Exploring Zirconia as a Column Packing Material

Zirconia is one of the most promising column packing materials for High Performance Liquid Chromatography (HPLC). The perfect HPLC support material should be energetically homogenous, have a high surface area on which different chemical species can reversibly attach and be physically and chemically stable over a wide range of pH, temperature and solvent conditions. Most existing supports do not have all of these properties. This project is also focused on a proteomics study. Zirconia, hafnium oxide and titanium oxide which are some of the more promising materials currently available, can be used for the separation and analysis of phosphorylated proteins. Adenosine triphosphate, Adenosine diphosphate and Adenosine monophosphate were used as prototypes for phosphorylated proteins. Separation, absorption, fluorescence and SEM studies were performed to determine the adsorption of Adenosine phosphates species at a particular pH on Zirconia. Zirconia was also used for the purification of Fibrinogen Growth Factor (FGF) protein, which are a family of growth factors involved in angiogenesis, wound healing, and embryonic development. The sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) technique was used to analyze the off-column purification and separation of this protein. This research suggests that, at acidic conditions, adenosine monophosphate has more favorable absorption on the Zirconia surface. On the other hand, the separation study suggests that basic conditions are more favorable for the absorption of ATP, ADP and AMP when mixed together on Zirconia 500. Furthermore, it was found that Zirconia is a very promising material for the purification of FGF protein

Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of View

Some reinforcement learning (RL) algorithms can stitch pieces of experience to solve a task never seen before during training. This oft-sought property is one of the few ways in which RL methods based on dynamic-programming differ from RL methods based on supervised-learning (SL). Yet, certain RL methods based on off-the-shelf SL algorithms achieve excellent results without an explicit mechanism for stitching; it remains unclear whether those methods forgo this important stitching property. This paper studies this question for the problems of achieving a target goal state and achieving a target return value. Our main result is to show that the stitching property corresponds to a form of combinatorial generalization: after training on a distribution of (state, goal) pairs, one would like to evaluate on (state, goal) pairs not seen together in the training data. Our analysis shows that this sort of generalization is different from i.i.d. generalization. This connection between stitching and generalisation reveals why we should not expect SL-based RL methods to perform stitching, even in the limit of large datasets and models. Based on this analysis, we construct new datasets to explicitly test for this property, revealing that SL-based methods lack this stitching property and hence fail to perform combinatorial generalization. Nonetheless, the connection between stitching and combinatorial generalisation also suggests a simple remedy for improving generalisation in SL: data augmentation. We propose a temporal data augmentation and demonstrate that adding it to SL-based methods enables them to successfully complete tasks not seen together during training. On a high level, this connection illustrates the importance of combinatorial generalization for data efficiency in time-series data beyond tasks beyond RL, like audio, video, or text.Comment: ICLR 2024, Project code: https://github.com/RajGhugare19/stitching-is-combinatorial-generalisatio

Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective

While reinforcement learning (RL) methods that learn an internal model of the environment have the potential to be more sample efficient than their model-free counterparts, learning to model raw observations from high dimensional sensors can be challenging. Prior work has addressed this challenge by learning low-dimensional representation of observations through auxiliary objectives, such as reconstruction or value prediction. However, the alignment between these auxiliary objectives and the RL objective is often unclear. In this work, we propose a single objective which jointly optimizes a latent-space model and policy to achieve high returns while remaining self-consistent. This objective is a lower bound on expected returns. Unlike prior bounds for model-based RL on policy exploration or model guarantees, our bound is directly on the overall RL objective. We demonstrate that the resulting algorithm matches or improves the sample-efficiency of the best prior model-based and model-free RL methods. While such sample efficient methods typically are computationally demanding, our method attains the performance of SAC in about 50\% less wall-clock time.Comment: 9 pages (without references and appendix), 17 figures, 25 Pages (total), Project website with code: \url{https://alignedlatentmodels.github.io/

Development and validation of analytical methods for the simultaneous estimation of Nimorazole and Ofloxacin in tablet dosage form

Two simple, rapid, accurate and precise spectrophotometric methods have been developed for simultaneous estimation of Nimorazole and Ofloxacin from tablet dosage form. Method І involves formation of ‘simultaneous equations’ at 304 nm (λ max of Nimorazole) and 287.5 nm (λ max of Ofloxacin); while Method ІІ involves, formation of ‘Absorbance ratio equation’ at 301(isoabsorptive point) and 287.5 nm (λ max of Ofloxacin) using distilled water as a solvent. The linearity was observed in the concentration range of 5 - 25 μg/ml for Nimorazole and 2 - 10 μg/ml for Ofloxacin. The results of analysis have been validated statistically and by recovery studies and were found satisfactory

Structural Investigation on Thermoresponsive PVA/Poly(methacrylate-co-N-isopropylacrylamide) Microgels across the Volume Phase Transition

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Structural and Dynamic Features of Thermoresponsive Microgels around the Volume Phase Transition Temperature

Sustained drug delivery requires the use of multi-functional devices with enhanced properties, including responsivity to external stimuli (such as temperature, pH, ionic strength), ability to target specific receptors, enhanced bioadhesion to cells and biocompatibility. Microgels represent one such multifunctional suitable as drug delivery and switchable microdevices. The fabrication of a stable colloidal aqueous suspension of biocompatible microgel spheres is based, for instance, on a poly(vinyl alcohol)/poly(methacrylate-co-N-isopropylacrylamide) network [1]. These microgel spheres undergo an entropy driven volume phase transition around physiological temperature, this process being driven by the incorporation of NiPAAm residues in the network. In this study the microgel was loaded with the anti cancer drug, doxorubicin. Upon microgel de-swelling, a marked increase in the amount of doxorubicin released was noted. Sieving and size exclusion effects were studied by laser scanning confocal microscopy with microgel particles exposed to fluorescent probes with different molecular weights (Figure 1). In this contribution we focus on some fundamental issues regarding modifications of the network structure at a nanoscopic level and of the diffusive behavior of water associated with the polymer network around the volume phase transition temperature (VPTT) [2]. Observations carried out at room temperature and at 40 °C (i.e. below and above the VPTT), provided an evaluation of the variation of the average pore size (from 5 nm to 3 nm). The diffusive behaviour of water molecules closely associated to the polymer network around the VPTT was investigated quasi-elastic neutron scattering. Nanostructured changes around VPTT of the microgel particles was probed in direct and reciprocal space, i.e. small angle neutron scattering (SANS) (Figure 2) and scanning transmission X-ray microscopy (STXM), respectively. A transition of the microgel interface from brush-like to smooth surface was evidenced by a power law change from 2 to 4 (Porod’s law)

Structural and Dynamic Features of Thermoresponsive Microgels around the Volume Phase Transition Temperature

Sustained drug delivery requires the use of multi-functional devices with enhanced properties, including responsivity to external stimuli (such as temperature, pH, ionic strength), ability to target specific receptors, enhanced bioadhesion to cells and biocompatibility. Microgels represent one such multifunctional suitable as drug delivery and switchable microdevices. The fabrication of a stable colloidal aqueous suspension of biocompatible microgel spheres is based, for instance, on a poly(vinyl alcohol)/poly(methacrylate-co-N-isopropylacrylamide) network [1]. These microgel spheres undergo an entropy driven volume phase transition around physiological temperature, this process being driven by the incorporation of NiPAAm residues in the network. In this study the microgel was loaded with the anti cancer drug, doxorubicin. Upon microgel de-swelling, a marked increase in the amount of doxorubicin released was noted. Sieving and size exclusion effects were studied by laser scanning confocal microscopy with microgel particles exposed to fluorescent probes with different molecular weights (Figure 1). In this contribution we focus on some fundamental issues regarding modifications of the network structure at a nanoscopic level and of the diffusive behavior of water associated with the polymer network around the volume phase transition temperature (VPTT) [2]. Observations carried out at room temperature and at 40 °C (i.e. below and above the VPTT), provided an evaluation of the variation of the average pore size (from 5 nm to 3 nm). The diffusive behaviour of water molecules closely associated to the polymer network around the VPTT was investigated quasi-elastic neutron scattering. Nanostructured changes around VPTT of the microgel particles was probed in direct and reciprocal space, i.e. small angle neutron scattering (SANS) (Figure 2) and scanning transmission X-ray microscopy (STXM), respectively. A transition of the microgel interface from brush-like to smooth surface was evidenced by a power law change from 2 to 4 (Porod’s law)

Ulnar neuropathy at wrist associated with a stab wound from iron fenced wall: A case report and review of electrodiagnostic methods to localize the lesion

Diagnosis of ulnar neuropathy at wrist remains challenging domain for neurophysicians as the clinical picture resembles proximal ulnar neuropathies. Inching across wrist and conduction to first dorsal interosseous remains mainstay of electrodiagnostic (EDX) procedures for distal ulnar neuropathy. Here, we present a case of distal ulnar neuropathy with review of its EDX procedures