Sexualidad animal y humana: análisis de un proyecto de formación no presencial

Se describe una experiencia de diseño y desarrollo de un curso virtual orientado a tratar cuestiones de sexualidad animal y humana con estudiantes pertenecientes de distintas carreras de la Universidadde Alicante (España). Se evalúan los recursos, las actividades desarrolladas, la participación en las mismas y el grado de satisfacción con los contenidos del curso

A Review of 2011 for PLoS Computational Biology

A Review of 2011 for <em>PLoS Computational Biology</em

Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images

[EN] Simple Summary Tumor segmentation is a key step in oncologic imaging processing and is a time-consuming process usually performed manually by radiologists. To facilitate it, there is growing interest in applying deep-learning segmentation algorithms. Thus, we explore the variability between two observers performing manual segmentation and use the state-of-the-art deep learning architecture nnU-Net to develop a model to detect and segment neuroblastic tumors on MR images. We were able to show that the variability between nnU-Net and manual segmentation is similar to the inter-observer variability in manual segmentation. Furthermore, we compared the time needed to manually segment the tumors from scratch with the time required for the automatic model to segment the same cases, with posterior human validation with manual adjustment when needed. Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (+/- 0.032 IQR). The median DSC for the automatic tool was 0.965 (+/- 0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%.This study was funded by PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, empowered by imaging biomarkers), a Horizon 2020 | RIA project (Topic SC1-DTH-07-2018), grant agreement no: 826494.Veiga-Canuto, D.; Cerdà-Alberich, L.; Sangüesa Nebot, C.; Martínez De Las Heras, B.; Pötschger, U.; Gabelloni, M.; Carot Sierra, JM.... (2022). Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images. Cancers. 14(15):1-15. https://doi.org/10.3390/cancers14153648115141

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

Systemic Type I IFN Inflammation in Human ISG15 Deficiency Leads to Necrotizing Skin Lesions

Most monogenic disorders have a primary clinical presentation. Inherited ISG15 deficiency, however, has manifested with two distinct presentations to date: susceptibility to mycobacterial disease and intracranial calcifications from hypomorphic interferon-II (IFN-II) production and excessive IFN-I response, respectively. Accordingly, these patients were managed for their infectious and neurologic complications. Herein, we describe five new patients with six novel ISG15 mutations presenting with skin lesions who were managed for dermatologic disease. Cellularly, we denote striking specificity to the IFN-I response, which was previously assumed to be universal. In peripheral blood, myeloid cells display the most robust IFN-I signatures. In the affected skin, IFN-I signaling is observed in the keratinocytes of the epidermis, endothelia, and the monocytes and macrophages of the dermis. These findings define the specific cells causing circulating and dermatologic inflammation and expand the clinical spectrum of ISG15 deficiency to dermatologic presentations as a third phenotype co-dominant to the infectious and neurologic manifestations.Fil: Martin Fernandez, Marta. Icahn School Of Medicine At Mount Sinai; Estados Unidos. King Saud University; Arabia SauditaFil: Bravo García Morato, María. Instituto de Investigacion del Hospital de la Paz.; EspañaFil: Gruber, Conor. Icahn School Of Medicine At Mount Sinai; Estados Unidos. King Saud University; Arabia SauditaFil: Murias Loza, Sara. Instituto de Investigacion del Hospital de la Paz.; EspañaFil: Malik, Muhammad Nasir Hayat. Twincore; Alemania. University Of Lahore; Países Bajos. Leibniz Universitat Hannover; Alemania. Helmholtz Gemeinschaft; AlemaniaFil: Alsohime, Fahad. King Saud University; Arabia SauditaFil: Alakeel, Abdullah. King Saud University; Arabia SauditaFil: Valdez, Rita. Gobierno de la Ciudad Autónoma de Buenos Aires. Hospital General de Agudos Doctor Cosme Argerich; ArgentinaFil: Buta, Sofija. Icahn School Of Medicine At Mount Sinai; Estados UnidosFil: Buda, Guadalupe. Bitgenia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Biología Celular e Histología; ArgentinaFil: Marti, Marcelo Adrian. Bitgenia; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Biología Celular e Histología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Larralde, Margarita. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; ArgentinaFil: Boisson, Bertrand. L'institut Des Maladies Génétiques Imagine; Francia. The Rockefeller University; Estados Unidos. Universite de Paris; FranciaFil: Feito Rodriguez, Marta. Instituto de Investigacion del Hospital de la Paz.; EspañaFil: Qiu, Xueer. Icahn School Of Medicine At Mount Sinai; Estados UnidosFil: Chrabieh, Maya. L'institut Des Maladies Génétiques Imagine; FranciaFil: Al Ayed, Mohammed. Najran University; Arabia SauditaFil: Al Muhsen, Saleh. King Saud University; Arabia SauditaFil: Desai, Jigar V.. National Institutes of Health; Estados UnidosFil: Ferre, Elise M.N.. National Institutes of Health; Estados UnidosFil: Rosenzweig, Sergio D.. National Institutes of Health; Estados UnidosFil: Amador-Borrero, Blanca. Icahn School Of Medicine At Mount Sinai; Estados UnidosFil: Bravo-Gallego, Luz Yadira. Instituto de Investigacion del Hospital de la Paz.; EspañaFil: Olmer, Ruth. Hannover Medical School; Alemania. German Center for Lung Research; AlemaniaFil: Merkert, Sylvia. Hannover Medical School; Alemania. German Center for Lung Research; AlemaniaFil: Bret, Montserrat. Instituto de Investigacion del Hospital de la Paz.; EspañaFil: Sood, Amika K.. University of North Carolina; Estados UnidosFil: Al-rabiaah, Abdulkarim. King Saud University; Arabia SauditaFil: Temsah, Mohamad Hani. King Saud University; Arabia SauditaFil: Halwani, Rabih. University of Sharjah; Emiratos Arabes UnidosFil: Hernandez, Michelle Marilyn. University of North Carolina; Estados UnidosFil: Pessler, Frank. Twincore; Alemania. Helmholtz Centre for Infection Research; AlemaniaFil: Casanova, Jean Laurent. The Rockefeller University; Estados Unidos. Necker Hospital for Sick Children; Francia. Howard Hughes Medical Institute; Estados Unidos. Universite de Paris; FranciaFil: Bustamante, Jacinta. The Rockefeller University; Estados Unidos. Necker Hospital for Sick Children; Francia. Universite de Paris; FranciaFil: Lionakis, Michail S.. National Institutes of Health; Estados UnidosFil: Bogunovic, Dusan. Icahn School Of Medicine At Mount Sinai; Estados Unido

Outcome of the First wwPDB Hybrid / Integrative Methods Task Force Workshop

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models