Effect of Ashwagandha and Aloe vera pretreatment on intestinal transport of buspirone across rat intestine

The transport of buspirone across rat intestine (duodenum, jejunum, ileum and colon) was studied by using the non-everted sac method. Rats were pretreated with ashwagandha (Withania somnifera) and Aloe vera juice for 7 days. The rats were sacrificed by using anesthetic ether, the intestinal segments were isolated and used for the studies. The probe drug (buspirone) solution was placed in the isolated intestinal sac. Samples were collected at preset time points and replaced with fresh buffer. The drug content in the samples was estimated using high performance liquid chromatography method. Control experiments were also performed. The results reveal that there was a significant (p < 0.05) difference compared to control, in the transport of buspirone from the intestinal sacs which were pretreated with ashwagandha and Aloe vera juice. It suggests that both ashwagandha and Aloe vera might be acting by inhibiting the transporters and enzymes which are responsible for transport/metabolism of buspirone.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Probabilistic Arithmetic and Energy Efficient Embedded Signal Processing

Probabilistic arithmetic, where the i th output bit of addition and multiplication is correct with a probability pi, is shown to be a vehicle for realizing extremely energy-efficient, embedded computing. Specifically, probabilistic adders and multipliers, realized using elements such as gates that are in turn probabilistic, are shown to form a natural basis for primitives in the signal processing (dsp) domain. In this paper, we show that probabilistic arithmetic can be used to compute the fft in an extremely energy-efficient manner, yielding energy savings of over 5.6X in the context of the widely used synthetic aperture radar (sar) application [1]. Our results are derived using novel probabilistic cmos (pcmos) technology, characterized and applied in the past to realize ultra-efficient architectures for probabilistic applications [2, 3, 4]. When applied to the dsp domain, the resulting error in the output of a probabilistic arithmetic primitive, such as an adder for example, manifests as degradation in the signal-to-noise ratio (snr) of the sar image that is reconstructed through the fft algorithm. In return for this degradation that is enabled by our probabilistic arithmetic primitives — degradation visually indistinguishable from an image reconstructed using conventional deterministic approaches — significant energy savings and performance gains are shown to be possible per unit of snr degradation. These savings stem from a novel method of voltage scaling, which we refer to as biased voltage scaling (or bivos), that is the major technical innovation on which our probabilistic designs are based

Efficient Program Transformations for Resilient Parallel Computation via Randomization

In this paper, we address the problem of automatically transforming arbitrary programs written for an ideal parallel machine to run on a completely asynchronous machine. We present a transformation which can be applied to an ideal program such that the resulting program &apos;s execution on an asynchronous machine is work and space efficient, relative to the ideal program from which it is derived. Above all, the transformation will guarantee that the ideal program will execute in a continually progressive manner on the asynchronous machine: the computation itself will make progress without waiting for slow or failed processors to complete their work. We ensure the above properties by requiring that only read and write instructions be primitives in the asynchronous machine; these instructions are not universal. Furthermore, the individual processors can get delayed for arbitrary amounts of time while execut

Opportunities for energy efficient computing: a study of inexact general purpose processors for high-performance and big-data applications

In this paper, we demonstrate that disproportionate gains are possible through a simple devise for injecting inexactness or approximation into the hardware architecture of a computing system with a general purpose template including a complete memory hierarchy. The focus of the study is on energy savings possible through this approach in the context of large and challenging applications. We choose two such from different ends of the computing spectrum---the IGCM model for weather and climate modeling which embodies significant features of a high-performance computing workload, and the ubiquitous PageRank algorithm used in Internet search. In both cases, we are able to show in the affirmative that an inexact system outperforms its exact counterpart in terms of its efficiency quantified through the relative metric of operations per virtual Joule (OPVJ)---a relative metric that is not tied to particular hardware technology. As one example, the IGCM application can be used to achieve savings through inexactness of (almost) a factor of 3 in energy without compromising the quality of the forecast, quantified through the forecast error metric, in a noticeable manner. As another example finding, we show that in the case of PageRank, an inexact system is able to outperform its exact counterpart by close to a factor of 1.5 using the OPVJ metric