Standardisation of elemental analytical techniques applied to provenance studies of archaeological ceramics: an inter laboratory calibration study

Chemical analysis is a well-established procedure for the provenancing of archaeological ceramics. Various analytical techniques are routinely used and large amounts of data have been accumulated so far in data banks. However, in order to exchange results obtained by different laboratories, the respective analytical procedures need to be tested in terms of their inter-comparability. In this study, the schemes of analysis used in four laboratories that are involved in archaeological pottery studies on a routine basis were compared. The techniques investigated were neutron activation analysis (NAA), X-ray fluorescence analysis (XRF), inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS). For this comparison series of measurements on different geological standard reference materials (SRM) were carried out and the results were statistically evaluated. An attempt was also made towards the establishment of calibration factors between pairs of analytical setups in order to smooth the systematic differences among the results

Effect of Frying and Roasting Processes on the Oxidative Stability of Sunflower Seeds (Helianthus annuus) under Normal and Accelerated Storage Conditions

The effect of different cooking processes such as frying and roasting on the oxidative stability of sunflower seeds was evaluated under accelerated oxidation and normal storage conditions. The fatty acid composition by GC-MS showed a higher amount of linoleic acid in fried samples due to the replacement of the seed moisture by the frying oil. On the other hand, roasted samples presented a higher oleic acid content. DSC and TGA results showed some decrease in the thermal stability of sunflower seed samples, whereas PV and AV showed the formation of primary and secondary products, with increasing oxidation time. Roasted sunflower seeds showed seven main volatile compounds characteristic of the roasting process by HS-SPME-GC-MS: 2-pentylfuran, 2,3-dimethyl-pyrazine, methyl-pyrazine, 2-octanone, 2-ethyl-6-methylpyrazine, trimethyl-pyrazine, and trans,cis-2,4-decadienal, whereas fried samples showed six volatile characteristic compounds of the frying process: butanal, 2-methyl-butanal, 3-methyl-butanal, heptanal, 1-hexanol, and trans,trans-2,4-decadienal. The generation of hydroperoxides, their degradation, and the formation of secondary oxidation products were also investigated by ATR-FTIR analysis. The proposed methodologies in this work could be suitable for monitoring the quality and shelf-life of commercial processed sunflower seeds with storage time

Gitana: a SQL-based Git Repository Inspector

International audienceSoftware development projects are notoriously complex and difficult to deal with. Several support tools such as issue tracking, code review and Source Control Management (SCM) systems have been introduced in the past decades to ease development activities. While such tools efficiently track the evolution of a given aspect of the project (e.g., bug reports), they provide just a partial view of the project and often lack of advanced querying mechanisms limiting themselves to command line or simple GUI support. This is particularly true for projects that rely on Git, the most popular SCM system today. In this paper, we propose a conceptual schema for Git and an approach that, given a Git repository, exports its data to a relational database in order to (1) promote data integration with other existing SCM tools and (2) enable writing queries on Git data using standard SQL syntax. To ensure efficiency, our approach comes with an incremental propagation mechanism that refreshes the database content with the latest modifications. We have implemented our approach in Gitana, an open-source tool available on GitHub

Entomopathogenic nematology in Latin America: A brief history, current research and future prospects

Since the 1980s, research into entomopathogenic nematodes (EPNs) in Latin America has produced many remarkable discoveries. In fact, 16 out of the 117 recognized species of EPNs have been recovered and described in the subcontinent, with many more endemic species and/or strains remaining to be discovered and identified. In addition, from an applied perspective, numerous technological innovations have been accomplished in relation to their implementation in biocontrol. EPNs have been evaluated against over 170 species of agricultural and urban insects, mites, and plant-parasitic nematodes under laboratory and field conditions. While much success has been recorded, many accomplishments remain obscure, due to their publication in non-English journals, thesis dissertations, conference proceedings, and other non-readily available sources. The present review provides a brief history of EPNs in Latin America, including current findings and future perspectives.Fil: San Blas, Ernesto. Instituto Venezolano de Investigaciones Científicas; VenezuelaFil: Campos Herrera, Raquel. Consejo Superior de Investigaciones Científicas; EspañaFil: Dolinski, Claudia. Universidade Estadual Do Norte Fluminense Darcy Ribeiro; BrasilFil: Monteiro, Caio. Universidade Federal de Goiás; BrasilFil: Andaló, Vanessa. Universidade Federal de Uberlandia; BrasilFil: Leite, Luis Garrigós. Universidade Estadual de Campinas; BrasilFil: Rodríguez, Mayra G.. Centro Nacional de Sanidad Agropecuaria; CubaFil: Morales Montero, Patricia. Instituto Venezolano de Investigaciones Científicas; VenezuelaFil: Sáenz Aponte, Adriana. Pontificia Universidad Javeriana; ColombiaFil: Cedano, Carolina. Universidad Nacional de Trujillo; PerúFil: López Nuñez, Juan Carlos. Centro Nacional de Investigaciones del Café; ColombiaFil: del Valle, Eleodoro Eduardo. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Doucet, Marcelo Edmundo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Zoología Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; ArgentinaFil: Lax, Paola. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Zoología Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; ArgentinaFil: Navarro, Patricia D.. Instituto de Investigaciones Agropecuarias; ChileFil: Báez, Francisco. Instituto Nacional Autonomo de Investigaciones Agropecuarias; EcuadorFil: Llumiquinga, Pablo. Instituto Nacional Autonomo de Investigaciones Agropecuarias; EcuadorFil: Ruiz Vega, Jaime. Instituto Politécnico Nacional ; MéxicoFil: Guerra Moreno, Abby. Laboratorio de Biotecnología; PanamáFil: Stock, S. Patricia. University of Arizona; Estados Unido

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

COVID-19 in breast cancer patients: a subanalysis of the OnCovid registry

BACKGROUND: Cancer patients are at higher risk of COVID-19 complications and mortality than the rest of the population. Breast cancer patients seem to have better prognosis when infected by SARS-CoV-2 than other cancer patients. METHODS: We report a subanalysis of the OnCovid study providing more detailed information in the breast cancer population. RESULTS: We included 495 breast cancer patients with a SARS-CoV-2 infection. Mean age was 62.6 years; 31.5% presented more than one comorbidity. The most frequent breast cancer subtype was luminal-like (n = 245, 49.5%) and 177 (35.8%) had metastatic disease. A total of 332 (67.1%) patients were receiving active treatment, with radical intent in 232 (47.6%) of them. Hospitalization rate was 58.2% and all-cause mortality rate was 20.3%. One hundred twenty-nine (26.1%) patients developed one COVID-19 complication, being acute respiratory failure the most common (n = 74, 15.0%). In the multivariable analysis, age older than 70 years, presence of COVID-19 complications, and metastatic disease were factors correlated with worse outcomes, while ongoing anticancer therapy at time of COVID-19 diagnosis appeared to be a protective factor. No particular oncological treatment was related to higher risk of complications. In the context of SARS-CoV-2 infection, 73 (18.3%) patients had some kind of modification on their oncologic treatment. At the first oncological reassessment (median time: 46.9 days ± 36.7), 255 (51.6%) patients reported to be fully recovered from the infection. There were 39 patients (7.9%) with long-term SARS-CoV-2-related complications. CONCLUSION: In the context of COVID-19, our data confirm that breast cancer patients appear to have lower complications and mortality rate than expected in other cancer populations. Most breast cancer patients can be safely treated for their neoplasm during SARS-CoV-2 pandemic. Oncological treatment has no impact on the risk of SARS-CoV-2 complications, and, especially in the curative setting, the treatment should be modified as little as possible