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"Kumar, Bikash"
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Indisch kochen : Rezepte aus dem Reich der Gewèurze ; mit allen beliebten Klassikern ; Warenkunde zu Gewèurzen und Krèautern ; mit den 10 GU-Erfolgstipps
Warenkunde, Hinweise zur indischen Esskultur und rund 40 Rezepte fèur Vorspeisen, Hauptgerichte, Beilagen; reich illustriert.
A detailed overview of xylanases: an emerging biomolecule for current and future prospective
Xylan is the second most abundant naturally occurring renewable polysaccharide available on earth. It is a complex heteropolysaccharide consisting of different monosaccharides such as l-arabinose, d-galactose, d-mannoses and organic acids such as acetic acid, ferulic acid, glucuronic acid interwoven together with help of glycosidic and ester bonds. The breakdown of xylan is restricted due to its heterogeneous nature and it can be overcome by xylanases which are capable of cleaving the heterogeneous β-1,4-glycoside linkage. Xylanases are abundantly present in nature (e.g., molluscs, insects and microorganisms) and several microorganisms such as bacteria, fungi, yeast, and algae are used extensively for its production. Microbial xylanases show varying substrate specificities and biochemical properties which makes it suitable for various applications in industrial and biotechnological sectors. The suitability of xylanases for its application in food and feed, paper and pulp, textile, pharmaceuticals, and lignocellulosic biorefinery has led to an increase in demand of xylanases globally. The present review gives an insight of using microbial xylanases as an “Emerging Green Tool” along with its current status and future prospective.
Journal Article
Handbook of Himalayan ecosystems and sustainability
\"Volume 1: Handbook on Spatio-Temporal Monitoring of Forests and Climate is aimed to describe the recent progress and developments of geospatial technologies (Remote Sensing and GIS) for assessing, monitoring and managing fragile Himalayan ecosystems and its sustainability under climate change. It is a collective research contribution from renowned researchers and academicians working in the Hindu Kush Himalayan (HKH) mountain range. The Himalayas ecosystems have been facing substantial transformation due to severe environmental conditions, land transformation, forest degradation and fragmentation. The authors utilized satellite datasets and algorithms to discuss the intricacy of Land use Land cover change, forest and agricultural ecosystems, canopy height estimation, above-ground biomass, wildfires, carbon sequestration, and landscape restoration. Furthermore, the potential impacts of climate change on ecosystems, biodiversity and future food and nutritional security are also addressed including the impact on the livelihood of people of the Himalayas. This comprehensive Handbook explains the advanced geospatial technologies for mapping and management of natural resources of the Himalayas\"-- Provided by publisher.
Book
Current perspective on production and applications of microbial cellulases: a review
The potential of cellulolytic enzymes has been widely studied and explored for bioconversion processes and plays a key role in various industrial applications. Cellulase, a key enzyme for cellulose-rich waste feedstock-based biorefinery, has increasing demand in various industries, e.g., paper and pulp, juice clarification, etc. Also, there has been constant progress in developing new strategies to enhance its production, such as the application of waste feedstock as the substrate for the production of individual or enzyme cocktails, process parameters control, and genetic manipulations for enzyme production with enhanced yield, efficiency, and specificity. Further, an insight into immobilization techniques has also been presented for improved reusability of cellulase, a critical factor that controls the cost of the enzyme at an industrial scale. In addition, the review also gives an insight into the status of the significant application of cellulase in the industrial sector, with its techno-economic analysis for future applications. The present review gives a complete overview of current perspectives on the production of microbial cellulases as a promising tool to develop a sustainable and greener concept for industrial applications.
Journal Article
Highly Active 2D Layered MoS 2 -rGO Hybrids for Energy Conversion and Storage Applications
The development of efficient materials for the generation and storage of renewable energy is now an urgent task for future energy demand. In this report, molybdenum disulphide hollow sphere (MoS 2 -HS) and its reduced graphene oxide hybrid (rGO/MoS 2 -S) have been synthesized and explored for energy generation and storage applications. The surface morphology, crystallinity and elemental composition of the as-synthesized materials have been thoroughly analysed. Inspired by the fascinating morphology of the MoS 2 -HS and rGO/MoS 2 -S materials, the electrochemical performance towards hydrogen evolution and supercapacitor has been demonstrated. The rGO/MoS 2 -S shows enhanced gravimetric capacitance values (318 ± 14 Fg −1 ) with higher specific energy/power outputs (44.1 ± 2.1 Whkg −1 and 159.16 ± 7.0 Wkg −1 ) and better cyclic performances (82 ± 0.95% even after 5000 cycles). Further, a prototype of the supercapacitor in a coin cell configuration has been fabricated and demonstrated towards powering a LED. The unique balance of exposed edge site and electrical conductivity of rGO/MoS 2 -S shows remarkably superior HER performances with lower onset over potential (0.16 ± 0.05 V), lower Tafel slope (75 ± 4 mVdec −1 ), higher exchange current density (0.072 ± 0.023 mAcm −2 ) and higher TOF (1.47 ± 0.085 s −1 ) values. The dual performance of the rGO/MoS 2 -S substantiates the promising application for hydrogen generation and supercapacitor application of interest.
Journal Article
Highly Active 2D Layered MoS2-rGO Hybrids for Energy Conversion and Storage Applications
The development of efficient materials for the generation and storage of renewable energy is now an urgent task for future energy demand. In this report, molybdenum disulphide hollow sphere (MoS
2
-HS) and its reduced graphene oxide hybrid (rGO/MoS
2
-S) have been synthesized and explored for energy generation and storage applications. The surface morphology, crystallinity and elemental composition of the as-synthesized materials have been thoroughly analysed. Inspired by the fascinating morphology of the MoS
2
-HS and rGO/MoS
2
-S materials, the electrochemical performance towards hydrogen evolution and supercapacitor has been demonstrated. The rGO/MoS
2
-S shows enhanced gravimetric capacitance values (318 ± 14 Fg
−1
) with higher specific energy/power outputs (44.1 ± 2.1 Whkg
−1
and 159.16 ± 7.0 Wkg
−1
) and better cyclic performances (82 ± 0.95% even after 5000 cycles). Further, a prototype of the supercapacitor in a coin cell configuration has been fabricated and demonstrated towards powering a LED. The unique balance of exposed edge site and electrical conductivity of rGO/MoS
2
-S shows remarkably superior HER performances with lower onset over potential (0.16 ± 0.05 V), lower Tafel slope (75 ± 4 mVdec
−1
), higher exchange current density (0.072 ± 0.023 mAcm
−2
) and higher TOF (1.47 ± 0.085 s
−1
) values. The dual performance of the rGO/MoS
2
-S substantiates the promising application for hydrogen generation and supercapacitor application of interest.
Journal Article
Biologically explainable multi-omics feature demonstrates greater learning potential by identifying tissue of origin, stages, and subtypes for pan-cancer classification
Cancer is a complex disease characterized by uncontrolled cell growth, which can invade surrounding tissues and spread to distant organs. Most of the conventional methods of diagnosis fails to identify the primary organ when cancer spreads to other organs, thereby adding another level of complexity for cancer detection. It is also critical to determine the stages and subtypes of cancer, and develop a clinically applicable model for precision therapy. With a dataset of 7632 samples from 30 different cancer originating from distinct organs, we have constructed a deep learning framework to solve all of these challenges. We have applied a hybrid feature selection method to identify cancer-associated features in the transcriptome, methylome, and microRNA datasets. This was achieved by combining both gene set enrichment analysis and Cox regression analysis to build an explainable AI model. We performed the early integration using an autoencoder to embed the cancer-associated multi-omics data into a lower-dimensional space; an ANN classifier was constructed using the latent features. In addition to correctly classifying 30 different cancer types by their tissue of origin, our framework also identifies individual subtypes and stages of cancer with an accuracy ranging from 87.31% to 94.0% and 83.33% to 93.64%, respectively. The current model demonstrates higher accuracy even when tested with external datasets, and shows better stability and accuracy in making predictions compared to the existing models. This approach offers explainable strategies for selecting features in AI-based prediction of tumor types for personalized therapy, aiding clinicians in making real-time treatment decisions.
Journal Article
The industrially important genus Kaempferia: An ethnopharmacological review
Kaempferia , a genus of the family Zingiberaceae, is widely distributed with more than 50 species which are mostly found throughout Southeast Asia. These plants have important ethnobotanical significance as many species are used in Ayurvedic and other traditional medicine preparations. This genus has received a lot of scholarly attention recently as a result of the numerous health advantages it possesses. In this review, we have compiled the scientific information regarding the relevance, distribution, industrial applications, phytochemistry, ethnopharmacology, tissue culture and conservation initiative of the Kaempferia genus along with the commercial realities and limitations of the research as well as missing industrial linkages followed by an exploration of some of the likely future promising clinical potential. The current review provides a richer and deeper understanding of Kaempferia , which can be applied in areas like phytopharmacology, molecular research, and industrial biology. The knowledge from this study can be further implemented for the establishment of new conservation strategies.
Journal Article
Solar-DG and DSTATCOM Concurrent Planning in Reconfigured Distribution System Using APSO and GWO-PSO Based on Novel Objective Function
The concurrent planning of multiple Distributed Generations (DGs), consisting of solar-DG and DSTATCOM with reconfiguration in IEEE 33 and 69 bus Radial Distribution Network (RDN), using Adaptive Particle Swarm Optimization (APSO) and hybrid Grey Wolf-Particle Swarm Optimization (GWO-PSO), is reported in this paper. For this planning, a novel multiple objective-based fitness-function (MOFF) is proposed based on various performance parameters of the system, such as power losses (both active, as well as reactive loss), system voltage profile, short circuit level of line current (SCLLCurrent), and system reliability. The economic perspective of the system has also been considered based on the various costs, such as fix, loss, and Energy Not Supplied (ENS) cost. Two case studies have been presented on IEEE 33 and 69 bus RDN to validate the efficacy of the proposed methodology. The results analysis of the system shows that better performance can be achieved with the proposed technique for 33 and 69 bus RDN, using GWO-PSO rather than APSO. From this results analysis, a vital point is noticed that the SCLLCurrent is reduced, which causes the short-circuit (fault) tolerance capacity (level) of the RDN to become enhanced. Finally, the comparative analysis of the obtained results, using the proposed method with other methods that exist in different literature, reveals that the proposed method has performed better from a techno-economic prospective.
Journal Article
A generalized protein identification method for novel and diverse sequencing technologies
Protein sequencing is a rapidly evolving field with much progress towards the realization of a new generation of protein sequencers. The early devices, however, may not be able to reliably discriminate all 20 amino acids, resulting in a partial, noisy and possibly error-prone signature of a protein. Rather than achieving de novo sequencing, these devices may aim to identify target proteins by comparing such signatures to databases of known proteins. However, there are no broadly applicable methods for this identification problem. Here, we devise a hidden Markov model method to study the generalized problem of protein identification from noisy signature data. Based on a hypothetical sequencing device that can simulate several novel technologies, we show that on the human protein database (N = 20 181) our method has a good performance under many different operating conditions such as various levels of signal resolvability, different numbers of discriminated amino acids, sequence fragments, and insertion and deletion error rates. Our results demonstrate the possibility of protein identification with high accuracy on many early experimental devices. We anticipate our method to be applicable for a wide range of protein sequencing devices in the future.
Journal Article