En un mundo en el que cada vez tienen lugar más relaciones y transacciones a través de
aplicaciones informáticas (compras, comunicaciones y relaciones personales), muchas
decisiones se toman en función del valor estético, el atractivo o el impacto de imágenes
digitales. Sin embargo, existen todavía pocos resultados sobre modelización estética; es
decir, sobre la capacidad de realizar valoraciones y juicios estéticos.
Diferentes grupos de investigadores han intentado crear sistemas informáticos capaces de
aprender la percepción estética de un grupo de seres humanos como parte de un sistema
generativo, con la intención de ser utilizados en la selección u ordenación automática de
imágenes. Dada la naturaleza subjetiva del problema estético, la selección del conjunto de
datos con el que se entrena el sistema es especialmente relevante.
Tras haber analizado el grado de generalización de algunos conjuntos muestrales utilizados
para la clasificación automática de imágenes, se ha concluido que no es suficiente para
tomarlos como referencia en el entrenamiento de sistemas de predicción y clasificación
automática de imágenes. Además, se han detectado también otras limitaciones funcionales
en dichos datasets.
Con la intención de ofrecer una solución a los problemas detectados, se presenta la
creación de un nuevo conjunto de imágenes procedentes del portal web DPChallenge.com,
con mayor coherencia estadística. Este nuevo dataset ha sido evaluado según criterios de
estética y de calidad por un grupo de individuos españoles en condiciones experimentales
controladas y por otro grupo de estadounidenses a través de encuestas online. Así, este
dataset se convierte en el primer conjunto de imágenes evaluado por tres poblaciones
diferentes (el que evalúa en el portal web DPChallenge.com, el español y el
estadounidense).
Se presenta entonces la cuestión sobre el método a utilizar para la realización de las
evaluaciones. Por una parte, un experimento presencial proporciona un control mucho
mayor sobre las condiciones de la evaluación, pero supone un gran gasto y complica la
tarea de recopilar evaluadores de un target específico. Por otra parte, un experimento vía
online facilita la evaluación por parte de un gran número de personas, pero elimina el control
sobre la evaluación.
Ante esta situación, es necesario elegir el método que mejor se adapte a cada ocasión.
Una herramienta muy útil para la realización de experimentos con encuestas online, es
Amazon Mechanical Turk. Se trata de una plataforma crowdsourcing que requiere
inteligencia humana. En dicha plataforma existen dos perfiles: los solicitantes y los
trabajadores. Los solicitantes publican las encuestas con la información que desean testear
y adjudican un precio de recompensa a cada tarea de la encuesta; los trabajadores realizan
las tareas publicadas contestando las preguntas de las encuestas y reciben la recompensa
correspondiente.

En esta presentación se muestra el procedimiento a seguir a la hora de realizar un
estudio de la influencia de un “tratamiento” en un grupo de personas o población.
Se explica la utilización de análisis no paramétricos cuando la distribución de la
población no puede ser definida a priori, y se muestran los dos tipos de análisis más
comunes: el estudio antes/después y el tratamiento/control.
Se expone un ejemplo realizado en colaboración con el KMi de la Open University
(Milton Keynes, Reino Unido), centrado en los efectos de una interfaz de hipervídeo que
pretende dar mayor sentido a los debates políticos y promover cambios actitudinales
que permitan desafiar las ideas preconcebidas de cada persona.
En este estudio participaron 113 personas y se midieron 9 variables, y permite llegar a
la conclusión de que el hipervídeo ayuda a aumentar el interés de las personas por un
nuevo tipo de compromiso con los debates electorales televisados.
El estudio también muestra que las analíticas visuales y la navegación por hipervídeo
mejoran el compromiso personal, ya que provocan el cuestionamiento de los supuestos
personales que las personas sostienen antes de ver los debates.

El mundo del análisis de series temporales ha tenido una gran repercusión debido a su utilidad en múltiples entornos que se han podido beneficiar de la aplicación de técnicas de Machine Learning (ML). Sin embargo, una aplicación tradicional implica una costosa y pesada tarea de análisis que se realiza de forma manual por parte del experto humano. En este trabajo se propone, bajo una nueva óptica, el uso de técnicas de ML y Computación Evolutiva (CE) para la extracción automática de características con el objetivo de mejorar la clasificación de señales. Finalmente, este trabajo se completa con la aplicación de técnicas de Deep Learning (DL).

La Figura Compleja de Rey (FCR) se ha convertido actualmente en una herramienta ampliamente utilizada en neuropsicología para, entre otras, determinar el deterioro cognitivo en pacientes con enfermedades tales como Deterioro Cognitivo Leve, Alzheimer, Demencia o Esclerosis Múltiple. Más concretamente, esta prueba consiste en que el paciente dibuje una determinada figura, conocida como FCR, para valorar 2 aspectos diferentes: la riqueza de la copia, es decir, cómo de fiel es la copia respecto a la original; y la planificación, es decir, cómo se enfrente el paciente al problema.

A diferencia de la riqueza de la copia, actualmente existen pocos estudios que tienen en cuenta la planificación, y los existentes, o son excesivamente intrusivos (es decir, influyen en la planificación del paciente) o son demasiados complejos para su uso clínico. El objetivo de la investigación que aquí se plantea es desarrollar un procedimiento que permita valorar la planificación de forma objetiva pero suficientemente ágil como para poder ser empleado en la clínica ordinaria. Una vez alcanzado este primer paso se pretenden determinar, mediante técnicas de machine learning, diferentes patrones de planificación que permitan discernir entre diferentes tipologías de alteraciones neuropsicológicas.

Los sistemas operativos presentan actualmente técnicas de protección de memoria que dificultan la explotación de las vulnerabilidades existentes. Una de estas técnicas es ASLR (Address Space Layout Randomization), cuya función es introducir aleatoriedad en el espacio de direcciones virtuales de un proceso.

El objetivo de este proyecto es medir, analizar y comparar el comportamiento de ASLR en dos sistemas operativos actuales, Windows 10 y Ubuntu 18.04 LTS, en las versiones de 64 bits. Para ello, utilizando la metodología Kanban, se ha realizado una revisión de los artículos científicos publicados hasta la fecha sobre esta temática, y se ha desarrollado una herramienta con la que obtener las direcciones de memoria para las áreas principales de un proceso durante un número suficientemente elevado y representativo de iteraciones.

Una vez concluidas las ejecuciones, se ha realizado un análisis, apoyado en datos, gráficas y tablas, que ha conducido esencialmente al siguiente resultado: la implementación de ASLR ha mejorado notablemente en estos dos sistemas operativos respecto a versiones anteriores, ya que las direcciones cuentan con más bits de entropía y casi todas las áreas de memoria se aleatorizan. Sin embargo, existen aspectos, como las correlaciones parciales o una distribución de frecuencias no siempre uniforme, que todavía son susceptibles de mejora. En Windows el mayor problema reside en el tamaño de su espacio de direcciones, que conlleva que se produzcan correlaciones totales o parciales entre las principales áreas de memoria. En Linux, que presenta un tamaño de direccionamiento mayor, teóricamente sí podría mejorarse la implementación de ASLR evitando por completo las correlaciones, pues existe espacio de direccionamiento suficiente para ello.

Por tanto, se demuestra que ASLR en la actualidad se comporta de manera mucho más eficiente que en sistemas operativos anteriores, pero sigue sin ser, de todas formas, óptimo. Este estudio podría extenderse tanto desde una perspectiva sincrónica, añadiendo nuevos sistemas operativos al análisis y comparación, como diacrónica, analizando y comparando los sistemas estudiados con otras versiones de Windows y Ubuntu anteriores. Asimismo, también se podría profundizar en las causas concretas que limitan la eficiencia de estas implementaciones de ASLR.

Synthetic Aperture Radar (SAR) remote sensing plays an important role in research areas such as resource management, forest conservation, crop monitoring, land hazards monitoring, elevation product generation, and military applications. SAR has active imaging capability with an ability to discriminate terrain features, along with recognition of selected natural as well as man-made targets. However, special abilities of SAR become ineffective in specific cases due to interference of SAR frequency bands with the same magnitude range of radio frequencies originating from other types of electronic equipment. This equipment may include air-traffic surveillance radars, meteorological radars, communication systems, Radio Local Area Network (RLAN), and other electromagnetic (EM) radiation sources. This process of SAR frequency band contamination is called Radio Frequency Interference (RFI). Due to the increasing communication applications based on EM radiation, a wide range of EM spectrum is being used for this purpose. SAR frequency bands are very closely packed and even overlapping with other operating frequency bands allotted for other applications. Due to gaps in the unified international planning for EM spectrum band allocation for different applications, the problem of RFI in every communication application is rising rapidly. The satellites of the Sentinel-1 constellation use a radar, which operates in the IEEE (Institute of Electrical and Electronics Engineers) standard defined C band (central frequency 5.405 GHz) which covers most civilian and defense use frequencies. The RFIs discussed in the study manifest themselves on Sentinel-1 data in the form of lines having bright signatures, which are always perpendicular to the satellite orbit trajectory. These patterns may be hundreds of kilometers long and signify that a powerful radio source close to 5.405 GHz (such as some radars) is active and emitting somewhere along those lines. These interference patterns rigorously reduce the SAR image quality, which results in reducing the usefulness of SAR images, especially for high-resolution data-based applications. Therefore, an effective RFI pattern detection method is necessary for prior identification of RFI contaminated SAR images. In this study, openly accessible Sentinel-1 dual polarimetric (GRD) SAR images taken over different busy maritime shipping ports having international trade such as in Dubai and Germany have been used for the semantic segmentation of RFI patterns. The RGB composite images of different experimental sites were used to test and train the U-Net architecture of Convolutional Neural Network (CNN) for RFI pattern recognition.

The growth of the foliar area is mainly for the plant development, particularly in Musaceae, which get sizes relatively bigger as compared to the other vegetable species. An estimate of the final behavior of the foliar area is made for a banana species, from experimental measurements carried out from the beginning of the plant development up to four months, considered in fifteen -day stages, using a model that describes the trend of final foliar growth which the plant can reach in the stage after the last experimental measurement. The first measurement is considered to run the model with each of the subsequent measurements. The model is selected in the class of functions of the type second order rational fractions, from which those that have a non-sigmoid geometric behavior are discarded, imposing conditions on the coefficients, among which are subsequently selected from the experimental data, those that they accurately represent the process.

Background: An ethnobotanical survey has been carried out in Moroccan Rif (northern Morocco). The aim of this study was to assess the potential of the with regard to medicinal and aromatic plants used in the treatment of osteoarticular diseases. Materials and Methods: The ethnobotanical survey was conducted in Moroccan Rif region for two periods from 2016 to 2018. I n total, 520 local traditional herbalists and users of these plants were interviewed. Information was collected using semi-structured interviews and group discussion, analyzed and compared by quantitative ethno-botanical indices such as family importance value (FIV), relative frequency of citation (RFC), plant part value (PPV), fidelity level (FL) and informant consensus factor (ICF) were used to analyze the obtained data. Results: The analysis of results identified 17 plants species distributed in 10 families with a dominance of the Poaceae (6 species). Concerning the diseases treated, rheumatism diseases have the highest ICF (0.98). The survey revealed that leaves were the most used part of the plants (PPV=0.37) and the majority preparation used was a decoction (40.9%). Conclusion: The results of the present study showed the existence of indigenous ethnomedicinal knowledge of medicinal and aromatic plants in the Moroccan Rif to treat osteoarticular diseases. Further research on phytochemical, pharmacological and other biological activities should be considered to discover new drugs from these documented plants.

The aim of this study was to assess the potential of the Moroccan Rif (northern Morocco) with regard to medicinal and aromatic plants used in the treatment of genito-urinary diseases. The ethnobotanical survey was conducted in Moroccan Rif region for two campaigns from 2016 to 2018. I n total, 548 local traditional healers were interviewed. Information was collected using open-ended and semi-structured interviews, analyzed and compared by quantitative ethnobotanical indices such as family importance value (FIV), relative frequency of citation (RFC), plant part value (PPV), fidelity level (FL) and informant consensus factor (ICF) were used to analyze the obtained data. The study identified a total of 27 medicinal and aromatic plant species belonging to 18 botanical families. The most important family is that of the Rutaceae represented by 04 species. Concerning the diseases treated, kidney stones diseases have the highest ICF (0.97), the leaf was considered the most used part of the plant (PPV=0.443) and the majority of the remedies were prepared in the form of decoction. The results of the present study showed the existence of indigenous ethnomedicinal knowledge of medicinal and aromatic plants in the Moroccan Rif to treat genito-urinary diseases. Further research on phytochemical, pharmacological and other biological activities should be considered to discover new drugs from these documented plants.

Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. Cheminformatic methods are able to design and create predictive models with high rate of accuracy saving time, costs and animal sacrifice. It has been applied on different disciplines including nanotechnology. Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning prediction models to guarantee public safety. A systematic study of the regulation and the incorporation of predictive models of biological activity of nanomaterials was carried out through the analysis of the express nanotechnology regulation on foods, applicable in European Union. It is concluded Machine Learning could improve the application of nanotechnology food regulation, especially methods such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development, European Union regulations and European Food Safety Authority. To our best knowledge this is the first study focused on nanotechnology food regulation and it can help to support technical European Food Safety Authority Opinions for complementary information.

Reference: Machine Learning as a Proposal for a Better Application of Food Nanotechnology Regulation in the European Union. Santana R, Onieva E, Zuluaga R, Duardo-Sánchez A, Gañán P. Curr Top Med Chem. 2020;20(4):324-332.

This work compares the oxidative stability of two margarines with vegetable oils of equal composition in both saturated and unsaturated fatty acids. For this purpose, weighted averages of the composition of the oils are calculated and their induction times at 120⁰C were previously computed by QSPR. The results obtained from this calculation are compared to those obtained for equal weight of margarine in Rancimat at the same temperature. These results show that margarines have lower oxidative stability than oils with the same composition in fatty acids, which is explained by the fact that the presence of water in margarines generates hydrolysis reactions that speed up the oxidative process.

This paper presents the sizing of a treatment plant for domestic wastewater generated in the urban planning zone of the Cooperativa de la Pequeña Empresa de Pastaza LTDA, located in Puyo, Ecuador. The population served will be 291 inhabitants and the average flow is 1.48 L/s. The most suitable option for the plant’s secondary treatment was selected using the prioritisation matrix methodology. The complete treatment proposal is composed of input, sieving and desanding, two parallel septic tanks, a trickling filter, a secondary decanter and finally a disinfection process of two 12GPM ultraviolet light lamps at the outlet of the secondary decanter. There is also an area for drying the sludge after it has been digested in the septic tank. The calculated cost of the plant was $57,032.88USD and it would occupy an area of 86m².

The Tereré wastewater treatment plant located in the city of Tena in the Ecuadorian Amazon region treats urban liquid inflows by means of a membrane bioreactor, which has an organic load removal efficiency of 98%. Within the treatment plant, sewage sludge is generated, which goes through a drying process and is disposed of at a landfill site. In this project, the sludge’s physical-chemical and microbiological characteristics were determined using three samples where nine parameters were analysed (pH, %Humidity, %N, %OM, %P, C/N, Faecal Coliforms, Salmonella spp. and Heavy metals). The results highlight the presence of a large amount of pathogens (faecal coliforms, Salmonella spp.), for which two treatment proposals were established, consisting of alkaline stabilisation and composting, where the latter presents greater viability due to its low investment costs and simple operation technique. In addition, the city council will be able to utilise and commercialise the compost bags that will be obtained from the treatment.

This research was carried out at the Amazon Research, Postgraduate and Conservation Center (CIPCA) Poultry Program, of the Amazon State University, Ecuador and aimed to “Evaluate the productive behavior and quality of quail egg (Coturnix coturnix japan) on stage of posture”, for which 72 quail were used, with 69 days (week 10) of age; Each experimental unit (cages) housed 18 quails, in which the variables of posture percentage, egg weight, egg mass, food consumption, food conversion and water consumption as productive indicators were evaluated. For egg quality, 72 eggs were evaluated for 6 weeks, which were measured weekly for shape index, yolk index, egg whites index and Unit Haugh. A Fully Randomized Design (DCA) with four replications was used. The statistical analysis applied was an ANOVA and for the differences the test of Tukey means comparisons was used, at a statistical significance (p <0.05). The productive indicators did not show significant differences for p <0.05 per week, however, among the cages, except for food and water consumption. The ratio of feed conversion and egg mass to percentage of posture was higher at weeks 10, 13 and 15. The quality manifest form indexes from 76.05 to 80%, indicating that they are elliptical and can be accepted in the market. The yolk index and Haugh expressed good quality and freshness of the egg with 0.42 and 81.6% respectively. The quality of the eggs in the different weeks is very good, favoring the commercialization process, when it creates added value to quail products.

The objective of the study was to carry out a chemical characterization and to quantify the fatty acid profile of Sacha inchi (Plukenetia volubilis) to be used as raw material, in the elaboration of diets for animal use. The research was developed with samples of the seed meal with and without the Sacha inchi capsule, acquired from the unused waste for export from the “Huamboya” collection center, located in the Amazon province of Morona Santiago in Ecuador. The determination of the chemical composition was analyzed the nutrients of: dry matter (DM), organic matter (OM); crude protein (CP); crude fiber (FB); grease; Nitrogen-free extracts (ELN); gross energy (EB) in the laboratory of the National Institute for Agricultural Research (INIAP). The fatty acid profile (Saturated; Polyunsaturated and Monounsaturated) was determined in the Laboratory of Analisis de Alimentos AVVE S.A., through the liquid chromatography / PDA technique. Sacha inchi seed flour with and without capsule from crops in the Huamboya canton, Morona Santiago Province, has an adequate protein for the swine species, as well as the fiber, fat and energy content are acceptable, to be used in The preparation of diets for Creole pigs in the growth and final fattening stage, in addition to the flour from the Sacha inchi seed with and without capsule, are an important source of polyunsaturated fatty acids, mainly α-linolenic acid (C18: 3 ω - 3) and linoleic acid (C18: 2 ω-6), which could provide beneficial effects due to their nutritional qualities and nutritional value, to be considered in the inclusion of diets for animal feed.

The aim of the present work was to evaluate the content of total phenols in a fermented orito banana (Musa acuminata AA) pulp drink. A central compound experimental design with nine treatments was carried out in the elaboration of an orito banana pulp fermented beverage. Total antioxidant activity was evaluated by means of Folin Ciocalteu, obtaining a higher content of polyphenols in T₀ (4.888 mg/L) and T₃ (4.616 mg/L) treatments, after eight days of fermentation. In conclusion, in the fermented beverage, T₀ treatment at 70ºC and & 0.6% citric acid concentration, as well as T₃ treatment at 60ºC & 0.9% citric acid concentration, preserved the organoleptic characteristics from orito banana, no enzymatic browning was visualized in the subsequent operations.

With their cosmovision, indigenous peoples in Amazon territory develop subsistence farming systems, that preserve biodiversity, which is considered to be the result between culture and control of the territory by local communities, an autonomy expression, knowledge, identity and economy factors (Escobar, 2010; Arias et al. 2018). The objective in this study was to analyze de resources coming from forest for feeding Ecuadorian Amazon communities. It shows the calendar of products from plants collected for food in communities in the Ecuadorian Amazon. The seasonality of these foods and the need for conservation through agro-industrial processes is observed. It is presented the main marketable products for consumption in the Ecuadorian Amazon region. Bananas constitute the largest production, 80,710 Tm in hole the region, while in the province of Pastaza 21,320 Tm are harvested

The purpose of this research was to carry out a retrospective analysis of the external and internal egg quality and its relationship with coloration, in the municipal markets of the Pastaza canton. The results of 247 creole eggs were analyzed, to which the indicators were measured: weight, height and width of the egg, shell thickness, shape index, yolk index, albumen index and Haugh units. An exploratory transectional or cross-sectional design was used and the data were processed in the statistical package SPSS version 22. The results indicate that there is a variation of the yolk diameter between the egg colors; the light brown and green coloration showed the highest values ​​with 4.4 ± 0.30 and 4.3 ± 0.27cm respectively. The yolk index was higher in the light brown and green colored eggs, however, only the green color corresponds to the highest quality standards. The analysis of the Haugh units showed 80.4%, which expresses very good quality for the marketing process and durability of the product; while the white and light brown eggs showed values ​​between 77.63 and 70.72% respectively. The indicators height and diameter of the yolk and height and diameter of the albumen were higher for the green color, favoring the indices of yolk and white or albumen, which is why it is manifested in the Haugh Units by presenting very good quality. A logical relation of the internal and external quality is not expressed for the green color, unlike the other colorations.

Background: Autism Spectrum Disorder (ASD) is a neurodevelopment disorder characterized by socio-communicative impairments as one of the core symptoms. Autistic symptoms may be seen in the first year of life, they vary in severity from mild to severe, and in a few instances, they may improve over time, even without treatment. The neuropeptide N-acetyl-aspartyl-glutamate (NAAG) modulates glutamate release which has been proposed as a key mechanism underlying symptoms of ASD. NAAG provides one of the components of the proton magnetic resonance spectrum (¹H-MRS) in humans. The signal of NAAG, however, largely overlaps with its precursor and degrading product N-acetyl aspartate (NAA) that by itself does not act in glutamatergic neurotransmission. Previously, we described the altered N-Acetyl-aspartyl-glutamate levels found in cingulated cortices by ¹H-MRS in individuals with ASD that suggested neuronal damage. Taken together, the findings of this study support our hypothesis and a role for NAA-NAAG imbalance and impairments in the Social-communication skills in autism, which lead the next step in our investigation to correlate imbalances neurochemistry linked to cingulated cortices in social and communicational skills in the autism spectrum disorders.

Aim: To study the imbalance of NAA-NAAG metabolism role in the cingulated cortices correlated with the AQ domains social skills and communication associated with ASD severity using ¹H-MRS.

Methods: We quantified NAAG and NAA separately from the ¹H-MRS signal in 22 patients with ASD and 44 healthy comparison subjects, matched for age, gender on a 3.0 Tesla MR scanner. Autism quotients (AQ) scores were assessed. Statistic one-way ANOVA and Bonferroni correction was applied. Furthermore, the Pearson correlation hallmarks the goal.

Results: The results of the Pearson correlation were represented graphically, where it was observed that there is no correlation between the Socio-communicative skills and the NAA/NAAG ratio in the ACC (r = -0.43, P = .005) in the ASD group (Fig.1). However, when was stratified ASD plus TD groups as AQ1, AQ2, AQ3, and AQ4, there was within groups differences (AQ1, AQ2, AQ3, and AQ4) of NAA/NAAG ratio; was increased significantly (P = .05) in AQ3 (Fig. 2) and, decreased in AQ4. Comparably, there was no differences of (NAAG, NAA, or NAA/NAAG) concentrations in the PCC, but a positive linear correlation with communication (r = .55, P = .049) was observed. In addition, in both ACC and PCC, the AQ2, AQ3, and AQ4 groups maintain a different correlation pattern than the AQ1 group, both in social skills and communication showing the severity level change within AQ domains.

These results make us suggest the relation of the deficit socio-communicational with the enlarged relative grey matter volumes (rGMV) of the auditory network in ASD adults; in accordance with that described by (Watanabe & Rees, 2016); who demonstrated the relation of the deficits associated with the severity of autistic socio-communicational core symptom. Since NAA is considered a marker of neurons, these results provide stronger support for neuron loss in the posterior cingulated cortex than volume measurements by MRI alone.

Conclusion: We conclude that the concentrations of NAAG and NAA act differently in ASD. The opportunity to measure NAAG in subjects with ASD creates a new and promising approach for intensified research on the glutamatergic systems and on the effects of novel drug candidates.

The accumulation of dry matter per organ of the Chinese potato plant (Colocasia esculenta (L.) Schtt) local white variety was determined in three moments of crop development, using three planting distances (1.0m x 0.40m, 1.0m x 0.60m and 1.0mx 0.80 m) in the conditions of the Ecuadorian Amazon, evidencing the physiological development of the crop. A factorial design in blocks was used completely at random, forming 9 plots of 30 m2, where the study factor was the planting distance representing the treatments. For data collection, five plants were randomly selected in perfect intraspecific competition. The variables were evaluated at 60, 120 and 180 days after planting. The analysis of double variance was carried out, using the Tukey test at a probability level of 95%. The highest values of dry matter accumulation by plant organs were obtained in the plantation distance of 1.0 m x 0.80m.

Menopause is an influential aspect of a woman's life, especially if she is a sportswoman. It is important to consider the role that women play in athletics and have also reached menopause, since the loss of muscle mass and gain of fat tissue influences their sports performance. In this line, nutrition is a fundamental component, so it is relevant to know how it influences the development of a particular somatotype. To improve the performance of menopausal women athletes, a specific somatotype is required, which should be provided not only by adequate training, but also by optimal nutrition. In the present investigation, a cross-sectional and quantitative observational study was conducted. The population sample consists of 5 women between 49-60 years old who are members of an athletic club in the province of Valencia (Spain) federated in the Royal Spanish Athletic Federation. The average caloric intake of the athletes was 2073 Kcal/day which meet 96% of the recommended intake. However, the distribution of the macronutrients in the diet did not meet the established recommendations: 55% of carbohydrates, 30% of fats and 15% of proteins. Instead, the athletes ingested 43% of carbohydrates, 41% of fats and 17% of proteins. These showed a significant excess in the intake of fats (37% extra) and carbohydrates (22% extra) which would lead to an abnormal distribution and accumulation of body fat that would be enhanced by the menopausal state of the athletes. All together will influence body composition that will be associated with an inadequate somatotype which would clearly impair the sports performance of the athletes.

Objective: Circulating microRNAs (miRNAs) have been proposed as emerging biomarkers for obesity and metabolic comorbidities. Our aim was to characterize miRNA signatures and assess their utility to discriminate between insulin resistance (IR) phenotypes in paediatric obesity and evaluate their role in diverse metabolic pathways.

Methods: Observational, case-control study, including prepubertal children between 6 and 10 years, divided in three study groups: a) healthy control (n=3), b) metabolically healthy obese children (n=3) and c) IR obese children (n=3). Obese patients were defined by a body mass index > 2 SD for age and sex. IR patients fulfilled at least one of the ADA’s insulin resistance criteria (Basal Insulin>15 U/mL; Insulin along the OGTT>150 U/mL; Insulin>75U/mL at 120’ on the OGTT; and/or, iHOMA>3,5). We first screened 179 miRNAs to identify differentially expressed miRNAs between groups. Total RNA was extracted from plasma using the miRNeasy Serum/Plasma Advanced Kit (Qiagen). Correlations between miRNA levels and clinical parameters were investigated.

Results: The established criteria for miRNA candidate’s selection were high expression levels (Max. Cq<39 and detected in at least 95% of all samples) and statistical significance (p<0.05). We found 19 miRNAs highly expressed and differentially detected between a) metabolically healthy obese vs. IR obese children and b) healthy subjects vs. obese children, including miRNAs with previously reported roles in iron and glucose metabolism, oxidative stress, inflammation and erythrocyte integrity.

Conclusion: The miRNA profile identified new candidates related to pediatric obesity, and enables to differentiate between IR phenotype and metabolically healthy obese children.

Traditional agriculture research programs have used classical breeding and molecular biology approaches for crop improvement. Besides, they are proved inadequate to deal collectively with a major number of problems. High throughput sequencing has shown a way towards overcoming those barriers along with storing and evaluating various big scale datasets on experimental basis. Artificial intelligence with Machine and deep learning techniques uses a training dataset as a calibrator for performing identification, classification, quantification and prediction. Different algorithms can interpret the same data to different desirable outputs; the output includes a simpler solution for the complex problems in link with a given dataset. Its application has moved research towards less biased and high precision results which are extensively accepted on a global level ^[1-3].

The sophisticated application of AI and machine learning is prevalent in genomics, transcriptomics, proteomics, metabolomics and systems biology^[4]. The approach of Interpreting a given dataset with deep learning algorithms mentioned in figure 1 has been used for predicting translational initiation site recognition^[5],signal peptide prediction^[6], subcellular localisation^[7], plant effectors^[8], fungal effectors^[9], promoter recognition^[10], mRNA based alternative splicing^[11], m5cap^[12], poly A site^[13], RNA editing^[14], epistatic state^[15], gene^[16] and protein function and interaction^[17],mutational analysis^[18], epigenetic interaction^[19], gene expression analysis^[20], transcription factor binding^[21], Chromatin signature^[21], gene–environment interactions^[22], SNP detection for QTL and interactome analysis^[23-25].

Single nucleotide polymorphism is one of the major molecular markers for the indication of genetic diversity for crop improvement programs. It is majorly used for the assessment of genomic breeding values. Approaches like NGS are used to locate SNP in economic improvement traits, for the easy and early domestication of beneficial crops. However, the error-prone fashion of the available NGS analysis tools is still a big concern which can lead to false-positive results. Machine learning methods have paved a way towards more precise SNP screening from the sequenced data available in large natural population ^[23-25]. Fig.1 depicts the available machine learning algorithm used in SNP detection. In addition to it, “Integrated SNP Mining and Utilization” (ISMU) Pipeline ^[26] and “SNP machine learning” (SNP‐ML)^[27] are two of the ML based models presently in use for SNP based QTL analysis. Use of molecular marker datasets with machine learning algorithm holds promising results in genetic analysis and hybrid breeding ^[28].

References

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2. Khandelwal, R., & Nayarisseri, A. (2019). A Machine learning approach for the prediction of efficient iPSC modeling. DOI: 10.3390/mol2net-05-06376
3. Nayarisseri, A., Khandelwal, R., Madhavi, M., Selvaraj, C., Panwar, U., Sharma, K., & Singh, S. K. (2020). Shape-based machine learning models for the potential novel COVID-19 protease inhibitors assisted by molecular dynamics simulation. Current topics in medicinal chemistry, 20(24), 2146-2167.
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28. Limaye, A., & Nayarisseri, A. (2019). Machine learning models to predict the precise progression of Tay-Sachs and Related Disease. DOI: 10.3390/mol2net-05-06180
29. Udhwani, T., & Nayarisseri, A. (2019). A Machine Learning approach for the identification of CRISPR/Cas9 nuclease off-target for the treatment of Hemophilia. DOI: 10.3390/mol2net-05-06179

Biomedical research often involves studying patient data that contain personal information. Inappropriate use of these data might lead to leakage of sensitive information, which can put patient privacy at risk. The problem of preserving patient privacy has received increasing attentions in the era of big data. Therefore, for a biomedical research with this type of technology to be carried out correctly, it is essential to take care of the personal rights of the population and it is a necessary point that should not be overlooked.

Ascariasis is a worldwide disease that causes a great impact in the pig sector and, also, in Public Health because it affects humans. This is the reason which makes that this disease must be controlled in slaughterhouses. The aim of the study was to determine the prevalence of ascariasis in pigs in a slaughterhouse in Valencia in 2018, which was 10.19 %. For this purpose, 464,659 animals from 525 farms in different Autonomous Communities were studied. In addition, we wanted to determine the influence of factors such as the origin of the farm and the month in which the animals are slaughtered in the onset of the disease.

Coffee (Coffea arabica L.) has a gelatinous layer, called mucilage, that covers the seed and that in its transformation process is eliminated as a waste. In the province of Pastaza two varieties of the species have been cultivated, respectively the "catuai" and the "castle", therefore the present research aims to determine the amount of caffeine, the total content of polyphenols and the antioxidant activity of the concentrate mucilage of the two varieties. The caffeine content was evaluated using the UV-Vis spectrophotometer method, the total polyphenols through the Folin-Ciocalteu method and the antioxidant activity with the FRAP method. The coffee mucilage concentrate presented respectively a caffeine content of 4.87mg / 10g for the Catuai variety and 2.37mg / 10g for the Castillo variety. As for the polyphenols, values ​​of 22.87 mg / g were obtained for the Catuai variety and 21.1 mg / g for the Castillo variety, while the antioxidants of the Catuai variety presented 17.78 mg / g and the Castillo variety 17.46 mg / g. The Catuai variety has a higher content of caffeine and polyphenols and both varieties show a promising antioxidant activity. The results of the study allow recommending new studies of the C. arabica mucilage concentrates as potential ingredients of functional or cosmetic foods.

NASADEM (NASA Digital Elevation Model) is a merged digital elevation product prepared by the National Aeronautics and Space Administration (NASA) from SRTM (Shuttle Radar Topography Mission) DEM as primary data along with other secondary datasets generated from remote sensing-based techniques like satellite photogrammetry and spaceborne LiDAR. These DEM products of NASADEM are reanalysis datasets produced from SRTM and datasets such as ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) DEM; Ice, Cloud, and land Elevation Satellite (ICESat) - Geoscience Laser Altimeter System (GLAS) elevation datasets; and Advanced Land Observing Satellite (ALOS) - Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) DEM datasets, available at other locations globally. Three sites were chosen, namely Kendrapara (Odisha), Jaipur (Rajasthan), and Dehradun (Uttarakhand) with plain, moderate, and highly undulating terrain conditions for the assessment of NASADEM. The RMSE results were compared with other merged DEM products namely EarthEnv-DEM90 and MERIT (Multi-Error-Removed Improved Terrain) DEM. The ground control points (GCPs) collected through differential GNSS (DGNSS) surveys were used for the assessment of vertical accuracy and the statistical parameters, such as mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The RMSE of 4.71m at the Dehradun site depicts that in undulating regions NASADEM is performing better than both EarthEnv-DEM90 and MERIT DEM. However, in the case of urban and plain regions, the performance of MERIT DEM and EarthEnv DEM is superior to that of NASADEM.

Objective To conduct data-mining of montelukast-related adverse events after marketing to provide a reference for safe clinical medication. Methods We use reporting odds ratio (ROR) and proportional reporting ratio (PRR) methods to mine the adverse reaction signals of montelukast on the adverse reaction report data of 22 quarters from 2015Q1 to 2020Q2, extracted from the FAERS database. Results Totally 467 signals were detected with ROR and PRR, and the most relevant 50 preferred terms are conducted based on the signal strength and signal frequency, 55.32% of signals were not reported in the proved label. Adverse reaction signals of montelukast involve 27 systems and organs, in addition to psychiatric diseases, majority of adverse events included respiratory, thoracic, and mediastinal disorders and examination. Conclusion Clinical use of montelukast should pay attention to the patient's neuropsychiatric symptoms, especially those not reported in the proved label, such as Separation anxiety disorder, Sleep terror and PANDAS. For patients with mental history, phenylketonuria and autoimmune diseases who use the montelukast，relevant workers should pay attention to monitoring to ensure safe and rational drug use.

Recurrent pregnancy loss (RPL) usually means two or more pregnancy failures occured within 20 weeks of conception, with an incidence as great as 3% to 5% of pregnancies [1]. RPL is a complex disease with diverse causes, including heredity, age, antiphospholipid syndrome, uterine anomalies, thrombosis, hormone or metabolic disorders, infection, autoimmunity, sperm quality, lifestyle, and mental, psychological, and environmental factors [2, 3], which brings a substantial adverse impact on society and female health care. Therefore, understanding the gene regulation network of RPL is beneficial to clarify the etiology of recurrent abortion and possible to prevent another miscarriage through some intervention, which will have great clinical significance and social benefits.

Genetic abnormalities are the main factor of RPL[4]. The correlation between chromosomal abnormalities and abortion has been clear and the relative genetic risk is easy to predict. For example, an abnormal karyotype in either partner, especially featuring a translocation and/or an inversion, is considered to be a cause of RPL, due to unbalanced chromosomal segregation in meiosis, which cause significant chromosomal imbalances (i.e., disomies and nullisomies) in their gametes with subsequent partial aneuploidies in the conceptuses [5, 6]. In theory, the incidence of normal, carrier and imbalances in the offspring of reciprocal translocation, Roche translocation and inversion were 1/18-1/18-16/18, 1/6-1/6-4/6 and 1/4-1/4-2/4, respectively, which are calculated according to the principle of chromosome separation and recombination. With the development of preimplantation genetic test (PGT) technology that derived from in-vitro fertilization (IVF), it is found that the actual genetic risk is quite different from theoretical calculation, which is not only related to the sex of the carrier, but also the location of the breakpoint and the chromosome involved[7-9]. A study by Xie etc. showed that the ratio of euploid and translocation balance embryos in reciprocal translocation and Robert translocation were 27.8% and 44%, the rest of all were aneuploid or translocation imbalance embryos [10]. Wang reported that 104 embryos from 11 Roche translocation carriers were detected, including normal, translocation carriers and unbalanced chromosome embryos were 22%, 19%, 59% [11].

Apart from chromosomal factors, abortion is also related to endocrine, immune, thrombotic, male sperm and other factors. With the development and clinical application of gene detection technology, current studies showed that these so-called clinical factors may eventually be attributed to genetic abnormalities. Examples of SNPs and copy number variants (CNVs) that may contribute to a genetic susceptibility to miscarriage include variances in the following genes: AR, DNMT3, FOXP3, CGB5, NLRP7, TIMP2 and CTNNA3[12-18]. A recent systematic review of 428 case-control studies from 1990 to 2015 evaluated 472 variants in 187 genes [19]. Meta-analysis could only be performed for 36 variants in 16 genes, because the other studies had never been replicated. The investigators reported modest associations between RPL and 21 variants in genes (odds ratio [OR] 0.51–2.37) involved in the immune response (IFNG, IL10, KIR2DS2, KIR2DS3, KIR2DS4, MBL, TNF), coagulation (F2, F5, PAI-1, PROZ), metabolism (GSTT1, MTHFR), and angiogenesis (NOS3, VEGFA). In addition, mutations in the thrombophilia genes, including MTHFR, F2, and F5, and deficiencies in protein C, protein S, and antithrombin III, may also increase the risk of second- or third-trimester loss[20-23].

Epigenetic modifications, including DNA methylation, noncoding RNA, genomic imprinting, and histone modification, refer to the heritable changes in gene functions without changing the genetic genes. Abnormal DNA methylation was found in the decidual chorionic villi of RPL with normal karyotype, especially at the loci of the imprinting genes [24, 25]. Aberrant microRNAs (miRNAs), which are endogenous small noncoding RNAs and ~22 nucleotides, were found in unexplained RPL [26]. Over the past few years, some studies have verified a clear correlation between lncRNAs and placental development, such as the lncRNAs HOTAIR, HOXA11-AS, and MEG3 and MALAT1, and these lncRNAs appear to be involved in some pregnancy pathologies[27-29].

Above all, genetic abnormalities or variants and related gene expression abnormalities play an important role in RPL. Understanding the correlation between the expression differences of these changes in different populations, different tissues (peripheral blood, amniotic fluid, villi, embryo, etc.) and abortion, is of great significance for clinical abortion counseling and genetic counseling, as well as for the exploration of potential factors of unexplained RPL and the development of beneficial interventions, so as to reduce or avoid abortion risks, improve women's survival quality.

Los modelos PTMLIF (Perturbation Theory, Machine Learning and Information Fusion) son la combinación de la teoría de la perturbación con el aprendizaje automático y la fusión de la información. Estos modelos se usaron en esta investigación para predecir la probabilidad de que los complejos nanopartícula decoradas con fármacos (Drugs-decorated Nanoparticles ó DDNP) tengan actividad antipalúdica en este caso de estudio contra el Plasmodium. Esta enfermedad es la causa de la malaria en los seres humanos que se transmite a través de la picadura del mosquito hembra del género Anopheles. Se fusionó 107 características de entrada y 249.990 ejemplos aproximadamente desde la base de datos ChEMBL. El mejor modelo de clasificación fue proporcionado por método Random Forest, con solo 27 características seleccionadas de fármacos / compuestos y nanopartículas en todas las condiciones experimentales consideradas. El alto rendimiento del modelo se demostró mediante el área media bajo las características operativas del receptor (AUC) en un subconjunto de prueba con un valor de 0,9921 ± 0,000244 (validación cruzada de 10 veces). En este trabajo también se demostró el poder de la fusión de información de las características experimentales de fármacos / compuestos y nanopartículas para la predicción de la actividad antipalúdica de nanopartículas-compuestos.

In a recent work we described the organic synthesis and experimental pharmacological assay of MIF-1 peptidomimetics modulators of D₂ receptors (D₂R). We measured their ability to enhance the maximal effect of tritiated N-propylapomorphine ([³H]-NPA) at D₂R. The 2-furoyl-l-leucylglycinamide (6a) showed increase maximal [³H]-NPA response at 10 pM (11 ± 1%) compared to MIF-1. Neurotoxicity assays of MIF-1 derivative 6a with cortex neurons of Wistar-Kyoto rat embryos suggest low neurotoxicity. Additionally, we reported a predictive model >20 000 outcomes of preclinical assays reported in ChEMBL for this kind of modulators. The model shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. The model is useful to predict this and similar compounds.

Ref: Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML). Sampaio-Dias IE, Rodríguez-Borges JE, Yáñez-Pérez V, Arrasate S, Llorente J, Brea JM, Bediaga H, Viña D, Loza MI, Caamaño O, García-Mera X, González-Díaz H.ACS Chem Neurosci. 2021 Jan 6;12(1):203-215. doi: 10.1021/acschemneuro.0c00687. Epub 2020 Dec 21.PMID: 33347281

Warfarin is an oral anticoagulant that is widely prescribed worldwide, and it has a large individual variability. Many factors contribute to the variability of warfarin. Although numerous algorithms of warfarin have also been developed by pharmacokinetics and pharmacodynamics (PKPD) and multivariate linear regression (MLR) models, there also exits some unknown factors that could affect the warfarin anticoagulant. In this study, the precision medication model of warfarin will be established based bioinformatics in cardiac surgery patients. About 200 cardiac surgery patients who will be administered warfarin will be enrolled. The demographic characters, combined drug and physiological factors will be collected from patients’ medical records. Some of single nucleotide polymorphisins (SNP) linked with warfarin PK and PD for pharmacogenetics will be performed using pyrosequencing. Fecal samples will be used for analyzing gut microbiota by 16S rDNA. S-warfarin, R-warfarin and vitamin K concentrations, and metabolomics will be detected by LC-MS/MS. International normalized ratio (INR) is the effective efficacy index for warfarin, and target INR should be within 1.5-2.5. All data will be analyzed by machine learning or neural networks. Expected the polymorphisms of pharmacogenomics (CYP2C9, VKORC1, and CYP4F2 etc.), vitamin K concentrations, and other biomarkers from gut microbiota and metabolomics were confirmed as the effect factor for the individual variability of warfarin. And an artificial intelligence model of warfarin would be established.

Currently, diseases transmitted by the aedes aegypti mosquito in tropical areas have become a great risk to public health in these areas, within these diseases we find members of the Flaviviridae family, the Dengue Virus is the most known among them. Currently, there are other members such as: Chikungunya (CHIKV), Yellow Fever Virus (YFV), ZIKA Virus (ZIKV), etc. Although these diseases are not new in the present, there is no specific treatment to deal with them. Several of these diseases are classified as serious by the World Health Organization - WHO, taking hundreds of lives today. From phylogenetic studies it is known that members of this family possess highly conserved sequences making them an optimal target for drug development. The drug discovery model is not something new, it has traditionally been carried out under the exercise of "trial and error" and, given the arrival of new and increasingly resistant diseases, it was necessary develop new methodologies to accelerate this process.

Computational chemistry was born from the need to reduce costs, reduce time in drug development and improve the discovery of new compounds, under these three requirements over the years several techniques such as QSAR methods were the fundamental axis of chemoinformatics, With the arrival of the Big Data era, a range of possibilities opens up for the study and development of drugs, such as the implementation of the perturbation theory (PT) and machine learning (ML) models - PTML. Through the use of databases such as ChEMBL it is possible to generate a sufficient data set for the development of a prediction model using PT operators, which are based on moving averages of multiple conditions (Moving Average), which combine the characteristics and simplify data management. In this study, several PTMLLDA prediction models were evaluated based on 47815 tests obtained from ChEMBL, taking as input variables: the reference function, three molecular descriptors: AlogP, MW, TPSA, six test conditions and the interaction between the disturbance conditions and operators. Three different PTML-LDA models were evaluated under different treatments and data processing, the proposed model presents precision values of 77.25%, on the contrary, models Nº2 and Nº3 did not exceed the 77% range in their training stages. The model was validated by ROC curve obtaining a value of 86.2% indicating that the discrimination is exact and not a random pattern. The proposed PTML-LDA model was selected with a specificity of 75.95% and a sensitivity of 78.88% (see table 9). With these values, the evaluation of the model obtained by using its resulting equation (See Eq. 12) was carried out, compared to 45 new compounds synthesized by our research group, obtaining 8 compounds with high probabilities of presenting activity against this type of diseases.

Currently drug discovery is a widely used tool in the pharmaceutical and medical industry, traditionally this was a trial and error method making the processes long and expensive, for this reason the development of virtual screening techniques based on the structure arises as one of the tools to speed up this process.
Zika has been considered a serious disease according to the WHO since 2016, due to its effects in neonates who presented microcephaly and Guillan Barre syndrome in other patients. During the investigation, a structure-based virtual screening was used to identify potential inhibitors of the enzymes protease and methyltransferase of ZIKV, the methodology used arose from a combination of several energy scoring functions using three different molecular coupling programs or Docking software’s : Dock6, GOLD and OpenEye.
In selecting the best combination of functions, 32 compounds that were reported as active for NS2B-NS3 Protease and 50 compounds for NS5 MethylTransferase were used. Using decoy compounds, the method was trained so that together with the ligands they were coupled to the respective enzymes and generated potentially active molecules for these enzymes where 15632 structures with favorable values were obtained. In the search to improve the methodology, a combination of "score" functions were implemented that maximized the enrichment of the compounds. Using the programs described above, it was determined that a combination of the functions 2-4-6 assigned from these molecular coupling software’s significantly improved the enrichment values of the molecules.
Subsequently, the methodology was evaluated to determine if this combination favors enrichment by calculating the BEDROC and the enrichment factor "EF". During this analysis, it was found that at 1% of the screening recovered three active compounds for NS2B-NS3 and four compounds for NS5. This indicated that the method works, and that the combination of the selected enrichment functions favors the discovery of new drug candidates that inhibit ZIKA.

In a previous work we discussed about the concerns emerging worlwide related to the now widespread use of CRISPR-Cas genome editions' technique. This technique is a very important tool in molecular biology now a days and is called to become an important tool in Molecular Pharmacology, Personalized Medicines, and Synthetic Biology. In Drug Discovery and Medicinal Chemistry and Chemical Biology may be used for resistance-selection studies of antimicrobial compounds; research on druggability of new compounds, or implementation of new laboratory animal models for assay of new compounds. The implications for Biotechnology an dSynthetic Biology are more bizarre. Totally new compounds not existing in nature may be created in a very fast-track way. In our previouspaper, we given an state-of-art discussion of literature with examples of CRISPR uses in chemical biology. We also discussed legal and ethical concerns still preset nowadays.

Ref: A. Duardo-Sanchez. CRISPR-Cas in Medicinal Chemistry: Applications and Regulatory Concerns Curr Top Med Chem, 2017;17(30):3308-3315.

The present study mainly aims at clustering of pre-monsoon thunderstorm (TS) and non thunderstorm (NTS) days over Kolkata (22⁰32´ N, 88⁰20´ E) (India) using hard k-mean technique, backward selection procedure and fuzzy c- mean algorithm (FCM). The study involves the numerical values of the parameters observed at 0000 UTC and is performed in two stages. In the first stage , the hard c-mean technique is applied to cluster the days of a semi-supervised data set in the above mentioned two categories and the backward selection procedure is used to find the best possible combination of the theoretically influential atmospheric parameters that play the dominant role in the categorization on basis of performance score (PC). Though FCM the technique is usually applied to supervised data set, but here, in the second stage of this study, this technique is applied to the semi-supervised data set of parameters to justify the result obtained in the first stage.

The final iteration in the first stage shows that the combination of maximum vertical velocity and P-PLCL at 1000 hpa level performs best in detecting the thunderstorm days so far the present data set is concerned. It is interesting to note that this finding is also supported by FCM in the second stage of the study, where in the final iteration the center of the cluster consisting of thunderstorm days moves closer to the parameters , maximum vertical velocity and P-P_LCL at 1000 hpa level (the parameters, P and P_LCL represent respectively the pressure at the reference level and that at the corresponding lifting condensation level which is also considered as the cloud base) than that of the other cluster containing the non- thunderstorm days.

The electrocardiogram (ECG) is a fundamental tool in daily clinical medicine practice, recording millions of ECGs annually. Computer-assisted interpretation is becoming more and more important in clinical ECG processing and interpretation, serving as a crucial adjunct to physician interpretation in many clinical settings. But the existing commercial ECG interpretation algorithms still show substantial rates of misdiagnosis, and there is still a lack of comprehensive evaluation of computer-aided interpretation. We recommend using an ensemble method to process and classify clinical ECGs for providing accuracy and increasing rhythm classes.

CHEMBIOMOL-01: Chem. Biol. & Med. Chem. Workshop, Galveston, Texas-Harvard, Boston, USA, 2020 is an inter-university workshop. It is aimed to become an online reference international science workshop series on both experimental and computational Biomolecular and Biomedical Sciences. On one side the workshop is chaired and co-hosted by professors of the Dept. of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, and the Department of Earth and Planetary Sciences, Harvard University (HARVARD), Boston, USA. On the other side, the workshop is chaired and co-hosted by professors of the Dept. of Pharmacology and Toxicology (PHTOX), University of Texas Medical Branch, Galveston, USA. This first edition is dedicated to Dr. Allen Reitz for his successful activity as Founder Editor in Chief of the journal Current Topics in Medicinal Chemistry. This workshop is associated to the MOL2NET International Conference Series on Multidisciplinary Sciences. MOL2NET (From Molecules to Networks) series is running this year MOL2NET-2020, International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI SciForum, Basel, Switzerland, 2020. Consequently, it is co-chaired and promoted as well by researchers of the University of The Basque Country (UPV/EHU) and IKERBASQUE, Basque Foundation for Science, Bilbao, Basque Country, Spain. The link of the workshop is: https://mol2net-06.sciforum.net/chembiomol-01

Phenolic compounds are considered as dangerous pollutants, which produces serious environmental problems by pollution of water streams because of their great water solubility and high toxicity. In this paper we present the modeling of inhibitory grown activity against Tetrahymena pyriformis with structural feature descriptors. Quantitative structure-toxicity relationship model for acute toxicity of phenol derivatives was performed using Multiple Linear Regression (MLR), Reduced Error Pruning Tree (REPTree), M5 Model Rules (M5R), Multilayer Perceptron (MLP), Instance-Based Learning algorithms using K nearest neighbor (IBk-ANN), Support vector machine (SVM), and Radial basis function network (RBF). The correlation coefficients (R²) of training sets and test sets were 0.88 and 0.86 for the best MLR model, 0.82 and 0.72 for the best machine learning model (SVM), respectively. Following to the obtained results, our proposed model may be useful to predict of toxicity and risk assessment of phenol derivatives compound.

In this report, we propose the Machine Learning FURIA-C as a cutting-edge to classify drug-like compounds with anti-diabetic inhibitory ability toward the main two pharmacological targets α-amylase, and α-glucosidase. This model was tested for its classification capability over each repository, achieving satisfactory accuracy scores of 94.5% and 96.5%, respectively. Another important outcome was to achieve various α-amylase and α-glucosidase fuzzy rules with high Certainty Factor values. Some of the rules derived from the training series and active classification rules were interpreted. An important external validation step, comparing our method with the ones already reported, was included as well. The Holm test comparison showed significant differences (p-value<0.05) between Furia C versus Linear Discriminating Analysis (LDA) and Bayes Network, the former beating the last two ones. According to the relative ranking score, the out-performing technique is FURIA-C. Our analysis suggests that Furia-C could be used as a cutting-edge technique to predict (classify or screen), the α-amylase and α-glucosidase inhibitory activity, leading to the discovery of potent antidiabetic agents.

Antimicrobial resistance has prompted research and the development of new antibiotic treatments. Efforts to discover new drugs with antibacterial activity have generated large data sets from multiple preclinical trials with different experimental conditions. Predicting the activity of new chemical compounds on pathogenic microorganisms with different Metabolic Reaction Networks (MRNs) has become an important objective in the field. PTMLIF (Perturbation Theory, Machine Learning and Information Fusion) models are the combination of perturbation theory with machine learning and information fusion. In this document, we merge >100000 preclinical antibacterial assays from the ChEMBL database with the structural information for >40 MRNs of different microorganisms reported by the Barabási group. Non-linear PTMLIF models were applied to apply Random Forest (RF), J48- decision tree, and Bayesian Network (BN) algorithms. BN and RF models presented better results, specificity (˃88%), sensitivity (˃95%), AUROC (˃95%), and accuracy (~90%), In this work, we also demonstrated the power of information fusion of experimental characteristics of drugs/compounds and MRN for the prediction of antibacterial activity of chemical compounds.

Nanoparticles may have anti-bacterial activity so they become interesting alternatives to drugs in a context of emergence of resistant bacteria. These bacteria have different metabolic networks. In a recent work we developed a information fusion perturbation-theory machine learning (IFPTML) model to predicting nanoparticles vs. bacteria with topological changes on metabolic networks. The dataset studied had 15 classes of nanoparticles (1-100 nm) with most cases in the range of 1-50 nm vs. >20 pathogenic bacteria species with different metabolic networks. The nanoparticles studied included metal nanoparticles of Au, Ag, and Cu; oxide nanoparticles of Zn, Cu, La, Al, Fe, Sn, Ti, Cd, and Si; and metal salt nanoparticles of CuI and CdS. We used the SOFT.PTML software (our own application) with a user-friendly interface for the IFPTML calculations and a control statistics package. Using SOFT.PTML, we found a random forest model with Sn and Sp = 98-99% in the training/validation series.

Ref: B. Ortega-Tenezaca, H. González-Díaz. IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks. Nanoscale 2021 Jan 7. doi: 10.1039/d0nr07588d.

Talk presented on the JClubBiofisika, Coordinated by Prof. Alvaro Villaroel, Basque Center for Biophysics, CSIC-UPVEHU, Leioa, Basque Country, Spain. https://balanbbb.corp.csic.es/playback/presentation/2.0/playback.html?meetingId=464d38fbcdb87ce995bb1094f6074c813a1c5f28-1609404921659

Talk presented on the JClubBiofisika, Coordinated by Prof. Alvaro Villaroel, Basque Center for Biophysics, CSIC-UPVEHU, Leioa, Basque Country, Spain. Please, follow the link to see the talk and slides, audio in Spanish: https://balanbbb.corp.csic.es/playback/presentation/2.0/playback.html?meetingId=464d38fbcdb87ce995bb1094f6074c813a1c5f28-1610458120252

A series of C-10 substituted pyrrolo[1,2-b]isoquinolines have been synthesized via palladium-catalyzed Heck/Suzuki and Heck/anion capture cascade reactions. These compounds have shown antileishmanial activity against cutaneous (L. amazonensis) leishmaniasis, being even 10-fold more potent and selective than the drug of reference, Miltefosine. A Perturbation Theory Machine Learning (PTML) model has also been developed for the prediction of the probability with which a query compound reaches a desired level for multiple parameters vs. different Leishmania species and target proteins.

Site selective monoacylation of pyrroles has been achieved via Pd(II)-catalyzed C-H activiation with aldehydes in the presence of TBHP as oxidant using the 3-methyl-2-pyridine as directing group. The reaction has been extended to different aromatic and heteroaromatic aldehydes for the synthesis of a series of di(hetero)aryl ketones. The utility of the methodloogy has been demonstrated in the synthesis of pyrrolomycins, as Celastramycin analogues and Tolmetin.

The variability of methicillin-resistant Staphylococcus aureus (MRSA), its rapid adaptive response against environmental changes, and its continued acquisition of antibiotic resistance determinants, have made it a habitual resident of hospitals, where it causes a problem of multidrug resistance.

In this study, molecular topology was used to develop several discriminant equations capable of classifying compounds according to
their anti-MRSA activity.

Topological indices were used as structural descriptors and their relationship to anti-MRSA activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds.

Four extra equations were constructed, named DFMRSA1, DFMRSA2, DFMRSA3 and DFMRSA4 (DFMRSA was built in a previous study), all with good statistical parameters such as Fisher-Snedecor F (> 68 in all cases), Wilk’s lambda (< 0.13 in all cases) and percentage of correct classification (> 94 % in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound.

The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of anti-MRSA activity.

In a recent paper we analyzed in detail the principles of EU food and nanotechnology regulations perspectives for Machine Learning application towards safety issues analysis. In-depth review and discussion of the regulation opportunities to apply ML models of nanoparticles was presented involving EU foods nanotechnology regulation. It is concluded Machine Learning could improve the application of nanotechnology food regulation. ML can be applied on this area following the principles lined up by the standards of OECD, EU regulations and EFSA.

Ref: Machine Learning as a Proposal for a Better Application of Food Nanotechnology Regulation in the European UnionRicardo Santana ^{1 2 3}, Enrique Onieva ¹, Robin Zuluaga ⁴, Aliuska Duardo-Sánchez ⁵, Piedad Gañán ⁶ Curr Top Med Chem. 2020;20(4):324-332. doi: 10.2174/1568026619666191205152538.