Towards the “Eldorado” of pKa Determination: A Reliable and Rapid DFT Model

The selection of a “perfect tool” for the theoretical determination of acid-base dissociation constants (Ka) is still puzzling. Recently, we developed a user-friendly model exploiting CAM-B3LYP for determining pKa with impressive reliability. Herein, a new challenge is faced, examining a panel of functionals belonging to different rungs of the “Jacob’s ladder” organization, which classifies functionals according to their level of theory. Specifically, meta-generalized gradient approximations (GGAs), hybrid-GGAs, and the more complex range-separated hybrid (RSH)-GGAs were investigated in predicting the pKa of differently substituted carboxylic acids. Therefore, CAM-B3LYP, WB97XD, B3PW91, PBE1PBE, PBEPBE and TPSSTPSS were used, with 6-311G+(d,p) as the basis set and the solvation model based on density (SMD). CAM-B3LYP showed the lowest mean absolute error value (MAE = 0.23) with relatively high processing time. PBE1PBE and B3PW91 provided satisfactory predictions (MAE = 0.34 and 0.38, respectively) with moderate computational time cost, while PBEPBE, TPSSTPSS and WB97XD led to unreliable results (MAE > 1). These findings validate the reliability of our model in predicting carboxylic acids pKa, with MAE well below 0.5 units, using a simplistic theoretical level and a low-cost computational approach

An accurate approach for computational pKa determination of phenolic compounds

Computational chemistry is a valuable tool, as it allows for in silico prediction of key parameters of novel compounds, such as pKa. In the framework of computational pKa determination, the literature offers several approaches based on different level of theories, functionals and continuum solvation models. However, correction factors are often used to provide reliable models that adequately predict pKa. In this work, an accurate protocol based on a direct approach is proposed for computing phenols pKa. Importantly, this methodology does not require the use of correction factors or mathematical fitting, making it highly practical, easy to use and fast. Above all, DFT calculations performed in the presence two explicit water molecules using CAM-B3LYP functional with 6-311G+dp basis set and a solvation model based on density (SMD) led to accurate pKa values. In particular, calculations performed on a series of 13 differently substituted phenols provided reliable results, with a mean absolute error of 0.3. Furthermore, the model achieves accurate results with -CN and -NO2 substituents, which are usually excluded from computational pKa studies, enabling easy and reliable pKa determination in a wide range of phenols

6-(7-Nitro-2,1,3-benzoxadiazol-4-ylthio)hexanol, a specific glutathione S-transferase inhibitor, overcomes the multidrug resistance (MDR)-associated protein 1-mediated MDR in small cell lung cancer

In the present work, we have investigated the antitumor activity of 6-(7-nitro-2,1,3-benzoxadiazol-4-ylthio)hexanol (NBDHEX) on aggressive small cell lung cancer. NBDHEX not only is cytotoxic toward the parental small cell lung cancer H69 cell line (LC50 of 2.3 +/- 0.6 mu mol/L) but also overcomes the multidrug resistance of its variant, H69AR, which overexpresses the ATP-binding cassette transporter multidrug resistance-associated protein 1 (MRP1; LC50 of 4.5 +/- 0.9 mu mol/L). Drug efflux experiments, done in the presence of a specific inhibitor of MRP1, confirmed that NBDHEX is not a substrate for this export pump. Interestingly, NBDHEX triggers two different types of cell death: a caspase-dependent apoptosis in the H69AR cells and a necrotic phenotype in the parental H69 cells. The apoptotic pathway triggered by NBDHEX in H69AR cells is associated with c-Jun NH2-terminal kinase and c-Jun activation, whereas glutathione oxidation and activation of p38(MAPK) is observed in the NBDHEX-treated H69 cells. In contrast to the parental cells, the higher propensity to die through apoptosis of the H69AR cell line may be related to the lower expression of the antiapoptotic protein Bcl-2. Therefore, down-regulation of a factor crucial for cell survival makes H69AR cells more sensitive to the cytotoxic action of NBDHEX, which is not a MRP1 substrate. We have previously shown that NBDHEX is cytotoxic toward P-glycoprotein-overexpressing tumor cell lines. Therefore, NBDHEX seems a very promising compound in the search for new molecules able to overcome the ATP-binding cassette family of proteins, one of the major mechanisms of multidrug resistance in cancer cells

Targeting GSTP1-1 induces JNK activation and leads to apoptosis in cisplatin-sensitive and -resistant human osteosarcoma cell lines

The effect of the glutathione transferase P1-1 (GSTP1-1) targeting has been investigated in both sensitive (U-2OS) and cisplatin-resistant (U-2OS/CDDP4μg) human osteosarcoma cell lines. Despite the different enzyme’s content, inhibition of GSTP1-1 by 6-(7-nitro-2,1,3-benzoxadiazol-4-ylthio)hexanol (NBDHEX) causes the activation of c-Jun N-terminal kinase (JNK) and apoptosis in both cell lines. However, different time courses of JNK activation and cell responses are observed. Whereas in the U-2OS/CDDP4μg cell line drug treatment results in an early increase of caspase activity and secondary necrosis, in the U-2OS cells it mainly causes cell cycle arrest followed by apoptosis. Thereafter, we detailed the action mechanism of NBDHEX in the U-2OS cell line. We report evidence of the interaction between GSTP1-1 and the TNF receptor associated factor 2 (TRAF2) and we demonstrate that NBDHEX is able to dissociate the GSTP1-1:TRAF2 complex. This restores the TRAF2:ASK1 signaling, thereby leading to the simultaneous and prolonged activation of JNK and p38. These mitogen-activated protein kinases (MAPKs) mediate different effects: JNK is crucial for apoptosis, whereas p38 causes an increase in the p21 level and a concomitant cell cycle arrest. Our study shows that GSTP1-1 plays an important regulatory role in TRAF signaling of osteosarcoma and discloses new features of the action mechanism of NBDHEX that suggest potentially practical consequences of these finding

In vitro and in vivo efficacy of 6-(7-nitro-2,1,3-benzoxadiazol-4-ylthio)hexanol (NBDHEX) on human melanoma

6-(7-Nitro-2,1,3-benzoxadiazol-4-ylthio)hexanol (NBDHEX) is a powerful inhibitor of the glutathione transferase P1-1 (GSTP1-1) and causes the disruption of the complex between GSTP1-1 and c-Jun N-terminal Kinase (JNK). This induces JNK activation and apoptosis in tumour cells. in the present work we assess the in vitro and in vivo effectiveness of NBDHEX on two human melanoma cell lines, Me501 and A375. NBDHEX shows IC50 values in the low micromolar range (IC50 of 1.2 +/- 0.1 mu M and 2.0 +/- 0.2 mu M for Me501 and A375, respectively) and is over 100 times more cytotoxic to these cell lines than temozolomide. Apoptosis is observed in Me501 cells within 3 h of the addition of NBDHEX, while in A375 cells the apoptotic event is rather late, and is preceded by a G2/M phase arrest. In both melanoma cell lines, INK activity is required for the ability of NBDHEX to trigger apoptosis, confirming that the JNK pathway is an important therapeutic target for this tumour. NBDHEX is also both effective and well tolerated in in vivo tumour models. A tumour inhibition of 70% is observed in vivo against Me501 human melanoma and a similar result is obtained on A375 model, with 63% of turnout inhibition. These findings indicate that the activation of the JNK pathway, through a selective GSTP1-1 targeting, could prove to be a promising new strategy for treating melanoma, which responds poorly to conventional therapies. (C) 2009 Elsevier Ltd. All rights reserved

Land use classification from Sentinel-2 imagery

[EN] Sentinel-2 (S2), a new ESA satellite for Earth observation, accounts with 13 bands which provide high-quality radiometric images with an excellent spatial resolution (10 and 20 m) ideal for classification purposes. In this paper, two objectives have been addressed: to determine the best classification method for S2, and to quantify its improve-ment with respect to the SPOT operational mission. To do so, four classifiers (LDA, RF, Decision Trees, K-NN) have been selected and applied to two different agricultural areas located in Valencia (Spain) and Buenos Aires (Argentina). All classifiers were tested using, on the one hand, all the S2 bands and, on the other hand, only selecting those bands from S2 closer to the four bands from SPOT. In all the cases, between 10%-50% of samples were used to train the classifier while remaining the rest for validation. As a result, a land use map was generated from the best classifier, according to the Kappa index, providing scientifically relevant information such as the area of each land use class.[ES] Sentinel-2 (S2) es un nuevo satélite de la ESA que cuenta con 13 bandas proporcionando imágenes de alta calidad radiométrica y excelente resolución espacial (10 y 20 m) ideal para trabajos de clasificación. En este trabajo se han abordado dos objetivos: determinar el mejor método de clasificación con S2, y cuantificar su mejora respecto a otras misiones operativas, como SPOT. Para ello se han seleccionado cuatro clasificadores (LDA, RF, Árboles de decisión, K-NN) que se han aplicado en dos zonas agrarias: una en la huerta de Valencia (España) y otra en la región de Buenos Aires (Argentina). Se han probado todos los clasificadores usando, por una parte, todas las bandas de S2, y por otra usando sólo las cuatro que coinciden con SPOT. En todos los casos se han aplicando porcentajes entre el 10 y el 50% de datos de entrenamiento y usado el resto de datos como validación. Como resultado se ha generado un mapa de usos del suelo a partir del mejor clasificador, basándose en el índice Kappa, proporcionando información científicamente relevante como es el área ocupada por cada una de las clases.Borràs, J.; Delegido, J.; Pezzola, A.; Pereira, M.; Morassi, G.; Camps-Valls, G. (2017). Clasificación de usos del suelo a partir de imágenes Sentinel-2. Revista de Teledetección. (48):55-66. doi:10.4995/raet.2017.7133.SWORD556648Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37-46. doi:10.1177/001316446002000104Comber, A., Fisher, P., & Wadsworth, R. (2005). You know what land cover is but does anyone else?…an investigation into semantic and ontological confusion. International Journal of Remote Sensing, 26(1), 223-228. doi:10.1080/0143116042000274032Delegido, J., Verrelst, J., Alonso, L., & Moreno, J. (2011). Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content. Sensors, 11(7), 7063-7081. doi:10.3390/s110707063Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random Forests for land cover classification. Pattern Recognition Letters, 27(4), 294-300. doi:10.1016/j.patrec.2005.08.011Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer Series in Statistics. doi:10.1007/978-0-387-84858-7Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data. Remote Sensing, 4(9), 2661-2693. doi:10.3390/rs4092661Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), 159. doi:10.2307/2529310Mena, A.J. 2014. Procesamiento de imágenes satelitales multiespectrales. Proyecto final de carrera, Facultad de Informática, Universidad del País Vasco.Quinlan, J.R. 1993. Programs for Machine Learning. 1st ed. San Mateo, CA, Morgan.Rees, G. 2005. The Remote Sensing Data Book. Cambridge University Press, 262 pp.Rodríguez-Galiano, V., Chica-Rivas, M. 2012. Clasificación de imágenes de satélite mediante software libre: Nuevas tendencias en algoritmos de Inteligencia artificial. Departamento de Geodinámica, Universidad de Granada

The economic impact of moderate stage Alzheimer's disease in Italy: Evidence from the UP-TECH randomized trial

Background: There is consensus that dementia is the most burdensome disease for modern societies. Few cost-of-illness studies examined the complexity of Alzheimer's disease (AD) burden, considering at the same time health and social care, cash allowances, informal care, and out-of-pocket expenditure by families. Methods: This is a comprehensive cost-of-illness study based on the baseline data from a randomized controlled trial (UP-TECH) enrolling 438 patients with moderate AD and their primary caregiver living in the community. Results: The societal burden of AD, composed of public, patient, and informal care costs, was about �20,000/yr. Out of this, the cost borne by the public sector was �4,534/yr. The main driver of public cost was the national cash-for-care allowance (�2,324/yr), followed by drug prescriptions (�1,402/yr). Out-of-pocket expenditure predominantly concerned the cost of private care workers. The value of informal care peaked at �13,590/yr. Socioeconomic factors do not influence AD public cost, but do affect the level of out-of-pocket expenditure. Conclusion: The burden of AD reflects the structure of Italian welfare. The families predominantly manage AD patients. The public expenditure is mostly for drugs and cash-for-care benefits. From a State perspective in the short term, the advantage of these care arrangements is clear, compared to the cost of residential care. However, if caregivers are not adequately supported, savings may be soon offset by higher risk of caregiver morbidity and mortality produced by high burden and stress. The study has been registered on the website www.clinicaltrials.org (Trial Registration number: NCT01700556). Copyright � International Psychogeriatric Association 2015

Socioeconomic Predictors of the Employment of Migrant Care Workers by Italian Families Assisting Older Alzheimer's Disease Patients: Evidence from the Up-Tech Study

Background: The availability of family caregivers of older people is decreasing in Italy as the number of migrant care workers (MCWs) hired by families increases. There is little evidence on the influence of socioeconomic factors in the employment of MCWs. Method: We analyzed baseline data from 438 older people with moderate Alzheimer's disease (AD), and their family caregivers enrolled in the Up-Tech trial. We used bivariate analysis and multilevel regressions to investigate the association between independent variables - education, social class, and the availability of a care allowance - and three outcomes - employment of a MCW, hours of care provided by the primary family caregiver, and by the family network (primary and other family caregivers). Results: The availability of a care allowance and the educational level were independently associated with employing MCWs. A significant interaction between education and care allowance was found, suggesting that more educated families are more likely to spend the care allowance to hire a MCW. Discussion: Socioeconomic inequalities negatively influenced access both to private care and to care allowance, leading disadvantaged families to directly provide more assistance to AD patients. Care allowance entitlement needs to be reformed in Italy and in countries with similar long-term care and migration systems. � 2015 The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved

Red Nacional de reconocedores de suelos.

Los relevamientos sistemáticos de suelos en Argentina comenzaron en la década de 1960, en el marco del Plan Mapa de Suelos. Dicho plan, desarrollado y liderado por el INTA, dio impulso a la formación de especialistas y a la producción de cartografía de suelos a diferentes escalas. Sin embargo, a partir del año 2000 las actividades se redujeron notablemente y gran parte de los equipos provinciales formados hasta ese momento se desarticularon. Desde entonces los relevamientos continuaron de manera aislada sólo en aquellas provincias donde se mantuvieron los grupos de trabajo. Este hecho condujo a que actualmente diferentes regiones del país no cuenten con información acerca de las propiedades y distribución de suelos a una escala adecuada para la toma de decisiones. En este contexto, en el 2018 se crea la Red Nacional de Reconocedores de Suelos (RNRS) que organiza las capacidades técnicas y operativas a nivel nacional para dar pronta respuesta a la creciente demanda de cartografía. Se trata de un equipo interinstitucional e interdisciplinario de especialistas distribuidos por todo el país, que realiza tareas de relevamiento, produce y difunde cartografía básica y utilitaria de suelos, ofrece capacitación y genera espacios de discusión y actualización metodológica. A la fecha, la RNRS ha relevado aproximadamente 760.000 ha en el sur de Córdoba, estimando completar durante el presente año el relevamiento del departamento Río Cuarto. Esta estrategia organizacional permitirá avanzar en el mapeo semidetallado de suelos en nuestro país, estableciendo vinculaciones sinérgicas entre profesionales de diferentes instituciones a fin de fortalecer y potenciar los equipos de trabajo en cada región. El motivo de esta contribución es presentar la RNRS, sus objetivos, avances a la fecha y desafíos a futuro, haciendo una breve revisión del estado actual de los relevamientos a escala semidetallada en nuestro país.Fil: Moretti, Lucas M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cerro Azul; ArgentinaFil: Rodriguez, Darío M. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Schulz, Guillermo A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Kurtz, Ditmar Bernardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Altamirano D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; ArgentinaFil: Amin, S. Universidad Nacional de Río Cuarto; ArgentinaFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Wageningen University. Soil Geography and Landscape group; Holanda. International Soil Reference and Information Centre. World Soil Information; HolandaFil: Babelis, German Claudio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; ArgentinaFil: Becerra, Alejandra Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Bedendo, Dante Julian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; ArgentinaFil: Boldrini, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez. Agencia de Extensión Rural Río Cuarto; AgentinaFil: Bongiovanni, C. Universidad Nacional de Río Cuarto; ArgentinaFil: Bozzer, S. Universidad Nacional de Río Cuarto; ArgentinaFil: Cabrera, A. Universidad Nacional de Río Cuarto; ArgentinaFil: Canale, A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez. Agencia de Extensión Rural Río Cuarto; AgentinaFil: Chilano, Y. Universidad Nacional de Río Cuarto; ArgentinaFil: Cholaky, Carmen. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; ArgentinaFil: Cisneros; José Manuel. Universidad Nacional de Río Cuarto. Cátedra de Uso y Manejo de Suelos; ArgentinaFil: Colazo, Juan Cruz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Luis; ArgentinaFil: Corigliano, J. Universidad Nacional de Río Cuarto; ArgentinaFil: Degioanni, Américo José. Universidad Nacional Río Cuarto. Facultad de Agronomía y Veterinaria. Departamento de Ecología Agraria; ArgentinaFil: de la Fuente, Juan Carlos Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Escobar, Dardo. Ministerio de Agricultura, Ganadería y Pesca; ArgentinaFil: Faule, L. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Córdoba. ArgentinaFil: Galarza, Carlos Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; ArgentinaFil: González, J. Universidad Nacional de Río Cuarto; ArgentinaFil: Holzmann, R. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Alto Valle; ArgentinaFil: Irigoin, Julieta. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad Nacional de Luján. Departamento Tecnología; ArgentinaFil: Lanfranco, M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; ArgentinaFil: León Giacosa, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Matteio, J.P. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Márquez, C. Gobierno de Córdoba. Ministerio de Agricultura y Ganadería; ArgentinaFil: Marzari, R. Universidad Nacional de Río Cuarto; ArgentinaFil: Mattalia, M.L. Universidad Nacional de Río Cuarto; ArgentinaFil: Morales Poclava, P.C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Muñoz, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; ArgentinaFil: Paladino, Ileana Ruth. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad Nacional de Lomas de Zamora. Facultad de Ciencias Agrarias; ArgentinaFil: Parra, B. Universidad Nacional de Río Cuarto; ArgentinaFil: Pérez, M. Gobierno de Córdoba. Ministerio de Agricultura y Ganadería; ArgentinaFil: Pezzola, A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Perucca, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez. Agencia de Extensión Rural Río Cuarto; ArgentinaFil: Porcel de Peralta, R. Gobierno de Córdoba. Ministerio de Agricultura y Ganadería; ArgentinaFil: Renaudeau, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Salustio, M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez. Agencia de Extensión Rural Río Cuarto; ArgentinaFil: Sapino, V. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Tenti Vuegen, L.M. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos. ArgentinaFil: Tosolini, R. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Vicondo, M.E. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina. Universidad Nacional de Córdoba. ArgentinaFil: Vizgarra, L.A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Quimili; ArgentinaFil: Ybarra, D.D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Winschel, C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Zamora, E. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentin

Objectification of the subjective riding comfort perception of motorcycles: Experimental analysis and international standards procedures

The present article refers to the study of the ride comfort of two-wheeled vehicles that represents an increasingly relevant aspect of the dynamic of the vehicle. The aim of the work is to develop a methodology to evaluate the ride comfort starting from data acquired during on road experimental tests. At the same time an investigation on a statistical population of drivers has been carried out in order to obtain a feedback about subjective perception of comfort for each tested vehicle. Several methods were proposed to identify objective indexes of comfort, including also analysis procedures proposed by International Standards. The obtained results were compared with the results coming from the subjective perception. At the end both the pros and cons of each proposed method were discusse