Detection and Enumeration of Bacterial Pathogens in the American Oyster (Crassostrea virginica)

American oyster (Crassostrea virginica) is a popular seafood for its delicacy and high nutritional value. Based on increasing concern about contamination of bacterial pathogens in raw oyster, my research objectives have been focused on detection and enumeration of two important bacterial pathogens, Escherichia coli and Salmonella spp. in the American oyster in south Texas waters, local markets and controlled laboratory studies. Immunohistochemical and RT-PCR analyses showed substantial bacterial pathogen’s presence in gills and digestive glands of oysters collected from San Martin Lake and South Padre Island as well as local markets. Laboratory studies showed increasing trend of both bacterial pathogens with elevated temperatures (28 and 32 °C) compared to control (24 °C). Extrapallial fluid, an important body fluid, glucose levels, pH, and protein concentration varied in oysters and appeared to be pertinent with pathogen intensity. Collectively, these results suggest that American oyster is prone to water-borne pathogen contamination in south Texas waters

The Socio-economic Status and Land Use Pattern: A Micro-level Analysis in Bangladesh

To investigate the micro level existing land use pattern, socio-economic status and differences between urban and rural land use pattern, the Dumuria upazila in Khulna district in Bangladesh has been selected. Total 340 samples have been taken through a questionnaire survey and the plot-to-plot survey was conducted on 2657 plots and about 267 acres to fulfill the objectives. This study demonstrates that land use pattern in both urban and rural areas was different due to various socio-economic factors. In urban areas, land use pattern was mainly dominated by residential or homestead use about 26% of land and a significant portion of land was occupied by the commercial and industrial use which is about 13% of the land. On contrary, rural area was dominated by wetlands and agricultural land (cropland and inter-culture) about 40% and 23% respectively. The study indicates that forest and woodland cover was relatively high in rural areas was about 15.39 acre out of 141 acre (11%) but it was only about 4% in urban areas. Moreover, this study reveals that the rural area was mostly covered with natural forest and woodland, on the contrary, urban area was mostly man-made. However, the findings of this study provide valuable information to support the sustainable development of urban and rural land use and planning processes and forecasting future possible land use changes on that locality

Effects of elevated temperature on 8-OHdG expression in the American oyster (Crassostrea virginica): Induction of oxidative stress biomarkers, cellular apoptosis, DNA damage and γH2AX signaling pathways

Global temperature is increasing due to anthropogenic activities and the effects of elevated temperature on DNA lesions are not well documented in marine organisms. The American oyster (Crassostrea virginica, an edible and commercially important marine mollusk) is an ideal shellfish species to study oxidative DNA lesions during heat stress. In this study, we examined the effects of elevated temperatures (24, 28, and 32 °C for one-week exposure) on heat shock protein-70 (HSP70, a biomarker of heat stress), 8-hydroxy-2’-deoxyguanosine (8-OHdG, a biomarker of pro-mutagenic DNA lesion), double-stranded DNA (dsDNA), γ-histone family member X (γH2AX, a molecular biomarker of DNA damage), caspase-3 (CAS-3, a key enzyme of apoptotic pathway) and Bcl-2-associated X (BAX, an apoptosis regulator) protein and/or mRNA expressions in the gills of American oysters. Immunohistochemical and qRT-PCR results showed that HSP70, 8-OHdG, dsDNA, and γH2AX expressions in gills were significantly increased at high temperatures (28 and 32 °C) compared with control (24°C). In situ TUNEL analysis showed that the apoptotic cells in gill tissues were increased in heat-exposed oysters. Interestingly, the enhanced apoptotic cells were associated with increased CAS-3 and BAX mRNA and/or protein expressions, along with 8-OHdG levels in gills after heat exposure. Moreover, the extrapallial (EP) fluid (i.e., extracellular body fluid) protein concentrations were lower; however, the EP glucose levels were higher in heat-exposed oysters. Taken together, these results suggest that heat shock-driven oxidative stress alters extracellular body fluid conditions and induces cellular apoptosis and DNA damage, which may lead to increased 8-OHdG levels in cells/tissues in oysters

Mapping and monitoring erosion-accretion in an alluvial river using satellite imagery – the river bank changes of the Padma river in Bangladesh

The Padma river is widely known for its dynamic and disastrous behaviour, and the river has been experiencing intense and frequent bank erosion and deposition leading to the changes and shifting of bank line. In this paper, a time series of Landsat satellite imagery MSS, TM and OLI and TIRS images and are used to detect river bank erosion-accretion and bank line shifting during the study period 1975-2015. This study exhibits a drastic increase of erosion and accretion of land along the Padma river. The results show that from 1975 to 2015, the total amount of river bank erosion is 49,951 ha of land, at a rate of 1,249 ha a-1 and the total amount of accretion is 83,333 ha of land, at a rate of 2,083 ha a-1. Throughout the monitoring period, erosion-accretion was more pronounced in the right part of the river and bank line had been shifting towards the southern direction. The paper also reveals that the total area of islands had been increased significantly, in 2015 there was about 50,967 ha of island area increased from 20,533 ha of island area in 1975, and the results evidence consistency of sedimentation in the river bed

Mapping and monitoring erosion-accretion in an alluvial river using satellite imagery – the river bank changes of the Padma river in Bangladesh

The Padma river is widely known for its dynamic and disastrous behaviour, and the river has been experiencing intense and frequent bank erosion and deposition leading to the changes and shifting of bank line. In this paper, a time series of Landsat satellite imagery MSS, TM and OLI and TIRS images and are used to detect river bank erosion-accretion and bank line shifting during the study period 1975-2015. This study exhibits a drastic increase of erosion and accretion of land along the Padma river. The results show that from 1975 to 2015, the total amount of river bank erosion is 49,951 ha of land, at a rate of 1,249 ha a-1 and the total amount of accretion is 83,333 ha of land, at a rate of 2,083 ha a-1. Throughout the monitoring period, erosion-accretion was more pronounced in the right part of the river and bank line had been shifting towards the southern direction. The paper also reveals that the total area of islands had been increased significantly, in 2015 there was about 50,967 ha of island area increased from 20,533 ha of island area in 1975, and the results evidence consistency of sedimentation in the river bed

Mapping and Monitoring Erosion-Accretion in an Alluvial River Using Satellite Imagery – The River Bank Changes of the Padma River in Bangladesh

The Padma river is widely known for its dynamic and disastrous behaviour, and the river has been experiencing intense and frequent bank erosion and deposition leading to the changes and shifting of bank line. In this paper, a time series of Landsat satellite imagery MSS, TM and OLI and TIRS images and are used to detect river bank erosion-accretion and bank line shifting during the study period 1975–2015. This study exhibits a drastic increase of erosion and accretion of land along the Padma river. The results show that from 1975 to 2015, the total amount of river bank erosion is 49,951 ha of land, at a rate of 1,249 ha a−1 and the total amount of accretion is 83,333 ha of land, at a rate of 2,083 ha a−1. Throughout the monitoring period, erosion-accretion was more pronounced in the right part of the river and bank line had been shifting towards the southern direction. The paper also reveals that the total area of islands had been increased significantly, in 2015 there was about 50,967 ha of island area increased from 20,533 ha of island area in 1975, and the results evidence consistency of sedimentation in the river bed

Session 2: \u3cem\u3eAI-driven wheat yield and protein content forecasting using UAV remote sensing\u3c/em\u3e

Preharvest forecasting of wheat grain yield, test weight and protein content is critical in terms of in season decision making and field management practices, as well as field-based high-throughput phenotyping toward enhanced yield and grain quality. In recent years, the rapid advancement of Unmanned Aerial Vehicle (UAV) and sensor technologies enabled high-resolution spatial, spectral, and temporal data collection with a lower cost. Coupled with cutting-edge artificial intelligence and deep learning (AI/DL) algorithms, UAV remote sensing has become an important tool in a variety of agricultural applications. This study aims to investigate the potential of UAV-based multitemporal multispectral data for preharvest wheat yield, test weight and protein content estimation under the framework of AI/DL. UAV-based multispectral images were collected throughout the 2022 winter wheat growing season over seven experimental winter wheat fields across South Dakota, USA. Plot-level canopy spectral and texture features were derived from UAV multispectral imagery. Traditional machine learning approaches such as Partial Least Squares Regression, Support Vector Regression, and Random Forest Regression were employed to develop prediction models using plot-level averaged spectral and texture features. Additionally, deep learning methods such as Convolutional Neural Networks (CNN) and hybrid CNN and Long-Short Term Memory (CNN-LSTM) were also implemented using plot-level reflectance imagery as input for prediction model development. This research highlights the potential of coupling high-resolution UAV remote sensing with cutting-edge AI/DL in predicting wheat yield, test wight and protein content. The results from this work deliver valuable insights for high-throughput phenotyping and crop field management with high spatial precision. Key words: artificial intelligence (AI), deep learning (DL), unmanned aerial vehicle (UAV), remote sensing, winter wheat, yield predictio