Search Results (134)

Post-disaster recovery is increasingly understood not as a simple return to pre-event conditions but as a dynamic reorganization of land systems, in which land cover and land use change (LCLUC) provides an operational signature of recovery trajectories. However, most existing assessments reduce recovery to a single dimension—typically vegetation greenness—which can conflate systems that differ fundamentally in their response behavior. This study develops a multi-index Recovery Resilience Index for Land Systems (RRI-LS) within Google Earth Engine and applies it to the catastrophic liquefaction zone of the 2018 Mw 7.5 Palu earthquake (Central Sulawesi, Indonesia). Combining Sentinel-2 spectral indices (NDVI, NDBI, BSI), Dynamic World land-cover labels, and a hybrid Top-of-Atmosphere/Surface-Reflectance baseline to overcome the sparse pre-event archive, we quantify three resilience dimensions—resistance, recovery, and stability—and classify recovery into qualitatively distinct pathways. The hybrid baseline is quantitatively validated: after removing a small systematic offset, the residual discrepancy between TOA- and SR-derived indices is 2.4–4.9 times smaller than the measured disturbance signal. Site-level analysis of the three principal liquefaction hotspots (Balaroa, Petobo, Jono-Oge) and a 1 km grid expansion (n = 962 cells) reveal that disturbance-affected areas did not converge on a single outcome but diverged into bounce-back, transformational, reconstructed (non-vegetated), and degraded pathways; a basin-wide re-run at 250 m (n = 14,157 cells) reproduced the same pathway hierarchy, confirming robustness to grid resolution. Initial disturbance intensity was a poor predictor of long-term recovery (R² = 0.07, p < 0.001), underscoring that recovery is multidimensional and not reducible to a single shock variable. The emergence of a reconstructed, non-vegetated pathway—where bare-soil disturbance is resolved through built surfaces rather than re-greening—demonstrates that vegetation metrics alone are insufficient in human-dominated landscapes. The framework supports a land-system perspective in which recovery is conceptualized as the establishment of new functional equilibria. Full article

Indonesia experiences some of the world’s highest seismic activity, making earthquakes a major source of physical, economic, and social losses. To better quantify these impacts, this study proposes a comprehensive evaluation framework with two new metrics: the Seismic Severity Index (SSI) and the Seismic Impact Index (SII). The indices are derived using four weighting methods—Grey Relational Analysis (GRA), Equal Weights, the Entropy Weight Method (EWM), and Criteria Importance Through Intercriteria Correlation (CRITIC)—and applied to 28 regencies and municipalities affected by damaging earthquakes from 2016 to 2022. Results show that the 2018 earthquake, intensified by a tsunami and liquefaction, caused the most severe losses. Palu Municipality and Donggala Regency consistently recorded high SSI values across all weighting schemes. The SII further identifies Sigi Regency, Donggala Regency, and Palu Municipality as the most heavily impacted areas, although rankings varied by method. Overall, Sigi Regency, Palu City, Donggala Regency, North Lombok, West Lombok and Cianjur exhibit the highest combined severity and impact, while Garut, Ciamis, and Cilacap experienced relatively minor effects. The study concludes that integrating GRA with EWM and CRITIC yields a robust earthquake loss index to support future disaster risk reduction policies. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder for which dopamine transporter single-photon emission computed tomography (DaT-SPECT) is widely used to support clinical diagnosis. Recent convolutional neural network (CNN)-based studies have reported high classification accuracy on DaT-SPECT datasets. However, the relative contributions of network architecture and training protocol design to these results remain insufficiently explored, particularly for small and moderately sized medical imaging datasets. In this study, a training-oriented evaluation of CNNs for PD classification is conducted using two DaT-SPECT datasets derived from the Parkinson’s Progression Markers Initiative (PPMI). First, a previously published experimental setup is faithfully reproduced on a curated dataset of 645 DaT-SPECT images using identical preprocessing procedures, data splits, and model architectures. Under the reproduced experimental setting, previously reported classification accuracies for individual CNN architectures ranged from 93.02% to 95.34%, while an ensemble approach achieved 98.45% accuracy. The same architectures are then evaluated using a unified training protocol incorporating standardized optimization and regularization strategies. Using this protocol, ResNet50 achieves 100% classification accuracy, with all evaluation metrics reaching 1.0, while VGG16, Inception V3, and Xception each achieve an accuracy of 99.22%. On a larger, independently constructed PPMI-derived dataset with higher spatial resolution, previously reported classification accuracies ranged from 93.27% for PD vs. SWEDD to 95.33% for PD vs. control. Using the proposed unified training protocol, the evaluated CNN architectures achieve classification accuracies of 98% for PD vs. SWEDD and 100% for PD vs. control. These results indicate that training protocol design has a stronger influence on DaT-SPECT-based PD classification performance than the specific choice of CNN architecture. Full article

Neuroblastoma is characterized by noticeable resistance to chemotherapy, largely driven by the ability of tumour cells to reorganize stress-adaptive signalling networks rather than relying on single oncogenic drivers. We conducted a study to investigate the pharmacological mode of action of doxorubicin in modifying adaptive signalling pathways in SH-SY5Y neuroblastoma cells, and whether the capacity of plant metabolites can exploit emergent biochemical vulnerabilities. Transcriptomic profiling through RNA sequencing conducted 48 h post-doxorubicin exposure unveiled the organized disruption of pathways linked with amyloidogenic processes, oncogenic signalling pathways, oxidative stress, and DNA repair. The protein–protein interactions, coupled with Kyoto Encyclopedia of Genes and Genomes pathway evaluations, revealed five network-central-hubs: BRAF, GSK3β, PARP1, BACE1, and MAOB. Structural docking integrated with 200 ns molecular dynamics simulations illustrated binding stability across multiple targets driven by three metabolites, Lactol binding to BRAF (−54.13 kcal/mol) and MAOB (−39.08 kcal/mol), Amino(1H-indol-2-yl)acetic acid to BACE1 (−41.07 kcal/mol) and GSK3β (−47.38 kcal/mol), and Quercetin-3-(6″-malonyl-glucoside) binding to PARP1 (−46.03 kcal/mol). In vitro Cell Counting Kit-8 proliferation assays validated the significant anti-neuroblastoma efficacy, with the lowest IC₅₀ (0.2397 µM) being exhibited by Amino(1H-indol-2-yl)acetic acid, followed by Lactol (1.226 µM) and Quercetin-3-(6″-malonyl-glucoside) (1.301 µM), which mirrored the cytotoxic action of doxorubicin (1.306 µM). These results suggest that plant-derived metabolites may interact with stress-adaptive signalling pathways connected with neuroblastoma. However, direct experimental validation of target engagement and pathway modulation will be required to confirm these predicted interactions. Full article

The SARS-CoV-2 papain-like protease (PL^pro) is an essential viral enzyme that promotes viral polyprotein processing while simultaneously suppressing the host innate immune response, which makes it a primary target for developing antiviral drugs. The present study employs a comprehensive approach integrating untargeted metabolomic profiling, in silico molecular docking and dynamics simulations, Molecular Mechanics Generalized Born Surface Area (MM-GBSA) energetic assessments, and biochemical enzyme assays. This integrated method aims to discover natural PL^pro inhibitors from two ethnomedicinal plants, Lippia javanica and Acorus calamus, which have long been utilized in African traditional medicine to treat respiratory diseases. Comprehensive metabolite profiling using untargeted Ultra-Performance Liquid Chromatography–Tandem Mass Spectrometry (UPLC-MS/MS) and Global Natural Products Social (GNPS) molecular networking revealed flavonoid glucuronides and phenylpropanoid derivatives as the major constituents in both plant species. In situ histochemical staining further offered spatial validation of phenolic- and lignin-associated tissues, supporting the phenolic-dominated molecular families detected by GNPS molecular networking. In silico evaluation of six selected compounds demonstrated spontaneous and thermodynamically favorable binding to PL^pro, with ΔG_bind values ranging from −5.63 to −6.43 kcal/mol. Catechin-7-glucoside emerged as the lead compound, establishing multiple hydrogen bond networks with Asp164, Gln269, Tyr264, and Asn267, supplemented by hydrophobic engagement with Pro247 and Pro248, and π-π stacking with the blocking loop 2 (BL2 loop). Molecular dynamics simulations confirmed the stability of the protein–ligand complexes. Biochemical enzyme assays confirmed concentration-dependent inhibition of PL^pro proteolytic and deubiquitinating activity by both crude plant extracts and isolated bioactive compounds. However, S-adenosyl-methionine showed comparatively high PL^pro proteolytic activity (IC₅₀ 5.872 µM) compared to catechin-7-glucoside, with an IC₅₀ of 7.493 µM, exhibiting efficacy similar to the reference inhibitor GRL0617. Both the extracts of L. javanica and A. calamus have shown significant inhibitory activity while maintaining cell viability in Human embryonic kidney 293T cell (HEK293T) culture models, indicating a favorable safety profile of the tested concentrations. Based on these results, catechin-based polyphenols and phenylpropanoid derivatives appear as promising lead compounds for the development of PL^pro inhibitors. To progress toward therapeutic use, further work is necessary in pharmacokinetics, structural optimization, and antiviral validation in cell models. Full article

Despite growing interest in South African medicinal plants, advanced metabolomic workflows that integrate positive (ESI+) and negative (ESI−) ionization modes in UPLC-MS/MS remain sparsely applied to South African flora, and especially to Acorus calamus and Lippia javanica species. Herein, application of a dual-polarity (positive (ESI+) and negative (ESI−) ionization modes) using an untargeted UPLC–MS/MS workflow, integrated with HEK293T cytotoxicity screening, to map their metabolomes, and rank potential signature metabolites for targeted antiviral follow-up. SwissADME supported in silico drug-likeness. Neither plant extract was cytotoxic across the concentration range, with absorbance-based cell viability of 73.82% for L. javanica and 77.23% for A. calamus at 250 µg/mL, and fluorescence-based cell viability ≥59.87% and ≥55.89%, respectively. Dual-polarity expanded coverage with ESI− yielded 312 features, compared with 225 with ESI+, consistent with the predominance of acidic phenolics in plant species. Unsupervised and supervised models segregated the plant species (PCA PC1/PC2 variance: ESI+ 89.4%/3.0%; ESI− 93.5%/1.8%; R²X(cum) = 0.799). Differential analysis identified 118 significant features in ESI+ with 80 up-regulated, 38 down-regulated, and 139 in ESI− with 96 up-regulated, 43 down-regulated. The ESI− showed the wider dynamic range. Chemotypes enriched among significant metabolites include flavonols of 3-O-methylkaempferol, apigenin, and conjugates of Pollenin A, iridoid glycosides of oleoside, forsythoside B, and jasmonate-pathway oxylipins of 7-epi-12-hydroxyjasmonic acid and its glucoside. These also include caryoptosidic acid and catechin-7-glucoside, which are ionized in both modes, pinning the increase in biomarker robustness. In conclusion, a dual-mode UPLC–MS/MS approach, integrated with cytotoxicity exploration, delivers a complementary metabolome coverage and a safety awareness for shortlisting of potential signature metabolites from L. javanica and A. calamus. Moreover, in vitro inhibition of SARS-CoV-2 papain-like protease (PL^pro) by these plants links chemical signatures to antiviral relevance. Shortlisted significant metabolites that demonstrated favorable drug-likeness include flavonol scaffolds of 3-O-methylkaempferol, Pollenin A, and jasmonate-pathway derivatives of 7-epi-12-hydroxyjasmonic acid. Moreover, the dual ionization mode may eliminate ionization bias, broaden metabolome coverage, and yield a mechanism-ready shortlist of metabolites from South African medicinal plants for downstream antiviral investigation. Full article

The reliable and economical operation of modern power systems is increasingly complex due to the integration of diverse energy sources and dynamic load patterns. A critical challenge is maintaining the balance between electricity supply and demand within various operational constraints. This study addresses the economic scheduling of generation units using a Mixed Integer Programming (MIP) optimization model. Key constraints considered include reserve requirements, ramp rate limits, and minimum up/down time. Simulations are performed across multiple scenarios, including systems with spinning reserves, responsive demand, renewable energy integration, and energy storage systems. For each scenario, the optimal mix of generation resources is determined to meet a 24 h load forecast while minimizing operating costs. The results show that incorporating demand responsiveness and renewable resources enhances the economic efficiency, reliability, and flexibility of the power system. Full article

The Atlantic blue crab (Callinectes sapidus) has rapidly expanded across the Mediterranean, raising concerns over its ecological and economic impacts. This study examines the morphometric characteristics and environmental influences on C. sapidus populations in the Palud-Palù swamp (western Istrian coast) from 2022 to 2024. A total of 203 specimens were analyzed for carapace width, length, depth, and body mass, alongside monthly measurements of temperature, salinity, oxygen saturation, and pH. Statistical analyses (t-tests, ANOVA, PCA, and RDA) revealed pronounced sexual dimorphism, with males consistently larger than females. Interannual differences in size distribution showed larger individuals in 2022, followed by a decline in 2023 and 2024, likely due to environmental stressors (e.g., salinity, temperature, hypoxia) and increased anthropogenic pressures (e.g., trapping and illegal harvesting). RDA identified temperature, oxygen saturation, and pH as key abiotic drivers of morphometric variation. These findings suggest that while C. sapidus demonstrates physiological plasticity, enabling its persistence in estuarine environments, its growth and invasive potential may be constrained under extreme or suboptimal local conditions. This study highlights the importance of long-term monitoring and integrated management to mitigate ecological disruption in sensitive coastal ecosystems. Full article

Background/Objectives: High sodium consumption increases the risk of hypertension and cardiovascular disease. Although food remains the primary source of intake, elevated sodium levels in drinking water can further contribute to excessive intake, particularly in populations already exceeding recommendations. This review examines the extent to which national drinking water standards account for sodium-related health risks and aims to inform discussion on the need for enforceable, health-based sodium limits. Methods: National standards for unbottled drinking water in 197 countries were searched for using the WHO 2021 review of drinking water guidelines, the FAOLEX database, and targeted internet and AI searches. For each country, data were extracted for the document name, year, regulatory body, regulation type, sodium limit (if stated), and rationale. Socio-geographic data were sourced from World Bank Open Data. A descriptive analysis was conducted using Microsoft Excel. Results: Standards were identified for 164 countries. Of these, 20% (n = 32), representing 30% of the global population, had no sodium limit. Among the 132 countries with a sodium limit, 92% (n = 121) adopted the WHO’s palatability-based guideline of 200 mg/L. Upper limits ranged from 50 to 400 mg/L. Only twelve countries (9%) cited health as a rationale. Three countries—Australia, Canada, and the United States—provided a separate recommendation for at-risk populations to consume water with sodium levels below 20 mg/L. Conclusions: Globally, drinking water standards give inadequate attention to sodium’s health risks. Most either lack sodium limits or rely on palatability thresholds that are too high to protect health. Updating national and international standards to reflect current evidence is essential to support sodium reduction efforts. Health-based sodium limits would empower communities to better advocate for safe water. Amid rising water salinity, such reforms must be part of a broader global strategy to ensure universal and equitable access to safe, affordable drinking water as a basic human right. Full article

Background/Objectives: Glioblastoma multiforme (GBM) is an aggressive and heterogeneous brain tumor with poor prognosis, emphasizing the need for reliable molecular biomarkers to improve patient stratification and treatment planning. This study aimed to identify key genes associated with overall survival in GBM by employing and comparing machine learning (ML) and deep learning (DL) approaches using RNA-Seq gene expression data. Methods: RNA-Seq expression and clinical data for primary GBM tumors were obtained from The Cancer Genome Atlas (TCGA). A univariate Cox proportional hazards regression was used to identify survival-associated genes. For survival prediction, ML-based feature selection techniques—RF, GB, SVM-RFE, RF-RFE, and PCA—were used to construct multivariate Cox models. Separately, DeepSurv, a DL-based survival model, was trained using the significant genes from the univariate analysis. Gradient-based importance scoring was applied to determine key genes from the DeepSurv model. Results: Univariate analysis yielded 694 survival-associated genes. The best ML-based Cox model (RF-RFE with 90% training data) achieved a c-index of 0.725. In comparison, DeepSurv demonstrated superior performance with a c-index of 0.822. The top 10 genes were identified from the DeepSurv analysis, including CMTR1, GMPR, and PPY. Kaplan–Meier survival curves confirmed their prognostic significance, and network analysis highlighted their roles in processes such as purine metabolism, RNA processing, and neuroendocrine signaling. Conclusions: This study demonstrates the effectiveness of combining ML and DL models to identify prognostic gene expression biomarkers in GBM, with DeepSurv providing higher predictive accuracy. The findings offer valuable insights into GBM biology and highlight candidate biomarkers for further validation and therapeutic development. Full article

Rapid and accurate detection of primary waves (P-waves) using high-rate Global Navigation Satellite System (GNSS) data is essential for earthquake monitoring and tsunami early warning systems, where traditional seismic methods are less effective in noisy environments. We applied a wavelet-based method using a Mexican hat wavelet and dynamic threshold to thoroughly analyze the three-component displacement waveforms of the 2009 Padang, 2012 Simeulue, and 2018 Palu Indonesian earthquakes. Data from the Sumatran GPS Array and Indonesian Continuously Operating Reference Stations were analyzed to determine accurate displacements and P-waves. Validation with Indonesian geophysical agency seismic records indicated reliable detection of the horizontal component, with a time delay of less than 90 s, whereas the vertical component detection was inconsistent, owing to noise. Spectrogram analysis revealed P-wave energy in the pseudo-frequency range of 0.02–0.5 Hz and confirmed the method’s sensitivity to low-frequency signals. This approach illustrates the utility of GNSS data as a complement to seismic networks for the rapid characterization of earthquakes in complex tectonic regions. Improving the vertical component noise suppression might further help secure their utility in real-time early warning systems. Full article

The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region’s vulnerability to such catastrophic events. Documenting damage from tsunamis is only meaningful shortly after the disaster has occurred because governmental agencies clean up debris and start the recovery process within a few hours after the destruction has occurred, deeming impact estimates unreliable. Sentinel-2 and Maxar WorldView-3 satellite images were used to calculate well-known environmental indices to delineate the tsunami-affected areas in Palu, Indonesia. The use of NDVI, NDSI, and NDWI indices has allowed for a quantifiable measure of the changes in vegetation, soil moisture, and water bodies, providing a clear demarcation of the tsunami’s impact on land cover. The final tsunami inundation map indicates that the areas most affected by the tsunami are found in the urban center, low-lying regions, and along the coast. This work charts the aftermath of one of Indonesia’s recent tsunamis but may also lay the groundwork for an easy, handy, and low-cost approach to quickly identify tsunami-affected zones. While previous studies have used high-resolution remote sensing methods such as LiDAR or SAR, our study emphasizes accessibility and simplicity, making it more feasible for resource-constrained regions or rapid disaster response. The scientific novelty lies in the integration of widely used environmental indices (dNDVI, dNDWI, and dNDSI) with threshold-based Decision Tree classification to delineate tsunami-affected areas. Unlike many studies that rely on advanced or proprietary tools, we demonstrate that comparable results can be achieved with cost-effective open-source data and straightforward methodologies. Additionally, we address the challenge of differentiating tsunami impacts from other phenomena (et, liquefaction) through index-based thresholds and propose a framework that is adaptable to other vulnerable coastal regions. Full article

Quercus, commonly known as oak, has great potential as one of the most widely cultivated plant species. However, the lack of superior varieties is a bottleneck for its usage and application in Southwest China. Here, this study aims to explore the growth and photosynthetic traits of 18 oak varieties with the goal of identifying the adaptable superior varieties for the region, focusing on nutrient growth, leaf morphology, chlorophyll content, and photosynthetic parameters over a 32-week growth period. The results showed that a significant diversity was observed among the varieties. Growth rhythm and fitted curves divided the 18 oak varieties into three patterns. Additionally, for the leaf morphological parameters, Q. denta boasted the maximum leaf area (167.24 cm²), leaf width (13.62 cm), and leaf aspect ratio (156.6); Q. mongo showed the greatest leaf length (20.37 cm); while Q. acutis had the largest leaf form factor (3.44) and leaf gap (0.39). Chlorophyll content was based on three-time-points investigation, with higher levels observed in Q. mongo, Q. robur 4, Q. wutai, Q. denta, Q. acutis, and Q. robur 1. The transpiration rate (E) (5.03 mmol m⁻²), stomatal conductance (gsw) (0.22 mol m⁻² s⁻¹), and total water vapor conductance (gtw) (0.19 mol m⁻² s⁻¹) were dominantly obtained in Q. robur 1, while Q. denta exhibited the highest intercellular CO₂ concentration (Ci) (564.67 µmol mol⁻¹). Conversely, Q. wutai displayed a significantly higher leaf chamber CO₂ concentration (Ca) (502.11 µmol mol⁻¹). Furthermore, growth traits showed a correlation with leaf morphological and photosynthetic traits. PCA analysis grouped the oak varieties into five clusters, with Q. acutis, Q. robur 1, Q. palus 3, Q. denta, Q. nutta, Q. mongo, and Q. wutai identified as superior varieties. These findings not only offer promising oak candidate varieties for Southwest China, but also provide insights for establishing efficient breeding program for other woody plants. Full article

Objectives: This study aimed to assess salt and sugar-related knowledge, attitudes and behaviours (KAB) of adults in the Central Division of Fiji. Methods: A randomised stratified sample of 700 adults were selected. Questions on salt and sugar-related KABs were developed. The survey took place from March to June 2022. For analyses, population and sample weights were applied, and differences between predefined subgroups (sex, age, ethnicity and area) were compared using weighted chi-square tests. Results: 534 adults participated (response rate 76%). Over 80% of participants (82% (95% CI 78.5 to 84.9%)) identified that consuming too much salt or salty sauce can lead to hypertension. More than 90% recognised that consuming too much sugar can lead to diabetes (92.3% (89.7 to 94.3%)). Approximately 80% of participants thought it was somewhat or very important to lower salt and sugar intake in their diet (79.8% (76.1 to 83.0) and 84.2% (80.8 to 87.1%), respectively). However, almost 40% reported adding salt or salty sauces when cooking (37.3% (32.7 to 42.2%)) and 65% (60.6 to 68.5%) adding sugar to drinks daily. Conclusions: Despite having positive knowledge and attitudes, many people reported behaviours likely to contribute to excess salt and sugar intake, highlighting the need for interventions that support behaviour change and the creation of healthier food environments. Full article

Gypsum is a common mineral at Gale crater on Mars, currently being explored by the Mars Science Laboratory (MSL) rover, Curiosity. In this paper, we summarize the associations of gypsum with other sulfate minerals (bassanite, anhydrite, jarosite, starkeyite, and kieserite) from the lowest levels of the crater’s northern moat zone (Aeolis Palus) up through ~0.8 km of the stratigraphic section in the lower slopes of the sedimentary mound developed around the central peak, Aeolis Mons (informally, Mount Sharp). The analysis is based on results from the CheMin X-ray diffraction instrument on Curiosity, supplemented with information from the rover’s versatile instrument suite. Gypsum does not occur with the same frequency as less hydrous Ca-sulfates, likely, in most cases, because of its dehydration to bassanite and possibly to anhydrite. All three of these Ca-sulfate phases often occur together and, along with other sulfates, in mixed assemblages that are evidence of limited equilibration on a cold, dry planet. In almost all samples, at least one of the Ca-sulfate minerals is present, except for a very limited interval where jarosite is the major sulfate mineral, with the implication of more acidic groundwater at a much later time in Gale crater’s history. Although observations from orbit reveal a sulfate-rich surface, currently active dark basaltic dunes at Gale crater have only small amounts of a single sulfate mineral, anhydrite. Gale crater has provided the most complete mineralogical analysis of a site on Mars so far, but the data in hand show that Gale crater mineralogy is not a blueprint with planet-wide application. The concurrent study of Jezero crater by the Mars 2020 mission and comparisons to what is believed to be the most extensive deposit of gypsum on Mars, in the dune fields at the north polar ice cap, show significant diversity. Unraveling the stories of gypsum and other sulfates on Mars is just beginning. Full article