes intersample behavior into account, reflecting the energy distributions of the signal. We present methods for computing optimal solutions which are guaranteed to be stable and causal. Detailed comparisons to alternative methods are provided. We discuss some applications in sound and image reconstruction

Online Algorithms for Dynamic Matching Markets in Power Distribution Systems

This paper proposes online algorithms for dynamic matching markets in power distribution systems, which at any real-time operation instance decides about matching -- or delaying the supply of -- flexible loads with available renewable generation with the objective of maximizing the social welfare of the exchange in the system. More specifically, two online matching algorithms are proposed for the following generation-load scenarios: (i) when the mean of renewable generation is greater than the mean of the flexible load, and (ii) when the condition (i) is reversed. With the intuition that the performance of such algorithms degrades with increasing randomness of the supply and demand, two properties are proposed for assessing the performance of the algorithms. First property is convergence to optimality (CO) as the underlying randomness of renewable generation and customer loads goes to zero. The second property is deviation from optimality, is measured as a function of the standard deviation of the underlying randomness of renewable generation and customer loads. The algorithm proposed for the first scenario is shown to satisfy CO and a deviation from optimal that varies linearly with the variation in the standard deviation. But the same algorithm is shown to not satisfy CO for the second scenario. We then show that the algorithm proposed for the second scenario satisfies CO and a deviation from optimal that varies linearly with the variation in standard deviation plus an offset

Hyperinsulinemia and insulin resistance : What comes first ?

Background

1)	Classical explanation :
Classical explanation of diabetic pathophysiology states that obesity induced insulin resistance develops first and is followed by compensatory hyperinsulinnemia. Further insulin resistance leads to prolonged, increased secretary demand on beta cells leading to subsequent secondary beta cell failure, giving rise to hyperglycaemia and diabetes^2^.

2)	 Neurobehavioral origin hypothesis :
The Neurobehavioral origin hypothesis suggests that insulin resistance mediates a shift from muscle dependent (soldier) to brain dependent (diplomat) strategies of making a livelihood. If nutrient limitation affects intrauterine development, brain development is the least affected among all the organs^4,5^. As a result, in IUGR babies muscle weight is poor but the brain is relatively well developed. Such a person is more likely to be a successful diplomat rather than a soldier and insulin resistance is adaptive for such an individual^3^. Since insulin is involved in brain development and cognitive functions, higher levels of insulin are needed. As insulin is having strong anti-lipolytic effect, hyperinsulinnemia is followed by subsequent excess fat accumulation. Also compensatory insulin resistance is needed to avoid hypoglycemia. This hypothesis predicts a reverse order of pathophysiology i.e. primary hyperinsulinnemia followed by compensatory insulin resistance^3^

Objective-
To determine in diabetes whether hyperinsulinnemia develops first or insulin resistance develops first.

Methods :
We searched literature for studies that investigated directly or indirectly the sequence of development of hyperinsulinnemia and insulin resistance in humans and animal models from an early stage. Meta-analysis was conducted on published data.

Results-
1)	In low birth weight neonates in humans as well as in rat models, hyperinsulinnemia is found at very early stage.^6^
2)	Development of insulin resistance is preceded by hyperinsulinnemia in mice, rats as well as in humans.^7, 8^
3)	In normoglycaemic hyperinsulinemia state if insulin production is suppressed insulin sensitivity increases rapidly maintaining the normoglycaemic state.^9,10^
4)	Beta cell expansion beginning in intrauterine life is independent of glucose, Insulin and Insulin receptors.^6^


Conclusion-
All the four lines of evidence indicate that hyperinsulinnemia precedes insulin resistance supporting the predictions of neurobehavioral origin hypothesis over the orthodox view.



References :
1)	DeFronzo RA, Ferrannini E (1991). Diabetes Care 14:173-194
2)	Kruszynska YT, Olefsky JM (1996). J Investig Med 44: 413-428.
3)	Watve MG, Yajnik CY (2007). BMC Evolutionary Biology.7: 61-74.
 4) Winick M, Rosso P, Waterlow JC (1970). Exp Neurol, 26:393-400.
 5) Winick M. (1969) J Pediatr,74:667-679.
 6) Chakravarthy MV et.al. (2008) Diabetes, 57:2698-2707.
 7) Ramin A et. al. (1998) J Clin Endo and Met, 83 :1911-1915.
 8) Hansen BC (1990) Am J Physiol Regul Integr Comp Physiol 259: 612-617.
 9) Stanley L (1981) Life Sciences, 28: 1829-1840.
 10) Ratzmann KP et. al. (1983) Int J Obes, 7 : 453-458

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Computational Modeling of Channelrhodopsin-2 Photocurrent Characteristics in Relation to Neural Signaling

Channelrhodopsins-2 (ChR2) are a class of light sensitive proteins that offer the ability to use light stimulation to regulate neural activity with millisecond precision. In order to address the limitations in the efficacy of the wild-type ChR2 (ChRwt) to achieve this objective, new variants of ChR2 that exhibit fast mono-exponential photocurrent decay characteristics have been recently developed and validated. In this paper, we investigate whether the framework of transition rate model with 4 states, primarily developed to mimic the bi-exponential photocurrent decay kinetics of ChRwt, as opposed to the low complexity 3 state model, is warranted to mimic the mono-exponential photocurrent decay kinetics of the newly developed fast ChR2 variants: ChETA (Gunaydin et al., Nature Neurosci, 13:387-392, 2010) and ChRET/TC (Berndt et al., PNAS, 108:7595-7600, 2011). We begin by estimating the parameters for the 3-state and 4-state models from experimental data on the photocurrent kinetics of ChRwt, ChETA and ChRET/TC. We then incorporate these models into a fast-spiking interneuron model (Wang and Buzsaki., J Neurosci, 16:6402-6413,1996) and a hippocampal pyramidal cell model (Golomb et al., J Neurophysiol, 96:1912-1926, 2006) and investigate the extent to which the experimentally observed neural response to various optostimulation protocols can be captured by these models. We demonstrate that for all ChR2 variants investigated, the 4 state model implementation is better able to capture neural response consistent with experiments across wide range of optostimulation protocol. We conclude by analytically investigating the conditions under which the characteristic specific to the 3-state model, namely the mono-exponential photocurrent decay of the newly developed variants of ChR2, can occurs in the framework of the 4-state model.Comment: 10 figure