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Article ; Online: Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States.

Maiti, Arabinda / Zhang, Qi / Sannigrahi, Srikanta / Pramanik, Suvamoy / Chakraborti, Suman / Cerda, Artemi / Pilla, Francesco

Sustainable cities and society

2021 Volume 68, Page(s) 102784

Abstract: Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been ...

Abstract	Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been 100,819,363 confirmed cases and 2,176,159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of the modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R
Language	English
Publishing date	2021-02-19
Publishing country	Netherlands
Document type	Journal Article
ISSN	2210-6715
ISSN (online)	2210-6715
DOI	10.1016/j.scs.2021.102784
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Article ; Online: Hypoxia-induced miR-210-3p expression in lung adenocarcinoma potentiates tumor development by regulating CCL2-mediated monocyte infiltration.

Arora, Leena / Patra, Debarun / Roy, Soumyajit / Nanda, Sidhanta / Singh, Navneet / Verma, Anita K / Chakraborti, Anuradha / Dasgupta, Suman / Pal, Durba

Molecular oncology

2022

Abstract: In most cancers, tumor hypoxia downregulates the expression of C-C motif chemokine 2 (CCL2), and this downregulation has been implicated in monocyte infiltration and tumor progression; however, the molecular mechanism is yet not clear. We compared non- ... ...

Abstract	In most cancers, tumor hypoxia downregulates the expression of C-C motif chemokine 2 (CCL2), and this downregulation has been implicated in monocyte infiltration and tumor progression; however, the molecular mechanism is yet not clear. We compared non-cancerous and lung-adenocarcinoma human samples for hypoxia-inducible factor 1-alpha (HIF-1A), microRNA-210-3p (mir-210-3p) and CCL2 levels. Mechanistic studies were performed on lung adenocarcinoma cell lines and 3D tumor spheroids to understand the role of hypoxia-induced miR-210-3p in the regulation of CCL2 expression and macrophage polarization. HIF-1 A stabilization increases miR-210-3p levels in lung adenocarcinoma and impairs monocyte infiltration by inhibiting CCL2 expression. Mechanistically, miR-210-3p directly binds to the 3'untranslated region (UTR) of CCL2 mRNA and silences it. Suppressing miR-210-3p substantially downregulates the effect of hypoxia on CCL2 expression. Monocyte migration is significantly hampered in miR-210-3p mimic-transfected HIF-1A silenced cancer cells. In contrast, inhibition of miR-210-3p in HIF-1A-overexpressed cells markedly restored monocyte migration, highlighting a direct link between miR-210-3p level and tumor monocyte burden. Moreover, miR-210-3p inhibition in 3D tumor spheroids promotes monocyte recruitment and skewing towards an anti-tumor M1 phenotype. Anti-hsa-miR-210-3p-locked nucleic acid (LNA) delivery in a lung tumor xenograft zebrafish model caused tumor regression, suggesting that miR-210-3p could be a promising target for immunomodulatory therapeutic strategies against lung adenocarcinoma.
Language	English
Publishing date	2022-06-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	2415106-3
ISSN	1878-0261 ; 1574-7891
ISSN (online)	1878-0261
ISSN	1574-7891
DOI	10.1002/1878-0261.13260
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Article: COVID-19 incidences and its association with environmental quality: A country-level assessment in India

Maiti, Arabinda / Chakraborti, Suman / Pramanik, Suvamoy / Sannigrahi, Srikanta

Abstract: This study explored the association between the five key air pollutants (Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Particulate Matter (PM2.5, PM10), and Carbon Monoxide (CO)) and COVID-19 incidences in India. The COVID-19 confirmed cases, air ... ...

Abstract	This study explored the association between the five key air pollutants (Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Particulate Matter (PM2.5, PM10), and Carbon Monoxide (CO)) and COVID-19 incidences in India. The COVID-19 confirmed cases, air pollution concentration and meteorological variables (temperature, wind speed, surface pressure) for district and city scale were obtained for 2019 and 2020. The location-based air pollution observations were converted to a raster surface using interpolation. The deaths and positive cases are reported so far were found highest in Mumbai (436 and 11394), followed by Ahmedabad (321 and 4991), Pune (129 and 2129), Kolkata (99 and 783), Indore (83 and 1699), Jaipur (53 and 1111), Ujjain (42 and 201), Surat (37 and 799), Vadodara (31 and 400), Chennai (23 and 2647), Bhopal (22 and 652), Thane (21 and 1889), respectively. Unlike the other studies, this study has not found any substantial association between air pollution and COVID-19 incidences at the district level. Considering the number of confirmed cases, the coefficient of determination (R2) values estimated as 0.003 for PM2.5, 0.002 for PM10 and SO2, 0.001 for CO, and 0.0002 for NO2, respectively. This suggests an absolute no significant association between air pollution and COVID-19 incidences (both confirmed cases and death) in India. The same association was observed for the number of deaths as well. For COVID-19 confirmed cases, none of the five pollutants has exhibited any statistically significant association. Additionally, except the wind speed, the climate variables have no produced any statistically significant association with the COVID-19 incidences.
Keywords	covid19
Publisher	ArXiv
Document type	Article
Database	COVID19

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Book ; Online: COVID-19 incidences and its association with environmental quality

Maiti, Arabinda / Chakraborti, Suman / Pramanik, Suvamoy / Sannigrahi, Srikanta

A country-level assessment in India

2020

Abstract	This study explored the association between the five key air pollutants (Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Particulate Matter (PM2.5, PM10), and Carbon Monoxide (CO)) and COVID-19 incidences in India. The COVID-19 confirmed cases, air pollution concentration and meteorological variables (temperature, wind speed, surface pressure) for district and city scale were obtained for 2019 and 2020. The location-based air pollution observations were converted to a raster surface using interpolation. The deaths and positive cases are reported so far were found highest in Mumbai (436 and 11394), followed by Ahmedabad (321 and 4991), Pune (129 and 2129), Kolkata (99 and 783), Indore (83 and 1699), Jaipur (53 and 1111), Ujjain (42 and 201), Surat (37 and 799), Vadodara (31 and 400), Chennai (23 and 2647), Bhopal (22 and 652), Thane (21 and 1889), respectively. Unlike the other studies, this study has not found any substantial association between air pollution and COVID-19 incidences at the district level. Considering the number of confirmed cases, the coefficient of determination (R2) values estimated as 0.003 for PM2.5, 0.002 for PM10 and SO2, 0.001 for CO, and 0.0002 for NO2, respectively. This suggests an absolute no significant association between air pollution and COVID-19 incidences (both confirmed cases and death) in India. The same association was observed for the number of deaths as well. For COVID-19 confirmed cases, none of the five pollutants has exhibited any statistically significant association. Additionally, except the wind speed, the climate variables have no produced any statistically significant association with the COVID-19 incidences.
Keywords	Statistics - Applications
Subject code	333
Publishing date	2020-10-29
Publishing country	us
Document type	Book ; Online
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Article ; Online: Using pupae as appetitive reinforcement to study visual and tactile associative learning in the Ponerine ant Diacamma indicum.

Chandak, Parth / Chakraborti, Udipta / Annagiri, Sumana

Scientific reports

2023 Volume 13, Issue 1, Page(s) 15609

Abstract: Associative learning is of great importance to animals, as it enhances their ability to navigate, forage, evade predation and improve fitness. Even though associative learning abilities of Hymenopterans have been explored, many of these studies offered ... ...

Abstract	Associative learning is of great importance to animals, as it enhances their ability to navigate, forage, evade predation and improve fitness. Even though associative learning abilities of Hymenopterans have been explored, many of these studies offered food as appetitive reinforcement. In the current study, we focus on tactile and visual cue learning in an ant Diacamma indicum using a Y-maze setup with pupa as a positive reinforcement. Using pupa as a reward resulted in a significantly higher proportion of ants completing the training in a shorter time as compared to using food as reinforcement. Ants spent significantly more time in the conditioned arm for both visual cues (white dots or black dots) and tactile cues (rough or smooth surfaces) presented on the floor when associated with pupa, thus showing that they were capable of associative learning. On encountering a conflict between visual and tactile cues during the test, ants chose to spend significantly more time on the arm with the tactile cues indicating that they had made a stronger association between pupa and the tactile cue as compared to the visual cue during training. Using pupa as an ecologically relevant reward, we show that these solitary foraging ants living in small colonies are capable of visual and tactile associative learning and are likely to learn tactile cues over visual cues in association with pupa.
MeSH term(s)	Animals ; Ants ; Pupa ; Reinforcement, Psychology ; Conditioning, Classical ; Spatial Learning
Language	English
Publishing date	2023-09-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-42439-w
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Article ; Online: Analysing the dynamic relationship of land surface temperature and landuse pattern

Dipendra Nath Das / Suman Chakraborti / Gourab Saha / Anushna Banerjee / Dharmaveer Singh

City and Environment Interactions, Vol 8, Iss , Pp 100046- (2020)

A city level analysis of two climatic regions in India

2020

Abstract: Intense urbanization in the Indian cities has brought about phenomenal changes in the existing landuse patterns, whereby increasing trends of surface temperature and the presence and absence of green cover and water bodies, their shape, pattern, ... ...

Abstract	Intense urbanization in the Indian cities has brought about phenomenal changes in the existing landuse patterns, whereby increasing trends of surface temperature and the presence and absence of green cover and water bodies, their shape, pattern, configuration significantly modifies the thermal properties of the city. The present study comprehendeds how different biophysical, anthropogenic, and landscape composition determine and interact with the surface temperature in different climatic zones in India, by taking Jaipur and Guwahati cities as examples. This study has also incorporated several landscape metrics of composition and configuration and spatial cluster analyses to identify the most fragmented landscape in the two cities. Such complex relationship among the landuse and landcover, bio-physical parameters and the surface temperature can be assessed by using the Generalised Additive Model (GAM). Result shows that Jaipur has experienced high temperature changes in city centre in comparison to Guwahati, which can be attributed to the differential presence of environmental sensitive land class in the city cores and peripheries. Results also indicate that medium patches of environmental sensitive land class, as well as close proximity and configuration with environmental non-sensitive class reduces the surface temperature in two heterogenous landscape. Thus, Policymakers and urban planners could be assisted by our finding to mitigate surface temperature in the city.
Keywords	Landscape metrics ; Land surface temperature ; Generalised additive model ; Jaipur and Guwahati ; Environmental sciences ; GE1-350 ; Urban groups. The city. Urban sociology ; HT101-395
Subject code	710
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Inhibition of pro-/active MMP-2 by green tea catechins and prediction of their interaction by molecular docking studies.

Chowdhury, Animesh / Nandy, Suman Kumar / Sarkar, Jaganmay / Chakraborti, Tapati / Chakraborti, Sajal

Molecular and cellular biochemistry

2017 Volume 427, Issue 1-2, Page(s) 111–122

Abstract: Matrix metalloproteinases (MMPs) play a crucial role in developing different types of lung diseases, e.g., pulmonary arterial hypertension (PAH). Green tea polyphenolic catechins such as EGCG and ECG have been shown to ameliorate various types of ... ...

Abstract	Matrix metalloproteinases (MMPs) play a crucial role in developing different types of lung diseases, e.g., pulmonary arterial hypertension (PAH). Green tea polyphenolic catechins such as EGCG and ECG have been shown to ameliorate various types of diseases including PAH. Our present study revealed that among the four green tea catechins (EGCG, ECG, EC, and EGC), EGCG and ECG inhibit pro-/active MMP-2 activities in pulmonary artery smooth muscle cell (PASMC) culture supernatant. Based on the above, we investigated the interactions of pro-/active MMP-2 with the green tea catechins by computational methods. In silico analysis revealed a strong interaction of pro-/active MMP-2 with EGCG/ECG, and galloyl group has been observed to be responsible for this interaction. The in silico analysis corroborated our experimental observation that EGCG and ECG are active in preventing both the proMMP-2 and MMP-2 activities. Importantly, these two catechins appeared to be better inhibitors for proMMP-2 in comparison to MMP-2 as revealed by gelatin zymogram and also by molecular docking studies. In many type of cells, activation of proMMP-2 occurs via an increase in the level of MT1-MMP (MMP-14). We, therefore, determined the interactions of MT1-MMP with the green tea catechins by molecular docking analysis. The study revealed a strong interaction of MT1-MMP with EGCG/ECG, and galloyl group has been observed to be responsible for the interaction.
MeSH term(s)	Animals ; Catechin/chemistry ; Catechin/pharmacology ; Cattle ; Enzyme Precursors/antagonists & inhibitors ; Enzyme Precursors/chemistry ; Enzyme Precursors/metabolism ; Gelatinases/antagonists & inhibitors ; Gelatinases/chemistry ; Gelatinases/metabolism ; Humans ; Matrix Metalloproteinase 2/chemistry ; Matrix Metalloproteinase 2/metabolism ; Molecular Docking Simulation ; Muscle, Smooth, Vascular/enzymology ; Myocytes, Smooth Muscle/enzymology ; Protease Inhibitors/chemistry ; Protease Inhibitors/pharmacology ; Tea/chemistry
Chemical Substances	Enzyme Precursors ; Protease Inhibitors ; Tea ; Catechin (8R1V1STN48) ; Gelatinases (EC 3.4.24.-) ; progelatinase (EC 3.4.24.-) ; MMP2 protein, human (EC 3.4.24.24) ; Matrix Metalloproteinase 2 (EC 3.4.24.24)
Language	English
Publishing date	2017-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	184833-1
ISSN	1573-4919 ; 0300-8177
ISSN (online)	1573-4919
ISSN	0300-8177
DOI	10.1007/s11010-016-2903-y
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Article ; Online: Mapping active paddy rice area over monsoon asia using time-series Sentinel – 2 images in Google earth engine; a case study over lower gangetic plain

Maiti, Arabinda / Acharya, Prasenjit / Sannigrahi, Srikanta / Zhang, Qi / Bar, Somnath / Chakraborti, Suman / Gayen, Bijoy K / Barik, Gunadhar / Ghosh, Surajit / Punia, Milap

Geocarto International. 2022 Dec. 13, v. 37, no. 25 p.10254-10277

2022

Abstract: We proposed a modification of the existing approach for mapping active paddy rice fields in monsoon-dominated areas. In the existing PPPM approach, LSWI higher than EVI at the transplantation stage enables the identification of rice fields. However, it ... ...

Abstract	We proposed a modification of the existing approach for mapping active paddy rice fields in monsoon-dominated areas. In the existing PPPM approach, LSWI higher than EVI at the transplantation stage enables the identification of rice fields. However, it fails to recognize the fields submerged later due to monsoon floods. In the proposed approach (IPPPM), the submerged fields, at the maximum greenness time, were excluded for better estimation. Sentinel–2A/2B time-series images were used for the year 2018 to map paddy rice over the Lower Gangetic Plain (LGP) using Google earth engine (GEE). The overall accuracy (OA) obtained from IPPPM was 85%. Further comparison with the statistical data reveals the IPPPM underestimates (slope (β₁) = 0.77) the total reported paddy rice area, though R² remains close to 0.9. The findings provide a basis for near real-time mapping of active paddy rice areas for addressing the issues of production and food security.
Keywords	Internet ; case studies ; food security ; monsoon season ; rough rice ; time series analysis ; Asia ; Paddy rice mapping ; Sentinel-2 ; Google earth engine ; lower gangetic plain ; random forest
Language	English
Dates of publication	2022-1213
Size	p. 10254-10277.
Publishing place	Taylor & Francis
Document type	Article ; Online
ISSN	1752-0762
DOI	10.1080/10106049.2022.2032396
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis.

Chakraborti, Suman / Maiti, Arabinda / Pramanik, Suvamoy / Sannigrahi, Srikanta / Pilla, Francesco / Banerjee, Anushna / Das, Dipendra Nath

The Science of the total environment

2020 Volume 765, Page(s) 142723

Abstract: Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most ... ...

Abstract	Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most critical factors which are responsible for the overall fatalities caused by COVID-19. The functional capabilities of ML models in epidemiological research, especially for COVID-19, are not substantially explored. To bridge this gap, this study has adopted two advanced ML models, viz. Random Forest (RF) and Gradient Boosted Machine (GBM), to perform the regression modelling and provide subsequent interpretation. Five successive steps were followed to carry out the analysis: (1) identification of relevant key explanatory variables; (2) application of data dimensionality reduction for eliminating redundant information; (3) utilizing ML models for measuring relative influence (RI) of the explanatory variables; (4) evaluating interconnections between and among the key explanatory variables and COVID-19 case and death counts; (5) time series analysis for examining the rate of incidences of COVID-19 cases and deaths. Among the explanatory variables considered in this study, air pollution, migration, economy, and demographic factor were found to be the most significant controlling factors. Since a very limited research is available to discuss the superiority of ML models for identifying the key determinants of COVID-19, this study could be a reference for future public health research. Additionally, all the models and data used in this study are open source and freely available, thereby, reproducibility and scientific replication will be achievable easily.
MeSH term(s)	COVID-19 ; Humans ; Machine Learning ; Pandemics ; Reproducibility of Results ; SARS-CoV-2
Keywords	covid19
Language	English
Publishing date	2020-10-06
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2020.142723
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Article: A neural network and landscape metrics to propose a flexible urban growth boundary: A case study

Chakraborti, Suman / Das, Dipendra Nath / Mondal, Biswajit / Shafizadeh-Moghadam, Hossein / Feng, Yongjiu

Ecological indicators. 2018 Oct., v. 93

2018

Abstract: Urban sprawl is a major barrier for the precise demarcation of administrative boundary in the world. In India, medium and small towns have so far developed outside the envisaged planning, resulting in a leapfrog and haphazard growth. This paper has ... ...

Abstract	Urban sprawl is a major barrier for the precise demarcation of administrative boundary in the world. In India, medium and small towns have so far developed outside the envisaged planning, resulting in a leapfrog and haphazard growth. This paper has attempted to simulate the spatial extent of urban expansion and boundary demarcation for the purpose of efficient urban planning and land resource management. An Artificial Neural Network (ANN) model and a set of landscape metrics were used to delineate the Urban Growth Boundary (UGB) and characterize the future patterns of growth in Siliguri Municipal Corporation (SMC, India). In particular, two urban boundaries – namely, Urban Hard Boundary (UHB) and Urban Soft Boundary (USB) – were simulated. The results suggest a USB with the area of 123 km2 to address the basic service delivery and a UHB with the area of 211.88 km2 to manage the ecological fragmentation.
Keywords	case studies ; environmental indicators ; land management ; landscapes ; neural networks ; towns ; urban planning ; urbanization ; India
Language	English
Dates of publication	2018-10
Size	p. 952-965.
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	2036774-0
ISSN	1872-7034 ; 1470-160X
ISSN (online)	1872-7034
ISSN	1470-160X
DOI	10.1016/j.ecolind.2018.05.036
Database	NAL-Catalogue (AGRICOLA)

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