Generation of Induced Pluripotent Stem (iPS) Cells by Nuclear Reprogramming

During embryonic development pluripotency is progressively lost irreversibly by cell division, differentiation, migration and organ formation. Terminally differentiated cells do not generate other kinds of cells. Pluripotent stem cells are a great source of varying cell types that are used for tissue regeneration or repair of damaged tissue. The pluripotent stem cells can be derived from inner cell mass of blastocyte but its application is limited due to ethical concerns. The recent discovery of iPS with defined reprogramming factors has initiated a flurry of works on stem cell in various laboratories. The pluripotent cells can be derived from various differentiated adult cells as well as from adult stem cells by nuclear reprogramming, somatic cell nuclear transfer etc. In this review article, different aspects of nuclear reprogramming are discussed

IRON-MAN: An Approach To Perform Temporal Motionless Analysis of Video using CNN in MPSoC

This paper proposes a novel human-inspired methodology called IRON-MAN ( Integrated RatiONal prediction and Motionless ANalysis ) for mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video ( TMAV ). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image ( ECTI ) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption and lifespan reliability of the device

TMAV: Temporal Motionless Analysis of Video using CNN in MPSoC

Analyzing video for traffic categorization is an important pillar of Intelligent Transport Systems. However, it is difficult to analyze and predict traffic based on image frames because the representation of each frame may vary significantly within a short time period. This also would inaccurately represent the traffic over a longer period of time such as the case of video. We propose a novel bio-inspired methodology that integrates analysis of the previous image frames of the video to represent the analysis of the current image frame, the same way a human being analyzes the current situation based on past experience. In our proposed methodology, called IRON-MAN (Integrated Rational prediction and Motionless ANalysis), we utilize Bayesian update on top of the individual image frame analysis in the videos and this has resulted in highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV) for most of the chosen test cases. The proposed approach could be used for TMAV using Convolutional Neural Network (CNN) for applications where the number of objects in an image is the deciding factor for prediction and results also show that our proposed approach outperforms the state-of-the-art for the chosen test case. We also introduce a new metric named, Energy Consumption per Training Image (ECTI). Since, different CNN based models have different training capability and computing resource utilization, some of the models are more suitable for embedded device implementation than the others, and ECTI metric is useful to assess the suitability of using a CNN model in multi-processor systems-on-chips (MPSoCs) with a focus on energy consumption and reliability in terms of lifespan of the embedded device using these MPSoCs

Lignocellulosic bioethanol production: Prospects of emerging membrane technologies to improve the process - A critical review

© 2018 Walter de Gruyter GmbH, Berlin/Boston. To meet the worldwide rapid growth of industrialization and population, the demand for the production of bioethanol as an alternative green biofuel is gaining significant prominence. The bioethanol production process is still considered one of the largest energy-consuming processes and is challenging due to the limited effectiveness of conventional pretreatment processes, saccharification processes, and extreme use of electricity in common fermentation and purification processes. Thus, it became necessary to improve the bioethanol production process through reduced energy requirements. Membrane-based separation technologies have already gained attention due to their reduced energy requirements, investment in lower labor costs, lower space requirements, and wide flexibility in operations. For the selective conversion of biomasses to bioethanol, membrane bioreactors are specifically well suited. Advanced membrane-integrated processes can effectively contribute to different stages of bioethanol production processes, including enzymatic saccharification, concentrating feed solutions for fermentation, improving pretreatment processes, and finally purification processes. Advanced membrane-integrated simultaneous saccharification, filtration, and fermentation strategies consisting of ultrafiltration-based enzyme recycle system with nanofiltration-based high-density cell recycle fermentation system or the combination of high-density cell recycle fermentation system with membrane pervaporation or distillation can definitely contribute to the development of the most efficient and economically sustainable second-generation bioethanol production process

Sauromatum horsfieldii (Araceae – Areae): an addition to the Flora of India

Sauromatum horsfieldii (Araceae – Areae) is reported here as a new record for India. A detailed description and photographic illustration are provided, along with an account and revised key of the Indian species of Sauromatum

Receptor-Binding and Oncogenic Properties of Polyoma Viruses Isolated from Feral Mice

Laboratory strains of the mouse polyoma virus differ markedly in their abilities to replicate and induce tumors in newborn mice. Major determinants of pathogenicity lie in the sialic binding pocket of the major capsid protein Vp1 and dictate receptor-binding properties of the virus. Substitutions at two sites in Vp1 define three prototype strains, which vary greatly in pathogenicity. These strains replicate in a limited fashion and induce few or no tumors, cause a disseminated infection leading to the development of multiple solid tumors, or replicate and spread acutely causing early death. This investigation was undertaken to determine the Vp1 type(s) of new virus isolates from naturally infected mice. Compared with laboratory strains, truly wild-type viruses are constrained with respect to their selectivity and avidity of binding to cell receptors. Fifteen of 15 new isolates carried the Vp1 type identical to that of highly tumorigenic laboratory strains. Upon injection into newborn laboratory mice, the new isolates induced a broad spectrum of tumors, including ones of epithelial as well as mesenchymal origin. Though invariant in their Vp1 coding sequences, these isolates showed considerable variation in their regulatory sequences. The common Vp1 type has two essential features: 1) failure to recognize “pseudoreceptors” with branched chain sialic acids binding to which would attenuate virus spread, and 2) maintenance of a hydrophobic contact with true receptors bearing a single sialic acid, which retards virus spread and avoids acute and potentially lethal infection of the host. Conservation of these receptor-binding properties under natural selection preserves the oncogenic potential of the virus. These findings emphasize the importance of immune protection of neonates under conditions of natural transmission

Receptor-Binding and Oncogenic Properties of Polyoma Viruses Isolated from Feral Mice

Laboratory strains of the mouse polyoma virus differ markedly in their abilities to replicate and induce tumors in newborn mice. Major determinants of pathogenicity lie in the sialic binding pocket of the major capsid protein Vp1 and dictate receptor-binding properties of the virus. Substitutions at two sites in Vp1 define three prototype strains, which vary greatly in pathogenicity. These strains replicate in a limited fashion and induce few or no tumors, cause a disseminated infection leading to the development of multiple solid tumors, or replicate and spread acutely causing early death. This investigation was undertaken to determine the Vp1 type(s) of new virus isolates from naturally infected mice. Compared with laboratory strains, truly wild-type viruses are constrained with respect to their selectivity and avidity of binding to cell receptors. Fifteen of 15 new isolates carried the Vp1 type identical to that of highly tumorigenic laboratory strains. Upon injection into newborn laboratory mice, the new isolates induced a broad spectrum of tumors, including ones of epithelial as well as mesenchymal origin. Though invariant in their Vp1 coding sequences, these isolates showed considerable variation in their regulatory sequences. The common Vp1 type has two essential features: 1) failure to recognize “pseudoreceptors” with branched chain sialic acids binding to which would attenuate virus spread, and 2) maintenance of a hydrophobic contact with true receptors bearing a single sialic acid, which retards virus spread and avoids acute and potentially lethal infection of the host. Conservation of these receptor-binding properties under natural selection preserves the oncogenic potential of the virus. These findings emphasize the importance of immune protection of neonates under conditions of natural transmission

SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology

Automated feature extraction from program source-code such that proper computing resources could beallocated to the program is very difficult given the current state of technology. Therefore, conventionalmethods call for skilled human intervention in order to achieve the task of feature extraction from programs.This research is the first to propose a novel human-inspired approach to automatically convert programsource-codes to visual images. The images could be then utilized for automated classification by visualconvolutional neural network (CNN) based algorithm. Experimental results show high prediction accuracyin classifying the types of program in a completely automated manner using this approach