Solubilidad e hidrólisis de La, Pr, Eu, Er y Lu en un medio de fuerza iónica 1M de NaCl a 303 k

Se presenta un estudio sobre la determinación de la primera constante de hidrólisis y de la solubilidad de La, Pr, Eu, Er y Lu en un medio 1M de cloruro de sodio a temperatura de 303 K y en condiciones libres de CO2. Mediante un método radiométrico, se construyeron los diagramas pLn-pCH y se determinó la frontera de las zonas no saturada y la saturada, es decir, el pCH de inicio de precipitación. Esos diagramas se ajustaron con un método gráfico que utiliza el polinomio de solubilidad, para determinar el producto de solubilidad y la primera constante de hidrólisis. Este método se emplea por primera vez y es una aportación a la investigación sobre la determinación de las constantes de hidrólisis de los lantánidos. Además, el valor de la primera constante de hidrólisis se determinó a partir de los datos de las valoraciones potenciométricas donde no hay precipitado. Dichos datos se trataron con el programa de cómputo SUPERQUAD y con el método gráfico que utiliza el número promedio de ligandos. También se empleó el método de extracción con disolventes para determinar la primera constante de hidrólisis del lutecio en el medio 1M de NaCl a 303K. Los valores de log Kps que se obtuvieron para La, Pr, Eu, Er y Lu son: –19.53, -20.92, -22.24, -22.62 y –23.05 y los valores promedio de log*β1 son: -8.86 , -8.54, -8.33, -8.11 y –8.06 respectivamente. Con estos datos se tiene un panorama sobre el comportamiento hidrolítico y con base en ellos, se obtuvo una relación empírica con la cual se puede predecir el comportamiento de los otros lantánidos, en el medio 1M de NaCl, a 303 K

“Métodos de remoción y degradación de antibióticos en medio acuoso”

En los últimos años, los antibióticos han sido considerados contaminantes emergentes debido a su aporte continuo y su persistente aparición en los ecosiste concentraciones. Estos contaminantes se han detectado en todos los cuerpos acuáticos del mundo, lo que indica su eliminación tratamiento convencionale

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease.

Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer’s Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.This work was partly supported by the MICINN un der the projects TEC2012-34306 and PSI2015-65848- R, and the Consejer´ıa de Innovaci´on, Ciencia y Em presa (Junta de Andaluc´ıa, Spain) under the Ex cellence Projects P09-TIC-4530, P11-TIC-7103 and the Universidad de M´alaga. Programa de fortalec imiento de las capacidades de I+D+I en las Uni versidades 2014-2015, de la Consejer´ıa de Econom´ıa, Innovaci´on, Ciencia y Empleo, cofinanciado por el fondo europeo de desarrollo regional (FEDER) un der the project FC14-SAF30. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Ini tiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bio engineering, and through generous contributions from the following: AbbVie, Alzheimer’s Associa tion; Alzheimer’s Drug Discovery Foundation; Ara clon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; Eu roImmun; F. Hoffmann-La Roche Ltd and its affili ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.;Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity ; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Re search is providing funds to support ADNI clinical sites in Canada. Private sector contributions are fa cilitated by the Foundation for the National Insti tutes of Health (www.fnih.org). The grantee organi zation is the Northern California Institute for Re search and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

Determination of the Kinetic Behavior of Diclofenac in Aqueous Solution by UV Light Radiation

Kinetic behavior and half-life of diclofenac, trimethoprim and 17-α-ethinyl estradiol in aqueous solution under UV light radiation were determined.Diclofenac (DCF) is one of the most widely used non-steroidal anti-inflammatory drugs worldwide, and several studies have reported adverse effects on the environment, in plants and animals; so, it is classified as an emerging pollutant. There are several alternatives for its removal; however, it is necessary to study the way in which the DCF is degrading to offer more effective removal techniques, since the traditional ones such as chlorination, activated sludge, and biofiltration offer low removal efficiency (20–40%). This work analyzes the kinetic behavior of the photodegradation of DCF and the thermodynamic parameters of the reaction under UV-C-type light radiation. The results obtained indicate that it presents a first-order kinetic promoted by the increase of the temperature. Also, within the evaluated interval (273 to 308 K), the values of the kinetic coefficient (k) range between 0.05 and 0.20 min−1 and the half-life ranges from 3 to 9 min. The reaction is exothermic and spontaneous and gives way to the formation of approximately 6 byproducts, being two with the reatest presence and stability. This suggests that its decomposition route occurs through the dechlorination of the molecule and originate compounds known as carbazoles that have been detected in revious works. It was also found that this mixture of byproducts remained after the degradation of the drug, which is released to the environment, so it is necessary to extend a study on its properties and its possible environmental impact.CONACYT (Proyecto 215997)

Determination of ketorolac in the effluent from a hospital treating plant and kinetics study of its physiolic degradation

In this work, two specific, sensitive and rapid analytical methods were developed. One of them for the determination of ketorolac in a hospital wastewater treatment plant where there is no interference with other organic substances; the other one for the determination of the degradation kinetics in aqueous medium. Ketorolac was extracted from wastewater samples through solid phase extraction cartridges (SPE), then it was identified and quantified by high performance liquid chromatography (HPLC). Ketorolac was detected in concentrations between 0.1376 and 0.2667 µg/L. Photolytic degradation was performed on aqueous solutions of ketorolac tromethamine, reference substance, at a concentration of 50 µg/mL. Samples were in direct contact with ultraviolet light in a dark chamber, equipped with two mercury lamps (254 nm) at a radiation source of 15W. The results of the photolytic degradation were adjusted to a first-order model, obtaining a half-life of 4.8 hrs

Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.This work was partly supported by the MINECO/ FEDER under TEC2015-64718-R and PSI2015- 65848-R projects and the Consejer´ıa de Innovaci´on, Ciencia y Empresa (Junta de Andaluc´ıa, Spain) under the Excellence Project P11-TIC-7103 as well as the Salvador deMadariaga Mobility Grants 2017. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Depart ment of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contribu tions from the following: AbbVie, Alzheimer’s Asso ciation; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myer Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Ho mann-La Roche Ltd and its ali ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; P zer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clin ical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coor dinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern Cali fornia. PPMI a public-private partnership is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]

Label Aided Deep Ranking for the Automatic Diagnosis of Parkinsonian Syndromes.

Parkinsonism is the second most common neurodegenerative disease in the world. Its diagnosis usually relies on visual analysis of Emission Computed Tomography (SPECT) images acquired using 123I − io f lupane radiotracer. This aims to detect a deficit of dopamine transporters at the striatum. The use of Computer Aided tools for diagnosis based on statistical data processing and machine learning methods have significantly improved the diagnosis accuracy. In this paper we propose a classification method based on Deep Ranking which learns an embedding function that projects the source images into a new space in which samples belonging to the same class are closer to each other, while samples from different classes are moved apart. Moreover, the proposed approach introduces a new cost-sensitive loss function to avoid overfitting due to class imbalance (an usual issue in practical biomedical applications), along with label information to produce sparser embedding spaces. The experiments carried out in this work demonstrate the superiority of the proposed method, improving the diagnosis accuracy achieved by previous methodologies and validate our approach as an efficient way to construct linear classifiers.This work was partly supported by the MINECO/FEDER under TEC2015-64718- R and PSI2015-65848-R projects. We gratefully acknowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research. PPMI - a pub435 lic - private partnership - is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Conservación de poblaciones singulares ante el cambio climático: el caso de las currucas capirotadas ibéricas

Depto. de Biodiversidad, Ecología y EvoluciónFac. de Ciencias BiológicasTRUEpu

Five-Compressions Protocol as a Valid Myotonometric Method to Assess the Stiffness of the Lower Limbs: A Brief Report.

The objective of this study was to evaluate the validity of a short assessment MyotonPRO protocol to measure the stiffness of the superficial muscles and tendons of the lower limbs. The stiffness of the dominant lower limb vastus lateralis (VL), rectus femoris (RF) and patellar tendon (PT) was evaluated in 52 healthy participants (26.9 ± 3.4 years) with two MyotonPRO protocols: the standard protocol (10 mechanical taps) and the short protocol (five mechanical taps). The myotonometry was performed at the midpoint of the length from the upper pole of the patella to the greater trochanter for the VL, and to the anterior superior iliac spine for the RF. The PT was evaluated 1 cm caudal from the inferior pole of the patella. Pearson’s correlation coefficients were calculated to determine the relationships between protocols. The validity of the short protocol was evaluated with Student’s t-test. High positive correlations were observed between the short and standard protocols in the stiffness of the VL (r = 0.959; p 0.05). Therefore, the five-compressions protocol is a valid protocol for the assessment of lower limb mechanical propertiespost-print1648 K