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This paper deals with the variability of Normalized Difference Vegetation Index (NDVI) and its association with rain rate and total evapotranspiration over Bangladesh during the period of 2003-2011 using MODerate-resolution Imaging Spectroradiometer (MODIS), Tropical Rainfall Measuring Mission (TRMM) and Global Land Assimilation System (GLDAS) data. NDVI shows higher concentration in eastern parts of the country. The maximum NDVI is found in the month of October and minimum in February. It reveals excellent periodic variation in relation to rain rate and total evapotranspiration. NDVI shows strong spatial and temporal correlation with rain rate and total evapotranspiration especially in northwestern part of the country. Total evapotranspiration is more strongly correlated with vegetation than rain rate as it integrates rainfall, temperature and soil water statistics during the entire period. Thus, NDVI is an important variable for agronomical and climate applications. Also, it is important to study the vegetation for different seasons and different agro-ecological areas to investigate the variables affecting the vegetation types and growth rate.

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Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github .com /hiddenntreasure /comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.

Trace gases are important components for climate change process, and Earth's climate is sensitive to change in their atmospheric concentrations; therefore, proper assessment of trace gases is essential for ongoing global climate simulation. The spatio-temporal variations of four trace gases, namely carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), and carbon dioxide (CO 2 ), over Bangladesh during the last decade are analysed using the remote-sensing data sets of the Atmospheric Infrared Sounder (AIRS) and Ozone Monitoring Instrument (OMI). Monthly, seasonal, and annual mean variations of trace gases were assessed. Higher CO, O 3 , and CO 2 concentrations show west-to-east gradient, indicating the impact of both local meteorology and emissions on variations in trace gases. On the other hand, total NO 2 concentration increases over Dhaka because of large population density, high traffic emission, larger industrial activities, and highly polluted air. The inter-annual variations of trace gases are mainly due to large-scale climatic phenomena such as El Niño and La Niña conditions. All the trace gases show strong seasonality, with higher levels during pre-monsoon season and lower levels during monsoon season, which are caused by the seasonal variations in biomass burning (BB), long-range transportation, and rainfall in South and Southeast Asia (S-SE Asia). However, O 3 concentration reveals minimum loading during winter season, associated with the reduction of O 3 formation in cold days due to insufficient heat. These findings are important to estimate regional climate variability due to trace gases.

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Aerosol-cloud interactions influence the global precipitation patterns that influence significantly the Earth's climate system. Anthropogenic aerosols alter the clouds and their optical properties. The present study has investigated the Aerosol Optical Depth (AOD), cloud parameters and rainfall interactions for three different monsoon periods (2008-2010) and also compared the satellite rainfall with ground based observations, by using MODIS and TRMM datasets. The highest average of AOD was in the month of June and lowest was in July for both Rajshahi and Sylhet divisions. Comparing between Rajshahi and Sylhet, Rajshahi was in the peak of aerosol contamination than Sylhet. The cloud parameters, such as COD and CER, were positively correlated with rainfall except CER in Rajshahi during the 2010 monsoon season and in Sylhet during the 2008 monsoon season. The investigation has showed complex interaction among AOD, cloud parameters and rainfall in both regions during the study period. In addition TRMM satellite-derived rainfall has compared with ground-measured values. The result indicated that TRMM rainfall data were in good agreement with ground measurements with correlation coefficient of above 0.90 in Rajshahi.

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