This study integrates the application of Dempster-Shafer-driven evidential belief function (DS-EBF) methodology with remote sensing and geographic information system techniques to analyze surface and subsurface data sets for the spatial prediction of groundwater potential in Perak Province, Malaysia. The study used additional data obtained from the records of the groundwater yield rate of approximately 28 bore well locations. The processed surface and subsurface data produced sets of groundwater potential conditioning factors (GPCFs) from which multiple surface hydrologic and subsurface hydrogeologic parameter thematic maps were generated. The bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training to 30% (9 wells) for model testing. Application results of the DS-EBF relationship model algorithms of the surface- and subsurface-based GPCF thematic maps and the bore well locations produced two groundwater potential prediction (GPP) maps based on surface hydrologic and subsurface hydrogeologic characteristics which established that more than 60% of the study area falling within the moderate-high groundwater potential zones and less than 35% falling within the low potential zones. The estimated uncertainty values within the range of 0 to 17% for the predicted potential zones were quantified using the uncertainty algorithm of the model. The validation results of the GPP maps using relative operating characteristic curve method yielded 80 and 68% success rates and 89 and 53% prediction rates for the subsurface hydrogeologic factor (SUHF)- and surface hydrologic factor (SHF)-based GPP maps, respectively. The study results revealed that the SUHF-based GPP map accurately delineated groundwater potential zones better than the SHF-based GPP map. However, significant information on the low degree of uncertainty of the predicted potential zones established the suitability of the two GPP maps for future development of groundwater resources in the area. The overall results proved the efficacy of the data mining model and the geospatial technology in groundwater potential mapping.
An estimate of the radiogenic heat production (RHP) across the different petrologic units of northeastern, Nigeria was previously not performed. Hence, their geothermal potentials are not widely known. However, an airborne radiometric data of equivalent uranium, (eU), equivalent thorium (eTh,) and percentage potassium (% K) acquired by Nigerian geological survey agency (NGSA) in the year 2009 was deployed in the evaluation of the RHP across the major petrologic outcrops of northeastern, Nigeria. The objective of this study is to estimate the quantity of RHP across the 13 petrologic units of the northeastern Nigerian terrain via the use of an empirical equation (RHP=ρ(0.0952Cu+0.0256CTh+0.0348Ck)). The petrologic units studied are; medium-coarse grained biotite-hornblende granites (OGe), porphyritic biotite-hornblende granites (OGp), banded gneiss (bG), charnokytes (Ch), ignimbrites (JYG), migmatites-gneiss (MG), basalts (bb), Gombe sandstones (GS), Pindiga Formation (PS), Yolde Formation (YL), Bima sandstones (BS), Keri-Keri Formation (KK), and alluvium (AL). Basic/preliminary processing such as; signal integration, signal validation, and examination of spurious data were applied prior to the RHP computation. The results of the heat production analysis performed show the range of RHP to be from 1.11μW/m3 to 3.35μW/m3 Hence, the maximum heat production value of 3.35μW/m3 was recorded along porphyritic biotite-hornblende granites (OGp) rock block, while the least value of 1.11μW/m3 was recorded over alluvium (AL) rock outcrops. Furthermore, the spatial distribution of the RHP values over the study location shows a gradual increase from the middle, low heat production (sedimentary zones) to the high heat producing areas (granitic and metamorphic zones) around eastern and western parts. The petrologic units arranged in order of decreasing magnitude of radiogenic heat generation are; OGp > MG > OGe/bG > bb > GS > Ch > JYG > BS > PS/YL > KK > AL. On a general note, the petrologic units studied were classified as low in terms of geothermal character based on comparison with other previous global RHP studies.
Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.
Forest ownership is considered as a vital aspect for sustainable management of forest and its associated biodiversity. The Global Forest Resources Assessment 2015 reported that privately owned forest area are increasing on a global scale, but deforestation was found very active in privately owned hill forest areas of Malaysia. Penang State was purposively chosen as it has been experiencing rapid and radical changes due to urban expansion over the last three decades. In this study, analyses of land-use changes were done by PCI Geomatica using Landsat images from 1991 to 2015, future trends of land-use change were assessed using EXCEL forecast function, and its impact on the surrounding environment were conducted by reviewing already published articles on changing environment of the study area. This study revealed an annual deforestation rate of 1.4% in Penang Island since 1991. Trend analysis forecasted a forest area smaller than the current forest reserves by the year 2039. Impact analysis revealed a rapid biodiversity loss with increasing landslides, mudflows, water pollution, flash flood, and health hazard. An immediate ban over hill-land development is crucial for overall environmental safety.
This study aims to investigate and establish a suitable model that can help to estimate aerosol optical depth (AOD) in order to monitor aerosol variations especially during non-retrieval time. The relationship between actual ground measurements (such as air pollution index, visibility, relative humidity, temperature, and pressure) and AOD obtained with a CIMEL sun photometer was determined through a series of statistical procedures to produce an AOD prediction model with reasonable accuracy. The AOD prediction model calibrated for each wavelength has a set of coefficients. The model was validated using a set of statistical tests. The validated model was then employed to calculate AOD at different wavelengths. The results show that the proposed model successfully predicted AOD at each studied wavelength ranging from 340 nm to 1020 nm. To illustrate the application of the model, the aerosol size determined using measure AOD data for Penang was compared with that determined using the model. This was done by examining the curvature in the ln [AOD]-ln [wavelength] plot. Consistency was obtained when it was concluded that Penang was dominated by fine mode aerosol in 2012 and 2013 using both measured and predicted AOD data. These results indicate that the proposed AOD prediction model using routine measurements as input is a promising tool for the regular monitoring of aerosol variation during non-retrieval time.
This study aimed to predict monthly columnar ozone (O3) in Peninsular Malaysia by using data on the concentration of environmental pollutants. Data (2003-2008) on five atmospheric pollutant gases (CO2, O3, CH4, NO2, and H2O vapor) retrieved from the satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) were employed to develop a model that predicts columnar ozone through multiple linear regression. In the entire period, the pollutants were highly correlated (R = 0.811 for the southwest monsoon, R = 0.803 for the northeast monsoon) with predicted columnar ozone. The results of the validation of columnar ozone with column ozone from SCIAMACHY showed a high correlation coefficient (R = 0.752-0.802), indicating the model's accuracy and efficiency. Statistical analysis was utilized to determine the effects of each atmospheric pollutant on columnar ozone. A model that can retrieve columnar ozone in Peninsular Malaysia was developed to provide air quality information. These results are encouraging and accurate and can be used in early warning of the population to comply with air quality standards.
Spinal tuberculosis is an uncommon extrapulmonary manifestation of tuberculosis infection, known as a great masquerade that often mimics other pathologies, such as pyogenic and non-pyogenic infection, bone metastasis, haematological malignancy, and metabolic bone disease. It presents great challenges in establishing a diagnosis, deciding on treatment, and monitoring the response to treatment. A tissue-proven diagnosis is the cornerstone of a definitive diagnosis before initiating medical antitubercular therapy, leading to successful treatment. Here, we present a distinct and rare instance of spinal tuberculosis with an atypical presentation of upper thoracic myelopathy. It involved the cervicothoracic junction, exhibiting minimal axial symptoms but intensive destruction of the affected levels radiologically, along with an incomplete neurological deficit and the possibility of catastrophic neurological complications. The ultimate distinctiveness of this case lies in the diagnostic challenge it posed. Despite undergoing three separate tissue biopsies, tuberculosis infection could not be established, as all results returned negative for cellular, molecular, and histopathological markers, leading to a delay in initiating empirical medical therapy. Nonetheless, the patient responded well to empirical antitubercular therapy, resulting in favourable outcomes. To the best of our knowledge, a case of spinal tuberculosis with numerous negative tissue diagnoses has not been previously reported.
Forest fires are sudden, destructive, hazardous, and challenging to manage and rescue, earning them a place on UNESCO's list of the world's eight major natural disasters. Currently, amid global warming, all countries worldwide have entered a period of high forest fire incidence. Due to global warming, the frequency of forest fires has accelerated, the likelihood of large fires has increased, and the spatial and temporal dynamics of forest fires have shown different trends. Therefore, the impact of climate change on the spatiotemporal dynamics of forest fires has become a hot issue in the field of forest fire research in recent years. Therefore, it is of great significance and necessity to conduct a review of the research in this area. This review delves into the interactions and impacts between climate change and the spatiotemporal dynamics of forest fires. To address this issue, scholars have mainly adopted the following research methods: first, statistical analysis methods, second, the establishment of spatiotemporal prediction models for meteorology and forest fires, and third, the coupling of climate models with forest fire risk forecasting models. The statistical analysis method relies on the analysis of historical meteorological and fire-related data to study the effects of climate change and meteorological factors on fire occurrence. Meanwhile, forest fire prediction models utilize technical tools such as remote sensing. These models synthesize historical meteorological and fire-related data, incorporating key meteorological factors such as temperature, rainfall, relative humidity, and wind. The models revealed the spatial and temporal distribution patterns of fires, identified key drivers, and explored the interactions between climate change and forest fire dynamics, culminating in the construction of predictive models. With the deepening of the study, the coupling of climate models and fire risk ranking systems became a trend in the prediction of forest fire risk trends. Moreover, as the climate warms, the increased frequency of extreme weather events like heatwaves, droughts, snow and ice storms, and El Niño-Southern Oscillation (ENSO) has accelerated forest fire occurrences and raised the risk of major fires. This review offers valuable technical insights by comprehensively analyzing the spatial and temporal characteristics of forest fires, elucidating key meteorological drivers, and exploring potential mechanisms. These insights serve as a scientific foundation for preventive measures and effective forest fire management. In the face of a changing climate, this synthesis contributes to the development of informed strategies to mitigate the escalating threat of forest fires.
Solitary plasmacytoma (SPC) account for only 5% of plasma cell neoplasms, and the literature hardly reports spinal SPC with a neurological deficit. Furthermore, spinal surgical intervention during pregnancy is rarely encountered and often requires multidisciplinary collaboration and management. The objective of this case report is to highlight this near-miss diagnosis and spinal surgical intervention during pregnancy. A 31-year-old woman with 24 weeks gestation presented with sudden paralysis and incontinence, with an underlying history of chronic backpain over a two-month period. Initially, she was treated for musculoskeletal back pain by obstetric colleagues during an antenatal visit, and no radiograph was performed. A non-contrasted spinal MRI was eventually requested when she started to show bilateral lower limb weakness, numbness and incontinence. The MRI highlighted thoracic vertebrae T11 vertebra plana with kyphotic deformity and a paraspinal soft tissue mass compressing the spinal cord causing spinal cord oedema. Our initial working diagnosis was spinal tuberculosis (TB), considering TB is highly endemic in Malaysia. However, TB workup was negative, and we proceeded with spinal surgery and transpedicular biopsy. Neurology improved significantly after surgery. Eventually, serum protein electrophoresis reported plasma dyscrasia, and HPE confirmed plasmacytoma. The patient was referred to a haematologist for steroidal and chemotherapy treatment.
Air surface temperature (AST) is a crucial importance element for many applications such as hydrology, agriculture, and climate change studies. The aim of this study is to develop regression equation for calculating AST and to analyze and investigate the effects of atmospheric parameters (O3, CH4, CO, H2Ovapor, and outgoing longwave radiation (OLR)) on the AST value in Iraq. Dataset retrieved from the Atmospheric Infrared Sounder (AIRS) at EOS Aqua Satellite, spanning the years of 2003 to 2016, and multiple linear regression were used to achieve the objectives of the study. For the study period, the five atmospheric parameters were highly correlated (R, 0.855-0.958) with predicted AST. Statistical analyses in terms of β showed that OLR (0.310 to 1.053) contributes significantly in enhancing AST values. Comparisons among selected five stations (Mosul, Kanaqin, Rutba, Baghdad, and Basra) for the year 2010 showed a close agreement between the predicted and observed AST from AIRS, with values ranging from 0.9 to 1.5 K and for ground stations data, within 0.9 to 2.6 K. To make more complete analysis, also, comparison between predicted and observed AST from AIRS for four selected month in 2016 (January, April, July, and October) has been carried out. The result showed a high correlation coefficient (R, 0.87 and 0.95) with less variability (RMSE ≤ 1.9) for all months studied, indicating model's capability and accuracy. In general, the results indicate the advantage of using the AIRS data and the regression analysis to investigate the impact of the atmospheric parameters on AST over the study area.
α-Mangostin, one of the major constituents of Garcinia mangostana, has been reported to possess several biological activities, including antioxidant, anti-inflammatory, antibacterial, and cytotoxic activities associated with the inhibition of cell proliferation and activation of apoptosis. However, the cellular signaling pathway mediated by α-mangostin has not been firmly established. To investigate the cellular activities of α-mangostin, human cancer cells, MCF-7 and MCF-7-CR cells, were treated with α-mangostin to measure the cellular responses, including cytotoxicity, protein-protein interaction, and protein expression. Cancer cells stably expressed Myc-BCL-XL and HA-MOAP-1 were also included in the studies to delineate the cell signaling events mediated by α-mangostin. Our results showed that the apoptosis signaling mediated by α-mangostin involves the upregulation of endogenous MOAP-1, which interacts with α-mangostin activated BAX (act-BAX) while downregulating the expression of BCL-XL. Moreover, α-mangostin was found to induce BAX oligomerization, the release of mitochondrial cytochrome C, and activation of caspase in MCF-7 cells. In overexpression studies, MCF-7 cells and spheroids stably expressed HA-MOAP-1 and Myc-BCL-XL exhibited differential chemosensitivity toward α-mangostin in which the stable clones expressing HA-MOAP-1 and MYC-BCL-XL were chemosensitive and chemoresistant to the apoptosis signaling events mediated by α-mangostin, respectively, when compared to untreated cells. Together, the data suggest that the cytotoxicity of α-mangostin involves the activation of MOAP-1 tumor suppressor and its interaction with act-BAX, leading to mitochondria dysfunction and cell death.
Microsatellites are the most popular markers for parentage assignment and population genetic studies. To meet the demand for international comparability for genetic studies of Asian seabass, a standard panel of 28 microsatellites has been selected and characterized using the DNA of 24 individuals from Thailand, Malaysia, Indonesia and Australia. The average allele number of these markers was 10.82 +/- 0.71 (range: 6-19), and the expected heterozygosity averaged 0.76 +/- 0.02 (range: 0.63-1.00). All microsatellites showed Mendelian inheritance. In addition, eight standard size controls have been developed by cloning a set of microsatellite alleles into a pGEM-T vector to calibrate allele sizes determined by different laboratories, and are available upon request. Seven multiplex PCRs, each amplifying 3-5 markers, were optimized to accurately and rapidly genotype microsatellites. Parentage assignment using 10 microsatellites in two crosses (10 x 10 and 20 x 20) demonstrated a high power of these markers for revealing parent-sibling connections. This standard set of microsatellites will standardize genetic diversity studies of Asian seabass, and the multiplex PCR sets will facilitate parentage assignment.