fairdataihub/poster-sentry-training-data huggingface dataset:
Categorizaci´on N IE-PETER Francisco Miralles Ferrer Pedro Abreu S´anchez Elisa Caballero Jos´e F. Reyes Rom´an 13 mayo 2024 1. Introducci´on Se presenta en este documento la naturaleza, funcionamiento y l´ogica de la funci´on de clasificaci´on en categor´ıas de los hallazgos N. Esta funci´on es utilizada en el proyecto IE-Peter, con objetivo de identificar los hallazgos N m´as relevantes entre los encontrados en el PET/CT. 2. Descripci´on de la funci´on de categorizaci´on N Se analiza en los siguientes subapartados diferentes aspectos relacionados con la l´ogica de la funci´on de categorizaci´on. 2.1. Atributos de entrada La funci´on acepta dos atributos de entrada que determina su comportamiento y resultados: 1. Hallazgo N, objeto que se evaluar´a con el objetivo de determinar su atributo categorizacion mediante el valor de retorno de la funci´on. No puede ser nulo. 2. Hallazgo T con el que el hallazgo N a categorizar se relaciona. Se identifica la relaci´on mediante el atributo idHallazgoTRelacionado del hallazgo N. Puede ser nulo. Se presenta el diagrama UML de las clases de hallazgoN y halazgoT con el que se relaciona. Ambas clases heredan de la clase hallazgo. Se indica la relaci´on que mantienen los hijos: un hallazgoN puede estar relacionado con cero o un hallazgoT mientras que este ´ultimo puedo ser relacionado con cero o muchos hallazgos N. 1
Single-cell migration data - import module in CellMissy This document describes in detail how to import single-cell experiments derived-data into the dedicated module of CellMissy. Which data is CellMissy expecting to receive as input The module expects text files containing four distinct columns: (i) the trajectory unique ID, (ii) the time point, (iii) the x coordinate (either in pixel or µm), and (iv) the y coordinate (either in pixel or µm): Formats accepted: tab-separated-values, comma-separated-values, XLS. Header of the text file
Integrated IoT monitoring system and Data Science platform to monitor plant conditions under biotic and abiotic factors Monia Santini1, Paola Nassisi1, Valentina Scardigno1, Carlo Trotta1, Alessandro D’Anca1, Arianna di Paola1,3, Sandro Fiore4, Giovanni Aloisio1,2, Riccardo Valentini1 1 Fondazione Centro euro-Mediterraneo sui Cambiamenti Climatici, Lecce, Italy 2 University of Salento, Lecce, Italy 3 National Research Council of Italy, Roma, Italy 4 University of Trento, Trento, Italy
Special issue on: Digital Technologies for Entrepreneurship and Social Change IMPORTANT DATES: Submission of Full Paper: until October 31, 2023. Initial decision sent to authors: until March 31, 2024 Deadline for revised papers: July 31, 2024 Notification of acceptance: until September 31, 2024 Deadline for final versions: October 31, 2024 Special Issue publication (expected): December 2024 Guest Editors: Dr. Amarolinda Zanela Klein Universidade do Vale do Rio dos Sinos, Business and Management School, Brazil (aczanela@unisinos.br) Dr. Cristiane Pedron Universidade Nove de Julho, Brazil (cdpedron@uni9.pro.br) Dr. Silvia Elaluf-Calderwood Florida International University, USA (selalufc@fiu.edu) Dr. Winnie Ng Picoto Universidade de Lisboa, Instituto Superior Economia e Gestão, Portugal (w.picoto@iseg.ulisboa.pt) BAR Editor-in-Chief: Dr. Ivan Lapuente Garrido Universidade do Vale do Rio dos Sinos, Business and Management School, Brazil (bar-eic@anpad.org.br)
AMS 2021 - Poster # 1019 Daniel M. Gilford, January 2021 Recording Transcript, including time stamps [Recording transcribed by https://otter.ai; lightly edited/formatted for readability/accessibility] 0:00 My name is Daniel Gilford. I'm a postdoctoral associate at Rutgers University and I'm really excited to share with you pyPI, which is a tropical cyclone potential intensity calculator coded up in Python and now available as a Python package. 0:16 Tropical Cyclone potential intensity is effectively the speed limit that a tropical cyclone can spin at, given its environmental conditions and pyPI is designed to supply a freely available set of codes and algorithms to both calculate potential intensity and do some simple analysis on potential intensity. [The] potential intensity algorithm has been around since about the 1980s, and in 2002, there were sort of a new version of it in Bister and Emanuel (2002). But the code from that which was originally coded in Fortran and then in MATLAB has never been really fully documented, so one of the purposes of this Python package that I developed, was to fully document the potential intensity algorithm, and make it as transparent as possible for users within the tropical cyclone and meteorological communities. I also want to really demonstrate the usefulness and utility of potential intensity as a research tool and pyPI makes that really transparent and I'm excited to share it with you today, as I do, as I sort of show you what it's capable of and what its purpose is. 1:23 So let's just start off with what potential intensity is. It’s fundamentally the speed limit of a tropical cyclone when we consider that it is a thermal heat engine. So it takes in energy it does work on the environment and it turns its potential energy into kinetic energy which is represented by the rotation of the wind, the wind speed of the tropical cyclone. And that maximum wind speed is really useful because we can compare it directly to actual intensity of tropical cyclones, and we can see there's a strong significant correlation between those two that is well documented in previous research. 2:01 And so what we can do is we can take environmental conditions, namely the temperature, water vapor, and sea surface temperatures, and also mean sea level pressures from an environmental profile---a single profile or we can take a gridded profile--and we can put those things together to calculate the potential intensity through the potential intensity algorithm. This is really exciting because you can take the potential energies from the environment convert them into kinetic energy you have some maximum speed at which the hurricane can spin, and you can learn a lot about the tropical cyclone that way; it's thermodynamic properties. 2:39 So if you're looking to calculate potential intensity one of the big questions you might ask, using pyPI, is how fast does this algorithm run. In the case of pyPI we are using numba to optimize potential intensity calculations; that's something that my colleague, Daniel Rothenberg helped me to set up within pyPI and I'm really grateful to him for his help, because it really has gotten
CDOCKER Tutorial CDOCKER is a molecular docking method based on CHARMm's position, which can produce highly accurate docking results. Taking the binding of natural ibuprofen ligand molecules to the COX-1 receptor as a point, the obtained docking conformation is compared with the natural conformation of the ligand in the crystal structure obtained by X-ray diffraction. 1. Prepare the docking system In the Files Explorer, find and double-click to open Samples| Tutorals| Receptor-Ligand Interations|1EQG.dsv. Open a three-dimensional protein structure with active sites in the branch window. (Figure 1) Figure 1 Three-dimensional structure of protein In the Tools Explorer, expand Receptor-Ligand Interactions|Define and Edit Binding Site, and then click Show/Hide Residues Outside Sphere and Show/Hide Sphere. Expand the menu bar View|Transform and click Fit To Screen to display the amino acid at the binding site in the center of the window. (Figure 2) Figure 2 Protein active site amino acids Find and double-click to open the Samples|Tutorials|Receptol-Ligand Interactions|1EQG-ibuprofen- conf.sd. file. A molecule of ibuprofen with an institutional image will open. (Figure 3)
●Title CBRAIN: a web-based, distributed computing platform for collaborative neuroinformatics research ● Authors and Affiliations Bryan Caron 1, Reza Adalat 1, Natacha Beck 1, Serge Boroday 1, Samir Das 1, Najmeh Khalili-Mahani 1, Darcy Quesnel 1, Pierre Rioux 1, Darius Valvicius 1, Alan Evans 1 1 McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada ● Introduction, Methods, Results, Conclusions Introduction CBRAIN (http://cbrain.ca) is an open source, web-based, collaborative research software platform designed to address major challenges in big data research. CBRAIN provides researchers in neuroinformatics, genomics and more the ability to easily collaborate, store and analyze data at scale using computational pipelines on heterogeneous computing and data resources around the world. CBRAIN is used in numerous major neuroinformatics projects and is a pillar component of NeuroHub (https://neurohub.ca), a core platform of McGill University’s Healthy Brains, Healthy Lives initiative (https://www.mcgill.ca/hbhl/). Methods In production since 2009, CBRAIN (Sherif et al., 2014) is a mature and flexible orchestration infrastructure seamlessly connecting scientists to a network of advanced computing and data resources. CBRAIN is a multi-tiered platform implemented in Ruby on Rails, a widely used RESTful, Ruby-based framework. See Figure 1 for a schematic of various components of CBRAIN. CBRAIN allows users to easily launch and manage the execution of complex computational pipelines and choreographs all associated data transfers across a distributed network of resources. CBRAIN is integrated with Globus Auth (Tuecke et al., 2016) for identity management, authenticating users with their institutional Identity Provider and authorizing access to CBRAIN services. With CBRAIN users are able to upload their own data, create datasets and share that data. Fine-grained access controls enable users to securely store and share data according to a study’s data management policy. A wide variety of open and controlled access datasets including those from the Canadian Open Neuroscience Platform (https://conp.ca) (Harding et al., 2022), the Human Connectome Project (Van Essen et al., 2013) and the UK Biobank (Miller
Wanted egg parasitoids: Ooctonus vulgatus parasitizes Philaenus spumarius in Corsica and is probably widely distributed in Europe Mesmin X.; Chartois M.; Genson G.; Rossi J.-P.; Cruaud A.; Rasplus J.-Y CBGP, INRAE, CIRAD, IRD, Montpellier SupAgro, Univ Montpellier, Montpellier, France
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/283259146 Environmental risks of biochar in soils: ecotoxicological effects on plants and microarthropods Conference Paper · October 2015 CITATIONS 0 READS 128 4 authors: Some of the authors of this publication are also working on these related projects: Sustainable Agriculture and Soil Conservation (SoCo) View project Soil sealing and land take View project Federica D. Conti Università degli studi di Parma 27 PUBLICATIONS 93 CITATIONS SEE PROFILE Ciro Gardi EFSA European Food Safety Authority 121 PUBLICATIONS 969 CITATIONS SEE PROFILE Giovanna Visioli Università degli studi di Parma 61 PUBLICATIONS 621 CITATIONS SEE PROFILE Cristina Menta University of East London 56 PUBLICATIONS 609 CITATIONS SEE PROFILE All content following this page was uploaded by Federica D. Conti on 27 October 2015. The user has requested enhancement of the downloaded file.
Digital Humanities Benelux 2022 conference University of Luxemburg June 1-3, 2022 Title Scholar-Curated Worksets for Analysis, Reuse & Dissemination (SCWAReD) from the HathiTrust Research Center Authors ● Ryan Dubnicek (University of Illinois at Urbana-Champaign, HathiTrust Research Center) rdubnic2@illinois.edu ● John A. Walsh (Indiana University, HathiTrust Research Center) jawalsh@indiana.edu ● Maryemma Graham (University of Kansas) - non-presenting author ● J. Stephen Downie (University of Illinois at Urbana-Champaign, HathiTrust Research Center) - non-presenting author ● Isabella Magni (Indiana University, HathiTrust Research Center) - non-presenting author ● Glen Layne-Worthey (University of Illinois at Urbana-Champaign, HathiTrust Research Center) - non-presenting author Abstract The Scholar-Curated Worksets for Analysis, Reuse & Dissemination (SCWAReD) project, generously supported by the Andrew W. Mellon Foundation, is producing a suite of curated, targeted worksets of materials from the HathiTrust Digital Library, facilitated by its Research Center (HTRC). HTRC worksets are user-created collections of HathiTrust volumes that can be treated as data and analyzed using a variety of tools and services. Worksets can be shared and cited, contributing to research reproducibility and enduring scholarship. In addition to their intrinsic value as focused digital collections, SCWAReD’s enhanced, scholar-curated worksets also serve as illustrative, reusable research models, and include not only the worksets themselves, but also scholarly introductions, derived datasets and related documentation, and research reports, demonstrating the collaborative workset-building, textual analysis, workflow development, and dataset-creation activities typically carried out by HTRC. The special mission of SCWAReD is to highlight and center the work of historically under- resourced and marginalized textual communities. For this purpose, a flagship project and four sub-projects were selected competitively; each of them explores new methods for creating, analyzing, and reusing curated digital library collections and the research data derived from them. SCWAReD aims to address inequities in both library collections and digital humanities research by identifying data that can support research in these traditionally marginalized fields as well as remediating gaps within HathiTrust, and using computationally-assisted efforts to recover content that is already part of the HathiTrust Digital Library but that may be difficult to discover with traditional metadata, in a traditional catalog, from within a massive digital collection.
5th PRESS RELEASE The SECRETed project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement No. 101000794. This publication reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained therein. Sustainable Exploitation of bio-based Compounds Revealed and Engineered from naTural sources © copyright A message from the Exploitation and Dissemination managers of SECRETed
Development of self-degradable lipid-like material equipped with environment sensing units Daniel Zucker 1*, Yuta Nakai 2, Kota Tange 2, Hiroki Yoshioka 2, Hiroki Tanaka 3, Hidetaka Akita 3. 1 NOF EUROPE GmbH, Mainzer Landstrasse 46, 60325 Frankfurt, Germany. 2 NOF CORPORATION, 3-3 Chidori-cho, Kawasaki-ku, Kawasaki, Kanagawa 210-0865, Japan. 3 Chiba University, Graduate School of Pharmaceutical Sciences, Inohana Campus: 1-8-1 Inohana, Chuo-ku, Chiba, 260-8675, Japan. * Presenter, e-mail: daniel.zucker@nofeurope.com An in-vitro transcribed messenger RNA (mRNA) is now becoming one of the most powerful tools for introducing a desired protein to cells and organisms. We previously developed a lipid-like material (COATSOME® SS-series) for the successful cytoplasmic delivery of the mRNA. COATSOME® SS-series contain two environment sensing units; tertiary amines for endosomal escape and a disulfide bonding for cytoplasmic collapse [1, 2]. In this study, we further improved the cytoplasmic release of the mRNA from the lipid nanoparticles by modifying the structure of COATSOME® SS-series. We have recently discovered that an enrichment of hydrophobic thiols in the particle under reducing environments induces an intra-particle hydrolysis reaction. In this study, we introduced a hydrolysable phenyl ester group to COATSOME® SS-OP (Fig. 1) as a self-degradation unit. The self-degradation reaction was confirmed using high performance liquid chromatography, liquid chromatography–mass spectrometry, and nuclear magnetic resonance spectroscopy. Transmission electron cryomicroscopy observation revealed that the mRNA-encapsulating particle has a spherical and homogenous morphology. The COATSOME® SS-OP showed potent gene expressions in both in-vitro and in-vivo assays. 0 20 40 60 80 100 120 140 0 3 6 9 12 Blood EPO concentration (ng/mL) Time post administration (hour) COATSOME® SS-OP MC3 In vivo Erythropoietin-encoding mRNA delivery to Liver COATSOME® SS-OP O O O O N S S N O O O O
Young, (metal-)rich ad not alone: formation of thin-disc RR Lyrae stars through binary evolution Giuliano Iorio In coll. with : Alexei Bobrick, Vasily Belokurov, Joris Vos, Maja Vuckovic Based on: Iorio & Belokurov, MNRAS, 2021, 502, 5686 Bobrick, Iorio et al., arXiv:2208.04332 Long story short: • Data from the Gaia satellite reveal a population of RR Lyrae stars with metallicity, kinematics and distribution typical of the Galactic thin-disc • Their existence is a conundrum for stellar evolution models, how do they form? Binary evolution is the solution! • Interesting implications for both Galactic and Binary studies Where are the plots? See next slides Wanna know more? Let’s talk! Where are the data? giuliano.iorio@unipd.it
A socio-semantic analysis of the research domain on Xylella fastidiosa Structure and scientific dynamics Marc Barbier3, Pauline Farigoule1,2, Astrid Cruaud2, Jean-Pierre Rossi2 1 AgroParisTech, Paris, France 2 CBGP, INRAE, CIRAD, IRD, Montpellier SupAgro, Univ. Montpellier, Montferrier-sur-Lez, France 3 UMR LISIS, INRAE, Marne-la-Vallée, France Contact : Marc Barbier – marc.barbier@inrae.fr
Practicing the Best Practice Designing Effective Health Care Experiences Susan L. Spraragen IBM T.J. Watson Research Center Hawthorne, New York, sprara@us.ibm.com Abstract: This paper offers a discussion on how to apply a service design technique to health care scenarios where positive service experiences are vital. When examining issues like patient compliance, applying techniques from the design world may afford health care providers with a clear visualization for understanding the patient’s perspective in interacting with their health care providers. Here we describe the Expressive Service Blueprinting method that links the patient’s journey with the actions and processes of the health care provider. The emotive responses of the patient as they progress through their journey are also reviewed as an element that augments the insight gained from constructing the blueprint. Key words: Service Design, Health Care, Emotion, Patient Compliance. 1. Introduction It is readily being recognized that design thinking can be applied to areas that reach beyond product design and into service experience design, where the service may be the use of public transportation, securing clean water for a community, or providing the best health care at a low cost [1]. This poster will demonstrate how the use of a design technique can bridge the chasm between design intentions and service processes in order to produce beneficial experiences for service providers and consumers. This discussion will focus on health care scenarios. As a health care provider constructs a regimen or intervention for a patient they have the hopes that the patient will readily adopt the procedures, show improvement, and grow along a planned path. But often the patient encounters many bumps and obstacles along that path. Thus the challenge becomes one of how to collaboratively design successful patient journeys, or at least ones that have better chances for being followed and producing positive outcomes. The answer lies in anchoring the design of the prescribed plan around the patient’s perspective while linking that perspective to provider intentions for their patient. A developing design technique that maps out the patient journey, as experienced from their viewpoint, is called Expressive Service Blueprinting. This technique, which I will illustrate in my poster, adds emotive states to traditional service blueprinting, where we consider the customer’s expected or observed emotional responses as they occur during their service interactions. Providers present emotions on the service stage as well, and that may represent opportunities for improved training and staffing. Service blueprinting is a method for visually representing the meaningful moments of a service engagement from the customer’s perspective [2, 3]. Linked to the steps of the customer’s experience, the map includes provider
Colección de ESMOS 1 Infografía Fosfatasa alcalina Verónica González Luna* iD Licenciatura en Biotecnología, Facultad de Ciencias Biológicas, Benemérita Universidad Autónoma de Puebla, Puebla, México. *Email: 202123056@viep.com.mx 01 de Febrero de 2023 DOI: http://doi.org/10.5281/zenodo.7593281 Editado por: Jesús Muñoz-Rojas (Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla). Revisado por: Victor Rivelino Juárez-González (Departamento de Medicina Molecular y Bioprocesos, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México). Colección de ESMOS Resumen Es una enzima proteica homodimérica formada por dos monómeros idénticos, cada monómero va a tener cinco residuos de cisteína, dos átomos de zinc y un átomo de magnesio. Esta enzima se encuentra en diversos individuos y a distinta concentración, esto va a depender de las condiciones del sistema, podrá estar presente en células procariotas y eucariotas entre los que destacan hongos y plantas [1]. En las bacterias Gram-negativas la fosfatasa alcalina se encuentra en el espacio
CENTRO DE CIÊNCIAS HUMANAS, LETRAS E ARTES DEPARTAMENTO DE ADMINISTRAÇÃO E CONTABILIDADE PROGRAMA DE PÓS-GRADUAÇÃO EM ADMINISTRAÇÃO VI Seminários InterNacionais de Administração Pública Prof. Dr. Roberto Kern Gomes IBGE / SC Intraempreendedorismo em Organizações Públicas Convidado Prof. Dr. Josiel Lopes Valadares Universidade Federal de Viçosa Moderador Tema 29/05/2019 - 16h00 - Sala de Conferências DAD/UFV Prof. Antônio Vagner Almeida Olavo Universidade Federal do Amazonas - UFAM Debatedor Aberto ao público / sem inscrição prévia Realização Organização Apoio PPGAdm
Colección de ESMOS 1 Infografía Aldrín, un viejo enemigo Edgar Elias Torres iD, Danna Michelle Hernández Cabrera iD, María Esther Hernández Huerta iD, Camila Ojeda Fernández iD, América Oropeza Vázquez iD Licenciatura en Biotecnología, Facultad de Ciencias Biológicas, Benemérita Universidad Autónoma de Puebla, Puebla, México. *Email: edgar.elias@alumno.buap.mx 8 de Marzo de 2024 DOI: http://doi.org/10.5281/zenodo.10795826 Editado por: Yolanda Elizabeth Morales-García (Facultad de Ciencias Biológicas, Benemérita Universidad Autónoma de Puebla). Revisado por: Jesús Muñoz-Rojas (Instituto de Ciencias, Benemérita Universidad Autónoma de Puebla). Apoyo en la maquetación: Gadriana Scarlett Martínez Gómez (Estudiante de la Licenciatura en Biología, Facultad de Ciencias Biológicas, Benemérita Universidad Autónoma de Puebla, Puebla, México). Colección de ESMOS Resumen El aldrín es un insecticida organoclorado que ya sea por contacto, inhalación o por ingestión es un veneno potencial y persistente [1, 2]. Su uso comenzó en los años 40 y se extendió hasta 1974, ya para los años 70’s y 80’s fue prohibido debido a su alta toxicidad en EUA, Malasia,