Search Results (30)

The scarcity of information on agricultural development in the semi-arid region of Alagoas limits the spatial understanding of this activity. Government data are generally numerical and lack spatial detail. Remote sensing emerges as an efficient alternative, providing accessible visualization of agricultural areas. This study evaluates the performance of MapBiomas in monitoring agricultural areas in the semi-arid region of Alagoas, comparing it to a Random Forest model adjusted for the region using higher-resolution images. The first methodology is based on land use and land cover (LULC) data from MapBiomas, an initiative that provides information on land use and land cover in Brazil. The second method employs the Random Forest model, calibrated for the region’s dry season, addressing cloud cover issues and allowing for the identification of irrigated agriculture. LULC data were subjected to a precision analysis using 200 points generated within the study areas, extracting LULC information for each coordinate. These points were overlaid on high-resolution images to assess model accuracy. Additionally, field visits were conducted to validate the identification of agriculture. The irrigated area data from the Random Forest model were correlated with irrigation records from SEMARH. MapBiomas presented a Kappa index of 0.74, with precision exceeding 90% for classes such as forest, natural pasture, non-vegetated area, and water bodies. However, the agriculture class obtained an F1 score of 0.56. The Random Forest model achieved a Kappa index of 0.82, with an F1 score of 0.79 for agriculture. The correlation between the total annual irrigated area data from Random Forest and SEMARH records was high (R = 0.85). The Random Forest model yielded better results in classifying agriculture in the semi-arid region of Alagoas compared to MapBiomas. However, classification limitations were observed in lowland areas due to spectral confusion caused by soil moisture accumulation. Full article

Modern multi-tiered data storage systems optimize file access by managing data across a hybrid composition of caches and storage tiers while using policies whose decisions can severely impact the storage system’s performance. Recently, different Machine-Learning (ML) algorithms have been used to model access patterns from complex workloads. Yet, current approaches train their models offline in a batch-based approach, even though storage systems are processing a stream of file requests with dynamic workloads. In this manuscript, we advocate the streaming ML paradigm for modeling access patterns in multi-tiered storage systems as it introduces various advantages, including high efficiency, high accuracy, and high adaptability. Moreover, representative file access patterns, including temporal, spatial, length, and frequency patterns, are identified for individual files, directories, and file formats, and used as features. Streaming ML models are developed, trained, and tested on different file system traces for making two types of predictions: the next offset to be read in a file and the future file hotness. An extensive evaluation is performed with production traces provided by Huawei Technologies, showing that the models are practical, with low memory consumption (<1.3 MB) and low training delay (<1.8 ms per training instance), and can make accurate predictions online (0.98 F1 score and 0.07 MAE on average). Full article

Background: A large number of patients suffer from neuropathic pain, and systemic therapy often remains ineffective while inducing severe side effects. Topical therapy with the TRPV1-agonist capsaicin is an established alternative, and the identification of co-therapeutics that modulate TRPV1 may be a promising approach to reduce the dose of capsaicin while maintaining efficacy. Here, we aimed to determine if the antidepressant amitriptyline displays properties rendering it a potential co-therapeutic agent. Methods: We performed patch clamp and calcium imaging experiments on HEK293T cells expressing human (h) TRPV1 as well as on dorsal root ganglion (DRG) neurons from adult mice. Results: Amitriptyline induced an increase in intracellular calcium in both HEK293T and mouse DRG neurons expressing TRPV1. Patch clamp experiments revealed a concentration-dependent activation of hTRPV1 by amitriptyline that was also evident in cell-free inside-out patches. When hTRPV1 was fully activated by capsaicin, amitriptyline induced concentration-dependent and partly reversible current inhibition. In contrast, amitriptyline potentiated small responses to capsaicin, heat and protons. We also found that amitriptyline desensitized hTRPV1 to capsaicin. This effect was reduced by the intracellular application of the strong calcium chelator BAPTA. Furthermore, the non-desensitizing mutant hTRPV1-Y672K displayed a reduced amitriptyline-induced desensitization. Conclusions: Our data showed that amitriptyline can activate, sensitize, desensitize and even inhibit TRPV1. Together with its property as a strong local anesthetic, our data suggest that amitriptyline may be a promising adjunct to topical capsaicin. Full article

The central nervous system (CNS) relies on complex and dynamic interactions between neurons and glial cells. Among glial cells, astrocytes regulate the chemical environment surrounding neurons and supply essential nutrients for brain metabolism whereas microglia, the resident macrophages of the CNS, play critical roles in homeostasis, defense, and responses to injury. Both microglia and astrocytes contribute to the regulation of excitotoxicity and inflammation mediated by the metabolism of tryptophan (Trp) via the kynurenine pathway. Trp metabolism generates several bioactive metabolites, including quinolinic acid (QUIN) and kynurenic acid (KYNA), which have opposing effects. QUIN, produced by activated microglia, acts as an agonist for NMDA receptors; excessive stimulation of these receptors can lead to excitotoxicity and neuronal death. Conversely, KYNA, primarily produced by astrocytes via kynurenine 2,3-aminotransferases (KAT), acts as an NMDA receptor antagonist, conferring neuroprotection by mitigating excitotoxicity. Dysregulation of the Trp metabolism is implicated in many neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis and amyotrophic lateral sclerosis, as well as in various neuropsychiatric disorders. This review examines the cellular and molecular mechanisms underlying Trp metabolism in glial cells, highlighting the unique contributions of each glial phenotype, the implications for CNS pathologies, and the potential biomarkers and therapeutic targets for restoring homeostasis and preventing disease progression. Full article

Background: We sought to explain the seroprevalence of Zika Virus (ZIKV) as a syndemic of socioeconomic, environmental, and health factors in a cohort of women living in Brazil. Methods: This is a cohort study comprising 1498 women between 15 and 39 years of age followed up in two waves between February 2018 and August 2019. Two questionnaires addressed the arbovirus’s socioeconomic, demographic, and behavioral aspects and participants’ arbovirus infection history. Blood samples were collected to detect IgM and IgG for ZIKV, chikungunya virus (CHIKV), and dengue virus (DENV), and RT-PCR for ZIKV. Results: The baseline prevalence for ZIKV was 43% (95%CI: 40.5, 45.6), increasing to 44.7% in the following period (95%CI: 42, 47.1). We found a prevalence of 44.1% among women having one syndemic factor, 49.9% for those having two, and 58% for women having three or more factors. Women reporting a single syndemic factor resulted in higher odds of acquiring ZIKV (OR = 1.6, 95%CI: 1.2–2.4). There were increased adjusted odds among women having two or three or more factors (OR = 2.1, 95%CI: 1.6–3.1; OR = 2.9, 95%CI: 2.0–4.3, respectively). Conclusions: Tailored interventions targeting syndemic conditions, such as the co-circulation of urban arboviruses and poor living conditions, are crucial to improving the burden produced by ZIKV. Full article

Cutaneous leishmaniasis caused by Leishmania amazonensis is a significant public health issue. This study aimed to evaluate an ecofriendly, thermosensitive nanogel, developed using a low-energy, solvent-free method, incorporating F127 and Carbopol 974P copolymers, and enriched with Pectis brevipedunculata essential oil (EOPb) for its leishmanicidal and anti-inflammatory properties. The nanogel was prepared and characterized through FTIR, DLS, SEM, and AFM to confirm the incorporation of EOPb as well as its stability and rheological properties. In vitro leishmanicidal activity was evaluated on Leishmania amazonensis promastigotes, and in vivo anti-inflammatory effects were assessed using a rat paw edema model. In vitro, nGF3 (EOPb-loaded nanogel) demonstrated significant leishmanicidal activity, with promastigote mortality rates exceeding 80% at 24 h and 90% at 48 h. In vivo, nGF1, nGF2, and nGF3 exhibited anti-inflammatory effects, with nGF2 and nGF3 reducing edema by 62.7% at 2 h post-treatment. The empty nanogel (nGF0) showed minimal anti-inflammatory activity. The ecofriendly EOPb-loaded nanogel (nGF3) demonstrated strong leishmanicidal and anti-inflammatory effects, presenting a promising candidate for cutaneous leishmaniasis treatment. Further studies are necessary to explore its clinical potential. Full article

Anthropogenic disturbances, such as forest conversion, have a profound impact on species distributions and biodiversity in tropical forests. This study aimed to determine the diversity and distribution of bark and ambrosia beetles (Curculionidae: Scolytinae and Platypodinae) across three forest types: Primary Forest (PF), Disturbed Forest (DF), and Rubber Plantation (RP) in southern Sabah, Malaysia. We analyzed biweekly data obtained from ethanol-baited traps over three years, from April 2017 to May 2020, which included 7257 individuals from 154 species. The dominant species remained the same across all forest types. However, species composition was highly stochastic and unpredictable between forest types. The abundance and number of species were highest in RP but lowest in DF. Indigenous forest use in DF mostly for fuel likely reduced the resources for the beetles. Open canopy structure in RP probably increased the number of flying beetles. Although adjacent to PF, RP displayed a distinct species composition predominantly associated with rubber trees. These findings underscore the anthropogenic impact on beetle assemblages due to forest use and emphasize the need for sustainable forest management practices to prevent biodiversity loss and maintain ecosystem stability. Full article

This study examines the structural relationship between a destination’s image (DI), social influence (SI), and tourists’ brand trust (BT) and brand loyalty (BL) in the destination of Tozeur, a Tunisian town located at the gateway to the Sahara and rooted in the Atlas Mountains, where George Lucas set scenes for the Star Wars saga. The structural correlations between the variables in the model were tested through structural equation modeling (SEM). Data from 1405 tourists, who had visited Tozeur, were analyzed through SEM using AMOS software (version 25). The results showed that DI significantly affects BT (β = 0.924, p < 0.001) and significantly affects BL (β = 0.481, p < 0.01). Additionally, SI significantly affects BT (β = 0.274, p < 0.001) and significantly affects BL (β = 0.234, p < 0.001). Furthermore, BT significantly affects the BL (β = 0.461, p < 0.01). Tourist’s trust in a brand was found to act as a partial mediator on the link between destination image and brand loyalty and between social influence and brand loyalty. The findings demonstrate the importance of the tourism destination as well as social influence in boosting tourism trust and increasing destination loyalty among tourists. The results have many practical implications for destination marketers. Full article

Active packaging has emerged as a promising technological alternative for coating fruits and extending their shelf life. In this study, we developed a Tara gum and chitosan nanostructure-based coating, assessing its effects on guava shelf life and antifungal activity. In vitro analyses showed that nanochitosan suspension concentrations between 4.5 and 5.625 mg·mL⁻¹ completely inhibited the growth of the fungus Colletotrichum gloeosporioides and reduced the growth of Puccinia psidii. In in vivo tests, guavas coated with a 1.5% (w/v) Tara gum coating with 0.5% (v/v) nanochitosan exhibited significantly delayed or no lesion growth, unlike the control group, where lesions developed continuously. Beyond antifungal results, the Tara gum and nanochitosan coatings maintained the guava’s luminescence for up to 16 days, reduced weight loss, and preserved firmness compared to the control group. Firmness tests indicated that Tara gum, both pure and at 0.1% and 0.3% (v/v) concentrations, was effective in preserving this characteristic. Chemically, the coating also contributed to increased pH and acidity of the guavas, although a reduction in reducing and total sugars was observed in all groups. These findings confirm that the Tara gum with nanochitosan-based coating is an effective nanotechnological biomaterial to extend the shelf life of seasonal fruits, acting as a physical and biological barrier against pathogens and supporting post-harvest preservation. Full article

The design and synthesis of antiviral compounds were guided by computationally predicted data against highly conserved non-structural proteins (Nsps) of the SARS-CoV-2 coronavirus. Chromenephenylmethanone-1 (CPM-1), a novel biphenylpyran (BPP), was selected from a unique natural product library based on in silico docking scores to coronavirus Nsps with high specificity to the methyltransferase protein (2′-O-MTase, Nsp10–16), which is responsible for viral mRNA maturation and host innate immune response evasion. To target the 2′-O-MTase, CPM-1, along with intermediate BPP regioisomers, tetrahydrophenylmethanones (TPMs), were synthesized and structurally validated via nuclear magnetic resonance (NMR) data and DP4+ structure probability analyses. To investigate the activity of these BPPs, the following in vitro assays were conducted: SARS-CoV-2 inhibition, biochemical target validation, mutagenicity, and cytotoxicity. CPM-1 possessed notable activity against SARS-CoV-2 with 98.9% inhibition at 10 µM and an EC₅₀ of 7.65 µM, as well as inhibition of SARS-CoV-2’s 2′-O-MTase (expressed and purified) with an IC₅₀ of 1.5 ± 0.2 µM. In addition, CPM-1 revealed no cytotoxicity (CC₅₀ of >100 µM) or mutagenicity (no frameshift or base-pair mutations). This study demonstrates the potential of computational modeling for the discovery of natural product prototypes followed by the design and synthesis of drug leads to inhibit the SARS-CoV-2 2′-O-MTase. Full article

Brazil, despite possessing the largest renewable freshwater reserves in the world (8.65 trillion m³ annually), faces growing challenges in water management due to increasing demand. Agriculture, responsible for 68.4% of water consumption, is one of the main drivers of this demand, especially in the São Francisco River Basin, where irrigation accounts for 81% of total water withdrawals. Water bodies play a crucial role in sustaining ecosystems and supporting life, particularly along the East-West axis of Alagoas, a water-rich region in the ENEB. This study aimed to map and quantify the spatiotemporal variations of water bodies in the ENEB region and assess the impacts of human activities using MODIS satellite data, applying hydrological indices such as NDWI, MNDWI, and AWEI. Between 2003 and 2022, significant variations in the extent of water bodies were observed, with reductions of up to 100 km² during dry periods and expansions of up to 300 km² during wet seasons compared to dry periods. AWEI and MNDWI proved to be the most effective indices for detecting water bodies with MODIS data, providing accurate insights into water dynamics. Additionally, the MapBiomas Rios dataset, despite being resampled from a 30 m to a 500 m resolution, offered the most accurate representation of water bodies due to its methodology for data acquisition. Changes in albedo and surface temperature were also detected, highlighting the influence of climate change on the region’s water resources. These findings are crucial for guiding the sustainable management of water resources, not only in Alagoas but also in other regions of Brazil and similar semi-arid areas around the world. The study demonstrates the hydrological variability in the state of Alagoas, indicating the need for adaptive strategies to mitigate the impacts of climate change and anthropogenic pressures, supporting the need for informed decision-making in water resource management at both local and national levels. Full article

Surface coal mining and reclamation have greatly reshaped eastern Kentucky’s landscape affecting its socioeconomic, environmental and climatic aspects. This study examined the land-cover changes, trends and patterns in Floyd, Knott, Letcher, Magoffin, Martin, Perry, and Pike counties from 2004 to 2019. Using a random forest classifier, land cover was categorized into seven major classes, i.e., water, barren land, developed land, forest, shrubland, herbaceous, and planted/cultivated, majorly based on Landsat images. The Kappa accuracy ranged from 75 to 89%. The results showed a notable increase in forest area from 5052 sq km to 5305 sq km accompanied by a substantial decrease in barren land from 179 sq km to 91 sq km from 2004 to 2019. These findings demonstrated that reclamation activities positively impacted the forest expansion and reduced the barren land of the study area. Key land-cover transitions included barren land to shrubland/herbaceous, forest to shrubland, and shrubland to forest, indicating vegetation growth from 2004 to 2019. An autocorrelation analysis indicated similar land-cover types clustered together, showing effective forest restoration efforts. As surface coal mining and reclamation significantly influenced the landscapes of the coalfield counties in eastern Kentucky, this study provides a holistic perspective for understanding the repercussions of these transformations, including their effects on humans, society, and environmental health. Full article

Monitoring areas susceptible to desertification contributes to the strategic development of regions located in environments of extreme hydric and social vulnerability. Therefore, the objective of this study is to evaluate the process of soil degradation in the Desertification Nucleus of Cabrobó (DNC) over the past three decades using remote sensing techniques. This study used primary climatic data from TerraClimate, geospatial data of land use and land cover (LULC), and vegetation indices (SAVI and LAI) via Google Earth Engine (GEE) from Landsat 5/TM and 8/OLI satellites, and established the aridity index (AI) from 1992 to 2022. The results indicated 10 predominant LULC classes with native vegetation suppression, particularly in agriculture and urbanization. SAVI ranged from −0.84 to 0.90, with high values influenced by La Niña episodes and increased rainfall; conversely, El Niño episodes worsened the rainfall regime in the DNC region. Based on the Standardized Precipitation Index (SPI), it was possible to correlate normal and severe drought events in the DNC with years under the influence of El Niño and La Niña phases. In summary, the AI images indicated that the DNC remained semi-arid and that the transition to an arid region is a cyclical and low-frequency phenomenon, occurring in specific periods and directly influenced by El Niño and La Niña phenomena. The Mann–Kendall analysis showed no increasing trend in AI, with a Tau of −0.01 and a p-value of 0.97. During the analyzed period, there was an increase in Non-Vegetated Areas, which showed a growing trend with a Tau of 0.42 in the Mann–Kendall analysis, representing exposed soil areas. Annual meteorological conditions remained within the climatic pattern of the region, with annual averages of precipitation and actual evapotranspiration (ETa) close to 450 mm and an average temperature of 24 °C, showing changes only during El Niño and La Niña events, and did not show significant increasing or decreasing trends in the Mann–Kendall analysis. Full article

Salmonella spp. are known to persist in the environment. Wild animals are believed to act as important reservoirs, with antimicrobial resistance frequently occurring in the environment. However, little is known about the role of the wildlife in Bahia as a reservoir for Salmonella in Brazil. This study aimed to isolate and characterize Salmonella spp. from wildlife in the Atlantic Forest and Caatinga biomes considering indicators such as the animal species, degree of anthropization, sampling area, and feeding habits. Convenience wildlife sampling and characterization were conducted, followed by microbiological and molecular identification of Salmonella isolates, serotyping, and antimicrobial susceptibility testing. A total of 674 fecal samples were collected from 12 municipalities during 2015–2021, and 4 were positive for the following Salmonella species: Salmonella enterica subspecies enterica serovar Agona (n = 1), Salmonella enterica subsp. enterica serogroup O:16 (n = 2), and Salmonella enterica subsp. enterica serovar Muenchen (n = 1). Antimicrobial susceptibility analysis revealed that one isolate was resistant to six antibiotics, including extended-spectrum penicillins and beta-lactamase inhibitors. These results indicated a low frequency of Salmonella spp. in the sampled forest fragments. The presence of Salmonella in wild animals increases the risk to public health and biodiversity and indicates that they can act as sentinels of environmental contamination or indicators of preservation. Full article

Healthcare decision-makers face difficult decisions regarding COVID-19 booster selection given limited budgets and the need to maximize healthcare gain. A constrained optimization (CO) model was developed to identify booster allocation strategies that minimize bed-days by varying the proportion of the eligible population receiving different boosters, stratified by age, and given limited healthcare expenditure. Three booster options were included: B₁, costing US $1 per dose, B₂, costing US $2, and no booster (NB), costing US $0. B₁ and B₂ were assumed to be 55%/75% effective against mild/moderate COVID-19, respectively, and 90% effective against severe/critical COVID-19. Healthcare expenditure was limited to US$2.10 per person; the minimum expected expense using B_1, B_2, or NB for all. Brazil was the base-case country. The model demonstrated that B₁ for those aged <70 years and B₂ for those ≥70 years were optimal for minimizing bed-days. Compared with NB, bed-days were reduced by 75%, hospital admissions by 68%, and intensive care unit admissions by 90%. Total costs were reduced by 60% with medical resource use reduced by 81%. This illustrates that the CO model can be used by healthcare decision-makers to implement vaccine booster allocation strategies that provide the best healthcare outcomes in a broad range of contexts. Full article