Learning to hash: a comprehensive survey of deep learning-based hashing methods

Explosive growth of big data demands efficient and fast algorithms for nearest neighbor search. Deep learning-based hashing methods have proved their efficacy to learn advanced hash functions that suit the desired goal of nearest neighbor search in large image-based data-sets. In this work, we present a comprehensive review of different deep learning-based supervised hashing methods particularly for image data-sets suggested by various researchers till date to generate advanced hash functions. We categorize prior works into a five-tier taxonomy based on: (i) the design of network architecture, (ii) training strategy based on nature of data-set, (iii) the type of loss function, (iv) the similarity measure and, (v) the nature of quantization. Further, different data-sets used in prior works are reported and compared based on various challenges in the characteristics of images that are part of the data-sets. Lastly, different future directions such as incremental hashing, cross-modality hashing and guidelines to improve design of hash functions are discussed. Based on our comparative review, it has been observed that generative adversarial networks-based hashing models outperform other methods. This is due to the fact that they leverage more data in the form of both real world and synthetically generated data. Furthermore, it has been perceived that triplet-loss-based loss functions learn better discriminative representations by pushing similar patterns together and dis-similar patterns away from each other. This study and its observations shall be useful for the researchers and practitioners working in this emerging research field.

     

Chitosan nanoparticles for the oral delivery of tenofovir disoproxil fumarate: formulation optimization,characterization and ex vivo and in vivo evaluation for uptake mechanism in rats

Objective: Design chitosan based nanoparticles for tenofovir disoproxil fumarate (TDF) with the purpose of enhancing its oral absorption.

Significance: TDF is a prodrug that has limited intestinal absorption because of its susceptibility to gut wall esterases. Hence, design of chitosan based polymeric novel nanocarrier systems can protect TDF from getting metabolized and also enhance the oral absorption.

Methods: The nanoparticles were prepared using the ionic gelation technique. The factors impacting the particle size and entrapment efficiency of the nanoparticles were evaluated using design of experiments approach. The optimized nanoparticles were characterized and evaluated for their ability to protect TDF from esterase metabolism. The nanoparticles were then studied for the involvement of active transport in their uptake during the oral absorption process. Further, in vivo pharmacokinetic studies were carried out for the designed nanoparticles.

Results: The application of design of experiments in the optimization process was useful to determine the critical parameters and evaluate their interaction effects. The optimized nanoparticles had a particle size of 156?±?5?nm with an entrapment efficiency of 48.2?±?1%. The nanoparticles were well characterized and provided metabolic protection for TDF in the presence of intestinal esterases. The nanoparticles were able to increase the AUC of tenofovir by 380%. The active uptake mechanisms mainly involving clathrin-mediated uptake played a key role in increasing the oral absorption of tenofovir.

Conclusions: These results show the ability of the designed chitosan based nanoparticles in enhancing the oral absorption of TDF along the oral route by utilizing the active endocytic uptake pathways.     

Plastic Deformation in a Molecular Crystal Enables a Piezoresistive Response

Organic materials are promising candidates for the development of efficient sensors for many medicinal and materials science applications. Single crystals of a small molecule, 4-trifluoromethyl phenyl isothiocyanate (4CFNCS), exhibit plastic deformation when bent, twisted, or coiled. Synchrotron micro-focus X-ray diffraction mapping of the bent region of the crystal confirms the mechanism of deformation. The crystals are incorporated into a flexible piezoresistive sensor using a composite constituting PEDOT: PSS/4CFNCS, which shows an impressive performance at high-pressure ranges (sensitivity 0.08 kPa⁻¹ above 44 kPa).