Curcumin is a natural polyphenol and essential curcuminoid derived from the rhizome of the medicinal plant Curcuma longa (L.) is universally acknowledged as “Wonder drug of life”. It is a vital consumable and restorative herb, commonly keened for several ailments such as cancer, arthritis, pain, bruises, gastrointestinal quandaries, swelling and much more. Despite its enormous curative potential, the poor aqueous solubility and consequently, minimal systemic bioavailability with rapid degradation are some of the major factors which restrict the utilization of curcumin at medical perspective. However, to improve its clinically relevant parameters, nanoformulation of curcumin is emerging as a novel substitute for their superior therapeutic modality. It enhances its aqueous solubility and targeted delivery to the tissue of interest that prompts to enhance the bioavailability, better drug conveyance, and more expeditious treatment. Subsequent investigations are endeavored to enhance the bio-distribution of native curcumin by modifying with felicitous nano-carriers for encapsulation. In this review, we specifically focus on the recent nanotechnology based implementations applied for overcoming the innate constraints of native curcumin and additionally the associated challenges which restrict its potential therapeutic applications both in vivo and in-vitro studies, as well as their detailed mechanism of action, have additionally been discussed.

This study was conducted to determine the effects of argon plasma on the growth of soybean [Glycine max (L.) Merr.] sprouts and investigate the regulation mechanism of energy metabolism. The germination and growth characteristics were modified by argon plasma at different potentials and exposure durations. Upon investigation, plasma treatment at 22.1 kV for 12 s maximized the germination and seedling growth of soybean, increasing the concentrations of soluble protein, antioxidant enzymes, and adenosine triphosphate (ATP) as well as up-regulating ATP a1, ATP a2, ATP b1, ATP b2, ATP b3, target of rapamycin (TOR), growth-regulating factor (GRF) 1–6, down-regulating ATP MI25 mRNA expression, and increasing the demethylation levels of the sequenced region of ATP a1, ATP b1, TOR, GRF 5, and GRF 6 of 6-day-old soybean sprouts. These observations indicate that argon plasma promotes soybean seed germination and sprout growth by regulating the demethylation levels of ATP, TOR, and GRF.

Abstract

DRAMs require periodic refresh for preserving data stored in them. The refresh interval for DRAMs depends on the vendor and the design technology they use. For each refresh in a DRAM row, the stored information in each cell is read out and then written back to itself as each DRAM bit read is self-destructive. The refresh process is inevitable for maintaining data correctness, unfortunately, at the expense of power and bandwidth overhead. The future trend to integrate layers of 3D die-stacked DRAMs on top of a processor further exacerbates the situation as accesses to these DRAMs will be more frequent and hiding refresh cycles in the available slack becomes increasingly difficult. Moreover, due to the implication of temperature increase, the refresh interval of 3D die-stacked DRAMs will become shorter than those of conventional ones.This paper proposes an innovative scheme to alleviate the energy consumed in DRAMs. By employing a time-out counter for each memory row of a DRAM module, all the unnecessary periodic refresh operations can be eliminated. The basic concept behind our scheme is that a DRAM row that was recently read or written to by the processor (or other devices that share the same DRAM) does not need to be refreshed again by the periodic refresh operation, thereby eliminating excessive refreshes and the energy dissipated. Based on this concept, we propose a low-cost technique in the memory controller for DRAM power reduction. The simulation results show that our technique can reduce up to 86% of all refresh operations and 59.3% on the average for a 2GB DRAM. This in turn results in a 52.6% energy savings for refresh operations. The overall energy saving in the DRAM is up to 25.7% with an average of 12.13% obtained for SPLASH-2, SPECint2000, and Biobench benchmark programs simulated on a 2GB DRAM. For a 64MB 3D DRAM, the energy saving is up to 21% and 9.37% on an average when the refresh rate is 64 ms. For a faster 32ms refresh rate the maximum and average savings are 12% and 6.8% respectively. 40th IEEE/ACM International Symposium on Microarchitecture

Encrypting data in unprotected memory has gained much interest lately for digital rights protection and security reasons. Counter Mode is a well-known encryption scheme. It is a symmetric-key encryption scheme based on any block cipher, e.g. AES. The scheme's encryption algorithm uses a block cipher, a secret key and a counter (or a sequence number) to generate an encryption pad which is XORed with the data stored in memory. Like other memory encryption schemes, this method suffers from the inherent latency of decrypting encrypted data when loading them into the onchip cache. One solution that parallelizes data fetching and encryption pad generation requires the sequence numbers of evicted cache lines to be cached on-chip. On-chip sequence number caching can be successful in reducing the latency at the cost of a large area overhead.In this paper, we present a novel technique to hide the latency overhead of decrypting counter mode encrypted memory by predicting the sequence number and pre-computing the encryption pad that we call one-time-pad or OTP. In contrast to the prior techniques of sequence number caching, our mechanism solves the latency issue by using idle decryption engine cycles to speculatively predict and pre-compute OTPs before the corresponding sequence number is loaded. This technique incurs very little area overhead. In addition, a novel adaptive OTP prediction technique is also presented to further improve our regular OTP prediction and precomputation mechanism. This adaptive scheme is not only able to predict encryption pads associated with static and infrequently updated cache lines but also those frequently updated ones as well. Experimental results using SPEC2000 benchmark show an 82% prediction rate.Moreover, we also explore several optimization techniques for improving the prediction accuracy. Two specific techniques, Two-level prediction and Context-based prediction are presented and evaluated. For the two-level prediction, the prediction rate was improved from 82% to 96%. With the context-based prediction, the prediction rate approaches 99%. Context-based OTP prediction outperforms a very large 512KB sequence number cache for many memory-bound SPEC programs. IPC results show an overall 15% to 40% performance improvement using our prediction and precomputation, and another 7% improvement when context-based prediction techniques is used.