Métodos para evaluar interacciones entre cuerpos de agua en un humedal y aplicación en dos casos de estudio

La comprensión de la dinámica de ecosistemas dependientes del recurso hídrico subterráneo, como pueden llegar a serlo algunos humedales, parte del conocimiento del sistema hidrológico. Para alcanzar esta meta se aplica una serie de métodos y procedimientos de análisis que comprenden la caracterización hi-drogeológica, la delimitación del área de captura de agua hacia el humedal, el monitoreo piezométrico, la rea-lización de balances de masas. La modelación numérica, los análisis hidrogeoquímicos y los métodos isotópi-cos permiten refinar y validar los modelos conceptuales. En el marco del proyecto Hydrochemical and isotopic techniques for assessment hidrological proccesses on wetlands, promovido entre los años 2006 y 2011 por la Agencia Internacional de Energía Atómica (IAEA), Colombia y Argentina compartieron conoci-miento y experiencias para entender la dinámica de los humedales Ciénaga Colombia y La Salada. En este texto se resumen aspectos metodológicos y los resultados de los dos casos de estudio considerados.The Understanding of ecosystem dynamics, for example the wetlands, depends of the knowledge of the hydrologic system. Many techniques can be used in order to obtain a good conceptual mod-el of the wetlands and its catchment area: hydrogeology, numerical modeling, hydrogeochemestry, and iso-tope hydrology. researchers of Argentina and Colombia studied -According with the project: ―Hydrochemical and isotopic techniques for assessment hidrological proccesses on wetlands‖ (IAEA, 2006 to 2011)- two wet-lands hydrogeology dependent: La salada Pond and Cienaga Colombia Weltand. These projects used method-ologies similar and they obtained validated hydrological models

Use of Transient Time Response as a Measure to Characterize Phononic Crystal Sensors

Phononic crystals are periodic composite structures with specific resonant features that are gaining popularity in the field as liquid sensors. The introduction of a structural defect in an otherwise periodic regular arrangement can generate a resonant mode, also called defect mode, inside the characteristic band gaps of phononic crystals. The morphology, as well as the frequency in which these defect modes appear, can give useful information on the composition and properties of an analyte. Currently, only gain and frequency measurements are performed using phononic crystal sensors. Other measurements like the transient response have been implemented in resonant sensors such as quartz microbalances showing great results and proving to be a great complimentary measure to the gain and frequency measurements. In the present paper, a study of the feasibility of using the transient response as a measure to acquire additional information about the analyte is presented. Theoretical studies using the transmission line model were realized to show the impact of variations in the concentration of an analyte, in this case, lithium carbonate solutions, in the transient time of the system. Experimental realizations were also performed showing that the proposed measurement scheme presents significant changes in the resulting data, indicating the potential use of this measure in phononic crystal sensors. This proposed measure could be implemented as a stand-alone measure or as a compliment to current sensing modalities

Tracking end-of-life stage of chemicals: A scalable data-centric and chemical-centric approach.

[EN]Chemical flow analysis (CFA) can be used for collecting life-cycle inventory (LCI), estimating environmental releases, and identifying potential exposure scenarios for chemicals of concern at the end-of-life (EoL) stage. Nonetheless, the demand for comprehensive data and the epistemic uncertainties about the pathway taken by the chemical flows make CFA, LCI, and exposure assessment time-consuming and challenging tasks. Due to the continuous growth of computer power and the appearance of more robust algorithms, data-driven modelling represents an attractive tool for streamlining these tasks. However, a data ingestion pipeline is required for the deployment of serving data-driven models in the real world. Hence, this work moves forward by contributing a chemical-centric and data-centric approach to extract, transform, and load comprehensive data for CFA at the EoL, integrating cross-year and country data and its provenance as part of the data lifecycle. The framework is scalable and adaptable to production-level machine learning operations. The framework can supply data at an annual rate, making it possible to deal with changes in the statistical distributions of model predictors like transferred amount and target variables (e.g., EoL activity identification) to avoid potential data-driven model performance decay over time. For instance, it can detect that recycling transfers of 643 chemicals over the reporting years (1988 to 2020) are 29.87%, 17.79%, and 20.56% for Canada, Australia, and the U.S. Finally, the developed approach enables research advancements on data-driven modelling to easily connect with other data sources for economic information on industry sectors, the economic value of chemicals, and the environmental regulatory implications that may affect the occurrence of an EoL transfer class or activity like recycling of a chemical over years and countries. Finally, stakeholders gain more context about environmental regulation stringency and economic affairs that could affect environmental decision-making and EoL chemical exposure predictions.USEP

Automatic translation of the dactilologic language of hearing impaired by adaptive systems

Una de las principales limitaciones que presentan las personas con discapacidad auditiva está directamente relacionada con su dificultad para interactuar con otras personas, ya sea de forma verbal o a través de sistemas auxiliares basados en la voz y el audio. En este artículo se presenta el desarrollo de un sistema integrado de hardware y software, para el reconocimiento automático del lenguaje dactilológico de señas utilizado por personas con este tipo de discapacidad. El hardware está compuesto por un sistema inalámbrico adherido a un guante, el cual posee un conjunto de sensores que capturan una serie de señales generadas por los movimientos gestuales de la mano, y un modelo por adaptación basado en los principios de la computación neuronal, el cual permite su reconocimiento en términos de un lenguaje dactilológico en particular. Los resultados arrojados por el sistema integrado mostraron gran efectividad en el reconocimiento de las vocales que conforman el lenguaje dactilológico en español, esto gracias a la capacidad que posee el modelo de asociar un conjunto de señales de entrada, con un movimiento dactilológico en particular.One of the main limitations of the people with hearing impairment is directly related to their difficulty interacting with others, either verbally or through auxiliary systems based on voice and audio. This paper presents the development of an integrated system of hardware and software for automatic fingerspelling sign language used by people with this type of disability. The hardware system comprises a glove which has a set of wireless sensors that capture a series of signals generated by the hand gestures, and a adaptive model based on the principles of neural computation, that allows recognition of a particular dactilologic language. Results from the integrated system showed great effectiveness in recognizing vowels from the dactilologic Spanish language. This recognition was influenced by the dimensionality reduction made by the neural model of the input signals representing movements, and the sensitivity factor that sets the limit between recognition and learning

Optimizing the phenotyping of rodent ASD models: enrichment analysis of mouse and human neurobiological phenotypes associated with high-risk autism genes identifies morphological, electrophysiological, neurological, and behavioral features

<p>Abstract</p> <p>Background</p> <p>There is interest in defining mouse neurobiological phenotypes useful for studying autism spectrum disorders (ASD) in both forward and reverse genetic approaches. A recurrent focus has been on high-order behavioral analyses, including learning and memory paradigms and social paradigms. However, well-studied mouse models, including for example <it>Fmr1 </it>knockout mice, do not show dramatic deficits in such high-order phenotypes, raising a question as to what constitutes useful phenotypes in ASD models.</p> <p>Methods</p> <p>To address this, we made use of a list of 112 disease genes etiologically involved in ASD to survey, on a large scale and with unbiased methods as well as expert review, phenotypes associated with a targeted disruption of these genes in mice, using the Mammalian Phenotype Ontology database. In addition, we compared the results with similar analyses for human phenotypes.</p> <p>Findings</p> <p>We observed four classes of neurobiological phenotypes associated with disruption of a large proportion of ASD genes, including: (1) Changes in brain and neuronal morphology; (2) electrophysiological changes; (3) neurological changes; and (4) higher-order behavioral changes. Alterations in brain and neuronal morphology represent quantitative measures that can be more widely adopted in models of ASD to understand cellular and network changes. Interestingly, the electrophysiological changes differed across different genes, indicating that excitation/inhibition imbalance hypotheses for ASD would either have to be so non-specific as to be not falsifiable, or, if specific, would not be supported by the data. Finally, it was significant that in analyses of both mouse and human databases, many of the behavioral alterations were neurological changes, encompassing sensory alterations, motor abnormalities, and seizures, as opposed to higher-order behavioral changes in learning and memory and social behavior paradigms.</p> <p>Conclusions</p> <p>The results indicated that mutations in ASD genes result in defined groups of changes in mouse models and support a broad neurobiological approach to phenotyping rodent models for ASD, with a focus on biochemistry and molecular biology, brain and neuronal morphology, and electrophysiology, as well as both neurological and additional behavioral analyses. Analysis of human phenotypes associated with these genes reinforced these conclusions, supporting face validity for these approaches to phenotyping of ASD models. Such phenotyping is consistent with the successes in <it>Fmr1 </it>knockout mice, in which morphological changes recapitulated human findings and electrophysiological deficits resulted in molecular insights that have since led to clinical trials. We propose both broad domains and, based on expert review of more than 50 publications in each of the four neurobiological domains, specific tests to be applied to rodent models of ASD.</p

FMECA and FTA analysis applied to the manufacturing process of pulsating heat pipes

Pulsating heat pipes (PHPs) offer significant advantages for the thermal control of electronic components due to their simple manufacturing and high heat transfer rates. The reliability of PHPs has traditionally been assessed through long-life testing, but detailed reliability analyses from an equipment perspective are limited. The study of PHP reliability is essential due to its application and operational conditions. For instance, in aerospace applications these devices operate under severe conditions, and maintenance or replacement is impossible during operation, making them critical components in system functionality. The reliability analysis of PHPs focuses on the manufacturing process, considering future operating conditions. Although preliminary PHP testing will be conducted on Earth, laboratory conditions are less stringent due to the difficulty of replicating launch acceleration and space conditions for long-term testing under microgravity. This study presents an FMECA (Failure Modes, Effects, and Criticality Analysis) of the pulsating heat pipe manufacturing process, breaking down the production of each component. The results indicate that the most critical point is concentrated in the assembly of these components, leading to a higher incidence of welding failures. It recommends further work to improve welding and analyze mechanical stresses within the heat pipe

Functional features defining the efficacy of cholesterol-conjugated, self-deliverable, chemically modified siRNAs

Progress in oligonucleotide chemistry has produced a shift in the nature of siRNA used, from formulated, minimally modified siRNAs, to unformulated, heavily modified siRNA conjugates. The introduction of extensive chemical modifications is essential for conjugate-mediated delivery. Modifications have a significant impact on siRNA efficacy through interference with recognition and processing by RNAi enzymatic machinery, severely restricting the sequence space available for siRNA design. Many algorithms available publicly can successfully predict the activity of non-modified siRNAs, but the efficiency of the algorithms for designing heavily modified siRNAs has never been systematically evaluated experimentally. Here we screened 356 cholesterol-conjugated siRNAs with extensive modifications and developed a linear regression-based algorithm that effectively predicts siRNA activity using two independent datasets. We further demonstrate that predictive determinants for modified and non-modified siRNAs differ substantially. The algorithm developed from the non-modified siRNAs dataset has no predictive power for modified siRNAs and vice versa. In the context of heavily modified siRNAs, the introduction of chemical asymmetry fully eliminates the requirement for thermodynamic bias, the major determinant for non-modified siRNA efficacy. Finally, we demonstrate that in addition to the sequence of the target site, the accessibility of the neighboring 3\u27 region significantly contributes to siRNA efficacy