Applicability domains of neural networks for toxicity prediction

In this paper, the term "applicability domain" refers to the range of chemical compounds for which the statistical quantitative structure-activity relationship (QSAR) model can accurately predict their toxicity. This is a crucial concept in the development and practical use of these models. First, a multidisciplinary review is provided regarding the theory and practice of applicability domains in the context of toxicity problems using the classical QSAR model. Then, the advantages and improved performance of neural networks (NNs), which are the most promising machine learning algorithms, are reviewed. Within the domain of medicinal chemistry, nine different methods using NNs for toxicity prediction were compared utilizing 29 alternative artificial intelligence (AI) techniques. Similarly, seven NN-based toxicity prediction methodologies were compared to six other AI techniques within the realm of food safety, 11 NN-based methodologies were compared to 16 different AI approaches in the environmental sciences category and four specific NN-based toxicity prediction methodologies were compared to nine alternative AI techniques in the field of industrial hygiene. Within the reviewed approaches, given known toxic compound descriptors and behaviors, we observed a difficulty in being able to extrapolate and predict the effects with untested chemical compounds. Different methods can be used for unsupervised clustering, such as distance-based approaches and consensus-based decision methods. Additionally, the importance of model validation has been highlighted within a regulatory context according to the Organization for Economic Co-operation and Development (OECD) principles, to predict the toxicity of potential new drugs in medicinal chemistry, to determine the limits of detection for harmful substances in food to predict the toxicity limits of chemicals in the environment, and to predict the exposure limits to harmful substances in the workplace. Despite its importance, a thorough application of toxicity models is still restricted in the field of medicinal chemistry and is virtually overlooked in other scientific domains. Consequently, only a small proportion of the toxicity studies conducted in medicinal chemistry consider the applicability domain in their mathematical models, thereby limiting their predictive power to untested drugs. Conversely, the applicability of these models is crucial; however, this has not been sufficiently assessed in toxicity prediction or in other related areas such as food science, environmental science, and industrial hygiene. Thus, this review sheds light on the prevalent use of Neural Networks in toxicity prediction, thereby serving as a valuable resource for researchers and practitioners across these multifaceted domains that could be extended to other fields in future research

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Estudio para la determinación de acrilamida mediante tecnología NIR en alimentos procesados

El pan es un alimento básico en muchos países, consumido por toda su población en una amplia gama de productos de panificación. Se ha encontrado que durante procesos de horneado y tostado a ciertas temperaturas se puede producir acrilamida, un compuesto que en determinadas concentraciones es nocivo para la salud. La acrilamida es un compuesto formado en la reacción de Maillard, cuando reaccionan la asparagina y los azures reductores (glucosa) presentes en alimentos con alto contenido en almidón, al someterlos a procesos superiores a 120ºC y escasa humedad. Actualmente, los métodos utilizados implican el uso de técnicas cromatográficas de gases o líquidos con detector de masas, lo que conlleva un proceso preparatorio de la muestra además de un alto coste en tiempo y medios. En este trabajo se pretende poner a punto un procedimiento para su determinación por cromatografía HPLC-UV/Vis con el objetivo de encontrar un método que permita contrastar los resultados con los obtenidos por espectroscopia NIR (infrarrojo cercano), realizando medidas en tiempo real, lo que supondría una gran ventaja en la industria alimenticia. Para desarrollar un método NIR es necesario realizar múltiples medidas espectroscópicas utilizando herramientas quimiométricas para el tratamiento de datos, permitiendo establecer así una relación entre los resultados obtenidos por los análisis espectroscópicos y los cromatográficos.Departamento de Química AnalíticaMáster en Técnicas Avanzadas en Química. Análisis y Control de Calidad Químico

7th Workshop on Agri-food Research-WiA.18

[SPA] Este libro contiene los resúmenes de los trabajos presentados al VII Workshop en Investigación Agroalimentaria (WiA. 18) organizado por el Programa de doctorado en Técnicas Avanzadas en Investigación y Desarrollo Agrario y Alimentario (TAIDA) de la UPCT y celebrado en Cartagena del 7 al 8 de mayo de 2018. El programa científico se estructura en cuatro sesiones: Biotecnología Agroalimentaria, Tecnología e Ingeniería de Alimentos, Tecnología e Ingeniería de Producción Vegetal y Ingeniería Agroforestal y Económica.[ENG] The scientific manuscripts presented in the 7th Workshop on Agri-Food Research (WIA.18) are here reported. The WiA.18 is an annual Workshop organized by the Doctoral Program in Advanced Techniques for Research and Development in Food and Agriculture (TAIDA) of the Universidad Politécnica de Cartagena - UPCT (Spain) in where PhD candidates present their research works. Presentations showed a great scientific interest and reflect the high potential of the Research Groups that belong to the several departments and institutions integrated into our PhD Program (UPCT, CEBAS-CSIC, and IMIDA). We expect that during the celebration of this Workshop, the coexistence and exchange of ideas and experiences among PhD candidates, research groups, researchers, technicians, etc.. has been favored. The scientific program is structured into four sessions: Agrifood Biotechnology, Food Technology and Engineering, Plant Production Technology and Engineering and Agroforestry and Economy Engineering, depending on the topics of interest involved in our Program. We had also two general invited lectures to strengthen the knowledge that researchers are gaining in their formation during this stage and we consider very useful for other researchers. The Scientific Organizing Committee consider that it is necessary that PhD candidates undertake this type of formative training activities to acquire the typical skills of a PhD remarking how to summarize their results, highlight the importance of them, presenting and disseminating clearly and concisely to a diverse audience in a limited time, as usually happens in the presentations at worldwide scientific conferences.The Organizing Committee acknowledges the Escuela Técnica Superior de Ingeniería Agronómica -ETSIA- and the Institute of Plant Biotechnology -IBV- of the Universidad Politécnica de Cartagena – UPCT- their assistance in making possible this event. To Mare Nostrum Campus -CMN- the dissemination done. Funding received from the International Doctorate School of UPCT is also appreciated

Molecular phylogeny of horseshoe crab using mitochondrial Cox1 gene as a benchmark sequence

An effort to assess the utility of 650 bp Cytochrome C oxidase subunit I (DNA barcode) gene in delineating the members horseshoe crabs (Family: xiphosura) with closely related sister taxa was made. A total of 33 sequences were extracted from National Center for Biotechnological Information (NCBI) which include horseshoe crabs, beetles, common crabs and scorpion sequences. Constructed phylogram showed beetles are closely related with horseshoe crabs than common crabs. Scorpion spp were distantly related to xiphosurans. Phylogram and observed genetic distance (GD) date were also revealed that Limulus polyphemus was closely related with Tachypleus tridentatus than with T.gigas. Carcinoscorpius rotundicauda was distantly related with L.polyphemus. The observed mean Genetic Distance (GD) value was higher in 3rd codon position in all the selected group of organisms. Among the horseshoe crabs high GC content was observed in L.polyphemus (38.32%) and lowest was observed in T.tridentatus (32.35%). We conclude that COI sequencing (barcoding) could be used in identifying and delineating evolutionary relatedness with closely related specie

Crab and cockle shells as heterogeneous catalysts in the production of biodiesel

In the present study, the waste crab and cockle shells were utilized as source of calcium oxide to transesterify palm olein into methyl esters (biodiesel). Characterization results revealed that the main component of the shells are calcium carbonate which transformed into calcium oxide upon activated above 700 °C for 2 h. Parametric studies have been investigated and optimal conditions were found to be catalyst amount, 5 wt.% and methanol/oil mass ratio, 0.5:1. The waste catalysts perform equally well as laboratory CaO, thus creating another low-cost catalyst source for producing biodiesel. Reusability results confirmed that the prepared catalyst is able to be reemployed up to five times. Statistical analysis has been performed using a Central Composite Design to evaluate the contribution and performance of the parameters on biodiesel purity