Search

The rapid development of information and communications technology has turned individuals into sensors, fostering the growth of human-generated geospatial big data. In disaster management, geospatial big data, mainly social media data, have opened new avenues for observing human responses to disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. However, less than 1% of users geotag their messages, leaving geolocating users through user profiles or message content addresses increasingly crucial. This paper evaluates and visualizes the margin of error incurred when using user profiles or message-mentioned addresses to geolocate social media data for disaster research. Using Twitter data during the 2017 Hurricane Harvey as an example, this research assessed the inconsistencies in predicting users' locations in various administrative units during each disaster phase using three geolocating strategies. The results reveal that the similarities between geotags, and user profile locations decrease from 94.07% to 64.56%, 43.9%, 31.82%, 27.05%, and 26.7% as the geographical scale changes from country to state, county, block group, 1-kilometer, and 30-meter levels. These similarities are overall higher than the agreements between locations derived from geotags and tweet content. The geolocation consistencies among the three methods remain stable across disaster phases. The impacts of uncertainties in geolocating Twitter data for disaster management applications were further unraveled. The findings offer valuable insights into the trade-off between spatial scale and geolocation accuracy and inform the selection of appropriate scales when applying different geolocating strategies in future social media-based investigations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic that emerged in 2020 has caused significant health crises worldwide. Countries and regions around the globe have practiced different policies to contain the pandemic. One commonly adopted strategy is the Zero-COVID policy, comprising two phases: an initial suppression phase, typically enforced through lockdowns, followed by a sustained containment phase. However, sudden and strict lockdown policies in the suppression phase would inevitably bring unexpected challenges to vulnerable populations. There is a need to identify the emergent needs of underprepared, vulnerable populations under lockdowns to inform future pandemic management. In this study, the messages posted on an online mutual help-requesting platform during the 2022 Shanghai lockdown were leveraged as the near real-time agent to represent the side effects of the lockdown policy. This work explores the spatiotemporal correlation of the posts to their potential influencing factors and mines knowledge from the textual content of the posts. The results indicate that the help requests were clustered in downtown Shanghai. The access to profuse medical resources and public services may be hindered in districts with more stringent lockdown policies. The help requests' content unveils a need for medication and groceries under lockdown. It also underscores that the elderly population was affected most by the lockdown. Based on these insights, policymakers can better anticipate and address the urgent residential needs that may arise in future lockdowns. This knowledge can contribute to more effective planning and preparedness efforts to mitigate the impact on vulnerable populations and ensure their well-being during similar crisis situations. [ABSTRACT FROM AUTHOR]

This paper investigates the chemical composition of Particulate Matter, Organic Carbon (OC), and Elemental Carbon (EC) in residential and traffic sites in Mumbai. The average PM2.5 and PM10 concentrations at the traffic site (Sakinaka) were 240 µg/m3 and 424 µg/m3, respectively. The observed levels of OC were 35 µg/m3, 22 µg/m3, and 15.5 µg/m3 at Sakinaka junction (high-density traffic), YP-Gate (low-density traffic), and Hostel Premise (Residential), respectively. The average OC/EC ratio value was high (4.5) at the residential site, indicating contributions from stationary combustion sources and secondary production of carbonaceous species to OC. The residential site has a higher percentage of low volatile OC fraction (57%) in total OC than the traffic sites. On the other hand, Sakinaka has a higher percentage of highly volatile OC fractions (36%) in total OC. The crustal-originated metals were dominating in all areas, but the concentration of metals from anthropogenic sources was highest at Sakinaka, i.e., As (381 ng/m3), Pb (352 ng/m3), Zn (679 ng/m3). The K/Al, Ca/Al, Mg/Al, and Fe/Al ratios were high in all the samples compared to the crustal ratio indicating biomass burning and traffic emission sources of these metals. PM originating from traffic was more enriched with heavy metals that are toxic to human health, increasing cancer risks (CR) through inhalation. The hazard quotient was above 1 at all the locations, and CR was above 1 × 10− 4, causing health risks. According to the dosimetry model, more PM was deposited in the lungs of traffic location occupants through inhalation, increasing the cancerous risk. [ABSTRACT FROM AUTHOR]

The Covid-19 has presented an unprecedented challenge to public health worldwide. However, residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts. This study analyzed the global Twitter data from January 1st to June 30th, 2020, to answer two research questions. What are the linguistic and geographical disparities of public awareness in the Covid-19 outbreak period reflected on social media? Does significant association exist between the changing Covid-19 awareness and the pandemic outbreak? We established a Twitter data mining framework calculating the Ratio index to quantify and track awareness. The lag correlations between awareness and health impacts were examined at global and country levels. Results show that users presenting the highest Covid-19 awareness were mainly those tweeting in the official languages of India and Bangladesh. Asian countries showed more disparities in awareness than European countries, and awareness in Eastern Europe was higher than in central Europe. Finally, the Ratio index had high correlations with global mortality rate, global case fatality ratio, and country-level mortality rate, with 21-31, 35-42, and 13–18 leading days, respectively. This study yields timely insights into social media use in understanding human behaviors for public health research. [ABSTRACT FROM AUTHOR]

As the usage of GPS-enabled portable devices has become an indispensable part of people's daily life, big usergenerated geospatial data collected by apps on those devices, e.g., location-based social media, provide a new avenue to observe human behaviours and reveal socioeconomic characteristics. These geospatial big data, referred to as social sensing data play an increasingly important role in understanding human-space-environment interactions, which can be applied in different fields, such as crisis management, disaster relief, political sway, religious and economic trends, and most recently, combating the Covid-19 crisis. To understand and visualize the raw data obtained, these are attached to geographical factors like location of origin, user details, subject of such information - to evaluate and glean information. Most social sensing research determines the location of each record through enabled GPS (reported by the device), address mentioned in the body of the data, or the user-filled profile location. For procuring this type of data from social media sites, many researchers turn to Twitter, given its large and ever-growing user base compared to other platforms as the general populace indulges a lot in social media. Recently in 2019, however, due to less and less usage of the precise geotagging feature, the popular social media site like Twitter has decided to call off the feature to make for a more effortless experience for the users while protecting their privacy. In the absence of precise geotagged tweets, the next approach to geolocate the information is to check if there is any kind of addresses mentioned in the tweet or use the user profile location as the origin of the tweet - leading to some algorithmic uncertainties in analysing those data, producing biased or even misleading results. Hence, it needs to be known how accurate it is to replace the precise geotags with the user profile location in text format. The purpose of this study is to quantify the accuracy of such geocoding method-to check if using user profile locations to locate the tweet is accurate - based on different cartographic boundaries, such as country, state, county, and examine if the geocoding uncertainty would exacerbate the modifiable area unit problem (MAUP). This research studies the tweets during Hurricane Harvey, the most devastating coastal hazard in the U.S.'s history, as there were a lot of geotagged tweets during its occurrence, providing a larger sample space to obtain more robust results. There are three objectives of this study: (1) to obtain Twitter data relevant to Hurricane Harvey and process it for geotagged tweets, alongside developing a visualization framework for accuracy discrepancies at multiple spatial-temporal scales; (2) to visualize the distribution of error distance for country boundaries from the geotagged location to the user profile location; (3) to visualize percentage agreement of locations of these two locators based on smaller administrative boundaries (e.g., state, counties). A total of 47 million tweets were purchased, which were already filtered by disaster-related keyword-based search. Each collected tweet was checked for geotags (500k tweets) and only those having it were kept. These were then geocoded to a location using Google geocoding Application Programming Interface (API). The error distance between the geotags and the geocoded user location was calculated using the Haversine formula. The error distances were visualized per country if the number of tweets was sufficient to avoid the small number problem. Focusing on the USA, these tweets were then checked if both locators lied within same administrative boundaries to find the accuracy for studies geared to such (Figure 1). Results show that on a country level, all countries having considerable number of tweets showed similar patterns. Tweets are highly reliable to have its geotagged locations be replaced with user profile locations. As such, for brevity, the error distribution of locators in USA is presented with y-axis in logarithmic scale (Figure 2). It is clear that majority of the data lies in the first two bins - 0km (exact match) and 0 - 240 km, respectively (~27% of the total data each). For state and county levels, as Figure 3 shows, accuracy levels drop sharply. The state of Texas has the highest sample space since it is the prime affected zone and shows the highest accuracy. This goes on to show that closer to impact zones user locations would be interchangeable - however if one moves further away, the accuracies vary (Figure 3). The county level accuracies, however, severely drop and the distribution is lop-sided to below fifty percentage accuracy. This highlights a severe problem in interchanging the locators on a county level study or effort. For instance, highly time sensitive efforts like rescue operations or disaster relief distribution operations would get displaced and would present a false image of severity if frameworks and tools for analysis are based on this. Findings from this project show that data from social media used during disasters can be used depending on the locational constraints and threshold limit of errors would be subject to each unique study type-while this study shows a keystone that can be referenced to and based upon while making such decisions. [ABSTRACT FROM AUTHOR]

Coastal areas serve as a vital interface between the land and sea or ocean and host about 40% of the world's population, providing significant social, economic, and ecological functions. Meanwhile, the sea-level rise caused by climate change, along with coastal erosion and accretion, alters coastal landscapes profoundly, threatening coastal sustainability. For instance, the Mississippi River Delta in Louisiana is one of the most vulnerable coastal areas. It faces severe long-term land loss that has disrupted the regional ecosystem balance during the past few decades. There is an urgent need to understand the land loss mechanism in coastal Louisiana and identify areas prone to land loss in the future. This study modeled the current and predicted the future land loss and identified natural–human variables in the Louisiana Coastal Zone (LCZ) using remote sensing and machine-learning approaches. First, we analyzed the temporal and spatial land loss patterns from 2001 to 2016 in the study area. Second, logistic regression, extreme gradient boosting (XGBoost), and random forest models with 15 human and natural variables were carried out during each five-year and the fifteen-year period to delineate the short- and long-term land loss mechanisms. Finally, we simulated the land-loss probability in 2031 using the optimal model. The results indicate that land loss patterns in different parts change through time at an overall decelerating speed. The oil and gas well density and subsidence rate were the most significant land loss drivers during 2001–2016. The simulation shows that a total area of 180 km2 of land has over a 50% probability of turning to water from 2016 to 2031. This research offers valuable information for decision-makers and local communities to prepare for future land cover changes, reduce potential risks, and efficiently manage the land restoration in coastal Louisiana. [ABSTRACT FROM AUTHOR]

Abstract: Three steroidal saponins, racemosides A (1), B (2) and C (3), were isolated from the methanolic extract of the fruits of Asparagus racemosus, and characterized as (25S)-5β-spirostan-3β-ol-3-O-{β-D- glucopyranosyl (1→6)-[α-l-rhamnopyranosyl (1→6)-β-d-glucopyranosyl (1→4)]-β-d-glucopyranoside}, (25S)-5β-spirostan-3β-ol-3-O-α-l-rhamnopyranosyl (1→6)-β-d-glucopyranosyl (1→6)-β-d-glucopyranoside and (25S)-5β-spirostan-3β-ol-3-O-{α-l-rhamnopyranosyl-(1→6)-[α-l-rhamnopyranosyl (1→4)]-β-d-glucopyranoside}, respectively, by spectrometric analysis and some chemical strategies. [Copyright &y& Elsevier]

Bassic acid, an unsaturated triterpene acid isolated from Mimusops elangii , was tested for its antileishmanial properties both in vitro and in vivo . The in vitro antileishmanial activity of bassic acid being encouraging, its activity in vivo was evaluated in hamster models of visceral leishmaniasis, both in free form, as well as incorporated in two different delivery systems, viz microemulsions and polylactide nanoparticles. The delivery systems were prepared by published protocols. The percentage intercalation of bassic acid in nanoparticles and microemulsion was found to be about 50 and 100, respectively, when determined at its absorption maxima (λ max ) 285 nm (ε m = 2.3 × 10 2 M - 1 cm - 1 ). At an equivalent dose of 2 mg kg - 1 body weight, when injected subcutaneously for a total of six doses in 15 days, bassic acid was found to reduce spleen parasite loads by 45, 62 and 78% in free, microemulsion-incorporated and nanoparticle-incorporated forms, respectively. A comparison of specific biochemical tests related to normal liver and kidney functions revealed that the nanoparticulate form was successful in significantly reducing the hepatotoxicity and nephrotoxicity of the free drug, but the microemulsion delivery system was less effective and toxic to liver and kidney to some extent. Confocal microscopic images of Leishmania donovani promastigotes treated with bassic acid revealed that the drug induced necrotic cell death due to non-specific membrane damage. Because of its high efficacy as well as non-hepatotoxicity and non-nephrotoxicity, the nanoparticulate form of bassic acid may be considered for clinical application in humans rather than the microemulsion incorporated form. [ABSTRACT FROM AUTHOR]

Abstract: Bioguided-fractionation of the methanol extract of the leaves of Careya arborea led to isolation of a triterpenoid saponin, designated arborenin, and characterized as 3-O-β-d-glucopyranosyl(1→2)-β-d-glucopyranosyl-2α,3β-dihydroxy-taraxast-20-en-28-oic acid (1), together with desacylescin III (2). The structures were determined on the basis of extensive 2D NMR spectroscopic analysis. The saponin showed in vitro antileishmanial activity against Leishmania donovani (strain AG 83). [Copyright &y& Elsevier]

The COVID-19 pandemic has dramatically changed human daily life. To mitigate the pandemic’s impacts, different countries and regions implemented various policies to contain COVID-19 and residents showed diverse responses. These human responses in turn shaped the uneven spatial-temporal spread of COVID-19. Such human-pandemic interaction is complex, dynamic, and interconnected. Delineating the reciprocal effects between human society and pandemics is crucial for preparing for and managing future epidemics. Geospatial big data acquired through mobile applications and sensor networks have facilitated near-real-time tracking and assessment of human responses to the pandemic, enabling a surge in researching human-pandemic interactions. However, these investigations involve inconsistent data sources, human activity indicators, relationship detection models, and analysis methods, leading to a fragmented understanding of human-pandemic dynamics. To assess the current state of human-pandemic interactions research using geospatial big data, we conducted a synthesis study based on 67 selected publications between 25 March 2020, and 9 January 2023. We extracted information from each article across six categories, i.e. publication details, research context, research area and time, data, methodological framework, and results and conclusions. Results reveal that the influence of stay-at-home policies on mobility decrease varied regionally, showing limited effectiveness in Europe compared to the US. The positive correlations between human mobility and COVID-19 case rates evolved through time and were highest in the initial outbreak in 2020. Public awareness generally peaked prior to the peaks in COVID-19 cases, with varying intervals of 0 to 19.8 days observed across different countries. This study summarizes the research characteristics of selected articles and highlights the need for future research to spatially and temporally model the long-term, bidirectional causal relationships within human-pandemic systems to inform evidence-based, hyperlocal pandemic mitigation strategies. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: Aqueous decoction of Chenopodium album seeds (CAD) was assessed for its sperm-immobilizing and contraceptive efficacy in laboratory mammals. Method: Spermicidal efficacy was evaluated in vitro by a modified Sander–Cramer test. The mode of spermicidal action was assessed by (a) supravital and double fluoroprobe staining of sperm, (b) hypoosmotic swelling tests and (c) transmission electron microscopy. Contraceptive efficacy was evaluated by intrauterine and vaginal application of CAD in rats and rabbits, respectively, followed by their mating and evaluation of pregnancy outcomes. Results: The minimum effective concentration of CAD that induced instantaneous immobilization of rat spermatozoa in vitro was 2 mg/mL. The mechanism of CAD action involved disintegration of sperm plasma membrane and dissolution of acrosomal cap causing sperm death. Fertilization of oocytes and establishment of implantation were prevented in the uterine horn that was administered with CAD, while these events occurred unhindered in the untreated contralateral side. In rabbit, intravaginal application of CAD significantly blocked the establishment of pregnancy. Conclusion: CAD possesses appreciable spermicidal potential, which may be explored as an effector constituent of vaginal contraceptive. [Copyright &y& Elsevier]