Development and Disease-Dependent Dynamics of Spermatogonial Subpopulations in Human Testicular Tissues

Cancer therapy and conditioning treatments of non-malignant diseases affect spermatogonial function and may lead to male infertility. Data on the molecular properties of spermatogonia and the influence of disease and/or treatment on spermatogonial subpopulations remain limited. Here, we assessed if the density and percentage of spermatogonial subpopulation changes during development (n = 13) and due to disease and/or treatment (n = 18) in tissues stored in fertility preservation programs, using markers for spermatogonia (MAGEA4), undifferentiated spermatogonia (UTF1), proliferation (PCNA), and global DNA methylation (5mC). Throughout normal prepubertal testicular development, only the density of 5mC-positive spermatogonia significantly increased with age. In comparison, patients affected by disease and/or treatment showed a reduced density of UTF1-, PCNA- and 5mC-positive spermatogonia, whereas the percentage of spermatogonial subpopulations remained unchanged. As an exception, sickle cell disease patients treated with hydroxyurea displayed a reduction in both density and percentage of 5mC- positive spermatogonia. Our results demonstrate that, in general, a reduction in spermatogonial density does not alter the percentages of undifferentiated and proliferating spermatogonia, nor the establishment of global methylation. However, in sickle cell disease patients', establishment of spermatogonial DNA methylation is impaired, which may be of importance for the potential use of this tissues in fertility preservation programs

Pilot scale production of Hermetia illucens (L.) larvae and frass using former foodstuffs

The food and feed sector requires new sustainable sources of protein and innovative solutions for upcycling of food waste (former foodstuffs), which today is downcycled into energy or even wasted. This study aimed at evaluating the use of former foodstuff waste streams as feed substrate for Hermetia illucens (L.) larvae (black soldier fly larvae, BSFL) under long-term and semi-industrial conditions. Different foodstuff-based mixtures and different stocking BSFL densities were used during 20 batches, and quality and safety assessments were performed on the main outputs, namely BSFL production performance, frass impurities, larval and frass nutrient profiles and heavy metal content. About 1400 kg of former foodstuffs (fresh weight) were used to produce 239 kg BSFL and 230 kg frass. The production of BSFL reared on former foodstuffs was highly efficient, with feed conversion rates (FCR) ranging between 2.3 and 5.5 (dry matter basis). The optimization experiment revealed that former foodstuffs-based mixture and high larval density (10 larvae/cm2) lead to highly efficient (FCR: 2.6) and heavy metal-free production of BSFL and frass. The quality of the derived BSFL meal was high in terms of protein and amino acids. Furthermore, the quality of the technical frass was high in terms of N, P, and K levels and minimal packaging material residuals (<2.65%). This investigation suggests that nutrients in former foodstuffs can be successfully and safely recycled in production of BSFL

Acute pain pathways:protocol for a prospective cohort study

INTRODUCTION: Opioid analgesics are often used to treat moderate-to-severe acute non-cancer pain; however, there is little high-quality evidence to guide clinician prescribing. An essential element to developing evidence-based guidelines is a better understanding of pain management and pain control among individuals experiencing acute pain for various common diagnoses. METHODS AND ANALYSIS: This multicentre prospective observational study will recruit 1550 opioid-naïve participants with acute pain seen in diverse clinical settings including primary/urgent care, emergency departments and dental clinics. Participants will be followed for 6 months with the aid of a patient-centred health data aggregating platform that consolidates data from study questionnaires, electronic health record data on healthcare services received, prescription fill data from pharmacies, and activity and sleep data from a Fitbit activity tracker. Participants will be enrolled to represent diverse races and ethnicities and pain conditions, as well as geographical diversity. Data analysis will focus on assessing patients’ patterns of pain and opioid analgesic use, along with other pain treatments; associations between patient and condition characteristics and patient-centred outcomes including resolution of pain, satisfaction with care and long-term use of opioid analgesics; and descriptive analyses of patient management of leftover opioids. ETHICS AND DISSEMINATION: This study has received approval from IRBs at each site. Results will be made available to participants, funders, the research community and the public. TRIAL REGISTRATION NUMBER: NCT04509115

Metrics reloaded: Pitfalls and recommendations for image analysis validation

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, our large international expert consortium created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool, which also provides a point of access to explore weaknesses, strengths and specific recommendations for the most common validation metrics. The broad applicability of our framework across domains is demonstrated by an instantiation for various biological and medical image analysis use cases

Common Limitations of Image Processing Metrics:A Picture Story

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.Comment: This is a dynamic paper on limitations of commonly used metrics. The current version discusses metrics for image-level classification, semantic segmentation, object detection and instance segmentation. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorshi

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

Joint Observation of the Galactic Center with MAGIC and CTA-LST-1

MAGIC is a system of two Imaging Atmospheric Cherenkov Telescopes (IACTs), designed to detect very-high-energy gamma rays, and is operating in stereoscopic mode since 2009 at the Observatorio del Roque de Los Muchachos in La Palma, Spain. In 2018, the prototype IACT of the Large-Sized Telescope (LST-1) for the Cherenkov Telescope Array, a next-generation ground-based gamma-ray observatory, was inaugurated at the same site, at a distance of approximately 100 meters from the MAGIC telescopes. Using joint observations between MAGIC and LST-1, we developed a dedicated analysis pipeline and established the threefold telescope system via software, achieving the highest sensitivity in the northern hemisphere. Based on this enhanced performance, MAGIC and LST-1 have been jointly and regularly observing the Galactic Center, a region of paramount importance and complexity for IACTs. In particular, the gamma-ray emission from the dynamical center of the Milky Way is under debate. Although previous measurements suggested that a supermassive black hole Sagittarius A* plays a primary role, its radiation mechanism remains unclear, mainly due to limited angular resolution and sensitivity. The enhanced sensitivity in our novel approach is thus expected to provide new insights into the question. We here present the current status of the data analysis for the Galactic Center joint MAGIC and LST-1 observations

I Jornada de Aulas Abiertas: Encuentro de Docentes de la Facultad de Ciencias Económicas

La Jornada de Aulas Abiertas quiere ser una oportunidad para que los docentes de la Facultad de Ciencias Económicas nos encontremos en un espacio de reflexión y revisión de nuestras prácticas, distendido, cálido y respetuoso, que nos permita compartir nuestras experiencias cotidianas en las aulas, tanto presenciales como virtuales. Es la posibilidad de conocernos, intercambiar, aprender y contagiarnos de las inquietudes y el entusiasmo que muchos docentes ponen en juego cotidianamente. En el marco de propuestas de enseñanza, se analizaron recursos multimediales, materiales de estudio, aulas virtuales, redes sociales, aplicaciones web, juegos y actividades de evaluación y coevaluación originales; también se abordaron problemáticas y propuestas para favorecer vinculaciones con la práctica profesional. Estas fueron algunas de las cuestiones abordadas y compartidas en las presentaciones de nuestros colegas. Distintas propuestas, pero siempre con el propósito de favorecer las oportunidades de aprendizaje de nuestros estudiantes. Esta publicación pretende ampliar el alcance de esta actividad. Es una invitación para que los y las docentes que participaron puedan revisar nuevamente aquellas actividades que les parecieron valiosas, o las que no pudieron presenciar. Y para aquellos/as que no tuvieron la posibilidad de estar presentes, puedan descubrir cuánto podemos hacer para que nuestros estudiantes aprendan más y mejor, y se animen a iniciar sus propios recorridos. Esperamos repetir este evento para seguir aprendiendo de las iniciativas de los/las docentes de nuestra Facultad, poder hablar de lo que nos preocupa y nos enorgullece, en particular de las propuestas que desarrollamos en el aula para favorecer la comprensión, promover el entusiasmo, abordar temas complejos y errores frecuentes de nuestros estudiantes. Desde el Área de Formación Docente y Producción Educativa queremos agradecer a las autoridades de nuestra Facultad por acompañarnos en este desafío y a los/las docentes que estuvieron presentes compartiendo sus experiencias.Fil: Sabulsky, Gabriela. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Margaría, Oscar A. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Iturralde, Ivan. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Domenech, Roberto. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Torrico, Julieta. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Estigarribia, Lucrecia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Gohlke, Guillermo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Rosenfeld, Valeria. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Montenjano, Franco. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Atienza, Bárbara. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Becerra, Natalia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Alonso, Micaela. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Tomatis, Karina. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Saunders, Shirley. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: David, María Laura. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Flores, Verónica Andrea. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Heckmann, Gerardo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Vega, Juan José. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Trucchi, Carlos. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ferro, Flavia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Díaz, Cecilia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Peretto, Claudia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Racagni, Josefina. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Guardiola, Mariana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: López, Sonia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Beltrán, Natacha. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Russo, Paulo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Sánchez, Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Rocha Vargas, Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Flores, Norma. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Arévalo, Eliana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Pacheco, Verónica. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Delmonte, Laura. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Stanecka, Nancy. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Caminos, Ana Belén. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ahumada, María Inés. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Caro, Norma Patricia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Bravino, Laura. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Giménez, Siria Miriam. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Perona, Eugenia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Cuttica, Mariela. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: García, Gladys Susana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Cohen, Natalia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Tapia, Sebastián. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Erazu, Damián. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Torres, César. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Casini, Rosanna Beatriz. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Rosales, Julio. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Infante, Roberto Adrián. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ricci, María Beatriz. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Römer, Gabriela. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Goyeneche, Noel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Marzo, Emanuel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Olmos, Mariano. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Bottino, Cecilia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Cacciagiú, Victor. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Scidá, María Florencia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Guajardo Molina, Vanesa. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Batistella, Silvana del V. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Huanchicay, Silvia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Jones, Carola. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Cassutti, Marcela Beatriz. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Sánchez, Juan Nicolás. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Arónica, Sandra. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ortega, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Peretti, Florencia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Tagle, María Mercedes. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Asís, Gloria Susana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ortiz Figueroa, Ana María. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Giménez, Miriam Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Magnano, Cecilia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Arias, Verónica. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina

MAGIC and H.E.S.S. detect VHE gamma rays from the blazar OT081 for the first time: a deep multiwavelength study

https://pos.sissa.it/395/815/pdfPublished versio