IndiText Boost: Text Augmentation for Low Resource India Languages

Text Augmentation is an important task for low-resource languages. It helps deal with the problem of data scarcity. A data augmentation strategy is used to deal with the problem of data scarcity. Through the years, much work has been done on data augmentation for the English language. In contrast, very less work has been done on Indian languages. This is contrary to the fact that data augmentation is used to deal with data scarcity. In this work, we focus on implementing techniques like Easy Data Augmentation, Back Translation, Paraphrasing, Text Generation using LLMs, and Text Expansion using LLMs for text classification on different languages. We focus on 6 Indian languages namely: Sindhi, Marathi, Hindi, Gujarati, Telugu, and Sanskrit. According to our knowledge, no such work exists for text augmentation on Indian languages. We carry out binary as well as multi-class text classification to make our results more comparable. We get surprising results as basic data augmentation techniques surpass LLMs

Metabolic heterogeneity in microbial populations

No two living cells are exactly the same. Even cells from a clonal population with identical genomes living in the same environment will express proteins in different numbers simply due to the random nature of the chemistry involved in gene expression. The consequences of this stochastic gene expression are complex and not well understood, especially at the level of large reaction networks like metabolism. Here we investigate how variability in the copy numbers of metabolic enzymes affects how individual cells extract nourishment from their environment and grow. We model independent microbial cells, each with their own set of enzyme copy numbers sampled from experimental distributions, and use flux balance analysis (FBA) to compute the optimal way that each cell can use its metabolic pathways—an approach we dubbed Population FBA. We find that enzyme variability gives rise to a wide distribution of growth rates, and several metabolic phenotypes—subpopulations relying on diverse metabolic pathways. First we use Population FBA in investigating the effects of single cell proteomics data on the metabolic behavior of an in silico E. coli population. We use the latest metabolic reconstruction integrated with transcriptional regulatory data to model realistic cells growing in a glucose minimal medium under aerobic conditions. The modeled population exhibits a broad distribution of growth rates, and principal component analysis was used to identify well-defined subpopulations that differ in terms of their pathway usage. The cells differentiate into slow-growing acetate-secreting cells and fast-growing CO2-secreting cells, and a large population growing at intermediate rates shift from glycolysis to Entner-Doudoroff (ED) pathway usage. Constraints imposed by integrating regulatory data have a large impact on NADH oxidizing pathway usage within the cell. Finally we find that stochasticity in the expression of only a few genes may be sufficient to capture most of the metabolic variability of the entire population. Next, we extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media. We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen), while FBA without proteomics constraints predicts respirative metabolism almost exclusively. The comparisons to the 13C study showed improvement upon inclusion of the correlations and motivated a technique to systematically identify inconsistent kinetic parameters in the literature. The minor secretion fluxes for glycerol and acetate are underestimated by our method, which indicate a need for further refinements to the metabolic model. For yeast cells grown in synthetic defined (SD) medium, the calculated broad distribution of growth rates matches experimental observations from single cell studies, and we characterize several metabolic phenotypes within our modeled populations that make use of diverse pathways. Fast growing yeast cells perform significant amount of respiration, use serine- glycine cycle and produce ethanol in mitochondria as opposed to slow growing cells. We also investigate the degeneracy of the sets of protein-associated constraints that are necessary to give rise to the growth rate distributions seen experimentally. We find that a core set of 51 constraints are essential but that additional constraints are still necessary to reproduce the observed growth rate distributions in SD medium

High catalytic activity of Pt–Pd containing USY zeolite catalyst for low temperature CO oxidation from industrial off gases

AbstractSmall amounts (0.15wt%) of platinum and palladium were incorporated in porous, high surface area, ultra–stable H–USY–Zeolite by ion exchange method, and their catalytic activity was studied for carbon monoxide (CO) oxidation reaction, under various conditions of industrial importance. The catalyst was characterized by p–XRD, chemical analysis, SEM, TEM, evaluated for catalytic activity using a steady state, fixed bed catalytic reactor. The catalysts show high CO oxidation activity and it was possible to convert 0.044 mmols of CO per gram of catalyst at 120 °C, at a space velocity of 60 000 h−1 and with 100 ppm CO concentration in feed gas. The high catalytic activity of this noble metal catalyst also appears to be a factor of porous structure of zeolite facilitating mass transfer; high surface area as well as highly dispersed catalyst sites of palladium and platinum on zeolite structure. Introduction of acidic sites in zeolites probably makes them more resistant towards SO2, while their surface area and pore characteristics make this catalyst efficient even under high space velocity conditions, thus suggesting the potential of larger pore size zeolites over conventional porous materials for industrial applications

Perspectives on the Role of Fospropofol in the Monitored Anesthesia Care Setting

Monitored anesthesia care (MAC) is a safe, effective, and appropriate form of anesthesia for many minor surgical procedures. The proliferation of outpatient procedures has heightened interest in MAC sedation agents. Among the most commonly used MAC sedation agents today are benzodiazepines, including midazolam, and propofol. Recently approved in the United States is fospropofol, a prodrug of propofol which hydrolyzes in the body by alkaline phosphatase to liberate propofol. Propofol liberated from fospropofol has unique pharmacological properties, but recently retracted pharmacokinetic (PK) and pharmacodynamic (PD) evaluations make it difficult to formulate clear conclusions with respect to fospropofol's PK/PD properties. In safety and efficacy clinical studies, fospropofol demonstrated dose-dependent sedation with good rates of success at doses of 6.5 mg/kg along with good levels of patient and physician acceptance. Fospropofol has been associated with less pain at injection site than propofol. The most commonly reported side effects with fospropofol are paresthesia and pruritus. Fospropofol is a promising new sedation agent that appears to be well suited for MAC sedation, but further studies are needed to better understand its PK/PD properties as well its appropriate clinical role in outpatient procedures

Ordered intermetallic Pt–Cu nanoparticles for the catalytic CO oxidation reaction

Platinum-based intermetallic nanoparticles (NPs), using the abundantly available element copper, with an average particle size of 4–5 nm on a g-Al2O3 support were prepared successfully to reduce the consumption of Pt for the removal of CO through the catalytic oxidation reaction from flue gases. Intermetallic Pt–Cu NPs (Pt3Cu, PtCu, and PtCu3) with a Pt loading weight of 5 wt% were prepared on the g-Al2O3 support by a simple wet impregnation method followed by calcination at various temperatures (500–800 �C) in a H2 environment and they were characterized by powder X-ray diffraction analysis (pXRD), high resolution transmission electron microscopy (HR-TEM), selective area electron diffraction (SAED) method, etc. Despite the higher synthesis temperature of these intermetallic NPs, they were not agglomerated and formed a highly ordered intermetallic structure. The surface of the intermetallic Pt–Cu NPs with cubic-type structure (Pt3Cu and PtCu3) is enclosed of {200} facets, regardless of the significant difference in their compositions. Whereas the surface of rhombohedral-type intermetallic PtCu NPs is enclosed of {104} facets. Although the Pt-loading weight of these intermetallic NPs was the same, Pt3Cu NPs showed a stable and enhanced catalytic activity compared to the other intermetallic PtCu and PtCu3 NPs. Pt3Cu NPs showed an onset and maximum conversion temperature of 50 and 125 �C, respectively. The intermetallic phase between Pt and Cu of Pt3Cu NPs did not decompose; however, the intermetallic phase did decompose for PtCu and PtCu3 NPs after catalytic CO oxidation. Unlike PtCu and PtCu3 NPs, the Pt3Cu NPs were not agglomerated and they were finely dispersed even after catalytic CO oxidation

Multi-objective optimization of genome-scale metabolic models: the case of ethanol production

Ethanol is among the largest fermentation product used worldwide, accounting for more than 90% of all biofuel produced in the last decade. However current production methods of ethanol are unable to meet the requirements of increasing global demand, because of low yields on glucose sources. In this work, we present an in silico multi-objective optimization and analyses of eight genome-scale metabolic networks for the overproduction of ethanol within the engineered cell. We introduce MOME (multi-objective metabolic engineering) algorithm, that models both gene knockouts and enzymes up and down regulation using the Redirector framework. In a multi-step approach, MOME tackles the multi-objective optimization of biomass and ethanol production in the engineered strain; and performs genetic design and clustering analyses on the optimization results. We find in silico E. coli Pareto optimal strains with a knockout cost of 14 characterized by an ethanol production up to 19.74mmolgDW−1h−1 (+832.88% with respect to wild-type) and biomass production of 0.02h−1 (−98.06% ). The analyses on E. coli highlighted a single knockout strategy producing 16.49mmolgDW−1h−1 (+679.29% ) ethanol, with biomass equals to 0.23h−1 (−77.45% ). We also discuss results obtained by applying MOME to metabolic models of: (i) S. aureus; (ii) S. enterica; (iii) Y. pestis; (iv) S. cerevisiae; (v) C. reinhardtii; (vi) Y. lipolytica. We finally present a set of simulations in which constrains over essential genes and minimum allowable biomass were included. A bound over the maximum allowable biomass was also added, along with other settings representing rich media compositions. In the same conditions the maximum improvement in ethanol production is +195.24%