Design, Synthesis, and Bio Relevant Applications of Zwitterionic Amphiphilic Macromolecular Assemblies

Supramolecular nanoassemblies capable of reducing non-specific interactions with biological macromolecules, such as proteins, are of great importance for various biological applications especially for targeted drug delivery therapeutics. Recently, zwitterionic materials have been shown to reduce non-specific interactions with biomolecules, owing both to their charge neutrality and their ability to form strong hydration layer around zwitterions via electrostatic interactions. This dissertation focuses on design, synthesis, thorough characterization, and applications of zwitterionic amphiphilic dendrimers and polymeric materials. Firstly, A new triazole-based zwitterionic moiety was conceived and incorporated as the hydrophilic functionality in facially amphiphilic dendrons. Self-assembly characteristics and the structural and functional characteristics of the zwitterionic dendrons were then evaluated by spectroscopic techniques and by comparison with the corresponding charge-neutral PEG and anionic carboxylate-based dendrons respectively. Surface charge measurements, temperature sensitivity and evaluation of interactions of these assemblies with proteins form the bases for these comparisons. Because of the charge neutral characteristics of zwitterionic moieties exposed to outer aqueous phase, these dendritic assemblies evade non-specific interactions with biological milieu while enhancing the responsiveness towards specific biological triggers, such as proteins. We further simplified this molecular design by developing synthetically accessible zwitterionic carboxybetaine based amphiphilic polymers. Zwitterionic amphiphilic polymers are synthesized in a simple one-pot reaction by treating reactive acrylate polymer with secondary amine and 2-bromoaceticacid. This zwitterionic polymer possesses hydrophilic glycinebetaine and hydrophobic decyl chains placed orthogonally and forms environment dependent self-assembled aggregates owing to its amphiphilicity. The zwitterionic assemblies stably encapsulate guest molecules and undergo pH-dependent zeta potential and size variations owing to the pH sensitivity of zwitterionic component. In a related project, we’ve developed degradable polycarbonate based amphiphilic polymers possessing hydrophilic peg and hydrophobic decyl components. This degradable polymers form self-assembled aggregates encapsulate hydrophobic guest molecules, undergo degradation upon changes in pH or in presence of enzymes (PLE, an esterase). Work is underway towards the design and synthesis of zwitterionic amphiphilic degradable polymers and their characterization. Over all the work presented in this thesis have implications in design and development of polymeric excipients for targeted drug delivery, controlled release of drugs, and stimuli-sensitive therapeutics

Optimization of Xylanase Production through Response Surface Methodology by Fusarium sp. BVKT R2 Isolated from Forest Soil and Its Application in Saccharification

AbstractXylanses are hydrolytic enzymes with wide applications in several industries like biofuels, paper and pulp, deinking, food and feed. The present study was aimed at hitting at high yield xylanase producing fungi from natural resources. Two highest xylanase producing fungal isolates - Q12 and L1were picked from collection of 450 fungal cultures for the utilization of xylan. These fungal isolates - Q12 and L1 were identified basing on ITS gene sequencing analysis as Fusarium sp. BVKT R2 (KT119615) and Fusarium strain BRR R6 (KT119619), respectively with construction of phylogenetic trees. Fusarium sp. BVKT R2 was further optimized for maximum xylanase production and the interaction effects between variables on production of xylanase were studied through response surface methodology. The optimal conditions for maximal production of xylanase were sorbitol 1.5%, yeast extract 1.5%, pH of 5.0, Temperature of 32.5ºC, and agitation of 175 rpm. Under optimal conditions, the yields of xylanase production by Fusarium sp. BVKT R2 was as high as 4560 U/ml in SmF. Incubation of different lignocellulosic biomasses with crude enzyme of Fusarium sp. BVKT R2 at 37°C for 72 h could achieve about 45% saccharification. The results suggest that Fusarium sp. BVKT R2 has potential applications in saccharification process of biomass.Key words: Fusarium sp., Optimization, Response Surface Methodology, Saccharification, Submerged fermentation, Xylanas

Novel Heuristic Recurrent Neural Network Framework to Handle Automatic Telugu Text Categorization from Handwritten Text Image

In the near future, the digitization and processing of the current paper documents describe efficient role in the creation of a paperless environment. Deep learning techniques for handwritten recognition have been extensively studied by various researchers. Deep neural networks can be trained quickly thanks to a lot of data and other algorithmic advancements. Various methods for extracting text from handwritten manuscripts have been developed in literature. To extract features from written Telugu Text image having some other neural network approaches like convolution neural network (CNN), recurrent neural networks (RNN), long short-term memory (LSTM). Different deep learning related approaches are widely used to identification of handwritten Telugu Text; various techniques are used in literature for the identification of Telugu Text from documents. For automatic identification of Telugu written script efficiently to eliminate noise and other semantic features present in Telugu Text, in this paper, proposes Novel Heuristic Advanced Neural Network based Telugu Text Categorization Model (NHANNTCM) based on sequence-to-sequence feature extraction procedure. Proposed approach extracts the features using RNN and then represents Telugu Text in sequence-to-sequence format for the identification advanced neural network performs both encoding and decoding to identify and explore visual features from sequence of Telugu Text in input data. The classification accuracy rates for Telugu words, Telugu numerals, Telugu characters, Telugu sentences, and the corresponding Telugu sentences were 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% consequently. Experimental evaluation describe extracted with revealed which are textured i.e. TENG shown considerable operations in applications such as private information protection, security defense, and personal handwriting signature identification

An intriguing autopsy case of gangrene intestine

Background Hashimoto’s thyroiditis is one of the most common causes of hypothyroidism. Hypothyroidism is a known cause of hyperlipidemia. There is a strong correlation between coronary and mesenteric vessel atherosclerosis. Acute mesenteric ischemia is a cause of intestinal hemorrhagic infarction. Case history We present an autopsy case of 35-year-old male who presented with features of obstruction and edema with previously undetected hypothyroidism. Conclusion Hypothyroidism associated with atherosclerosis can lead to fatal intestinal gangrene as corroborated by this autopsy case. Key words –Hypothyroidism, intestinal gangrene, autopsy, atherosclerosi

In-network computation in sensor networks

Sensor networks are an important emerging class of networks that have many applications. A sink in these networks acts as a bridge between the sensor nodes and the end-user (which may be automated and/or part of the sink). Typically, convergecast is performed in which all the data collected by the sensors is relayed to the sink, which in turn presents the relevant information to the end-user. Interestingly, some applications require the sink to relay just a function of the data collected by the sensors. For instance, in a fire alarm system, the sinks needs to monitor the maximum of the temperature readings of all the sensors. For these applications, instead of performing convergecast, we can let the intermediate nodes process the data they receive, to significantly reduce the volume of traffic transmitted and increase the rate at which the data is collected and processed at the sink: this is known as in-network computation. Most of the current literature on this novel technique focuses on asymptotic results for large networks and for very elementary functions. In this dissertation, we study a new class of functions for which we want to compute explicit solutions for networks of practical size. We consider the applications where the sink is interested in the first M statistical moments of the data collected at a certain time. The k-th statistical moment is defined as the expectation of the k-th power of the data. The M=1 case represents the elementary functions like MAX, MIN, MEAN, etc. that are commonly considered in the literature. For this class of functions, we are interested in explicitly computing the maximum achievable throughput including routing, scheduling and queue management for any given network when in-network computation is allowed. Flow models have been routinely used to solve optimal joint routing and scheduling problems when there is no in-network computation and they are typically tractable for relatively large networks. However, deriving such models is not obvious when in-network computation is allowed. Considering a single rate wireless network and the physical model of interference, we develop a discrete-time model for the real-time network operation and perform two transformations to obtain a flow model that keeps the essence of in-network computation. This model gives an upper bound on the maximum achievable throughput. To show the tightness of that upper bound, we derive a numerical lower bound by computing a feasible solution to the discrete-time model. This lower bound turns out to be close to the upper bound proving that the flow model is an excellent approximation to the discrete-time model. We then adapt the flow model to a wired multi-rate network with asynchronous transmissions on links with different capacities. To compute the lower bound for wired networks, we propose a heuristic strategy involving the generation of multiple trees and effective queue management that achieves a throughput close to the one computed by the flow model. This cross validates the tightness of the upper bound and the goodness of our heuristic strategy. Finally, we provide several engineering insights on what in-network computation can achieve in both types of networks

An intriguing autopsy case of gangrene intestine

Hypothyroidism, or myxedema, is the clinical syndrome that results from decreased production of T4 and T3.Most patients have primary hypothyroidism .The etiology of adult primary hypothyroidism includes autoimmune hypothyroidism (Hashimoto’s thyroiditis), post – ablation after radio-active iodine, thyroid surgery and drugs such as Amiodarone and Lithium. Manifestations are variable and proportionate to the degree and duration of thyroid hormone deficiency as well as age of onset. The presence of goiter is common in younger patients (Hashimoto’s) but often absent in the elderly. Clinical features of hypothyroidism are insidious and often missed, particularly in the elderly

Effect of strain path on the evolution of microstructure and texture of equiatomic CoCrFeMnNi high entropy alloy

The effect of strain path on the evolution of microstructure and texture during cold rolling and subsequent annealing was investigated in equiatomic CoCrFeMnNi high entropy alloy. For this purpose the alloy was subjected to cold rolling up to 90% reduction in thickness by four different strain path routes namely (i) unidirectional rolling (UCR); (ii) Multi step cross rolling (MSCR); (iii) two step cross rolling (90°) (TSCR [90°]); (iv) two step cross rolling (45°) (TSCR [45°]). These deformed samples were subjected to isochronal annealing(for 1 h)at different temperatures ranging from 700°C to 1000°C and were characterized by using electron back scattered diffraction (EBSD) technique. Development of ultrafine microstructure and presence of shear bands in all deformed materials attributes to the grain subdivision at finer scale. The differently cross rolled materials shown weaker brass and stronger ND rotated brass components in contrast to UCR processed material where strong brass type texture was observed

EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning

Large language models primarily rely on incontext learning to execute tasks. We introduce EchoPrompt, a simple yet effective approach to prompt the model to rephrase its queries before answering them. EchoPrompt is inspired by self-questioning, a cognitive strategy humans use to vocalize queries before providing answers, thereby reducing misconceptions. Experimental results demonstrate that EchoPrompt leads to substantial improvements in both zero-shot and few-shot in-context learning with standard and chain-of-thought prompting on four families of causal language models. These improvements are observed across various numerical reasoning (GSM8K, SVAMP, MultiArith, SingleOp), reading comprehension (DROP, SQuAD), and logical reasoning (Shuffled Objects, Date Understanding, Coin Flipping) tasks. On average, EchoPrompt improves the Zero-shot-CoT performance of code-davinci-002 by 5% in numerical tasks and 13% in reading comprehension tasks. We investigate the effectiveness of EchoPrompt through ablation studies, which reveal the significance of both original and rephrased queries for EchoPrompt's efficacy. Our empirical results show that EchoPrompt is an effective technique that can easily augment in-context learning for better performance

Histopathological prognostic factor comparison of endometrial cancer patients in a tertiary hospital in India

Background: The objective of this study was to describe the histopathological features of patients with endometrial cancer in a tertiary hospital in South India.Methods: This retrospective study included cases diagnosed and operated in a tertiary hospital in the period of 3 years. Histopathological data was retrieved from records and analyzed. The study included both endometrial biopsy and post hysterectomy specimens of which prognostic factor comparison was performed on the latter following TNM and FIGO staging systems.Results: The sample consisted of 43 patients which includes 28 resected and 15 biopsy specimens. Age ranged from a minimum of 27 years to a maximum of 75 years (Mean around 52 years). Endometrioid adenocarcinoma was the predominant histological subtype (80 – 85%), while other types included papillary serous adenocarcinoma, stromal sarcoma and malignant mixed mullerian tumour (MMMST). Grade I tumours were 19 in number constituting 79.16% and stage IB tumours were the commonest. Pelvic nodal involvement, lymphatic invasion and recurrence were individually noted in one patient each.Conclusions: This study highlights the prognostic characteristics of endometrial cancer patients with most of them presenting in early stages thereby having a good prognostic outcome

CAPABLE CLOUD DATA EXCEEDING MAIN EXPOSURE

This focuses on encrypted data search, which is a vital means of protecting the privacy of file encryption before outsourcing in cloud computing or, in general, on almost all network-based information systems in which servers cannot be totally reliable. We officially demonstrate that our proposed plan is selectively secure against the selected keyword attack. We've created a single, authoritative, scalable search in an encrypted data plan that supports multiple data users and multiple data collaborators. We differentiate between features and keywords in our design. Keywords are the actual content of the files, while the attributes refer to the attributes of the user. In addition, using proxy encryption and slow file encryption techniques, the proposed plan is more relevant to the cloud outsourcing model and is efficient at eliminating the user. Unlike the existing public key search scheme, our plan can achieve scalability and system improvement at the same time. Unlike a search plan with native file encryption, our plan lets you search for approved, adaptable keywords in arbitrarily structured data. The appearance of complexity is a straight line to the amount of attributes within the system versus the number of authorized users. Thus, the one-to-many license mechanism is best suited for any huge system, for example, cloud. Proposed ABKS-UR plan and verification of the results verification mechanism through a real-world dataset and the complexity of an approximation calculation in relation to the conjugation process