Areas Of High Density Research Articles

Evidence of repeated zoonotic pathogen spillover events at ecological boundaries

Anthropogenic modifications to the landscape have altered several ecological processes worldwide, creating new ecological boundaries at the human/wildlife interface. Outbreaks of zoonotic pathogens often occur at these ecological boundaries, but the mechanisms behind new emergences remain drastically understudied. Here, we test for the influence of two types of ecosystem boundaries on spillover risk: (1) biotic transition zones such as species range edges and transitions between ecoregions and (2) land use transition zones where wild landscapes occur in close proximity to heavily impacted areas of high human population density. Using ebolavirus as a model system and an ensemble machine learning modeling framework, we investigated the role of likely reservoir (bats) and accidental host (primates) range edges and patterns of land use (defined using SEDAC categories) on past spillover events. Our results show that overlapping species range edges and heightened habitat diversity increase ebolavirus outbreaks risk. Moreover, we show that gradual transition zones, represent by high proportion of rangelands, acts as a buffer to reduces outbreak risks. With increasing landscape changes worldwide, we provide novel ecological and evolutionary insights into our understanding of zoonotic pathogen emergence and highlight the risk of aggressively developing ecological boundaries.

Brainstem hemorrhage associated with venous hypertensive myelopathy without dural arteriovenous fistula: illustrative case.

Venous hypertensive myelopathy (VHM), formerly known as "Foix-Alajouanine syndrome," is a spinal cord dysfunction caused by impaired perfusion of the spinal cord. Most cases are attributed to spinal dural arteriovenous fistulas (dAVFs), but there are scattered reports of VHM without concomitant dAVF. Furthermore, no cases of VHM associated with intracranial hemorrhage exist. A 68-year-old man with a history of hypertension presented with a sudden headache, proximal paresis of the left upper extremity, impaired pain and temperature sensation in the right upper extremity, dysphagia, and dysarthria. Computed tomography scans showed intraparenchymal hemorrhage in the left medulla oblongata and a linear, continuous high-density area extending from the medulla oblongata to the cervical spinal cord. Magnetic resonance images showed cervical spondylosis at the C5-6 and C6-7 levels, with high signal intensity changes from the medulla oblongata to the lower cervical cord on T2-weighted images. Cerebral angiography showed no abnormal vessels. Conservative treatment gradually improved symptoms and the high signal intensity areas. This case highlights intracranial hemorrhage occurring from extracranial causes and the possibility of VHM due to cervical spondylosis. When hemorrhagic lesions of the craniovertebral junction or spinal parenchymal lesions are encountered, the underlying pathology should be investigated thoroughly and systematically. https://thejns.org/doi/10.3171/CASE24441.

Nanocellulose and its modified forms in the food industry: Applications, safety, and regulatory perspectives.

Nanocellulose (NC), known for its unique properties including high mechanical strength, low density, and extensive surface area, presents significant potential for broad application in the food sector. Through further modification, NC can be enhanced and adapted for various purposes. Applications in the food industry include stabilizing, encapsulating, and packaging material. Additionally, due to its unique characteristics during digestion in the gastrointestinal tract, NC and its derivatives exhibit the potential to be used as health-promotion food ingredients. However, while the safety data on unmodified NC is readily available, the safety of modified forms of NC for use in food remains uncertain. This review offers a comprehensive analysis of recent breakthroughs in NC and its derivatives for innovative food applications. It synthesizes existing research on safety evaluations, with a particular emphasis on the latest findings on toxicity and biocompatibility. Furthermore, the paper outlines the regulatory landscape for NC-based food ingredients and food contact materials in the United States and European Union and provides recommendations to expedite regulatory authorization and commercialization. Ultimately, this work offers valuable insights to promote the sustainable and innovative application of NC compounds in the food sector.

Rethinking residential building design in high-density cities for enhancing pandemic resilience: Balancing importance and practicality

The relationship between the built environment and epidemic transmission is particularly pronounced within the context of lockdown policies and high-density urban areas. Non-pharmaceutical interventions have become increasingly important during and after the COVID-19 pandemic. This study proposes an evaluation index system for pandemic-resilient residential building designs in high-density cities, covering the entire design process including site planning, architecture design, building equipment and management. The system includes 64 design strategies, categorized into 15 sub-categories and 4 categories. Then, we evaluate the preference of each strategy by synthesizing the dimensions of importance and implementation difficulty. A questionnaire survey was conducted among experts who have experience with pandemic-resilient designs in high-density cities. Fuzzy synthetic evaluation analysis was implemented along with the “preference index” concept to identify the most important and practical interventions during the pandemic outbreak. The results indicate that “service and management” is the category that requires priority, followed by “property management,” “common areas” and “building morphology” in sub-categories. Among the design strategies, “waste receptacle,” “publicity and education,” “floor drain and water seal” and “environmental cleaning and disinfection” are the top four recommended strategies. This study provides policymakers and practitioners with effective and easily implementable prevention measures during an outbreak of respiratory tract infections. It also offers specific policy recommendations and application suggestions for new and existing buildings, contributing to creating a safer and healthier living environment

Role of Charge Density and Surface Area of Tailored Ionic Porous Organic Polymers for Adsorption and Antibacterial Actions.

The development of high-performance adsorbents for environmental remediation is a current need, and ionic porous organic polymers (iPOPs), due to their high physicochemical stability, high surface area, added electrostatic interaction, and easy reusability, have already established themselves as a better adsorbent. However, research on the structural design of high-performance iPOP-based adsorbents is still nascent. This study explored the building blocks' role in optimizing the polymers' charge density and surface area to develop better polymeric adsorbents. Among the three synthesized polymers, iPOP-ZN1, owing to its high surface area and high charge density in its active sites, proved to be the best adsorbent for adsorbing inorganic and organic pollutants in an aqueous medium. The polymers were efficient enough to capture and store iodine vapor in the solid state. Further, this study tried to address using iodine-loaded polymers in antibacterial action. Iodine-loaded iPOPs show impressive antibacterial behavior against E. coli, B. subtilis, and H. pylori.

Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.

Will new tram lines enhance the connectivity and walkability? A case study of Sakarya

<p>This study investigates the impact of planned tram lines on walkability and accessibility in Sakarya, utilizing Geographic Information Systems (GIS) for spatial analysis. The research evaluates the catchment areas of proposed tram stops, assessing their suitability and coverage, and examines how these areas integrate with existing public transport routes. By calculating time-oriented catchments for 5, 8, and 10-minute walking distances, the study identifies service gaps and redundancies in the public transport network. The findings reveal that the new tram lines will enhance accessibility by extending coverage to high-density areas and potentially reducing the demand on existing bus services. The analysis also highlights the overlap between bus transit lines and tram stops, suggesting optimizations to improve network efficiency. The results offer valuable insights for urban planners, aiming to optimize Sakarya’s public transport system to be more inclusive and sustainable. This study contributes to urban mobility planning by providing a detailed understanding of how new tram lines can improve connectivity and support sustainable urban development.</p>

Phenolic Ecotoxins in River Water: Survey on Knowledge and Practices of Road-Side Food Vendors Operating Along River Chania Bank Fringes in Thika Sub- County, Kenya

Purpose: Phenolic ecotoxins consumed in raw surface water from river sources has been implicated in causing diseases of high burden such as cancers, in Kenya. This study was conducted in Thika Sub County, and data collected from roadside food vendors and food hawkers who operate small scale street food business along the fringes and banks of River Chania. The study’s objective was to establish the knowledge on Phenolic ecotoxins present in river water and practices of the study respondents. Methodology: A crossectional descriptive study design was used, in which combined interviews and questionnaire enquiries conducted from, n=341, small-scale road side food vendors and hawkers was analyzed. Findings: Data reveals that none of the respondents had knowledge on Phenolic ecotoxin contaminants and additionally a strongly associated correlation (P<0.001, χ222.6 and df 1), further augments the disclosure that, poor knowledge of raw river water safety and hygiene does result to body harm and ill health. Unique Contribution to Theory, Policy and Practice: There is increased need and effort to scale up the sensitization and awareness on exposure to harmful ecotoxin contaminants from the use of raw river water. Water decontamination mobile units should be considered and introduced especially in urban areas of high population density, transversed by rivers.

Decentralized Multi-Agent Search for Moving Targets Using Road Network Gaussian Process Regressions

Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates.

Extracting Meso- and Microscale Patterns of Urban Morphology Evolution: Evidence from Binhai New Area of Tianjin, China

Understanding and recognizing urban morphology evolution is a crucial issue in urban planning, with extensive research dedicated to detecting the extent of urban expansion. However, as urban development patterns shift from incremental expansion to stock optimization, related studies on meso- and microscale urban morphology evolution face limitations such as insufficient spatiotemporal data granularity, poor generalizability, and inability to extract internal evolution patterns. This study employs deep learning and meso-/microscopic urban form indicators to develop a generic framework for extracting and describing the evolution of meso-/microscale urban morphology. The framework includes three steps: constructing specific urban morphology datasets, semantic segmentation to extract urban form, and mapping urban form evolution using the Tile-based Urban Change (TUC) classification system. We applied this framework to conduct a combined quantitative and qualitative analysis of the internal urban morphology evolution of Binhai New Area from 2009 to 2022, with detailed visualizations of morphology evolution at each time point. The study identified that different locations in the area exhibited seven distinct evolution patterns: edge areal expansion, preservation of developmental potential, industrial land development pattern, rapid comprehensive demolition and construction pattern, linear development pattern, mixed evolution, and stable evolution. The results indicate that in the stock development phase, high-density urban areas exhibit multidimensional development characteristics by region, period, and function. Our work demonstrates the potential of using deep learning and grid classification indicators to study meso-/microscale urban morphology evolution, providing a scalable, cost-effective, quantitative, and portable approach for historical urban morphology understanding.

Patterns of animal rabies in the Nizhny Novgorod region of Russia (2012-2022): the analysis of risk factors.

Animal rabies is a viral disease that poses a significant threat to domestic and wild animal populations, with devastating consequences for animal health and human life. Understanding and assessing the risk factors associated with the transmission and persistence of the rabies virus in wild and domestic animal populations is crucial for developing effective strategies to control and mitigate cases. Studies of the spatial and temporal distribution of rabies cases in the Nizhny Novgorod region during 2012-2022 provided epidemiological evidence of the circulation of infection between animals in the presence of vaccination. Among the wild animals in the area, red foxes play a major role in the spread of rabies, accounting for 96.4% of all wild animal cases. We used spatiotemporal cluster analysis and a negative binomial regression algorithm to study the relationships between animal rabies burden by municipality and a series of environmental and sociodemographic factors. The spatiotemporal cluster analysis suggests the concentration of wild animal rabies cases in the areas of high fox population density and insufficient vaccination rates. The regression models showed satisfactory performance in explaining the observed distribution of rabies in different animals (R 2 = 0.71, 0.76, and 0.79 in the models for wild, domestic and all animals respectively), with rabies vaccination coverage and fox population density being among the main risk factors. We believe that this study can provide valuable information for a better understanding of the geographical and temporal patterns of rabies distribution in different animal species, and will provide a basis for the development of density-dependent planning of vaccination campaigns.

Impact of COVID-19 Pandemic on Airbnb Listings in New York City: Challenges and Opportunities for Urban Housing Sustainability

Short-term rental (STR) platforms like Airbnb have significantly impacted urban housing sustainability, particularly in cities like New York City. The COVID-19 pandemic disrupted the STR market, raising questions about its resilience and effects on sustainable urban housing. This study addresses the following research questions: (1) How did unit and neighborhood characteristics influence the survival of Airbnb listings during the pandemic? (2) What changes occurred in the factors determining the emergence of new listings during the pandemic? Using data from Inside Airbnb, we applied Cox proportional hazard models and negative binomial regression to analyze changes before and after the pandemic. We found that during the pandemic, price discounts became crucial for listing survival, while traditional quality indicators like superhost status and high ratings lost significance. The importance of subway accessibility decreased, reflecting shifts in traveler preferences. Additionally, new listings were less likely to emerge in high-density Airbnb areas and more likely in neighborhoods with higher crime rates. These findings highlight the need for sustainable regulatory approaches that balance the benefits of STR platforms with protecting housing affordability and community well-being. Our study provides insights for policymakers aiming to promote sustainable urban housing during global crises.

Vertical Plant Configuration: Its Impact on Microclimate and Thermal Comfort in Urban Small Green Spaces

The presence of small green spaces throughout high-density urban areas is essential for enhancing thermal comfort and combating the urban heat island effect. Extensive research has been conducted on plants, which play a key role in cooling green spaces. However, the majority of research focuses on trees, overlooking the efficiency of vertical plant configurations, especially shrubs. Hence, this paper selected a typical small green space in Tokyo’s high-density urban area and employed scenario simulation to examine how various vertical plant configurations impact the microclimate and thermal comfort of small green spaces, with a particular emphasis on quantifying the efficiency of shrubs. The research primarily discovered four findings. First, trees significantly outperform shrubs in improving thermal comfort, especially during periods of strong sunlight during the day, leading to a direct modification of the thermal comfort levels. Second, while shrubs (height < 2 m) usually do not make a substantial difference in the overall thermal comfort levels of small green spaces, they are effective in reducing areas classified as “hot” and “very hot” during the day, with their cooling effect becoming more pronounced in the late afternoon. Third, when small green spaces combine trees and shrubs in their plant configuration, taller shrubs (within 0–2 m) exhibit a more pronounced cooling effect and improvement in thermal comfort. The conclusions of this study could offer guidance for the planning, design, and renovation of small green spaces in high-density urban areas.

Analysis of the spatial characteristics and driving forces of underground consumer service space in Chinese megacities based on multi-source data

Underground consumer service spaces (UCSS) offer new solutions for urban residents’ daily needs, but existing studies on their distribution and driving forces are often fragmented and overshadowed by research on other underground spaces, lacking targeted analysis. This study examines UCSS in the central urban areas of seven representative Chinese megacities. Using spatial analysis methods like kernel density estimation, multi-distance spatial clustering, and geographical detectors, the spatial characteristics and driving forces of UCSS are analyzed alongside aboveground consumer service spaces (ACSS). Results show that both ACSS and UCSS exhibit multi-centered, concentric spatial patterns, though UCSS demonstrates higher spatial aggregation. Unlike other underground public spaces (UPS), UCSS relies more on service industry agglomeration and market factors, while other UPS are more influenced by surrounding development intensity. UCSS follows the core principles of central place theory but deviates from the market-driven patterns typical of ACSS. Socioeconomic conditions and transportation infrastructure form the foundational basis for UCSS distribution, while service industry agglomeration, market dependence, and land development intensity exert more direct influence. The commercial atmosphere and existing underground space development play critical roles in UCSS distribution. Two key spatial scales for understanding UCSS distribution are the strong influence zones of shopping malls and metro stations, and high-density urban areas.

Exploring the LULC dynamics and its relation with land surface temperature variation using split window algorithm: A study of Barasat subdivision, West Bengal, India.

Rapid urban growth in an unplanned way, decreased forest cover and consequential changes in land use in urban and rural areas have become pivotal obstacles for policymakers and urban planners to a great extent. Specifically, in a developing country like India, the repercussions of these changes have generated a substantial alteration in climate in the form of increased land surface temperature (LST), heat islands and associated human health risks. This research was initiated to assess the interrelationship between LST and LULC dynamics using spatial techniques over the Barasat Sub-Division in West Bengal (India). This study encompasses the extraction of LST using satellite images, seasonal variations of LST, the relationship of different land indices (i.e. NDVI, NDBI, NDMI and NDWI) with LST, and hotspot analysis of the landscape to get a comprehensive understanding of LST changes from 1988 to 2023, seasonal patterns, and relationships with LULC dynamics in the study area. The results of this research signify a substantial percentage increase in built-up spaces (51%), a decline in fallow areas (- 73%), relatively higher LST changes (3.41°C) in high-density built-up areas (e.g. Madhyamgram), a positive relationship (e.g. r2 = 0.59) between NDBI and LST, a transition from a cold spot to a hot spot share of 20% and a persisting hot spot of 42% throughout the summer season (1988 to 2023) in the selected landscape. The findings of this study will improve our present knowledge of seasonal fluctuations in land surface temperature (LST) and the variables that influence them. These findings will also draw attention to the growing worries about the fast variations in LST and the climatic and health hazards they pose. It is advised to establish planned urban expansion, efficient land use and land cover (LULC) management laws and encourage green areas that support sustainable development in India's urban surroundings in order to reduce these dangers.

Low cost and real-time surveillance of enteric infection and diarrhoeal disease using rapid diagnostic tests in Cox’s Bazar, Bangladesh

BackgroundReal-time disease surveillance is an important component of infection control in at-risk populations. However, data on cases or from lab testing is often not available in many low-resource settings. Rapid diagnostic tests (RDT), including immunochromatographic assays, may provide a low cost, expedited source of infection data.MethodsWe conducted a pilot survey-based prevalence mapping study of enteric infection in Camp 24 of the camps for the forcibly displaced Rohingya population from Myanmar in Cox’s Bazar, Bangladesh. We randomly sampled the population and collected and tested stool from under-fives for eight pathogens using RDTs in January–March 2021 and September–October 2021. A Bayesian geospatial statistical model allowing for imperfect sensitivity and specificity of the tests was adapted.ResultsWe collected and tested 396 and 181 stools in the two data collection rounds. Corrected prevalence estimates ranged from 0.5% (Norovirus) to 27.4% (Giardia). Prevalence of Escherichia coli O157, Campylobacter, and Cryptosporidium were predicted to be higher in the high density area of the camp with relatively high probability (70–95%), while Adenovirus, Norovirus, and Rotavirus were lower in the areas with high water chlorination. Clustering of cases of Giardia and Shigella was also observed, although associated with relatively high uncertainty.ConclusionsWith an appropriate correction for diagnostic performance RDTs can be used to generate reliable prevalence estimates, maps, and well-calibrated uncertainty estimates at a significantly lower cost than lab-based studies, providing a useful approach for disease surveillance in these settings.

Assessing the efficiency of urban blue-green space in carbon-saving: Take a high-density urban area in a cold region as an example

Urban blue-green space responds to climate change by providing relatively cooler surrounding environments to indirectly reduce residents' energy consumption and directly adsorb CO2 from the air. However, the carbon-saving potential of urban blue-green space at the urban scale needs to be addressed in the literature. In this study, an improved ideal quantitative model of carbon-saving is constructed from the user's perspective, considering the cooling effects of urban blue-green space and integrating surface temperature, building information, and urban public space layout. A high-density urban area in a northern cold region of China (Tianjin center urban area) is taken as an example to estimate the annual carbon-saving by performing Landsat surface temperature inversions with the delineation of the effective cooling area, as well as regression analyses based on four seasons of data. The results show that the annual carbon-saving in 2021 is about 78 tonneC/km2, equivalent to 2.21 times the carbon sequestration of the same region. Compared with the edge of the center urban area, the urban blue-green space in the center has a carbon-saving with a 1.4 times higher fluctuation value and a cumulative annual difference of about 394 tonneC/km2. Large blue-green patches in the cold region, while capable of conserving more carbon in spring, summer, and fall, become carbon-source in winter due to increased heating energy consumption and reduced willingness of residents to adopt zero-carbon transportation. The indicators that have the most significant impact on the carbon-saving of urban blue-green space are the cooling area and the background surface temperature. The urban blue-green space coverage and the percentage of impervious surfaces, such as buildings and open spaces, will indirectly impact carbon-saving by affecting the cooling area. The above finding recognizes the carbon-saving potential of urban blue-green space in cold regions. It provides a feasible estimation scheme for comparing the carbon-saving capacity in different seasons.

Distributions of Fecal Indicators at Aquaculture Areas in a Bay of Republic of Korea.

Aquaculture products, such as clams, scallops, and oysters, are major vectors of fecal-derived pathogens. Male-specific and somatic coliphages are strongly correlated with human noroviruses, the major enteric viruses worldwide. Geographic information system with local land-use patterns can also provide valuable information for tracking sources of fecal-derived pathogens. We examined distributions of four fecal indicator microorganisms, i.e., male-specific and somatic coliphage, total coliform, and Escherichia coli (E. coli) in three river and seawater sampling sites located on the coast of Gomso Bay in the Republic of Korea during the sampling period (from March 2015 to January 2016). Geospatial analyses of fecal indicators and correlation between environmental parameters and fecal indicators or among fecal indicators were also performed. Overall, river water samples showed highest concentrations of both types of coliphage in summer (July 2015). High concentrations of both total coliform and E. coli were detected in river water during the period from July to September 2015. High concentrations of all fecal indicators were found at site GL02, located in the innermost part of Gomso Bay, which has high-density agriculture and residential areas. Environmental factors related to precipitation-cumulative precipitation on and from 3 days before the sampling day (Prep-0 and Prep-3, respectively)-and salinity were strongly correlated with concentrations of all fecal indicators. The present results suggest that investigations of multiple fecal indicators with a systemic geospatial information are necessary for precisely tracking fecal contaminations of aquaculture products.

Prevalence, Radiological, and Pathological Findings of Ossification and Calcification of the Lumbar Spinal Ligamentum Flavum: A Comparative Study with Thoracic Lesions.

This study aimed to determine the prevalence of lumbar ligamentum flavum lesions and identify correlations between radiological and pathological findings. We conducted an observational cross-sectional study of 349 patients (lumbar: n = 296, thoracic: n = 39, lumbar and thoracic: n = 14, mean age: 69 ± 12 years, male: 74%) who underwent posterior surgery for thoracolumbar spinal canal stenosis between January 2008 and April 2023 at our hospital.Computed tomography (CT) revealed that the prevalence of ligamentum flavum lesions defined as a high-density area with a CT value of 200 Hounsfield Unit or higher in the lumbar and thoracic spine was 47% (147/310) and 85% (45/53), respectively. CT showed that most patients had radiologically suspected ossification in the lumbar (90%) and thoracic spine (98%) than radiologically suspected calcification. Lumbar lesions were thinner than the thoracic lesions (2.5 vs 3.7 mm, p < 0.01). Pathological examinations were performed in specimens collected from 34 cases (lumbar: n = 13, thoracic: n = 21), and ossification was found in 62% (8/13) and 95% (20/21) of lumbar and thoracic lesions (p = 0.02), respectively. Lastly, ossification was confirmed pathologically in 72% (8/11) and 95% (19/20) of lumbar and thoracic lesions that showed ossification on imaging (p = 0.13), respectively. The literature review revealed that the prevalence of the lumbar ligamentum flavum lesions varied from 1.5 to 35% and the patient population was mostly asymptomatic.Collectively, we found that the prevalence of lumbar ligamentum flavum lesions in symptomatic patients was greater than previously reported. Histologically confirmed ossification was less common in lumbar lesions than in thoracic lesions.

Hydrothermal Liquefaction of Microalgae to Bio-oil Using Zeolite Catalysts

Mesoporous zeolite silica-alumina catalysts were investigated for their impact on the hydrothermal liquefaction (HTL) to convert Nannochloropsis oculata into bio-oil. The commercial catalysts were characterized using characterization instruments which are XRD, SAP, and TPD-NH3 analysis, revealing distinctive structural and physicochemical properties. The catalytic screening was conducted at varied reaction temperatures (160–240 °C), durations (60–120 min), and catalyst loadings (1–5 wt.%). The result revealed that ZSM-5 demonstrates effectiveness as a catalyst for the HTL of Nannochloropsis oculata, yielding high bio-oil production under optimized conditions. Strong acid sites and high surface area density are highlighted as the key factors contributing to the catalyst's enhanced performance in promoting valuable bio-oil components.