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"Kumar, Dinesh"
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Large-scale data streaming, processing, and blockchain security
\"This book explores the latest methodologies, modeling, and simulations for coping with the generation and management of large-scale data in both scientific and individual applications\"-- Provided by publisher.
Book
Generating the Blood Exposome Database Using a Comprehensive Text Mining and Database Fusion Approach
Blood chemicals are routinely measured in clinical or preclinical research studies to diagnose diseases, assess risks in epidemiological research, or use metabolomic phenotyping in response to treatments. A vast volume of blood-related literature is available via the PubMed database for data mining.
We aimed to generate a comprehensive blood exposome database of endogenous and exogenous chemicals associated with the mammalian circulating system through text mining and database fusion.
Using NCBI resources, we retrieved PubMed abstracts, PubChem chemical synonyms, and PMC supplementary tables. We then employed text mining and PubChem crowdsourcing to associate phrases relating to blood with PubChem chemicals. False positives were removed by a phrase pattern and a compound exclusion list.
A query to identify blood-related publications in the PubMed database yielded 1.1 million papers. Matching a total of 15 million synonyms from 6.5 million relevant PubChem chemicals against all blood-related publications yielded 37,514 chemicals and 851,999 publications records. Mapping PubChem compound identifiers to the PubMed database yielded 49,940 unique chemicals linked to 676,643 papers. Analysis of open-access metabolomics papers related to blood phrases in the PMC database yielded 4,039 unique compounds and 204 papers. Consolidating these three approaches summed up to a total of 41,474 achiral structures that were linked to 65,957 PubChem CIDs and to over 878,966 PubMed articles. We mapped these compounds to 50 databases such as those covering metabolites and pathways, governmental and toxicological databases, pharmacology resources, and bioassay repositories. In comparison, HMDB, the Human Metabolome Database, links 1,075 compounds to blood-related primary publications.
This new Blood Exposome Database can be used for prioritizing chemicals for systematic reviews, developing target assays in exposome research, identifying compounds in untargeted mass spectrometry, and biological interpretation in metabolomics data. The database is available at http://bloodexposome.org. https://doi.org/10.1289/EHP4713.
Journal Article
Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets
Metabolomics answers a fundamental question in biology: How does metabolism respond to genetic, environmental or phenotypic perturbations? Combining several metabolomics assays can yield datasets for more than 800 structurally identified metabolites. However, biological interpretations of metabolic regulation in these datasets are hindered by inherent limits of pathway enrichment statistics. We have developed ChemRICH, a statistical enrichment approach that is based on chemical similarity rather than sparse biochemical knowledge annotations. ChemRICH utilizes structure similarity and chemical ontologies to map all known metabolites and name metabolic modules. Unlike pathway mapping, this strategy yields study-specific, non-overlapping sets of all identified metabolites. Subsequent enrichment statistics is superior to pathway enrichments because ChemRICH sets have a self-contained size where
p
-values do not rely on the size of a background database. We demonstrate ChemRICH’s efficiency on a public metabolomics data set discerning the development of type 1 diabetes in a non-obese diabetic mouse model. ChemRICH is available at
www.chemrich.fiehnlab.ucdavis.edu
Journal Article
Silver decorated CeO2 nanoparticles for rapid photocatalytic degradation of textile rose bengal dye
High quality silver (Ag) decorated CeO
2
nanoparticles were prepared by a facile one-step chemical method. The samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), High resolution transmission electron microscopy (HR-TEM), fourier transform infrared spectrometer (FT-IR), electron paramagnetic resonance (EPR), X-ray photoelectron spectroscopy (XPS), UV–Visible absorption (UV–Vis), photoluminescence (PL) and thermogravimetric analysis. The decoration of Ag on CeO
2
surface was confirmed by XRD, EPR and HR-TEM analysis. Harmful textile pollutant Rose Bengal dye was degraded under sunlight using the novel Ag decorated CeO
2
catalyst. It was found that great enhancement of the degradation efficiency for Ag/CeO
2
compared to pure CeO
2
, it can be ascribed mainly due to decrease in its band gap and charge carrier recombination rate. The Ag/CeO
2
sample exhibited an efficient photocatalytic characteristic for degrading RB under visible light irradiation with a high degradation rate of 96% after 3 h. With the help of various characterizations, a possible degradation mechanism has been proposed which shows the effect of generation of oxygen vacancies owing to the decoration of Ag on the CeO
2
surface.
Journal Article
Services Marketing and Customer Relationship Management
CRM enables businesses to build lasting relationships with both new and existing customers, while optimizing corporate efficiency. This book explores the key concepts of customer relationship management and provides practical tools and techniques for managing customer relationships. It also discusses the importance of effective service delivery and how it can be used to build strong customer relationships. With real-world cases and examples, this book is an essential resource for anyone interested in the field of services marketing and customer relationship management.
eBook
Sentiment analysis using deep learning architectures: a review
Social media is a powerful source of communication among people to share their sentiments in the form of opinions and views about any topic or article, which results in an enormous amount of unstructured information. Business organizations need to process and study these sentiments to investigate data and to gain business insights. Hence, to analyze these sentiments, various machine learning, and natural language processing-based approaches have been used in the past. However, deep learning-based methods are becoming very popular due to their high performance in recent times. This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis. We present a taxonomy of sentiment analysis and discuss the implications of popular deep learning architectures. The key contributions of various researchers are highlighted with the prime focus on deep learning approaches. The crucial sentiment analysis tasks are presented, and multiple languages are identified on which sentiment analysis is done. The survey also summarizes the popular datasets, key features of the datasets, deep learning model applied on them, accuracy obtained from them, and the comparison of various deep learning models. The primary purpose of this survey is to highlight the power of deep learning architectures for solving sentiment analysis problems.
Journal Article
Research Methods for Successful PhD
A PhD is the start of the research careers, and these students are the backbone of Universities and research institutions. It is the opportunity for youthful energy and creativity to make global impact and train the future researchers to make a difference. However, the candidature can also be the period of confusion and regret because of lack of structure and understanding. Research Methods for Successful PhD is written to help the PhD students and other young researchers navigate their path through this phase that will give them a direction and purpose. It is a candid conversation and developed over the experience of supervising 30 research students and publishing 400 papers over 20 years. The book recognizes that every student is different and has unique circumstances. It teases out the fundamental questions that we forget to ask, the method of relating to the supervisor, discusses methods to improve communication skills and explains the how to get the work published.
eBook
Continuing search for new physics in b → sμμ decays: two operators at a time
A
bstract
The anomalies in the measurements of observables involving
b
→
sμμ
decays, namely
R
K
,
R
K
*
,
P
5
′
, and
B
s
ϕ
, may be addressed by adding lepton-universality-violating new physics contributions to the effective operators
O
9
,
O
10
,
O
9
′
,
O
10
′
. We analyze all the scenarios where the new physics contributes to a pair of these operators at a time. We perform a global fit to all relevant data in the
b
→
s
sector to estimate the corresponding new Wilson coefficients,
O
9
N
P
,
O
10
N
P
,
O
9
′
,
O
10
′
. In the light of the new data on
R
K
, and
R
K
*
, presented in Moriond 2019, we find that the scenarios with new physics contributions to the
O
9
N
P
O
9
′
or
O
9
N
P
O
10
′
pair remain the most favored ones. On the other hand, though the competing scenario
O
9
N
P
O
10
N
P
remains attractive, its advantage above the SM reduces significantly due to the tension that emerges between the
R
K
and
R
K
*
measurements with the new data. The movement of the
R
K
measurement towards unity would also result in the re-emergence of the one-parameter scenario
C
9
NP
= −
C
9
′
.
Journal Article
Efficient Virus-Induced Gene Silencing in Arabidopsis
Virus-induced gene silencing (VIGS) is a plant RNA-silencing technique that uses viral vectors carrying a fragment of a gene of interest to generate double-stranded RNA, which initiates the silencing of the target gene. Several viral vectors have been developed for VIGS and they have been successfully used in reverse genetics studies of a variety of processes occurring in plants. This approach has not been widely adopted for the model dicotyledonous species Arabidopsis (Arabidopsis thaliana), possibly because, until now, there has been no easy protocol for effective VIGS in this species. Here, we show that a widely used tobacco rattle virus-based VIGS vector can be used for silencing genes in Arabidopsis ecotype Columbia-0. The protocol involves agroinfiltration of VIGS vectors carrying fragments of genes of interest into seedlings at the two- to three-leaf stage and requires minimal modification of existing protocols for VIGS with tobacco rattle virus vectors in other species like Nicotiana benthamiana and tomato (Lycopersicon esculentum). The method described here gives efficient silencing in Arabidopsis ecotype Columbia-0. We show that VIGS can be used to silence genes involved in general metabolism and defense and it is also effective at knocking down expression of highly expressed transgenes. A marker system to monitor the progress and efficiency of VIGS is also described.
Journal Article
Novel Positive Regulatory Role for the SPL6 Transcription Factor in the N TIR-NB-LRR Receptor-Mediated Plant Innate Immunity
Following the recognition of pathogen-encoded effectors, plant TIR-NB-LRR immune receptors induce defense signaling by a largely unknown mechanism. We identify a novel and conserved role for the SQUAMOSA PROMOTER BINDING PROTEIN (SBP)-domain transcription factor SPL6 in enabling the activation of the defense transcriptome following its association with a nuclear-localized immune receptor. During an active immune response, the Nicotiana TIR-NB-LRR N immune receptor associates with NbSPL6 within distinct nuclear compartments. NbSPL6 is essential for the N-mediated resistance to Tobacco mosaic virus. Similarly, the presumed Arabidopsis ortholog AtSPL6 is required for the resistance mediated by the TIR-NB-LRR RPS4 against Pseudomonas syringae carrying the avrRps4 effector. Transcriptome analysis indicates that AtSPL6 positively regulates a subset of defense genes. A pathogen-activated nuclear-localized TIR-NB-LRR like N can therefore regulate defense genes through SPL6 in a mechanism analogous to the induction of MHC genes by mammalian immune receptors like CIITA and NLRC5.
Journal Article