Identifying plant-based natural medicine against oxidative stress and neurodegenerative disorders

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Polar question particle -aa in Malabar Malayalam

This paper provides an account for the properties of the polar question particle -aa in Malabar Malayalm, which is, in some crucial aspects, similar to its Hindi counterpart kyaa. Using instances of its occurrence in polar and alternative questions, and non-occurrence in wh- questions and declarative disjuncts, we discuss the unique manner in which -aa attaches only to clausal disjuncts and try to provide a semantic account for this pattern. Data from other major Dravidian languages have also been used for this purpose. We argue that -aa qualifies as a polar question particle since it resides in ForceP and has a presuppositional requirement of a singleton- set question as its complement. An additional supporting argument for this claim is that it exhibits all the diagnostic patterns of a root phenomenon. The second claim of the paper, that -oo in Malayalam is a polar question operator, is supported by the fact that it occurs only in polar and alternative questions. Like in more standard Hamblin semantics, we take the line that there is a distinction between the question operator that forms polar questions and the question operator that forms wh-questions, because the first takes a single proposition for its complement, whereas the second takes a set of propositions

Nutritional Composition and Antioxidant Properties of Cucumis dipsaceus

The leaf of C. dipsaceus was evaluated for its nutritional and antioxidant properties. From the present investigation, significant amount of almost all essential amino acids and important minerals were quantified. Low levels of trypsin inhibitory units, phenolics, and tannins content were found as antinutritional content. Further, hot water extract of C. dipsaceus showed good activity especially in ABTS+, metal chelating, nitric oxide, and DPPH assays. Hence, the results conclude that C. dipsaceus could be a valuable nutraceutical supplement to the human diet

Efficient ML Models for Practical Secure Inference

ML-as-a-service continues to grow, and so does the need for very strong privacy guarantees. Secure inference has emerged as a potential solution, wherein cryptographic primitives allow inference without revealing users' inputs to a model provider or model's weights to a user. For instance, the model provider could be a diagnostics company that has trained a state-of-the-art DenseNet-121 model for interpreting a chest X-ray and the user could be a patient at a hospital. While secure inference is in principle feasible for this setting, there are no existing techniques that make it practical at scale. The CrypTFlow2 framework provides a potential solution with its ability to automatically and correctly translate clear-text inference to secure inference for arbitrary models. However, the resultant secure inference from CrypTFlow2 is impractically expensive: Almost 3TB of communication is required to interpret a single X-ray on DenseNet-121. In this paper, we address this outstanding challenge of inefficiency of secure inference with three contributions. First, we show that the primary bottlenecks in secure inference are large linear layers which can be optimized with the choice of network backbone and the use of operators developed for efficient clear-text inference. This finding and emphasis deviates from many recent works which focus on optimizing non-linear activation layers when performing secure inference of smaller networks. Second, based on analysis of a bottle-necked convolution layer, we design a X-operator which is a more efficient drop-in replacement. Third, we show that the fast Winograd convolution algorithm further improves efficiency of secure inference. In combination, these three optimizations prove to be highly effective for the problem of X-ray interpretation trained on the CheXpert dataset.Comment: 10 pages include references, 4 figure

Anti-Proliferative, Analgesic and Anti-Inflammatory Properties of Syzygium mundagam Bark Methanol Extract

Abstract: Cancer, pain and inflammation have long been a cause for concern amongst patients, clinicians and research scientists. There is an alarming increase in the demand for medicines suppressing these disease conditions. The present study investigates the role of Syzygium mundagam bark methanol (SMBM) extract against MCF-7 breast cancer cells, pain and inflammation. The MCF-7 cells treated with SMBM were analyzed for adenosine triphosphate (ATP), lactate dehydrogenase (LDH) levels, changes in cell morphology and nuclear damage. Hot plate, acetic acid and formalin-induced pain models were followed to determine the analgesic activity. Anti-inflammatory activity was studied using carrageenan, egg albumin and cotton pellet induced rat models. Microscopic images of cells in SMBM treated groups showed prominent cell shrinkage and nuclear damage. Hoechst stain results supported the cell death morphology. The decline in ATP (47.96%) and increased LDH (40.96%) content indicated SMBM induced toxicity in MCF-7 cells. In the in vivo study, a higher dose (200 mg/kg) of the extract was found to be effective in reducing pain and inflammation. The results are promising and the action of the extract on MCF-7 cells, pain and inflammation models indicate the potential of drugs of natural origin to improve current therapies

The influence of light on reactive oxygen species and NF-êB in disease progression

Abstract: Abstract: Reactive oxygen species (ROS) are important secondary metabolites that play major roles in signaling pathways, with their levels often used as analytical tools to investigate various cellular scenarios. They potentially damage genetic material and facilitate tumorigenesis by inhibiting certain tumor suppressors. In diabetic conditions, substantial levels of ROS stimulate oxidative stress through specialized precursors and enzymatic activity, while minimum levels are required for proper wound healing. Photobiomodulation (PBM) uses light to stimulate cellular mechanisms and facilitate the removal of oxidative stress. Photodynamic therapy (PDT) generates ROS to induce selective tumor destruction. The regulatory roles of PBM via crosstalk between ROS and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-êB) are substantial for the appropriate management of various conditions

EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation for Machine Learning

We present EZPC: a secure two-party computation (2PC) framework that generates efficient 2PC protocols from high-level, easy-to-write, programs. EZPC provides formal correctness and security guarantees while maintaining performance and scalability. Previous language frameworks, such as CBMC-GC, ObliVM, SMCL, and Wysteria, generate protocols that use either arithmetic or boolean circuits exclusively. Our compiler is the first to generate protocols that combine both arithmetic sharing and garbled circuits for better performance. We empirically demonstrate that the protocols generated by our framework match or outperform (up to 19x) recent works that provide hand-crafted protocols for various functionalities such as secure prediction and matrix factorization

SIRNN: A Math Library for Secure RNN Inference

Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs) use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciprocal of square root. Although prior work on secure 2-party inference provides specialized protocols for convolutional neural networks (CNNs), existing secure implementations of these math operators rely on generic 2-party computation (2PC) protocols that suffer from high communication. We provide new specialized 2PC protocols for math functions that crucially rely on lookup-tables and mixed-bitwidths to address this performance overhead; our protocols for math functions communicate up to 423x less data than prior work. Some of the mixed bitwidth operations used by our math implementations are (zero and signed) extensions, different forms of truncations, multiplication of operands of mixed-bitwidths, and digit decomposition (a generalization of bit decomposition to larger digits). For each of these primitive operations, we construct specialized 2PC protocols that are more communication efficient than generic 2PC, and can be of independent interest. Furthermore, our math implementations are numerically precise, which ensures that the secure implementations preserve model accuracy of cleartext. We build on top of our novel protocols to build SIRNN, a library for end-to-end secure 2-party DNN inference, that provides the first secure implementations of an RNN operating on time series sensor data, an RNN operating on speech data, and a state-of-the-art ML architecture that combines CNNs and RNNs for identifying all heads present in images. Our evaluation shows that SIRNN achieves up to three orders of magnitude of performance improvement when compared to inference of these models using an existing state-of-the-art 2PC framework