Search Results (167)

Salt stress is one of the most common factors reducing the productivity of crops. We tested the effect of Rapamycin, an mTOR inhibitor and autophagy inducer, for the possible amelioration of high-salinity stress in Eruca sativa. We analyzed the germination rate, the macro- and micro-morphology of seedlings, and the ultrastructure of cotyledons with a Transmission Electron Microscope. The most striking observation was that salt stress blocked the catabolism of the lipid droplets stored in the embryos of E. sativa, also dramatically reducing the starch storage capability in the plastids. As a consequence, lipid droplets remained in the developing seedlings until a late stage. On the contrary, the catabolism of the lipid storage in the embryos in the presence of rapamycin and salt stress was comparable to the control, even if the starch stored in the plastids was lower. Rapamycin-induced autophagic activity was shown by characteristic ultrastructural changes, such as increased membrane recycling. Part of this activity was interpreted as pexophagy, i.e., the autophagy of peroxisomes, where an increase in their turnover rate could be necessary to maintain an active glyoxylate cycle. Full article

Impact cratering determined by collisions with meteorites and asteroids is considered one of the main natural processes in the Solar System, modifying the planets and their satellites surface during time. The Earth includes in its impact record a small number of such events due to active plate tectonics, sedimentation, and volcanism, with these geological processes destroying and burying their impact geomorphological signatures. To enlarge the Earth’s impacts database, new concepts and research methods are necessary, as well as the reinterpretation of old geological and geophysical models. Geomorphological, Geological, and Geophysical (3G) signatures in concealed impacted areas are discussed in this paper; the first offers the target characteristics, while the others give means for detecting their unseen remnants. The 3G signatures have been applied to the Transylvanian Depression, a fascinating geological structure, with difficulties in explaining the direct overlapping of regionally developed thick tuff and thick salt layers, and undecided interpretation of the regional magnetic anomaly. Large and deep sedimentary basins, such as the Precaspian, Alexandria and Transylvanian depressions, are interpreted to have started as impacted areas during the Permian or the Lower Neogene. Geophysical and geological existing information have been reinterpreted, offering a new way in understanding deeply located geological structures. Full article

Global energy transition has driven exponential growth in lithium demand, fueled by advancements in new energy vehicles and battery technologies. Despite abundant lithium resources, volatile market fluctuations underscore the critical need for the accurate monitoring of production capacity. Brine-type lithium resources, accounting for approximately 65% of the global reserves, are concentrated in the “lithium triangle” region of South America (Chile, Argentina, and Bolivia). This region typically employs solar evaporation ponds to extract lithium from brine, where lithium production directly correlates with the pond area, enabling remote sensing-based capacity monitoring. This study focuses on Chile’s Atacama Salt Lake, utilizing long-term Landsat and Sentinel satellite data (1985–2019) to extract evaporation pond areas through visual interpretation and support vector machine (SVM) classification. We further investigated the relationship between salt pond area and lithium production capacity by establishing a linear conversion formula. The results demonstrated a strong correlation (R² = 0.91), with over 97% of the data points falling within the 95% prediction band, validating the effectiveness of the method. This study proposes a semi-automated monitoring framework for lithium production capacity in salt lake brine systems, offering novel insights for sustainable lithium resource management and supporting the stable development of energy transition. Full article

Random noise degrades the quality and reduces the interpretability of Ground Penetrating Radar (GPR) data. The Block Matching Three Dimension (BM3D) algorithm is effective at suppressing Gaussian noise, but ineffective at handling salt-and-pepper noise. On the other hand, the Improved Adaptive Median (IAM) filter is suitable for eliminating salt-and-pepper noise, but performs poorly against Gaussian noise. In this paper, we introduce and implement JBI, a joint denoising algorithm that integrates both BM3D and improved adaptive median filtering, exploiting the advantages of both algorithms to effectively remove both Gaussian and salt-and-pepper noise from GPR data. Applying the proposed joint filter to both synthetic and real field GPR data, infested with various proportions of different noise types, shows that the proposed joint denoising algorithm yields significantly better results than these two filters when used separately, and better than other commonly used denoising filters. Full article

The Lower Congo Basin (LCB) is a rift-type basin with petroleum systems that developed at the western African margin in association with the opening of the South Atlantic. Two pre-salt siliciclastic units of the LCB, Lucula (uppermost Jurassic to Lower Cretaceous) and Chela (Aptian) formations, were sampled in deep wells and outcrops. Heavy mineral assemblages, XRD mineralogy and geochemistry indicate prevailing source in high rank metamorphic rocks from western regions of the Lower Congo Belt. However, sediment composition reveals some provenance heterogeneity. For the Chela Formation, occasionally abundant amphibole in the heavy mineral fraction, coupled with relatively high Fe and Ti proportions, suggest that it formed when deeper crustal units were exhumed. The Lucula Formation collected in outcrops have composition substantially different from Lucula and Chela samples collected in deep wells, indicating distinct provenance and the incorporation of recycled material. A significant diagenetic overprint compromises the interpretation of compositional features in terms of paleoclimate. The presence of a chemical component with dolomite, halite and diverse sulphates and the stratigraphic position of the Chela Formation at the transition to a thick evaporitic succession are compelling evidence of deposition under warm and dry conditions, which are probably more extreme than those associated with the original stages of rifting recorded by the Lucula Formation. Full article

Proper geologic reservoir characterization is crucial for energy generation and climate change mitigation efforts. While conventional techniques like core analysis and well logs provide limited spatial reservoir information, seismic data can offer valuable 3D insights into fluid and rock properties away from the well. This research focuses on identifying important structural and stratigraphic variations at the Mississippi Canyon Block 118 (MC-118) field, located on the northern slope of the Gulf of Mexico, which is significantly influenced by complex salt tectonics and slope failure. Due to a lack of direct subsurface data like well logs and cores, this area poses challenges in delineating potential reservoirs for carbon storage. The study leveraged seismic multi-attribute analysis and machine learning on 3-D seismic data and well logs to improve reservoir characterization, which could inform field development strategies for hydrogen or carbon storage. Different combinations of geometric, instantaneous, amplitude-based, spectral frequency, and textural attributes were tested using Self-Organizing Maps (SOM) to identify distinct seismic facies. SOM Models 1 and 2, which combined geometric, spectral, and amplitude-based attributes, were shown to delineate potential storage reservoirs, gas hydrates, salt structures, associated radial faults, and areas with poor data quality due to the presence of the salt structures more than SOM Models 3 and 4. The SOM results presented evidence of potential carbon storage reservoirs and were validated by matching reservoir sands in well log information with identified seismic facies using SOM. By automating data integration and property prediction, the proposed workflow leads to a cost-effective and faster understanding of the subsurface than traditional interpretation methods. Additionally, this approach may apply to other locations with sparse direct subsurface information to identify potential reservoirs of interest. Full article

Heavy metal pollution from anthropogenic sources poses significant risks to ecosystems and human health. Biosorption offers a sustainable removal method, but kinetics are poorly captured by traditional neural networks. This study introduces optimized Bidirectional Long Short-Term Memory (Bi-LSTM) networks for multivariate modeling of Ni(II) biosorption on Quercus crassipes acorn shells, trained using experimental (EKD), synthetic (SKD), and combined (CKD) datasets. A two-stage hyperparameter optimization with Optuna yielded models with R² above 0.995 and low RMSE in 5-fold cross-validation. Second-stage models showed high stability, with coefficient of variation (CoV) values below 10% for RMSE. Based on unseen kinetics, production models showed slightly lower performance (R² = 0.89–0.996): EKD1, EKD2, and CKD1 showed the most consistent performance across challenging conditions with R² values of 0.9617, 0.9769, and 0.9415, respectively; SKD models achieved strong results under standard conditions (kinetic 1, SKD1 R² = 0.9963). SHapley Additive exPlanations (SHAP) analysis identified contact time and initial Ni(II) concentration as key variables, with temperature, cation charge, and a salt interference code also contributing to model interpretability. These findings demonstrate that optimized Bi-LSTM networks offer a robust and interpretable data-driven solution for modeling Ni(II) removal under multivariate conditions, including the presence of salts. Full article

Soil salinization is a global ecological and environmental problem, which is particularly serious in arid areas. The formation process of soil salinity is complex, and the interactive effects of natural causes and anthropogenic activities on soil salinization are elusive. Therefore, we propose an automated machine learning framework for predicting soil salt content (SSC), which can search for the optimal model without human intervention. At the same time, post hoc interpretation methods and graph theory knowledge are introduced to visualize the nonlinear interactions of variables related to SSC. The proposed method shows robust and adaptive performance in two typical arid regions (Central Asia and Xinjiang Province in western China) under different environmental conditions. The optimal algorithms for the Central Asia and Xinjiang regions are Extremely Randomized Trees (ET) and eXtreme Gradient Boosting (XGBoost), respectively. Moreover, precipitation and minimum air temperature are important feature variables for salt-affected soils in Central Asia and Xinjiang, and their strongest interaction effects are latitude and normalized difference water index. In both study areas, meteorological factors exhibit the greatest effect on SSC, and demonstrate strong spatiotemporal interactions. Soil salinization intensifies with long-term climate warming. Regions with severe SSC variation are mainly distributed around the irrigation water source and in low-terrain basins. From 1950 to 2100, the regional mean SSC (g/kg) varies by +20.94% and +64.76% under extreme scenarios in Central Asia and Xinjiang, respectively. In conclusion, our study provides a novel automated approach for interaction analysis of driving factors on soil salinization in drylands. Full article

Orientation tools in borehole imaging logs acquire magnetic information that is currently used for spatial and geographical orientation of the images. We propose to use this magnetic field information to estimate both magnetic susceptibility and remanent magnetization of rocks inside wells. Measurements of these magnetic parameters are not often available in hydrocarbon exploration to support forward modeling of magnetic data, an interpretation tool that has played important role in the exploration risk reduction in the Pre-Salt prospects of Campos Basin, Brazil. The acquired magnetic data requires corrections for tool rotation and diurnal variation of the Earth’s magnetic field before calculation. Then, using a set of simple equations and reasonable assumptions we were able to estimate the magnetic susceptibility of carbonates and basalts, as well as the remanent magnetization of the basalts, from a Pre-Salt well in Campos Basin. When compared to susceptibility values measured in laboratory for the same rock interval, our results show a significant match. This promising result shows the importance of our methodology in providing reliable information that can minimize uncertainties in forward modeling of magnetic data, which contributes to reduction of hydrocarbon exploration risks. Given that direct susceptibility and remanence measurements require oriented samples, a complex and expensive operation in wells, our results offer this rock information without any extra costs since imaging logs are commonly acquired in exploration wells. Besides its use in hydrocarbon exploration, our methodology can be applied to mineral exploration where magnetic susceptibility is an important property for rock identification. Full article

The unique features of mitochondrial DNA (mtDNA), including its circular and multicopy nature, the possible coexistence of wild-type and mutant molecules (i.e., heteroplasmy) and the presence of nuclear mitochondrial DNA segments (NUMTs), make the diagnosis of mtDNA diseases particularly challenging. The extensive deployment of next-generation sequencing (NGS) technologies has significantly advanced the diagnosis of mtDNA-related diseases. However, the vast amounts and diverse types of sequencing data complicate the interpretation of these variants. From sequence alignment to variant calling, NGS-based mtDNA sequencing requires specialized bioinformatics tools, adapted for the mitochondrial genome. This study presents the use of new bioinformatics approaches, optimized for short- and long-read sequencing data, to enhance the accuracy of mtDNA analysis in diagnostics. Two recent and emerging free bioinformatics tools, Mitopore and MitoSAlt, were evaluated on patients previously diagnosed with single nucleotide variants or large-scale deletions. Analyses were performed in Linux-based environments and web servers implemented in Python, Perl, Java, and R. The results indicated that each tool demonstrated high sensitivity and specific accuracy in identifying and quantifying various types of pathogenic variants. The study suggests that the integrated and parallel use of these tools offers a significant advantage over traditional methods in interpreting mtDNA genetic variants, reducing the computational demands, and provides an accurate diagnostic solution. Full article

Experimental studies have shown that osmosis could be one of the mechanisms of water transport in porous materials that act, to a certain extent, as semipermeable membranes. In this paper, an experimental apparatus and the corresponding model to measure and determine the osmotic efficiency, σ, of bulk porous materials are described. Both the apparatus and model to interpret water transport in samples are modifications of those of Sherwood and Craster. In addition to σ, the transport parameters of the model include Darcy permeability and water and salt diffusivity. These parameters are used to calculate the ratio of the individual components of the total molar flow. We used the apparatus to measure cylindrical samples made from an illitic clay with a diameter of 45 mm and thickness of 5 mm. The measured transport coefficients were then used to estimate the relative importance of the individual contributions to the total flow of water through the samples. Our results show that the contribution of the osmosis is 82–88%, while the diffusion contributes only 11–13% and the Darcy flow caused by the pressure difference contributes only 1–5%. Even after considering the uncertainties in the measurement of the transport coefficients, which are estimated to be up to 22%, the results show that osmosis makes an important contribution to the total water flow and should not be neglected in general. Full article

Background/Objectives: This study investigates the metabolic profile of a single dose of etodolac in healthy volunteers, focusing on pharmacokinetics, clinical parameters, and metabolomic variations to identify biomarkers and pathways linked to drug response, efficacy, and safety. Methods: Thirty-seven healthy volunteers, enrolled after rigorous health assessments, received a single dose of etodolac (Flancox^® 500 mg). Pharmacokinetic profiles were determined using tandem mass spectrometry analysis, and the metabolomic profiling was conducted using baseline samples (pre-dose) and samples at maximum drug concentration (post-dose) via liquid chromatography coupled with a quadrupole time-of-flight mass spectrometer. Network analysis was employed to interpret the data. Results: Correlations were observed between metabolomic profiles and pharmacokinetic parameters as well as clinical characteristics. Notably, metabolites derived from arachidonic acid, such as prostaglandins and leukotrienes, were linked to etodolac’s pharmacokinetics. Other metabolites involved in pathways like cholesterol biosynthesis, bile salts, riboflavin, and retinoic acid signaling were correlated with hematological and liver function parameters. These findings are consistent with the infrequent adverse events reported by participants, including hematological and biochemical changes in liver function. Conclusions: A set of metabolites was identified in possible associations between specific pathways and unusual side effects, comparing the metabolic profiles before and after doses of etodolac. Our results highlight the importance of optimizing drug therapy and minimizing adverse events by taking into account individual metabolic profile information. Full article

A comprehensive hydrogeological, geophysical, and hydrochemical investigation was conducted in southeastern Hitchcock County, Nebraska, within the Driftwood Creek alluvial aquifer. This study assessed groundwater contamination stemming from the surface disposal of saline wastes from oilfield activities. A contaminated area, initially identified through regional groundwater sampling, was examined in detail. Monitoring wells were installed, and groundwater and soil samples were collected for chemical analysis. Surface electrical resistivity surveys were also performed to delineate contamination patterns. The findings revealed that the groundwater contamination originated from the leaching of residual evaporative salts through the vadose zone, beneath an abandoned emergency-evaporation brine storage pit. Data from down-hole specific conductance logs, water quality analyses, and computer-generated interpretations of surface electrical resistivity indicated that contaminant migration was primarily influenced by gravity, bedrock topography, and the local hydraulic gradient. An initial surface electrical resistivity profile survey was conducted to optimize the placement of monitoring wells and soil sampling sites within the vadose zone. Following well installation, a contaminant source with complex brine contamination patterns was detected within the shallow aquifer. Vertical electrical soundings were then carried out as the final investigative step. The data from these soundings, combined with test hole records, water level measurements, brine contaminant distribution, and soil analyses, were refined through a computer program employing the method of steepest descent. By incorporating known layer thicknesses and resistivities as constraints, this approach minimized the common issue of non-unique electrical sounding interpretations, providing information on the distribution of brine contaminants within the alluvial aquifer. Full article

Pimozide is a first-generation antipsychotic used in the treatment of schizophrenia, Gilles de la Tourette syndrome, and other chronic psychoses. Its in vivo efficacy is limited by poor solubility and consequent poor bioavailability. Therefore, adipic acid was used as a coformer for the preparation of a binary product with improved pharmaceutical properties. The thermal behavior of the liquid-assisted grinding products of compositions included in the range 0.1 < X_PMZ < 0.9 has been interpreted using a thermo-dynamic model according to which the two components originate a new crystalline entity in molar ratio pimozide:adipic acid 0.66:0.33, which forms an eutectic system with adipic acid. The model was confirmed using the quantitative analysis of the melting peaks and using the X-ray diffraction measurements from powders and single crystals. In particular, the latter have demonstrated that the new entity resulting from the pimozide:adipic acid 0.66:0.33 composition is actually salt [PMZH]₂[adipate]. The crystalline product was characterized, from a pharmaceutical perspective, in terms of solubility and wettability (contact angle). Then, a tablet formulation was developed, and its dissolution behavior was compared to a commercial product considered as a reference. The new entity showed improved pharmaceutical properties in terms of solubility and wettability compared to the pure drug in both deionized water and bio-relevant fluids simulating oral administration in fed and fasted conditions. The tablets containing the new crystalline form can make this virtually insoluble drug available for absorption within minutes regardless of the variability in gastric conditions. Full article

This study aims to tackle the challenge of explaining the underlying mechanisms behind the time-varying impedance phenomenon in animals experiencing low-voltage electric shocks. A dynamic impedance model that considers the effect of water electrolysis (WEDI) has been developed. First, we conducted root cause analyses through progressive validation experiments, identifying water within the shocked body as the key factor influencing impedance variation. Monitoring hydrogen concentration above the electrodes and measuring mass changes in the shocked body revealed that the time-varying impedance is closely related to internal water electrolysis. Second, we quantitatively analyzed the impact of water molecule decomposition during electrolysis on the salt concentration and conductivity within the electrically shocked body. A mathematical relationship between the variable resistance within the body and time was derived. A dynamic impedance model for animal electric shock that considered the effects of water electrolysis was subsequently established, explaining the underlying mechanism behind the time-varying impedance phenomenon. Finally, the Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) between the electric shock current predicted by the WEDI model and actual measurements were 0.00357, 0.00350, and 0.00446. Compared to existing models, the WEDI model demonstrates superior accuracy and interpretability. Full article