Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study.

White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.

Sensing the dependable surficial signatures of temporal groundwater variations in arid coastal regions through geospatial techniques with respect to microclimate changes.

Reports on Groundwater level variations and quality changes have been a critical issue, especially in arid regions. An attempt has been made in this study to determine the surface manifestations of groundwater variations through processing imageries for determining the changes in land use, Normalized Differential Building Index (NDBI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), along with Groundwater level (GWL) and Electrical conductivity (EC). Decadal variation between these parameters for 2013 and 2023 shows that the average water level had increased by 1.03amsl, while the EC values of groundwater decreased by 418 µS/cm. The decrease in EC values indicates freshwater recharge, promoting natural vegetation, thus reducing the LST values by 3.28 °C. In addition, urban landscaping and relatively lesser emissivity from built-up surfaces than the sandy desert have further reduced the LST. The interrelationship of the parameters indicates that an increase in LST correlates with an increase in NDBI and with less significant changes in NDVI. The lowering of the LST along the coastal regions was inferred to be due to the influence of Sea breeze, adjacent moisture from the ocean, shallow water level, and the shadow effect of the buildings. Further, the increase in water level was mainly attributed to the recent increase in rainfall and the extreme event in 2018. The higher EC in the lesser NDBI regions is attributed to the anthropogenic contamination from agriculture and landfill leachates. Though there was an increase in NDBI, the LST of the region was inferred to be reduced mainly due to the increase in water level and reduction of emission from desert sand by recent urban developments.

Deep Interpolation of Remote Sensing Land Surface Temperature Data with Partial Convolutions.

Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors' view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the research area and learning local dependencies that can help with the interpolation process. We propose a novel approach to interpolating remote sensing LST data in a fixed research area considering local ground-site air temperature measurements. The two-step approach consists of learning the LST from air temperature measurements, where the ground-site weather stations are located, and interpolating the remaining missing values with partial convolutions within a U-Net deep learning architecture. Our approach improves the interpolation of LST for our research area by 44% in terms of RMSE, when compared to state-of-the-art statistical methods. Due to the use of air temperature, we can provide coverage of 100%, even when no valid LST measurements were available. The resulting gapless coverage of high resolution LST data will help unlock the full potential of remote sensing LST data.

Integrating meteorological and geospatial data for forest fire risk assessment.

Understanding the factors that cause fire is crucial for minimizing the fire risk. In this research, a comprehensive approach was adopted to recognize factors influencing forest fires. Golestan National Park (GNP) was considered as a representative area with a humid climate in this study. Initially, using the Multi-Criteria Evaluation Method, a fire risk map was created by analyzing natural and human factors, and vulnerable areas were identified. Then, the relationship between key elements such as meteorological conditions, Land Surface Temperature (LST), and precipitation, with the occurrence of fire in different years was investigated. CHIRPS and Landsat data were utilized to assess LST changes and precipitation. 23-year changes in fire occurrence areas in GNP were acquired using MODIS products. The results of the data analysis showed that the highest number of fires occurred in forest areas, and in the fire risk prediction map, the extremely high-risk class is completely consistent with the ground truth data. The assigned weights, derived from expert opinions, highlight the substantial significance of elevation, and distance from roads and settlements. Additionally, the effectiveness of the model in providing reliable forecasts for fire risks in GNP is highlighted by the ROC curve with an AUC value of 0.83. Forest fires within GNP exhibit a distinct seasonality, predominantly occurring from July to December. During the warmer months, by coinciding with summer excursions, human activities may contribute to the ignition of fires. In 2013 and 2014, rising fire incidents correlated with elevated temperatures, hinting at a potential connection. GNP fires showed an upward trend with higher monthly LST and a downward trend with increased annual precipitation. The results showed that there is a relationship between LST, precipitation, and the occurrence of fire in GNP. Approximately 176.15 ha of GNP's forest areas have been destroyed by fires over the last two decades. This research demonstrated that there is a dynamic interaction between environmental conditions and fire incidents. By considering these factors, managers and environmental planners can develop effective strategies for managing and preventing forest fire risks.

Urban heat load assessment in Zagreb, Croatia: a multi-scale analysis using mobile measurement and satellite imagery.

A limited number of meteorological stations and sparse data challenge microclimate assessment in urban areas. Therefore, it is necessary to complement these data with additional measurements to achieve a denser spatial coverage, enabling a detailed representation of the city's microclimatic features. In this study, conducted in Zagreb, Croatia, mobile air temperature measurements were utilized and compared with satellite-derived land surface temperature (LST). Here, air temperature measurements were carried out using bicycles and an instrument with a GPS receiver and temperature probe during a heat wave in June 2021, capturing the spatial pattern of air temperature to highlight the city's microclimate characteristics (i.e. urban heat load; UHL) in extremely hot weather conditions. Simultaneously, remotely sensed LST was retrieved from the Landsat-8 satellite. Air temperature measurements were compared to city-specific street type classification, while neighbourhood heat load characteristics were analysed based on local climate zones (LCZ) and LST. Results indicated significant thermal differences between surface types and urban forms and between street types and LCZs. Air temperatures reached up to 35 °C, while LST exceeded 40 °C. City parks, tree-lined streets and areas near blue infrastructure were 1.5-3 °C cooler than densely built areas. Temperature contrasts between LCZs in terms of median LST were more emphasised and reached 9 °C between some classes. These findings highlight the importance of preserving green areas to reduce UHL and enhance urban resilience. Here, exemplified by the city of Zagreb, it has been demonstrated that the use of multiple datasets allows a comprehensive understanding of temperature patterns and their implications for urban climate research.

Explaining the role of land use changes on land surface temperature in an arid and semi-arid metropolitan area with multi-scale spatial regression analysis.

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and Spatial Error Model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30×30 m2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC changes and LST in an ASA environment, contributing significantly to the literature on environmental change in arid regions and the methodologies for monitoring such changes. Future research should aim to validate and expand additional LST-affecting factors and test our approach and findings in other ASA regions, considering the unique characteristics of these areas and the importance of tailored environmental monitoring and assessment approaches.

Impact of the COVID-19 outbreak on urban air, Land surface temperature and air pollution in cold climate zones.

The objective of this study was to analyze air pollution and thermal environment in Turkey's cold region before, during, and after COVID-19 in 2019, 2020 and 2021. The CO, NO2, O3, PM10 and SO2 data from the state air quality stations, as well as ground air temperature data from six weather stations, and land satellite images from the USGS website using ArcGIS 10.4.1 software were collected in January, March, April and August of 2019, 2020 an 2021. In order to evaluate the impact of COVID-19 measures and restrictions on cold region cities, air pollution indicators, land surface temperature and air temperature as well as statistical data were analyzed. The results indicated that the CO, NO2, PM10 and SO2 emissions decreased by 14.9%, 14.3%, 47.1% and 28.5%, but O3 increased by 16.9%, respectively, during the COVID-19 lockdown in 2020 as compared to these of the pre-COVID-19 levels in 2019. A positive correlation between air temperature and O3 in 2019 (r2 = 0.80), and in 2020 and 2021 (r2 = 0.64) was obtained. Air temperature in 2020 and 2021 decreased due to lockdowns and quarantine measures that led to lower O3 emissions as compared to 2019. Negative correlations were also found between air temperature and NO2 (r2 = 0.60) and SO2 (r2 = 0.5). There was no correlation between air temperature and PM10. During the COVID-19 lockdown and intense restrictions in April 2020, the average LST and air temperature values dropped by 14.7 °C and 1.6 °C respectively, compared to April 2019. These findings may be beneficial for future urban planning, particularly in cold regions.

Exploring knowledge, attitude, and intention towards advance care planning, advance directive, and the patient self-determination act among hemodialysis patients.

BACKGROUND: Hemodialysis holds the highest incidence and prevalence rate in Taiwan globally. However, the implementation of advance care planning (ACP), advance directives (AD), and patient self-determination acts (PSDA) remains limited. Our objective was to examine the current status of ACP, AD and PSDA and potential opportunities for enhancement. METHODS: We developed a novel questionnaire to assess individuals' knowledge, attitudes, and intentions regarding ACP, AD, and PSDA. We also collected baseline characteristics and additional inquiries for correlation analysis to identify potential factors. Student's t-test and Analysis of Variance were employed to assess significance. RESULTS: Initially, a cohort of 241 patients was initially considered for inclusion in this study. Subsequently, 135 patients agreed to participate in the questionnaire study, resulting in 129 valid questionnaires. Among these respondents, 76 were male (59.9%), and 53 were female (41.1%). Only 13.2% had signed AD. A significant portion (85.3%) indicated that they had not discussed their dialysis prognosis with healthcare providers. Additionally, a mere 14% engaged in conversations about life-threatening decisions. Ninety percent believed that healthcare providers had not furnished information about ACP, and only 30% had discussed such choices with their families. The findings revealed that the average standardized score for ACP and AD goals was 84.97, while the attitude towards PSDA received a standardized score of 69.94. The intention score stood at 69.52 in standardized terms. Potential candidates for ACP initiation included individuals aged 50 to 64, possessing at least a college education, being unmarried, and having no history of diabetes. CONCLUSION: Patients undergoing hemodialysis exhibited a significant knowledge gap concerning ACP, AD, and the PSDA. Notably, a substantial number of dialytic patients had not received adequate information on these subjects. Nevertheless, they displayed a positive attitude, and a considerable proportion expressed a willingness to sign AD. It is imperative for nephrologists to take an active role in initiating ACP discussions with patients from the very beginning.

Spatiotemporal Thermal Variations in Moroccan Cities: A Comparative Analysis.

This study examines the Land Surface Temperature (LST) trends in eight key Moroccan cities from 1990 to 2020, emphasizing the influential factors and disparities between coastal and inland areas. Geographically weighted regression (GWR), machine learning (ML) algorithms, namely XGBoost and LightGBM, and SHapley Additive exPlanations (SHAP) methods are utilized. The study observes that urban areas are often cooler due to the presence of urban heat sinks (UHSs), more noticeably in coastal cities. However, LST is seen to increase across all cities due to urbanization and the degradation of vegetation cover. The increase in LST is more pronounced in inland cities surrounded by barren landscapes. Interestingly, XGBoost frequently outperforms LightGBM in the analyses. ML models and SHAP demonstrate efficacy in deciphering urban heat dynamics despite data quality and model tuning challenges. The study's results highlight the crucial role of ongoing urbanization, topography, and the existence of water bodies and vegetation in driving LST dynamics. These findings underscore the importance of sustainable urban planning and vegetation cover in mitigating urban heat, thus having significant policy implications. Despite its contributions, this study acknowledges certain limitations, primarily the use of data from only four discrete years, thereby overlooking inter-annual, seasonal, and diurnal variations in LST dynamics.

A Spatial-Temporal Graph Convolutional Recurrent Network for Transportation Flow Estimation.

Accurate estimation of transportation flow is a challenging task in Intelligent Transportation Systems (ITS). Transporting data with dynamic spatial-temporal dependencies elevates transportation flow forecasting to a significant issue for operational planning, managing passenger flow, and arranging for individual travel in a smart city. The task is challenging due to the composite spatial dependency on transportation networks and the non-linear temporal dynamics with mobility conditions changing over time. To address these challenges, we propose a Spatial-Temporal Graph Convolutional Recurrent Network (ST-GCRN) that learns from both the spatial stations network data and time series of historical mobility changes in order to estimate transportation flow at a future time. The model is based on Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) in order to further improve the accuracy of transportation flow estimation. Extensive experiments on two real-world datasets of transportation flow, New York bike-sharing system and Hangzhou metro system, prove the effectiveness of the proposed model. Compared to the current state-of-the-art baselines, it decreases the estimation error by 98% in the metro system and 63% in the bike-sharing system.

Impact of COVID lockdowns on spatio-temporal variability in land surface temperature and vegetation index.

In urban areas, industrial and human activities are the prime cause that exacerbates the heating effects, also called the urban heat island (UHI) effect. The land surface temperature (LST), normalized difference vegetation index (NDVI), and the proportion of vegetation (Pv) are indicators of measurement of the heating/urbanization effects. In the present work, we investigated the impact of the COVID-19 lockdown, i.e., restricted human activities. We used Landsat-8 OLI/TIRS (level 1) data to investigate spatial and temporal heterogeneity changes in these urbanization indicators during full and partial lockdown periods in 2020 and 2021, with 2019 as the base year. We have selected three cities in India's eastern coal mining belt, Bokaro, Dhanbad, and Ranchi, for the study. Results showed a significant decrease in LST values over all sites, with a maximum reduction over mining sites, i.e., Bokaro and Dhanbad. The LST value decreased by about 13-19% during the lockdown period. Vegetation indices (i.e., NDVI and Pv) showed a substantial increase of about 15% overall sites. With decreased LST values and increased NDVI values, these quantities' correlations became more negative during the lockdown period. More positive changes are noticed over mining sites than non/less mining sites. This indirectly indicates the reduction in the heat-absorbing particles in the environment and surface of these sites, a possible cause for the reduction in LST values substantially. Reduction in coal particles at the land and vegetation surface likely contributed to decreased LST and enhanced vegetation indices. To check the statistical significance of changes in the UHI indicators in the lockdown period, statistical tests (ANOVA and Tukey's test) are performed. Results indicate that most of the case changes have been significant. The study's finding suggests the lockdown's positive impact on the heating/UHI effects. It emphasizes the need for planned lockdowns as city mitigation strategies to overcome pollution and environmental issues.

Ecological monitoring of urban thermal field variance index and determining the surface urban heat island effects in Lahore, Pakistan.

Lahore is the second major metropolitan city in Pakistan in terms of urban population and built-up area, making the city a more ideal place to form the surface urban heat island (SUHI) effects. In the last two decades, the considerable land-use conversion from a natural surface (vegetation) and permeable (waterbody) surface into an impervious (built-up area) surface has lead to an increase in land surface temperature (LST) in Lahore. The human thermal comfort (HTC) of the residents is also impacted by the higher LST. The present study uses multi-temporal Landsat (5&8) satellite imageries to examine the ecological and thermal conditions of Lahore between 2000 and 2020. The ecological and thermal conditions of Lahore are assessed by calculating the urban heat islands and UTFVI (urban thermal field variance index), based on LST data which quantitatively assessed the UHI effect and the quality of human life. The outcomes establish that the urban built-up area has increased by 18%, while urban vegetation, vacant land, and waterbody decreased by 13%, 4%, and 0.04%, respectively. In the last 20 years, the mean LST of the study region has risen by about 3.67 °C. The UHI intensity map shows intensification and a rise in surface temperature variation from 4.5 °C (2000) to 5.9 °C (2020). Furthermore, the finding shows that the ecological and thermal conditions are worse in construction sites, transition zones, and urban areas in comparison to nearby rural areas. The lower UTFVI was observed in dense vegetation cover areas while a hot spot of higher UTFVI was predominantly observed in the areas of transition zones and built-up area expansion. Those areas with higher hot spots are more vulnerable to the urban heat island effect. The main conclusions of this study are essential for educating city officials and urban planners in developing a sustainable urban land development plan to reduce urban heat island effects by investing in open green spaces for urban areas of cities.

A geographic information systems and remote sensing-based approach to assess urban micro-climate change and its impact on human health in Bartin, Turkey.

Increasing land surface temperature (LST) is one of the major urban climatology problems arising in urban development. In this paper, the impact of vegetation and built-up areas on the LST and impact of LST on human health are assessed using the Landsat thermal data in Bartin, Turkey. The results show that there is a constant change in the share of vegetation and built-up areas due to rapid urbanization in Bartin. Strong positive correlation has been found between NDBI and LST while strong negative correlation has been found between NDVI and LST, suggesting their strong impacts on land surface temperatures. Similarly, a strong positive correlation has been observed between LST, sleep deprivation, and heat stress. This study provides precise information on effects of urbanization and man-made activities, which cause major changes in micro-climate and human health in the city. This study can assist decision-makers or planners to plan future developments sustainably.

Spatiotemporal variation in the vegetation cover of Peshawar Basin in response to climate change.

Climate factors like temperature, precipitation, humidity, and sunshine time exert a profound influence on vegetation. The intricate interplay between the two is crucial to understand in the face of changing climate to develop mitigation strategies. In the current exploration, we delve how climate variability (CV) has impacted the vegetation in the Peshawar Basin (PB) using remote sensing data tools. The trend of climatic variability was investigated using the modified Mann-Kendall test and Sen's slope statistics. The changing climatic parameters were regressed on the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The NDVI was further analyzed for spatiotemporal variability under land surface temperature (LST) influence. Results revealed that among the climate factors, average annual temperature and solar radiation have a significant (p < 0.05) negative impact on vegetation while precipitation and relative humidity significantly (p < 0.05) influence NDVI positively. The overall positive trend shows that vegetation improved between 2001 and 2020 with time, however some years (2010, 2012, 2014, 2016, and 2017) with low NDVI. NDVI varied in space considerably due to climatic extremes brought on by CV and the urbanization of agricultural land. NDVI regressed on LST showed that there was no or very little vegetation in the grids with high LST. The study concluded that the region is significantly impacted by both CV-related extreme weather events and anthropogenic activities. The vegetation is improving, but it is in danger of being destroyed by deforestation due to CV and human activities that exacerbate the risk of future calamities. To protect vegetation and avoid disasters, there is an immense need for adaptation and mitigation measures to deal with the region's fast-changing environment. The study urges local authorities to create climate-resilient governmental policies and supports regional sustainable development and vegetation restoration.

Machine learning models for predicting vegetation conditions in Mahanadi River basin.

The vegetation of a river basin is affected by various climate factors, such as precipitation and land surface temperature (LST). This study explores the best machine learning model for the prediction of normalized difference vegetation index (NDVI) with LST and precipitation as input parameters. The study also determines the correlation between NDVI, LST, and precipitation of the Mahanadi basin from 2003 to 2021. Monthly precipitation data was extracted from the Center for Hydrometeorology and Remote Sensing (CHRS) portal. The Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to derive the LST and NDVI using Google Earth Engine (GEE). Four different machine learning models were used to predict the NDVI of the Mahanadi basin: linear regression (LR), random forest (RF), support vector regression (SVR), and k-nearest neighbors (KNN). The coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and explained variance score (EVS) were calculated to evaluate the performance of the models. The results show that the RF model has the highest R2 value in both the training and testing sets among these models, indicating that it is the most optimal among these models for predicting NDVI. The SVR model has the lowest RMSE value in the training set, but the KNN model has the lowest RMSE value in the testing set. The results also show that there is a positive correlation between precipitation and NDVI, a negative correlation between precipitation and LST, and between NDVI and LST. This study provides insights into the relationship between NDVI, LST, and precipitation, and the best machine-learning model for predicting NDVI. The findings of this study can be used to improve the management of river basins and to predict the effects of climate change on vegetation.

Investigating the relationship between COVID-19 shutdown and land surface temperature on the Anatolian side of Istanbul using large architectural impermeable surfaces.

Artificial impermeable surfaces are becoming more prevalent, especially in urban areas, as a result of shifting land use and cover, roads, roofs, etc. The modification of land surface temperature (LST) can also be accomplished through artificially impermeable surfaces. Large artificial impermeable surfaces, such as rooftops, parking lots, and other areas of use, can be found in industrial zones, shopping malls, industrial airports, and other locations. For the Anatolian side of Istanbul, 14 Landsat 8 OLI/TIRS imagery images over the years 2016-2022 were investigated. To evaluate how well the study's images could be utilized, correlation and cosine similarity approaches were employed. A total of 12 images may be employed for research LST studies, it was discovered. We looked at closure dates during the COVID-19 epidemic to find out how human migration affected the LST. In addition, the LST value was estimated using the ordinary least squares (OLS) method employing LST and other biophysical indices. A decrease in LST values was seen as a result of the investigation. High levels of similarity and correlation were found between the images used. Results from the Google Mobility Index also provide support to the study. All of these facts provide support to Istanbul's Anatolian side experiencing lower surface temperature values, which consequently affects the city's massive structures.

ALS-linked FUS mutations dysregulate G-quadruplex-dependent liquid-liquid phase separation and liquid-to-solid transition.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the accumulation of protein aggregates in motor neurons. Recent discoveries of genetic mutations in ALS patients promoted research into the complex molecular mechanisms underlying ALS. FUS (fused in sarcoma) is a representative ALS-linked RNA-binding protein (RBP) that specifically recognizes G-quadruplex (G4)-DNA/RNAs. However, the effects of ALS-linked FUS mutations on the G4-RNA-binding activity and the phase behavior have never been investigated. Using the purified full-length FUS, we analyzed the molecular mechanisms of multidomain structures consisting of multiple functional modules that bind to G4. Here we succeeded to observe the liquid-liquid phase separation (LLPS) of FUS condensate formation and subsequent liquid-to-solid transition (LST) leading to the formation of FUS aggregates. This process was markedly promoted through FUS interaction with G4-RNA. To further investigate, we selected a total of eight representative ALS-linked FUS mutants within multidomain structures and purified these proteins. The regulation of G4-RNA-dependent LLPS and LST pathways was lost for all ALS-linked FUS mutants defective in G4-RNA recognition tested, supporting the essential role of G4-RNA in this process. Noteworthy, the P525L mutation that causes juvenile ALS exhibited the largest effect on both G4-RNA binding and FUS aggregation. The findings described herein could provide a clue to the hitherto undefined connection between protein aggregation and dysfunction of RBPs in the complex pathway of ALS pathogenesis.

Lelliottia steviae sp. nov. isolated from Stevia rebaudiana Bertoni.

A Gram-negative, aerobic, chemoheterotrophic, rod-shaped, and motile bacterium, designated as LST-1T, was isolated from wild Stevia rebaudiana Bertoni and subjected to a polyphasic taxonomic analysis. The LST-1 strain grew optimally at 37 °C and pH 6.0-7.0 in the presence of 0.5% (w/v) NaCl. Phylogenetic analysis based on the 16S rDNA sequence indicated that LST-1 is closely related to Lelliottia jeotgali PFL01T (99.85%), Lelliottia nimipressuralis LMG10245T (98.82%), and Lelliottia amnigena LMG2784T (98.54%). Multi-locus sequence typing of concatenated partial atpD, infB, gyrB, and rpoB genes was performed to improve the resolution, and clear distinctions between the closest related type strains were observed. The results of average nucleotide identify analyses and DNA-DNA hybridization with four species (16S rDNA similarity > 98.65%) were less than 90 and 40%, respectively, verifying the distinct characteristics from other species of Lelliottia. The cellular fatty acid profile of the strain consisted of C16:0, Summed Feature3, and Summed Feature8 (possibly 16:1 w6c/16:1 w7c and 18:1 w6c) as major components. The major polar lipids included phosphatidylethanolamine, phosphatidylglycerol, an aminophospholipid, three non-characteristic phospholipids, and a non-characteristic lipid. The genome of LST-1T was 4,611,055 bp in size, with a G + C content of 55.02%. The unique combination of several phenotypic, chemotaxonomic, and genomic characteristics proved that strain LST-1T belongs to a novel species, for which the name Lelliottia steviae sp. nov. is proposed. The type strain is LST-1T (= CGMCC 1.19175T = JCM 34938T).Repositories: The genbank accession numbers for the 16S rRNA gene and genome sequences of strain LST-1T are MZ497264 and CP063663, respectively.

Urban Greening Effect on Land Surface Temperature.

Urbanization has accelerated the conversion of vegetated land to built-up regions. The purpose of this study was to evaluate the effects of urban park configuration on the Land Surface Temperature of the park and adjacent areas. In urban parks, the study analyzed the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and the Land Surface Temperature (LST). The NDVI categorization process resulted in the development of a vegetation density distribution. The majority of Medan's urban areas were categorized as low density, as seen by their low NDVI values. The NDBI values were significantly higher in the majority of the area. This shows that the majority of places are experiencing a decline in vegetation cover. The density of vegetation varies according to the placement of park components such as trees, mixed plants, recreation, and sports areas. According to LST data, the temperature in the urban park was cooler than in the surrounding areas. Although the surrounding areas are densely populated, urban parks are dominated by trees. Additionally, there is a green space adjacent to the park, which is a green lane that runs alongside the main roadways.

Urban Warming of the Two Most Populated Cities in the Canadian Province of Alberta, and Its Influencing Factors.

Continuous urban expansion transforms the natural land cover into impervious surfaces across the world. It increases the city's thermal intensity that impacts the local climate, thus, warming the urban environment. Surface urban heat island (SUHI) is an indicator of quantifying such local urban warming. In this study, we quantified SUHI for the two most populated cities in Alberta, Canada, i.e., the city of Calgary and the city of Edmonton. We used the moderate resolution imaging spectroradiometer (MODIS) acquired land surface temperature (LST) to estimate the day and nighttime SUHI and its trends during 2001-2020. We also performed a correlation analysis between SUHI and selected seven influencing factors, such as urban expansion, population, precipitation, and four large-scale atmospheric oscillations, i.e., Sea Surface Temperature (SST), Pacific North America (PNA), Pacific Decadal Oscillation (PDO), and Arctic Oscillation (AO). Our results indicated a continuous increase in the annual day and nighttime SUHI values from 2001 to 2020 in both cities, with a higher magnitude found for Calgary. Moreover, the highest value of daytime SUHI was observed in July for both cities. While significant warming trends of SUHI were noticed in the annual daytime for the cities, only Calgary showed it in the annual nighttime. The monthly significant warming trends of SUHI showed an increasing pattern during daytime in June, July, August, and September in Calgary, and March and September in Edmonton. Here, only Calgary showed the nighttime significant warming trends in March, May, and August. Further, our correlation analysis indicated that population and built-up expansion were the main factors that influenced the SUHI in the cities during the study period. Moreover, SST indicated an acceptable relationship with SUHI in Edmonton only, while PDO, PNA, and AO did not show any relation in either of the two cities. We conclude that population, built-up size, and landscape pattern could better explain the variations of the SUHI intensity and trends. These findings may help to develop the adaptation and mitigating strategies in fighting the impact of SUHI and ensure a sustainable city environment.