Evaluation of wild type and mutants of β-Glucuronidase (GUS) against natural and synthetic substrates

Modifying substrate specificity of β-glucuronidase (GUS) would be helpful in various enzyme prodrug systems in delivering drug dose to the site of action in the cancer treatment. Due to the presence of endogenous enzyme in human tissues, GUS-based Antibody-Directed Enzyme Prodrug Therapy (ADEPT) requires a novel substrate to avoid undesirable systemic activation. GUS is a glycosyl hydrolase, highly specific towards the glucuronide derivatives. It catalyzes the glycosidic cleavage of β-D-glucuronides to β-D-glucuronic acid and aglycone moiety. In order to gain insight on the substrate specificity of GUS, C6 carboxyl group of glucuronic acid was modified to C6 carboxamide (amide derivative). We have examined amide derivatized substrates with a variety of different aglycone groups including p-nitrophenyl, phenyl and 4-methylumbelliferone to further probe the activity profile of GUS. In an effort to optimize GUS activity, docking studies have been performed which indicated that amino acid point mutations near C6 carboxyl group of glucuronic acid could improve binding of the derivatized substrates. As a result point mutations to Arg-562 and Lys-568 which make the active site less positively charged either by glutamine or glutamate lead to an enzyme with much lower native substrate activity but abolished activity for the amide-derivatized substrate. This research study showed that there is still a further need of finding appropriate mutations required to make glucuronamide a better substrate for the mutated version of GUS

On the relationship between pain variability and relief in randomized clinical trials

Previous research suggests greater baseline variability is associated with greater pain relief in those who receive a placebo. However, studies that evidence this association do not control for confounding effects (natural history and regression-to-the-mean); for this reason, we analyzed data from two randomized clinical trials (Placebo I and Placebo II, N = 134) while adjusting for confounding effects via a no-treatment group. Results agree between the two placebo groups: both placebo groups showed a negligible correlation between baseline variability and adjusted response (r sp (CI 95% ) = 0.13 (−0.09, 0.37) and 0.01 (−0.15, 0.20) for Placebo I and II, respectively). Drug groups also showed similar, weak correlations (rsp = −0.16–0.08; max CI 95% = −0.39–0.31). When modeled as a linear covariate, variability only accounted for an additional 1% of the variance in post-intervention pain across both studies; the inability of variability to account for substantial variance in pain response highlights that previous results concerning variability and treatment response may be inconsistent. Indeed, the relationship appears to be neither consistently specific nor sensitive to improvements in the placebo group. Researchers and clinicians should not rely on using baseline pain variability as a prognostic factor for improvement following placebo

Green Carbon Footprint for Model Inference Serving via Exploiting Mixed-Quality Models and GPU Partitioning

This paper presents a solution to the challenge of mitigating carbon emissions from large-scale high performance computing (HPC) systems and datacenters that host machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to datacenter compute cycles and carbon emissions. We introduce Clover, a carbon-friendly ML inference service runtime system that balances performance, accuracy, and carbon emissions through mixed-quality models and GPU resource partitioning. Our experimental results demonstrate that Clover is effective in substantially reducing carbon emissions while maintaining high accuracy and meeting service level agreement (SLA) targets. Therefore, it is a promising solution toward achieving carbon neutrality in HPC systems and datacenters

SYNTHESIS AND ANTICANCER SCREENING OF TRIAZINE ANALOGUES

Objective: The study was aimed to investigate the cytotoxic effect of S-5H-[1,2,4]-triazino (5,6-b) indol-3-yl-3,4-phenylethane-thioate derivatives as epidermal growth factor Receptor (EGFR) inhibitors. Methods: In the present study 14 novel triazine analogues were synthesized and characterized using different spectroscopic techniques such as FT-IR, NMR and Mass Spectroscopy. The anticancer activity was performed using MCF-7 (breast cancer) and K-562 (leukaemia) cell lines. Further, molecular docking was carried out using Vlife Molecular Docking Software (MDS) on crystal structure of epidermal growth factor receptor (EGFR) to identify the binding mode of interaction with an active site. Results: Compounds MA-7, MA-8, MA-12, MA-13 and MA-14 show potent activity against cancer cell lines in the range of<10 to 84.4 µg/ml. Further molecular docking on EGFR also supports that there is a strong correlation between in silico and in vitro biological activity. The results of this study may be further useful for lead optimization process. Conclusion: The results of this study indicates that the synthesized triazine analogues can give a potential lead as an anticancer agent

EutroPod

EutroPod is a highly concentrated solution of denitrifying enzymes used to combat eutrophication. This product alters the chemical structure of nitrates and nitrites by converting them into a form that is safe for the environment

Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models

The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an urgent need for methods that reduce the energy needs of NLP and machine learning more broadly. In this article, we investigate techniques that can be used to reduce the energy consumption of common NLP applications. In particular, we focus on techniques to measure energy usage and different hardware and datacenter-oriented settings that can be tuned to reduce energy consumption for training and inference for language models. We characterize the impact of these settings on metrics such as computational performance and energy consumption through experiments conducted on a high performance computing system as well as popular cloud computing platforms. These techniques can lead to significant reduction in energy consumption when training language models or their use for inference. For example, power-capping, which limits the maximum power a GPU can consume, can enable a 15\% decrease in energy usage with marginal increase in overall computation time when training a transformer-based language model

What is the numerical nature of pain relief?

Pain relief, or a decrease in self-reported pain intensity, is frequently the primary outcome of pain 10 clinical trials. Investigators commonly report pain relief in one of two ways: using raw units (additive) 11 or using percentage units (multiplicative). However, additive and multiplicative scales have different 12 assumptions and are incompatible with one-another. In this work, we describe the assumptions and 13 corollaries of additive and multiplicative models of pain relief to illuminate the issue from statistical 14 and clinical perspectives. First, we explain the math underlying each model and illustrate these points 15 using simulations, for which readers are assumed to have an understanding of linear regression. Next, we 16 connect this math to clinical interpretations, stressing the importance of statistical models that accurately 17 represent the underlying data; for example, how using percent pain relief can mislead clinicians if the data are actually additive. These theoretical discussions are supported by empirical data from four 19 longitudinal studies of patients with subacute and chronic pain. Finally, we discuss self-reported pain 20 intensity as a measurement construct, including its philosophical limitations and how clinical pain differs 21 from acute pain measured during psychophysics experiments. This work has broad implications for 22 clinical pain research, ranging from statistical modeling of trial data to the use of minimal clinically important differences and patient-clinician communication

Sustainable HPC: Modeling, Characterization, and Implications of Carbon Footprint in Modern HPC Systems

The rapid growth in demand for HPC systems has led to a rise in energy consumption and carbon emissions, which requires urgent intervention. In this work, we present a comprehensive framework for analyzing the carbon footprint of high-performance computing (HPC) systems, considering the carbon footprint during both the hardware production and system operational stages. Our work employs HPC hardware component carbon footprint modeling, regional carbon intensity analysis, and experimental characterization of the system life cycle to highlight the importance of quantifying the carbon footprint of an HPC system holistically

Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service

This paper presents a solution to the challenge of mitigating carbon emissions from hosting large-scale machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to carbon footprint. We introduce, Clover, a carbon-friendly ML inference service runtime system that balances performance, accuracy, and carbon emissions through mixed-quality models and GPU resource partitioning. Our experimental results demonstrate that Clover is effective in substantially reducing carbon emissions while maintaining high accuracy and meeting service level agreement (SLA) targets