Assessment Of Degradation Research Articles

Mechanism and toxicity assessment of carbofuran degradation by persulfate-based advanced oxidation process.

The advanced oxidation process based on persulfate has been proven to be a promising method for degrading the highly toxic carbamate pesticide carbofuran (CBF). However, the mechanism of CBF degradation by sulfate radicals (SO4·-) and hydroxyl radicals (·OH) is still unclear and requires further research and discussion. This study investigated the mechanism and toxicity assessment of CBF degradation using density functional theory (DFT) theory calculation methods. The results indicated that SO4·- and ·OH can undergo addition and abstraction reactions with CBF. Thermodynamic and kinetic analysis showed that the abstraction reaction between SO4·- and the secondary H atom is the optimal reaction pathway, exhibiting the highest branching ratio (Γ = 41.84%). The rate constants for the reactions of CBF with SO4·- and ·OH at room temperature were found to be 3.66 × 109 and 8.96 × 108 M-1 s-1, respectively, which are consistent with experimental data reported in previous studies. The acute and chronic toxicity of CBF and its degradation products to aquatic organisms was predicted through an ecological toxicity assessment model. The toxicity of the degradation products was lower than that of the parent CBF, confirming the viability of using persulfate-based advanced oxidation processes for water treatment.

Erosion of rigid plastics in turbid (sandy) water: quantitative assessment for marine environments and formation of microplastics.

Mechanical degradation (erosion) of plastics in the marine environment has been reported in many literature studies but without quantitative information. This type of degradation is crucial as it accounts for most of the initial microplastic products, in marine environments (e.g., rivers and oceans). Here, we quantify the erosion of plastics by water-borne sediments under typical perpendicular water velocities and sand loads of turbid rivers and coastal oceans. Polypropylene (PP) shows the highest response to water-borne erosion, with a surface degradation rate of 5160 μm per year (4.44 mg per mm2 per year), compared with high-density polyethylene (HDPE) with a degradation rate of 1874 μm per year (1.79 mg per mm2 per year), resulting in the formation of microplastics (MPs). The rate of formation of such microplastic particles (>10 μm), as characterised by a laser direct infrared (LDIR) chemical imaging system, amounts to 669 particles per mm2 per year for PP and 187 particles per mm2 per year for HDPE, exhibiting average particle sizes of 60 μm and 23 μm in the same order. Furthermore, surface microscopy provided valuable insights into the dominant erosion mechanisms, revealing three distinct zones and the surface features reveal the brittle erosion behaviours. These results will enable a better assessment of degradation and lifetime prediction of plastics in turbid rivers and coastal oceans, allowing precise estimation of the rate of formation of MPs.

KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch.

Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they often underutilize existing knowledge from the medical and signal processing community. In this paper, we address three shortcomings of deep learning models: motion artifact removal, degradation assessment, and physiologically plausible analysis of the PPG signal. We propose KID-PPG, a knowledge-informed deep learning model that integrates expert knowledge through adaptive linear filtering, deep probabilistic inference, and data augmentation. We evaluate KID-PPG on the PPGDalia dataset, achieving an average mean absolute error of 2.85 beats per minute, surpassing existing reproducible methods. Our results demonstrate a significant performance improvement in heart rate tracking through the incorporation of prior knowledge into deep learning models. This approach shows promise in enhancing various biomedical applications by incorporating existing expert knowledge in deep learning models.

Fault diagnosis technology of CNC electromechanical system in mechanical engineering equipment manufacturing under structural coupling

Abstract This study addresses the fault diagnosis technology of CNC electromechanical systems in mechanical engineering equipment manufacturing, and explores the fault detection methods under the influence of structural coupling to improve the accuracy and efficiency of fault diagnosis. The study first analyzes the time-domain and frequency-domain features for fault diagnosis, including quantitative and dimensionless features used to identify different types of faults. Subsequently, the study explores feature dimensionality reduction methods, including algorithms such as PCA, LLE and t-SNE, and compares the effectiveness of their application in fault diagnosis. The research focuses on proposing a lightweight deep learning fault diagnosis framework called LTCN-BLS, which combines 2-DLTCN and 1-DLTCN branches, and an ILAEN-based BLS classifier to effectively extract and fuse time-domain and time-frequency-domain features of the data. The experimental results show that the LTCN-BLS framework has high accuracy and low network complexity in fault diagnosis, and has obvious advantages in early fault monitoring, degradation assessment, and robustness compared with traditional methods.

Assessment of Soil Degradation for Biomass Production in Tanjungpinang, Indonesia

Land management, especially land that is not in accordance with the rules can cause damage/change in soil properties which in turn impacts the disruption of biomass production. The lack of available data regarding soil degradation, especially in Tanjungpinang City, has resulted in less than optimal supervision and control over land management. Mapping the condition of soil degradation is necessary to exercise control over land management. The purpose of this research is to assess soil degradation in Tanjungpinang City, Riau Archipelago for biomass production. Soil degradation can be modeled by scoring and overlaying four parameters, namely land use, slope, soil type, and rainfall, producing a tentative map of potential soil degradation. Next, a tentative map of soil degradation is corrected using a field survey. Field surveys are used to measure the standard criteria for soil degradation according to Government Regulation No. 150 of 2000. The results show that the criteria for soil degradation in Tanjungpinang City are light damage and moderate damage. The criteria for moderate damage have the largest area spread over Dompak Sub-District, Batu Sembilan, Melayu City Plate, Bukit Cermin, West Tanjungpinang, East Tanjungpinang, Kampung Baru, and Tanjung Ayun Sakti. The main limiting factors that limit land use in Tanjungpinang City are permeability, surface rocks, and redox.

Assessment of Environmental-induced Degradation on Reinforced Concrete Tie Rods in a 70-Year-Old Arch Bridge

Given the present landscape of existing bridges in Italy, a significant portion of which dates back to the mid-20th century, the need to conduct detailed investigations on the current deterioration state and on the safety of these infrastructures becomes indispensable. This work, focusing on a case study involving reinforced concrete tie rods removed from a 70-year-old arch bridge and preserved for mini-invasive diagnostics, addresses the critical need of estimating the current condition of these elements while verifying whether the common non-destructive techniques can be used to monitor the progress of deterioration. Both electrochemical measurements (e.g. corrosion potential and electrical resistivity) were used to detect the current corrosion conditions of steel bars; small core samples were also extracted to assess the presence of carbonation. In addition, this study allowed to investigate the effectiveness of the past restoration interventions, determining whether they achieved the designed outcomes. This research questions the effectiveness of these diagnostic measures to propose their repetition in situ for reliable, non-continuous monitoring of the structural health of these elements over time. The next steps involve visual inspection and mechanical characterization of the reinforcement to demonstrate the effectiveness of the diagnostics presented.

Condition assessment of timber in the old built heritage - a case study

Where the rehabilitation and repair of ancient buildings is prevalent, the primary focus is on preserving existing materials and construction techniques. To achieve this, it is crucial that technicians have the expertise and tools to assess the value of traditional materials. This article introduces a practical methodology for identifying and evaluating the physical-mechanical properties of wood in ancient ordinary buildings. The study is grounded in the latest technical-scientific guidelines at the European level and adheres to grading rules defined by European regulations, which have been adapted for structures in service. The methodology also addresses the assessment of widespread biological degradation. The study provides a robust foundation for the technical implementation of in-service inspection actions for wooden structures in built heritage in Portugal and Europe, but its findings can be extrapolated to other countries as well. As a practical illustration, we present and discuss the assessment of a beam representative of a 19th-century ordinary building's wooden floor structure. In this case study, we applied a practical and viable assessment strategy, supported by an on-site inspection procedure. We conclude that inspection work is fundamental to underpinning a well-structured project and that equipping technicians with experience in working with ancient buildings and a practical and straightforward methodology is key to successful condition monitoring. This work forms part of the research activity conducted at Civil Engineering Research and Innovation for Sustainability (CERIS) and has been funded by Fundação para a Ciência e a Tecnologia (FCT) as part of project UIDB/04625/2020.

Assessment of microbial degradation of various types of bitumen using FTIR

ABSTRACT Pseudomonas aeruginosa (P. aeruginosa), a common bacterium found in the environment, is known to be a potential cause of bitumen degradation. Hence, the effect of P. aeruginosa on bitumen is to be evaluated. For this purpose, Reclaimed Asphalt Pavement (RAP) is collected from the pavement surface and it is tested for the presence of P. aeruginosa. Subsequently, this isolated P. aeruginosa strain is employed in experiments to evaluate its effect on three types of bitumen, namely: Viscosity Grade-30 (VG-30), and two types of Polymer Modified Binders (PMBs) viz., PMB-K and PMB-T. Bitumen degradation, Fourier Transform Infrared Spectroscopy (FTIR), and Scanning Electron Microscope (SEM) studies show that P. aeruginosa has degraded PMB-T more than PMB-K and VG-30. Degradation occurs due to the breakage of bonds between polymer and bitumen and confirmed that P. aeruginosa has the ability to degrade PMBs, which may alter their physical properties. The degradation test results presented that PMB-T has 12.5% degradation compared to the control sample. The FTIR findings indicate that P. aeruginosa has modified the chemical composition of PMBs, and has converted them into base bitumen. It is recommended to study, molecular analyses (i.e. metagenomic and transcriptomic) of P. aeruginosa on PMBs.

ASSESSMENT OF THE IMPACT OF ARMED AGGRESSION OF THE RF ON THE SOIL COVER OF UKRAINE

The purpose of this study is to assess the impact of armed aggression by the rf on the soil resources of Ukraine and to develop a map regarding the impact of military actions on the soil cover at different levels of management: the country, region, district, and territorial community. In this study, we used the following main methods: analysis, synthesis, monographic, abstract-logical, and cartographic. The article introduces a new type of soil degradation: degradation caused by armed aggression (military type), which includes the following types: (1) mechanical degradation; (2) physical degradation; (3) chemical degradation; (4) physical and chemical degradation; (5) biological degradation; and (6) other directions of influence on soils and land plots. For the first time, maps have been developed regarding the impact of military actions on the soil cover at different levels of management: Ukraine, oblast, district, and territorial community. Areas of each type of soil affected by military operations and requiring further examination and control have been determined. It was established that as of March 2023, the largest area affected by hostilities is characteristic of chernozem soils: ordinary chernozems (5.0M ha), southern (2.1M ha), typical and podzolized (1.9 million ha), meadow soils and meadow and chernozem (0.2M ha). Considering the area, chernozems were the most affected; in some areas, the soils were destroyed. The practical value of the obtained results is that their application should contribute to the comprehensive assessment of military soil degradation in Ukraine. Spatial specificities of interstate and interregional migrations in the population of Ukraine are defined for the prewar period, the period of russia’s hybrid war against Ukraine (2014–2021), and the full-scale russian-Ukrainian war (beginning on February 24, 2022). The scope and the directions of migrant departures/arrivals are disclosed, as well as their effect on the dynamics of population number in Ukraine and its regions.

Rubia-inspired biogenic synthesis of Cu–ZnO nanocomposites: Dual-modelling of visible light photocatalytic degradation and antibacterial assessment

Cu-doped ZnO nanoparticles (NPs) were synthesised using Rubia cordifolia root extract and their photocatalytic degradation and antimicrobial properties were evaluated. Among all dopant concentrations, UV-VIS analysis of 5 % Cu–ZnO NPs revealed a clean shift towards the visible range with a reduction in the band gap from 3.2 eV for pristine ZnO to 2.98 eV. Formed NPs were identified as wurtzite crystal structure (size of 16.67 nm) having functional group of ZnO, using XRD and FTIR analysis. Highest photocatalytic degradation efficiencies of both Alizarine Red (AZ) (80 %) and Rhodamine B (RhB) (82 %) dyes were by 5 % Cu–ZnO NPs. Statistical modelling and optimization were conducted using Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), resulting in development of models having >90 % predictive accuracy. Furthermore, the biogenic Cu–ZnO nanoparticles exhibits effective antimicrobial properties against both gram-positive (S. aureus) and gram-negative (E. coli) bacteria. The biogenic synthesis approach demonstrated enhanced photocatalytic efficiency and antimicrobial properties, suggesting its potential for environmentally friendly applications.

Analytical quality by design approach to develop an eco-friendly RP-HPLC method for estimation of irbesartan in chitosan polymeric nanoparticles: forced degradation studies and assessment of in vitro release mathematical modelling.

Irbesartan is an angiotensin converting enzyme blocker, primarily utilized for the management of hypertension and the mitigation of diabetic nephropathy progression. The present study introduces rapid, robust and environmentally sustainable reverse phase high performance liquid chromatography (RP-HPLC) validated under the analytical quality by design (AQbD) framework according to ICH guidelines. Utilizing a central composite design, the method's systemic optimization was achieved, ensuring reproducibility and accuracy. Chromatographic separation was accomplished utilizing an ethanol and sodium acetate buffer (60 : 40 v/v) isocratic mobile phase system on a zorbax sb C18 column, with a flow rate of at 0.6 mL min-1. Studies on forced degradation outlined stability of irbesartan and its degradation processes, enhancing our understanding of its chemical robustness under varied conditions. Complementing the green chemistry paradigm, the method's environmental impact was critically assessed, affirming its alignment with sustainability objectives. The validated method proved pivotal in determining the percent entrapment and loading efficiency of the formulated nanoparticles and holds potential for application in biological matrices. Furthermore, the encapsulation of IRB within chitosan nanoparticles was explored to assess release kinetics and enhance bioavailability. This study not only advances the analytical sciences by merging eco-friendly practices with method development but also broadens the applicative landscape of HPLC methodologies in drug delivery research.

Assessment of potential soil erosion in Mongolia based on the RUSLE model and RCP 8.5 scenario

AbstractDue to its relatively high altitude and a continental climate, environmental issues related to soil erosion and land degradation more seriously affect the ecosystem and crop production in Mongolia. As detailed soil erosion and land degradation assessments have only been performed in some regions of Mongolia, the purpose of this study is to evaluate the soil vulnerability on a national scale and predict future soil erosion under climate change prior to establishing an appropriate management plan. The revised universal soil loss equation model was chosen for this study, which reflects geographic and climatic characteristics in a vast area as the Mongolian territory. We evaluated soil erosion using historical data from 1993 and 2013 and generated predictions in the near (2041–2060) and far future (2061–2080) periods under the representative concentration pathway 8.5 scenario. The soil erosion rates were divided into six classes: very low, low, medium, high, very high and extreme. The total of 46,039 km2 of the Mongolian territory was classified as very high and extreme risk, which is predicted to increase up to 48,961 and 51,769 km2, in the near and far future periods, respectively. At the national scale, most of the soil erosion appeared in bare area, as it covers more than 60% of the total area. Meanwhile, the highest soil erosion risk was expected in high mountain ranges and ecologically vulnerable regions of the Gobi Desert and steppe area. This study identifies three land cover types with high priority for management and areas that should be considered first in the sustainable national land use and soil management plans considering regional characteristics.

Assessing the accuracy of spectral indices obtained from Sentinel images using field research to estimate land degradation.

Wind erosion resulting from soil degradation is a significant problem in Iran's Baluchistan region. This study evaluated the accuracy of remote sensing models in assessing degradation severity through field studies. Sentinel-2 Multispectral Imager's (MSI) Level-1C satellite data was used to map Rutak's degradation severity in Saravan. The relationship between surface albedo and spectral indices (NDVI, SAVI, MSAVI, BSI, TGSI) was assessed. Linear regression establishes correlations between the albedo and each index, producing a degradation severity map categorized into five classes based on albedo and spectral indices. Accuracy was tested with 100 ground control points and field observations. The Mann-Whitney U-Test compares remote sensing models with field data. Results showed no significant difference (P > 0.05) between NDVI, SAVI, and MSAVI models with field data, while BSI and TGSI models exhibited significant differences (P ≤ 0.001). The best model, BSI-NDVI, achieves a regression coefficient of 0.86. This study demonstrates the advantage of remote sensing technology for mapping and monitoring degraded areas, providing valuable insights into land degradation assessment in Baluchistan. By accurately identifying severity levels, informed interventions can be implemented to mitigate wind erosion and combat soil degradation in the region.

Joint optimization of degradation assessment and remaining useful life prediction for bearings with temporal convolutional auto-encoder

Remaining useful life (RUL) prediction and degradation assessment are pivotal components of prognostic and health management (PHM) and represent vital tasks in the implementation of predictive maintenance for bearings. In recent years, data-driven PHM techniques for bearings have made substantial progress through the integration of deep learning methods. However, modeling the temporal dependencies inherent in raw vibration signals for both degradation assessment and RUL prediction remains a significant challenge. Hence, we propose a joint optimization architecture that uses a temporal convolutional auto-encoder (TCAE) for the degradation assessment and RUL prediction of bearings. Specifically, the architecture includes a sequence-to-sequence model to extract degradation-sensitive features from the raw signal and utilizes temporal distribution characterization (TDC) and a nonlinear regressor to determine the degradation stages and predict RUL, respectively. Our framework integrates the tasks of degradation assessment and RUL prediction in a unified, end-to-end manner, using raw signals as input, resulting in high RUL prediction accuracy (RMSE = 0.0832) on publicly available and self-built datasets. Our approach outperforms state-of-the-art methods, indicating its potential to significantly advance the field of PHM for bearings.

Dynamics and degradation of pastures in the microregion of Goiânia through vegetation indices and satellite data

The quality of pasture directly reflects on the productivity of meat, milk, and their derivatives, as well as the socio-environmental quality of a particular region. The assessment of pasture degradation in extensive areas can be conducted through vegetation indices obtained from sensors installed on orbiting satellites. This study aimed to understand the dynamics of pastureland in the Goiânia Microregion between the years 1985 and 2019, as well as to assess the quality of these pastures in 2019. Satellite images obtained from Mapbiomas for the years 1985, 1997, 2008, and 2019 were used to determine pasture areas by municipality. To assess pasture degradation, the average annual NDVI was obtained from Sentinel 2A satellite images with a resolution of 10 meters. A gradual reduction in pastureland area was observed in 15 out of the 17 municipalities in the Microregion during the study years. According to the average NDVI values in 2019, only 26% of the pasture area showed no degradation, while 44%, 22%, and 8% exhibited mild, moderate, and severe degradation, respectively. Sentinel 2A satellite images prove to be an efficient tool for assessing pasture degradation in the studied region.

Enhancing Temperature-Dependent Li-Ion Battery Behavior Predictions with Transfer Learning

Lithium-ion batteries (LiBs) are crucial in modern energy applications, with their performance significantly influenced by temperature variations. LiBs undergo temperature-dependent chemo-mechanical processes that contribute to capacity fade. Accurate assessment of capacity degradation during cycling necessitates consideration of various factors, including temperature, depth of discharge, cell chemistry, charge/discharge rate, and others. Publicly accessible datasets often encompass only a restricted spectrum of the aforementioned battery health influencers. However, obtaining comprehensive real-world data encompassing diverse temperature conditions poses challenges and requires substantial resources. To address the data scarcity, we propose a novel approach utilizing transfer learning to improve predictive models for temperature-dependent behavior in Li-ion batteries. In this study, we generate synthetic data simulating battery behavior under specific temperature ranges. The pre-training of the machine learning model on this synthetic data enables the capture of complex interplays between temperature and battery phenomena, identifying unique patterns and relationships. Subsequently, we transfer the acquired knowledge to the target domain, facilitating predictions for battery behavior under varying temperature conditions. Fine-tuning the pre-trained model using real-world data from the target domain ensures adaptability to new temperature conditions. The data efficiency gained from synthetic data generation in the source domain allows effective pre-training, even with limited real-world data in specific temperature regimes. Moreover, the model with its transfer learning method acquires a robust understanding of the underlying features governing temperature-dependent battery behavior, enabling better generalization and enhanced predictions for effective battery management across diverse environmental conditions. In addition to the sensitivity analysis to identify the most significant impact on the predictive model’s performance in the focused temperature range, we compare the performance of the fine-tuned model with other predictive models that do not utilize transfer learning. Our findings could offer cost-effective, rapid, and accurate predictions, ensuring robust performance in scenarios with limited real-world data. As the demand for Li-ion batteries grows across various applications, these insights hold significant potential in advancing the development of efficient and reliable energy storage systems.

THE MAIN DIRECTIONS OF SCIENTIFIC RESEARCH AT THE DEPARTAMENT OF EROSION AND SOILPROTECTION: CURRENT STATE AND PROSPECTS OF DEVELOPMENT

The Department of Erosion and Soil Protection has developed the main scienti c directions - «Assessment of erosion processes in soils of various bioclimatic zones», «Study of the basics of sustainable land use» and «Ecological and economic assessment of soil and land degradation». It is proposed to develop both existing areas of scienti c research with a certain adjustment of their names and problems («Analysis and modeling of erosion processes in soils, including in the context of climate change», «Development of the concept of sustainable land use in the context of food security», «Ecology, economics and socio-demographic features of land use in the context of climate change and soil degradation»), as well as new directions that have some research history at the department - «Development of principles of soil protection», «Development of methodological foundations of land reclamation». e necessity of creating a holistic concept of soil protection is considered in particular detail. is concept should include both the formulation of legal (legislative and regulatory-methodological) principles of soil protection, and the de nition of criteria for assessing soils under which their protection can be carried out, and the development of soil protection systems ( rst of all, from the manifestation of erosion processes) in various bioclimatic and administrative-territorial conditions.

RETRACTED: An assessment of photovoltaic module degradation for life expectancy: A comprehensive review

Determining the life expectancy of a photovoltaic (PV) system involves many factors, including the assessment of photovoltaic module degradation. Degradation of PV modules is a normal process that develops over time as a result of a number of factors, including UV radiation exposure, thermal cycling, and weathering. The degradation rate, which is the percentage decline in module efficiency per year, is an often-used indicator for evaluating PV module deterioration. Depending on the type of module (crystalline silicon (c-Si), copper indium gallium selenide (CIGS) and cadmium telluride (CdTe) and the environment, degradation rates can vary greatly, although studies have indicated that most modules degrade at a rate of 0.5% to 1% per year. Additionally, the review focuses on developing PV technologies, like dye-sensitized solar cells (DSSCs), organic PVs (OPVs), and metal halideperovskite (MHP) solar cells, where relevant long-term degradation strategies have not yet entirely developed and the majority principles are not completely understood owing to their lower to medium technological maturity levels. Nonetheless, a thorough overview of the established stability issues related to every new photovoltaic technology is provided. There are a number of techniques that can be employed, including visual inspection, electrical performance testing, and infrared imaging, to evaluate the degradation of a PV module. Through the use of accelerated stress testing (AST), photovoltaic (PV) modules can be subjected to the consequences of years of operation in a fraction of the time. The goal of AST is to assess how well PV modules hold up over the long term in situations like high temperatures, humidity, and UV exposure. A PV module is put through a series of environmental circumstances that are more demanding than ordinary working conditions in order to perform an accelerated stress test on it. To calculate the module's rate of deterioration over time, data from an accelerated stress test can be used. This data is crucial for estimating the module's long-term performance and dependability as well as formulating plans to increase the lifespan of PV systems. Manufacturers, researchers, and designers of PV systems might benefit from using AST to assess the performance of PV modules in challenging environments and make sure they adhere to industry standards for toughness and dependability. Therefore, in this study, degradation factors, along with other parameters like the accelerated-stress tests linked to PV module degradation, to predict the life expectancy of various PV system especially matured PV systems, has been examined. This paper concludes by reviewing the lessons learnt from established photovoltaic technologies and discussing potential applications for these insights in the research and development of forthcoming technologies. Specifically, industry-standard accreditation tests must ultimately be passed by any developing PV technology, and advanced c-Si-based module warranties may be prolonged beyond the 25-year limit.

Integrated impact assessment of anthropogenic degradation in livestock agroecosystems of the Arid Chaco (Argentina)

Objetivos: desarrollar un procedimiento de evaluación integral del impacto de la degradación antropogénica utilizando un indicador compuesto, y conocer la magnitud de los impactos, en tres agroecosistemas pecuarios. Metodología: se aplicó un diseño de estudio de casos múltiples integrando métodos mixtos. Se definieron participativamente, y utilizaron métodos matemáticos de cálculo y estandarización sencillos para construir el indicador compuesto. Para la recopilación de información de variables humanas, la muestra fue por redes y evaluada; la unidad de recolección de datos, los actores sociales involucrados en cada caso; y la recopilación de información, se realizó por entrevistas semiestructuradas y estructuradas. Para las variables biológicas-físicas, la muestra fue la transecta; el universo de datos, la finca; y la recolección de información, se realizó por muestreo observacional sistemático. Resultados: se obtuvo impacto moderado con riesgo de degradación crítica para dos de las fincas, indicando la necesidad de modificación del plan de manejo para evitar cambios irreversibles. La otra, mostró impacto crítico con riesgo de impacto severo, indicando la detención de actividades y su intervención, para evitar el avance de cambios irreversibles y permitir la rehabilitación. Conclusiones: el indicador construido, permitió evaluar integralmente los impactos y riesgos de ocurrencia de la degradación antropogénica. la simplicidad de los métodos utilizados, promueve su uso público abierto. El procedimiento aplicado promueve una mejor comprensión del comportamiento del fenómeno estudiado, mayor efectividad de los planes de gestión, y la viabilidad y conservación de los agroecosistemas de zonas áridas, los más vulnerables ante la acción de la degradación antropogénica.

Assessment of Invadopodium Formation and Gelatin Degradation in Vitro.

Some cancer cells form highly regulated structures, termed invadopodia, which mediate local, enzymatic degradation of extracellular matrix and facilitate cancer cell invasion and migration during metastatic progression. Understanding invadopodium formation and function in cancer cells is therefore an important strategy to find novel clinical approaches to interfere with metastasis. Invadopodia are F-actin-rich protrusions that form on the advancing edge of cells, supported by complex molecular interactions at the cell membrane. Invadopodia formation, structure, and function can be studied in vitro, using commonly cultured cancer cell lines and standard microscopic techniques. Here, these approaches are described in detail.