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Article ; Online: Integrating Multi-Sensors Data for Species Distribution Mapping Using Deep Learning and Envelope Models

Akash Anand / Manish K. Pandey / Prashant K. Srivastava / Ayushi Gupta / Mohammed Latif Khan

Remote Sensing, Vol 13, Iss 3284, p

2021 Volume 3284

Abstract: The integration of ecological and atmospheric characteristics for biodiversity management is fundamental for long-term ecosystem conservation and drafting forest management strategies, especially in the current era of climate change. The explicit ... ...

Abstract	The integration of ecological and atmospheric characteristics for biodiversity management is fundamental for long-term ecosystem conservation and drafting forest management strategies, especially in the current era of climate change. The explicit modelling of regional ecological responses and their impact on individual species is a significant prerequisite for any adaptation strategy. The present study focuses on predicting the regional distribution of Rhododendron arboreum , a medicinal plant species found in the Himalayan region. Advanced Species Distribution Models (SDM) based on the principle of predefined hypothesis, namely BIOCLIM, was used to model the potential distribution of Rhododendron arboreum . This hypothesis tends to vary with the change in locations, and thus, robust models are required to establish nonlinear complex relations between the input parameters. To address this nonlinear relation, a class of deep neural networks, Convolutional Neural Network (CNN) architecture is proposed, designed, and tested, which eventually gave much better accuracy than the BIOCLIM model. Both of the models were given 16 input parameters, including ecological and atmospheric variables, which were statistically resampled and were then utilized in establishing the linear and nonlinear relationship to better fit the occurrence scenarios of the species. The input parameters were mostly acquired from the recent satellite missions, including MODIS, Sentinel-2, Sentinel-5p, the Shuttle Radar Topography Mission (SRTM), and ECOSTRESS. The performance across all the thresholds was evaluated using the value of the Area Under Curve (AUC) evaluation metrics. The AUC value was found to be 0.917 with CNN, whereas it was 0.68 with BIOCLIM, respectively. The performance evaluation metrics indicate the superiority of CNN for species distribution over BIOCLIM.
Keywords	spatial distribution modelling ; convolutional neural network ; Rhododendron arboreum ; biodiversity management ; ecological responses ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Exploring the endogenous potential of Hemidesmus indicus against breast cancer using in silico studies and quantification of 2-hydroxy-4-methoxy benzaldehyde through RP-HPLC

Bansod, Akash Anand / Ramasamy, Gnanam / Nathan, Bharathi / Kandhasamy, Rajamani / Palaniappan, Meenakshisundaram / Vichangal Pridiuldi, Santhanakrishnan

3 Biotech. 2021 May, v. 11, no. 5

2021

Abstract: Being a woman and getting older are the main risk factors for breast cancer. While admitting the increasing prevalence of breast cancer among females globally, there is an increasing urge for widening the range of chemical compounds that can act as ... ...

Abstract	Being a woman and getting older are the main risk factors for breast cancer. While admitting the increasing prevalence of breast cancer among females globally, there is an increasing urge for widening the range of chemical compounds that can act as potential inhibitors for certain cancer target receptors. Current investigation involves virtually screening of 19 protein receptors having major role in signal transduction pathway of breast cancer development against 47 compounds present in Hemidesmus indicus. Virtual screening and supplementary analysis were performed using freely available softwares, tools and online servers. To obtain meaningful results, a comparative scenario was created by screening FDA-approved drugs/drug analogues against the same 19 receptors by keeping all the parameters same as to that of ligands. Two ligands namely Taraxasteryl acetate and Rutin were found to be the best ligands with high binding affinity towards six protein receptors establishing strong receptor ligand interactions. Furthermore, the major volatile compound, a high demand flavouring agent and an isomer of vanillin, namely 2-hydroxy-4-methoxy benzaldehyde (MBALD) specifically found in the roots of Hemidesmus, was quantified by RP-HPLC using a reverse phase C-18 column. The methanolic extract of fresh roots was found to contain 0.221 mg of MBALD/gram of tissue. From the current investigation, it could be surmised that Hemidesmus indicus had demonstrated its potential in both pharmaceuticals and the food industry.
Keywords	Hemidesmus indicus ; acetates ; benzaldehyde ; breast neoplasms ; carcinogenesis ; computer simulation ; drugs ; food industry ; isomers ; ligands ; rutin ; signal transduction ; vanillin ; volatile compounds ; women
Language	English
Dates of publication	2021-05
Size	p. 235.
Publishing place	Springer International Publishing
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	2600522-0
ISSN	2190-5738 ; 2190-572X
ISSN (online)	2190-5738
ISSN	2190-572X
DOI	10.1007/s13205-021-02768-x
Database	NAL-Catalogue (AGRICOLA)

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Article: Exploring the endogenous potential of

Bansod, Akash Anand / Ramasamy, Gnanam / Nathan, Bharathi / Kandhasamy, Rajamani / Palaniappan, Meenakshisundaram / Vichangal Pridiuldi, Santhanakrishnan

3 Biotech

2021 Volume 11, Issue 5, Page(s) 235

Abstract	Being a woman and getting older are the main risk factors for breast cancer. While admitting the increasing prevalence of breast cancer among females globally, there is an increasing urge for widening the range of chemical compounds that can act as potential inhibitors for certain cancer target receptors. Current investigation involves virtually screening of 19 protein receptors having major role in signal transduction pathway of breast cancer development against 47 compounds present in
Language	English
Publishing date	2021-04-24
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2600522-0
ISSN	2190-5738 ; 2190-572X
ISSN (online)	2190-5738
ISSN	2190-572X
DOI	10.1007/s13205-021-02768-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

Ramandeep Kaur M. Malhi / Akash Anand / Prashant K. Srivastava / G. Sandhya Kiran / George P. Petropoulos / Christos Chalkias

ISPRS International Journal of Geo-Information, Vol 9, Iss 530, p

2020 Volume 530

Abstract: Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and ... ...

Abstract	Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.
Keywords	forest degradation ; fragmentation statistics ; Land cover prediction ; remote sensing ; CA-Markov ; FRAGSTATS ; Geography (General) ; G1-922
Subject code	333 ; 910
Language	English
Publishing date	2020-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve

Akash Anand / Prem Chandra Pandey / George P. Petropoulos / Andrew Pavlides / Prashant K. Srivastava / Jyoti K. Sharma / Ramandeep Kaur M. Malhi

Remote Sensing, Vol 12, Iss 4, p

A Contribution Towards Blue Carbon Initiative

2020 Volume 597

Abstract: Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion ... ...

Abstract	Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy.
Keywords	blue carbon ; hyperspectral data ; mangrove forest ; carbon stock ; bhitarkanika forest reserve ; regression models ; machine learning ; Science ; Q
Subject code	333
Language	English
Publishing date	2020-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Pre-existing conditions are associated with COVID-19 patients’ hospitalization, despite confirmed clearance of SARS-CoV-2 virus

Colin Pawlowski / AJ Venkatakrishnan / Eshwan Ramudu / Christian Kirkup / Arjun Puranik / Nikhil Kayal / Gabriela Berner / Akash Anand / Rakesh Barve / John C. O'Horo / Andrew D. Badley / Venky Soundararajan

EClinicalMedicine, Vol 34, Iss , Pp 100793- (2021)

2021

Abstract: Background: Consecutive negative SARS-CoV-2 PCR test results are being considered to estimate viral clearance in COVID-19 patients. However, there are anecdotal reports of hospitalization from protracted COVID-19 complications despite such confirmed ... ...

Abstract	Background: Consecutive negative SARS-CoV-2 PCR test results are being considered to estimate viral clearance in COVID-19 patients. However, there are anecdotal reports of hospitalization from protracted COVID-19 complications despite such confirmed viral clearance, presenting a clinical conundrum. Methods: We conducted a retrospective analysis of 222 hospitalized COVID-19 patients to compare those that were readmitted post-viral clearance (hospitalized post-clearance cohort, n = 49) with those that were not re-admitted post-viral clearance (non-hospitalized post-clearance cohort, n = 173) between February and October 2020. In order to differentiate these two cohorts, we used neural network models for the ‘augmented curation’ of comorbidities and complications with positive sentiment in the Electronic Hosptial Records physician notes. Findings: In the year preceding COVID-19 onset, anemia (n = 13 [26.5%], p-value: 0.007), cardiac arrhythmias (n = 14 [28.6%], p-value: 0.015), and acute kidney injury (n = 7 [14.3%], p-value: 0.030) were significantly enriched in the physician notes of the hospitalized post-clearance cohort. Interpretation: Overall, this retrospective study highlights specific pre-existing conditions that are associated with higher hospitalization rates in COVID-19 patients despite viral clearance and motivates follow-up prospective research into the associated risk factors. Funding: This work was supported by Nference, inc.
Keywords	Medicine (General) ; R5-920
Subject code	610
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology

Ram Kumar Singh / Martin Drews / Manuel De la Sen / Prashant Kumar Srivastava / Bambang H. Trisasongko / Manoj Kumar / Manish Kumar Pandey / Akash Anand / S. S. Singh / A. K. Pandey / Manmohan Dobriyal / Meenu Rani / Pavan Kumar

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 12

Abstract: Abstract The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict ... ...

Abstract	Abstract The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.
Keywords	Medicine ; R ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mapping each pre-existing condition’s association to short-term and long-term COVID-19 complications

A. J. Venkatakrishnan / Colin Pawlowski / David Zemmour / Travis Hughes / Akash Anand / Gabriela Berner / Nikhil Kayal / Arjun Puranik / Ian Conrad / Sairam Bade / Rakesh Barve / Purushottam Sinha / John C. O‘Horo / Andrew D. Badley / John Halamka / Venky Soundararajan

npj Digital Medicine, Vol 4, Iss 1, Pp 1-

2021 Volume 11

Abstract: Abstract Understanding the relationships between pre-existing conditions and complications of COVID-19 infection is critical to identifying which patients will develop severe disease. Here, we leverage ~1.1 million clinical notes from 1803 hospitalized ... ...

Abstract	Abstract Understanding the relationships between pre-existing conditions and complications of COVID-19 infection is critical to identifying which patients will develop severe disease. Here, we leverage ~1.1 million clinical notes from 1803 hospitalized COVID-19 patients and deep neural network models to characterize associations between 21 pre-existing conditions and the development of 20 complications (e.g. respiratory, cardiovascular, renal, and hematologic) of COVID-19 infection throughout the course of infection (i.e. 0–30 days, 31–60 days, and 61–90 days). Pleural effusion was the most frequent complication of early COVID-19 infection (89/1803 patients, 4.9%) followed by cardiac arrhythmia (45/1803 patients, 2.5%). Notably, hypertension was the most significant risk factor associated with 10 different complications including acute respiratory distress syndrome, cardiac arrhythmia, and anemia. The onset of new complications after 30 days is rare and most commonly involves pleural effusion (31–60 days: 11 patients, 61–90 days: 9 patients). Lastly, comparing the rates of complications with a propensity-matched COVID-negative hospitalized population confirmed the importance of hypertension as a risk factor for early-onset complications. Overall, the associations between pre-COVID conditions and COVID-associated complications presented here may form the basis for the development of risk assessment scores to guide clinical care pathways.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	610 ; 616
Language	English
Publishing date	2021-07-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

AJ Venkatakrishnan / Arjun Puranik / Akash Anand / David Zemmour / Xiang Yao / Xiaoying Wu / Ramakrishna Chilaka / Dariusz Murakowski K. / Kristopher Standish / Bharathwaj Raghunathan / Tyler Wagner / Enrique Garcia-Rivera / Hugo Solomon / Abhinav Garg / Rakesh Barve / Anuli Anyanwu-Ofili / Najat Khan / Venky Soundararajan

Abstract: The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a ... ...

Abstract	The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes and olfactory epithelial cells are likely under-appreciated targets of SARS-CoV-2 infection, correlating with reported loss of sense of taste and smell as early indicators of COVID-19 infection, including in otherwise asymptomatic patients. Airway club cells, ciliated cells and type II pneumocytes in the lung, and enterocytes of the gut also express ACE2. This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses.
Keywords	covid19
Publisher	arxiv
Document type	Article
Database	COVID19

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Article ; Online: Knowledge synthesis of 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

AJ Venkatakrishnan / Arjun Puranik / Akash Anand / David Zemmour / Xiang Yao / Xiaoying Wu / Ramakrishna Chilaka / Dariusz K Murakowski / Kristopher Standish / Bharathwaj Raghunathan / Tyler Wagner / Enrique Garcia-Rivera / Hugo Solomon / Abhinav Garg / Rakesh Barve / Anuli Anyanwu-Ofili / Najat Khan / Venky Soundararajan

eLife, Vol

2020 Volume 9

Abstract: The COVID-19 pandemic demands assimilation of all biomedical knowledge to decode mechanisms of pathogenesis. Despite the recent renaissance in neural networks, a platform for the real-time synthesis of the exponentially growing biomedical literature and ... ...

Abstract	The COVID-19 pandemic demands assimilation of all biomedical knowledge to decode mechanisms of pathogenesis. Despite the recent renaissance in neural networks, a platform for the real-time synthesis of the exponentially growing biomedical literature and deep omics insights is unavailable. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations from unstructured text, and triangulation with insights from single-cell RNA-sequencing, bulk RNA-seq and proteomics from diverse tissue types. A hypothesis-free profiling of ACE2 suggests tongue keratinocytes, olfactory epithelial cells, airway club cells and respiratory ciliated cells as potential reservoirs of the SARS-CoV-2 receptor. We find the gut as the putative hotspot of COVID-19, where a maturation correlated transcriptional signature is shared in small intestine enterocytes among coronavirus receptors (ACE2, DPP4, ANPEP). A holistic data science platform triangulating insights from structured and unstructured data holds potential for accelerating the generation of impactful biological insights and hypotheses.
Keywords	COVID-19 ; SARS-CoV-2 ; single cell RNA-seq ; natural language processing ; artificial intelligence ; machine learning ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2020-05-01T00:00:00Z
Publisher	eLife Sciences Publications Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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