Hydrotreating of Guaiacol and Acetic Acid Blends over Ni2P/ZSM-5 Catalysts: Elucidating Molecular Interactions during Bio-Oil Upgrading

[EN] Catalytic hydrodeoxygenation (HDO) is an effective technology for upgrading pyrolysis bio-oils. Although, in the past years, this process has been extensively studied, the relevance of the cross-reactivity between the numerous chemical components of bio-oil has been scarcely explored. However, molecular coupling can be beneficial for improving the bio-oil characteristics. With the aim of gaining a better understanding of these interactions, this work investigates the catalytic hydrodeoxygenation of mixtures of two typical components of pyrolysis bio-oils: guaiacol and acetic acid. The catalytic tests were carried out employing a bifunctional catalyst based on nickel phosphide (Ni2P) deposited over a commercial nanocrystalline ZSM-5 zeolite. The influence of both hydrogen availability and temperature on the activity and product distribution, was evaluated by carrying out reactions under different H2 pressures (40¿10 bar) and temperatures (between 260 and 300 °C). Using blends of both substrates, a partial inhibition of guaiacol HDO occurred because of the competence of acetic acid for the catalytic active sites. Nevertheless, positive interactions were also observed, mainly esterification and acylation reactions, which could enhance the bio-oil stability by reducing acidity, lowering the oxygen content, and increasing the chain length of the components. In this respect, formation of acetophenones, which can be further hydrogenated to yield ethyl phenols, is of particular interest for biorefinery applications. Increasing the temperature results in an increment of conversion but a decrease in the yield of fully deoxygenated molecules due to the production of higher proportion of catechol and related products. Additional experiments performed in the absence of hydrogen revealed that esterification reactions are homogeneously self-catalyzed by acetic acid, while acylation processes are mainly catalyzed by the acidic sites of the zeolitic support.The authors thank to the Spanish “Ministry of Economy and Competiveness” for their financial support through the project CATPLASBIO (CTQ2014-60209-R), as well as to the “Regional Government of Madrid” and European Structural Funds for the RESTOENE2 (S2013/MAE-2882) project

Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis

The objective of this study was to evaluate the performance of a new version of quantusFLM®, a software tool for prediction of neonatal respiratory morbidity (NRM) by ultrasound, which incorporates a fully automated fetal lung delineation based on Deep Learning techniques. A set of 790 fetal lung ultrasound images obtained at 24 + 0-38 + 6 weeks' gestation was evaluated. Perinatal outcomes and the occurrence of NRM were recorded. quantusFLM® version 3.0 was applied to all images to automatically delineate the fetal lung and predict NRM risk. The test was compared with the same technology but using a manual delineation of the fetal lung, and with a scenario where only gestational age was available. The software predicted NRM with a sensitivity, specificity, and positive and negative predictive value of 71.0%, 94.7%, 67.9%, and 95.4%, respectively, with an accuracy of 91.5%. The accuracy for predicting NRM obtained with the same texture analysis but using a manual delineation of the lung was 90.3%, and using only gestational age was 75.6%. To sum up, automated and non-invasive software predicted NRM with a performance similar to that reported for tests based on amniotic fluid analysis and much greater than that of gestational age alone

Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother's cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients, making it the largest ultrasound dataset to date. We then evaluated a wide variety of state-of-the-art deep Convolutional Neural Networks on this dataset and analyzed results in depth, comparing the computational models to research technicians, which are the ones currently performing the task daily. Results indicate for the first time that computational models have similar performance compared to humans when classifying common planes in human fetal examination. However, the dataset leaves the door open on future research to further improve results, especially on fine-grained plane categorization

Automatic deep learning-based pipeline for automatic delineation and measurement of fetal brain structures in routine mid-trimester ultrasound images

Introduction: The aim of this study was to develop a pipeline using state-of-the-art deep learning methods to automatically delineate and measure several of the most important brain structures in fetal brain ultrasound (US) images. Methods: The dataset was composed of 5,331 images of the fetal brain acquired during the routine mid-trimester US scan. Our proposed pipeline automatically performs the following three steps: brain plane classification (transventricular, transthalamic, or transcerebellar plane); brain structures delineation (9 different structures); and automatic measurement (from the structure delineations). The methods were trained on a subset of 4,331 images and each step was evaluated on the remaining 1,000 images. Results: Plane classification reached 98.6% average class accuracy. Brain structure delineation obtained an average pixel accuracy higher than 96% and a Jaccard index higher than 70%. Automatic measurements get an absolute error below 3.5% for the four standard head biometries (head circumference, biparietal diameter, occipitofrontal diameter, and cephalic index), 9% for transcerebellar diameter, 12% for cavum septi pellucidi ratio, and 26% for Sylvian fissure operculization degree. Conclusions: The proposed pipeline shows the potential of deep learning methods to delineate fetal head and brain structures and obtain automatic measures of each anatomical standard plane acquired during routine fetal US examination.The research leading to these results has received funding from the Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales,UK) and ASISA foundation.Peer ReviewedPostprint (published version

Pezizales (Ascomycota) asociados a bosque de pino-encino en Yécora, Sonora, México

Background and Aims: Pezizales is one of the most studied orders of the kingdom Fungi in the world and the second-best known group of ascomycetes in Mexico with 185 species; on the contrary, there are only 23 species registered for Sonora. The aim of this study was determined the taxonomic richness of Pezizales associated with the pine-oak forest in one locality of municipality of Yécora, Sonora, Mexico.Methods: Five seasonal samplings were carried out in pine-oak forest of Los Pilares, Yécora, Sonora during the years 2020-2021. The taxonomic determination was made with specialized keys from the macro- and micromorphological characterization of the specimens.Key results: Sixteen Pezizales species distributed in seven families were determined: Helvellaceae (5), Pezizaceae (2), Pseudombrophilaceae (1), Pulvinulaceae (1), Pyronemataceae (4), Sarcosomataceae (1), and Sarcoscyphaceae (2). Twelve species are new records for Sonora and six for Mexico: Geopyxis deceptiva, Helvella dryophila, Plectania milleri, Pseudombrophila fuscolilacina, Pseudopithyella magnispora and Tricophaeopsis latispora. In addition, the last three taxa are cited for the first time for the American continent.Conclusions: The Sonoran Pezizales catalogue increased to 35 taxa; however, it is important to continue with studies, which include ecological and phylogenetic analyzes of this group of fungi.Antecedentes y Objetivos: Pezizales es uno de los órdenes del reino Fungi más estudiados en el mundo y el segundo grupo de ascomicetos mejor conocido en México con 185 especies. En contraste, existen solo 23 especies registradas para Sonora. El objetivo de este estudio fue determinar la riqueza taxonómica de Pezizales asociados al bosque de pino-encino en una localidad del municipio de Yécora, Sonora, México.Métodos: Se realizaron cinco muestreos estacionales en bosque de pino-encino de Los Pilares, Yécora, Sonora durante los años 2020-2021. La determinación taxonómica se hizo con claves especializadas con base en la caracterización macro- y micromorfológica de los especímenes.Resultados clave: Se determinaron 16 especies de Pezizales distribuidas en siete familias: Helvellaceae (5), Pezizaceae (2), Pseudombrophilaceae (1), Pulvinulaceae (1), Pyronemataceae (4), Sarcosomataceae (1) y Sarcoscyphaceae (2). Doce especies son nuevos registros para Sonora y seis para México: Geopyxis deceptiva, Helvella dryophila, Plectania milleri, Pseudombrophila fuscolilacina, Pseudopithyella magnispora y Tricophaeopsis latispora. Además, los tres últimos taxones se citan por primera vez para el continente americano.Conclusiones: El catálogo de Pezizales de Sonora se incrementó a 35 especies; no obstante, es importante dar continuidad a estudios que incluyan análisis ecológicos y filogenéticos de este grupo de hongos

Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age

Background: Optimal prenatal care relies on accurate gestational age dating. After the first trimester, the accuracy of current gestational age estimation methods diminishes with increasing gestational age. Considering that, in many countries, access to first trimester crown rump length is still difficult owing to late booking, infrequent access to prenatal care, and unavailability of early ultrasound examination, the development of accurate methods for gestational age estimation in the second and third trimester of pregnancy remains an unsolved challenge in fetal medicine. Objective. This study aimed to evaluate the performance of an artificial intelligence method based on automated analysis of fetal brain morphology on standard cranial ultrasound sections to estimate the gestational age in second and third trimester fetuses compared with the current formulas using standard fetal biometry. Study Design: Standard transthalamic axial plane images from a total of 1394 patients undergoing routine fetal ultrasound were used to develop an artificial intelligence method to automatically estimate gestational age from the analysis of fetal brain information. We compared its performance—as stand alone or in combination with fetal biometric parameters—against 4 currently used fetal biometry formulas on a series of 3065 scans from 1992 patients undergoing second (n=1761) or third trimester (n=1298) routine ultrasound, with known gestational age estimated from crown rump length in the first trimester. Results: Overall, 95% confidence interval of the error in gestational age estimation was 14.2 days for the artificial intelligence method alone and 11.0 when used in combination with fetal biometric parameters, compared with 12.9 days of the best method using standard biometrics alone. In the third trimester, the lower 95% confidence interval errors were 14.3 days for artificial intelligence in combination with biometric parameters and 17 days for fetal biometrics, whereas in the second trimester, the 95% confidence interval error was 6.7 and 7, respectively. The performance differences were even larger in the small-for-gestational-age fetuses group (14.8 and 18.5, respectively). Conclusion: An automated artificial intelligence method using standard sonographic fetal planes yielded similar or lower error in gestational age estimation compared with fetal biometric parameters, especially in the third trimester. These results support further research to improve the performance of these methods in larger studies.The research leading to these results was partially funded by Transmural Biotech S.L. In addition, the research has received funding from “la Caixa” Foundation under grant agreements LCF/PR/GN14/10270005 and LCF/PR/GN18/10310003, the Instituto de Salud Carlos III (PI16/00861, PI17/00675) within the Plan Nacional de I+D+I and cofinanced by Instituto de Salud Carlos III— Subdirección General de Evaluación together with the Fondo Europeo de Desarrollo Regional (FEDER) “Una manera de hacer Europa,” Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales, United Kingdom), Cellex Foundation, ASISA Foundation, and Agency for Management of University and Research Grants under grant 2017 SGR number 1531. In addition, E.E. has received funding from the Departament de Salut under grant number SLT008/18/00156.Peer ReviewedPostprint (published version

Concordance between circulating tumor cells and clinical status during follow-up in anaplastic lymphoma kinase (ALK) non-small-cell lung cancer patients

Background: The identification of anaplastic lymphoma kinase (ALK) rearrangements is found in approximately 5% of non-small-cell lung cancers (NSCLCs). However, the development of liquid biopsies as a diagnostic tool is less developed in these cases. This study investigates the use of CTCs during treatment, together with an extended follow-up to correlate with clinical evolution. Patients and Methods: A total of 13 patients out of a cohort of 212 patients with lung adenocarcinoma, presented ALK rearrangements (6%) confirmed by tumor biopsy. A total of 60 serial blood samples were collected from these patients who were prospectively enrolled in the study. Results: All patients had a positive CTC count at baseline (mean = 3). The median follow-up was 9 months (range 1-17 months). Three patients underwent surgery and their CTC counts decreased after the procedure but still remained detectable. After radiotherapy, 3 cases showed an average decrease of 5 CTCs. A total of 6 patients were treated with ALK inhibitors and a partial response was observed in 3 of them, who also presented decreased CTC counts. The other 3 patients presented primary resistance, and their CTC counts were higher than those obtained prior to progression. Conclusion: We believe that the use of CTCs for dynamic monitoring of NSCLC with ALK rearrangement and to detect disease persistence or recurrence may be a reliable technique. CTC counts may also have potential use to monitor the efficacy of ALK inhibitors, facilitating detection of resistance to treatmentThis study was supported by Carlos III Institute of Health, Spanish Ministry of Science and Innovation, and European Regional Development Fund (grant number: PI16/01818 and PIE14/00064), D. Pérez-Callejo is supported by SEOM-Río-Hortega contract, A Romero is supported by Joan Rodés fellowship (grant number: JR14/00017) and M Sánchez-Beato is supported by Miguel Servet contract (CP11/00018 and CPII16/00024

Dual purpose cattle production in Mexico

Cattle production is one of the most important livestock activities in the rural areas of Mexico, with most of the national territory dedicated to it, in addition to the use of the most agricultural supplies and forages resources, as well as agricultural and agro-industrial by-products. Mexico is placed among the ten first meat and milk producer countries worldwide, being the Mexican tropical zone one of the main suppliers of such products. One of the main milk sources is the dual purpose cattle, such systems can be described as those that produce milk (daily milking) and meat (calf after weaning), on every productive cycle. They are mainly located in developing regions and characterized by using low-technology and in poor environments, consequently productive levels are considered low. Milk is destined for self-consumption or for sale at local markets and calf after weaning is sold at local feedlots or for export. Regarding to the little information available about the dual purpose systems, the present work is intended to discuss the main characteristics of cattle production in dual purpose systems in Mexico

Concordance of the risk of neonatal respiratory morbidity assessed by quantitative ultrasound lung texture analysis in fetuses of twin pregnancies

To evaluate the concordance of the risk of neonatal respiratory morbidity (NRM) assessed by quantitative ultrasound lung texture analysis (QuantusFLM) between twin fetuses of the same pregnancy. Prospective study conducted in twin pregnancies. There was good concordance of the risk of NRM between twins 34.0 weeks. From 30.0 to 33.6 weeks 26.5% of the twin pairs had discordant results, with moderate concordance of the risk of NRM

Una mirada a las diferentes perspectivas de los negocios internacionales: estrategias, principios y casos de internacionalización

Este libro consolida resultados de investigación que permiten mirar los negocios internacionales a partir de diferentes perspectivas, proporcionando un aporte sistémico para la academia y el mercado. A partir de discusiones del punto de vista de relaciones internacionales, comercio exterior, logística, estrategias de internacionalización y negociación, esta obra discute aspectos actuales aplicables y relacionados a la actuación de las empresas colombianas en el mercado internacional, sus oportunidades y potencialidades competitivas. A lo largo de sus siete capítulos e introducción, el libro “Una mirada a las diferentes perspectivas de los Negocios Internacionales: Estrategias, principios y casos de internacionalización” presenta diferentes metodologías, herramientas, fundamentos y casos de referencia que pueden apoyar profesionales en el estudio y en la práctica de los negocios internacionales