Raniere Gaia Costa da Silva

As computational methods become increasingly central to social science research, there is a growing demand for research software infrastructures that are sustainable, grounded in principles of open science, and embedded in the community. In response, we are introducing the Methods Hub as an open-source, community-driven collection of computational tools designed to support social science research in solving complex data-related tasks. The Methods Hub integrates three complementary components: First, quality-curated content, including computational methods and accompanying tutorials, which are specifically presented from a social science research perspective. Second, the methods and tutorials are hosted on a web portal enabling researchers to discover, apply, learn, and publish computational methods. Third, interactive, browser-based execution backends allow users to directly run, test, and adapt the code of methods and tutorials. These components are guided by a set of core principles: a long-term infrastructure perspective, reproducibility requirements, and a focus on community. The Methods Hub builds a long-term infrastructure that extends beyond the scope of single projects. Further, reproducibility functions as a quality assurance measure while advancing the practices of open science. Finally, the Methods Hub is built on public, community-based submissions by treating research software as citable academic output and providing visibility, accessibility, and formal recognition. We are also broadening the community by fostering learning and ease of access. Looking ahead, the Methods Hub is exploring the potential of AI as both a research tool and to further strengthen the infrastructure, while maintaining transparency, reproducibility, and quality standards for computational social science research.

Resorption within cortices of long bones removes excess mass and damaged tissue and increases during periods of reduced mechanical loading. Returning to high‐intensity exercise may place bones at risk of failure due to increased porosity caused by bone resorption. We used point‐projection X‐ray microscopy images of bone slices from highly loaded (metacarpal, tibia) and minimally loaded (rib) bones from 12 racehorses, 6 that died during a period of high‐intensity exercise and 6 that had a period of intense exercise followed by at least 35 days of rest prior to death, and measured intracortical canal cross‐sectional area (Ca.Ar) and number (N.Ca) to infer remodelling activity across sites and exercise groups. Large canals that are the consequence of bone resorption (Ca.Ar >0.04 mm²) were 1.4× to 18.7× greater in number and area in the third metacarpal bone from rested than exercised animals (p = 0.005–0.008), but were similar in number and area in ribs from rested and exercised animals (p = 0.575–0.688). An intermediate relationship was present in the tibia, and when large canals and smaller canals that result from partial bony infilling (Ca.Ar >0.002 mm²) were considered together. The mechanostat may override targeted remodelling during periods of high mechanical load by enhancing bone formation, reducing resorption and suppressing turnover. Both systems may work synergistically in rest periods to remove excess and damaged tissue.

Purpose: To assess the capability of deep convolutional neural networks to classify anatomical location and projection from a series of 48 standard views of racehorse limbs. Materials and methods: Radiographs ( N = 9504) of horse limbs from image sets made for veterinary inspections by 10 independent veterinary clinics were used to train, validate and test (116, 40 and 42 radiographs, respectively) six deep learning architectures available as part of the open source machine learning framework PyTorch. The deep learning architectures with the best top‐1 accuracy had the batch size further investigated. Results: Top‐1 accuracy of six deep learning architectures ranged from 0.737 to 0.841. Top‐1 accuracy of the best deep learning architecture (ResNet‐34) ranged from 0.809 to 0.878, depending on batch size. ResNet‐34 (batch size = 8) achieved the highest top‐1 accuracy (0.878) and the majority (91.8%) of misclassification was due to laterality error. Class activation maps indicated that joint morphology, not side markers or other non‐anatomical image regions, drove the model decision. Conclusions: Deep convolutional neural networks can classify equine pre‐import radiographs into the 48 standard views including moderate discrimination of laterality, independent of side marker presence.

Background: Object detection in radiograph computer vision has largely benefited from progress in deep convolutional neural networks and can, for example, annotate a radiograph with a box around a knee joint or intervertebral disc. Is deep learning capable of detect small (less than 1% of the image) in radiographs? And how many radiographs do we need use when re-training a deep learning model? Methods: We annotated 396 radiographs of left and right carpi dorsal 75 medial to palmarolateral oblique (DMPLO) projection with the location of radius, proximal row of carpal bones, distal row of carpal bones, accessory carpal bone, first carpal bone (if present), and metacarpus (metacarpal II, III, and IV). The radiographs and respective annotations were splited into sets that were used to leave-one-out cross-validation of models created using transfer learn from YOLOv5s. Results: Models trained using 96 radiographs or more achieved precision, recall and mAP above 0.95, including for the first carpal bone, when trained for 32 epochs. The best model needed the double of epochs to learn to detect the first carpal bone compared with the other bones. Conclusions: Free and open source state of the art object detection models based on deep learning can be re-trained for radiograph computer vision applications with 100 radiographs and achieved precision, recall and mAP above 0.95.

Resorption within cortices of long bones removes excess mass and damaged tissue, and increases during periods of reduced mechanical loading. Returning to high-intensity exercise may place bones at risk of failure due to increased porosity caused by bone resorption. We used microradiographs of bone slices from highly-loaded (metacarpal, tibia) and minimally-loaded (rib) bones from 12 racehorses, 6 that died during a period of high-intensity exercise and 6 that had a period of intense exercise followed by at least 35 days of rest prior to death, and measured intracortical canal cross-sectional area (Ca.Ar) and number (N.Ca) to infer remodelling activity across sites and exercise groups. Large canals that are the consequence of bone resorption (Ca.Ar > 0.04 mm ² ) were 1.4× to 18.7× greater in number and area in the third metacarpal bone from rested than exercised animals (p = 0.005– 0.008), but were similar in number and area in ribs from rested and exercised animals (p = 0.575–0.688). An intermediate relationship was present in the tibia, and when large canals and smaller canals that result from partial bony infilling (Ca.Ar > 0.002 mm ² ) were considered together. The mechanostat may override targeted remodelling during periods of high mechanical load by enhancing bone formation, reducing resorption and suppressing turnover. Both systems may work synergistically in rest periods to remove excess and damaged tissue.

Computational biology has gained traction as an independent scientific discipline over the last years in South America. However, there is still a growing need for bioscientists, from different backgrounds, with different levels, to acquire programming skills, which could reduce the time from data to insights and bridge communication between life scientists and computer scientists. Python is a programming language extensively used in bioinformatics and data science, which is particularly suitable for beginners. Here, we describe the conception, organization, and implementation of the Brazilian Python Workshop for Biological Data. This workshop has been organized by graduate and undergraduate students and supported, mostly in administrative matters, by experienced faculty members since 2017. The workshop was conceived for teaching bioscientists, mainly students in Brazil, on how to program in a biological context. The goal of this article was to share our experience with the 2020 edition of the workshop in its virtual format due to the Coronavirus Disease 2019 (COVID-19) pandemic and to compare and contrast this year’s experience with the previous in-person editions. We described a hands-on and live coding workshop model for teaching introductory Python programming. We also highlighted the adaptations made from in-person to online format in 2020, the participants’ assessment of learning progression, and general workshop management. Lastly, we provided a summary and reflections from our personal experiences from the workshops of the last 4 years. Our takeaways included the benefits of the learning from learners’ feedback (LLF) that allowed us to improve the workshop in real time, in the short, and likely in the long term. We concluded that the Brazilian Python Workshop for Biological Data is a highly effective workshop model for teaching a programming language that allows bioscientists to go beyond an initial exploration of programming skills for data analysis in the medium to long term. Bioscientists analyzing research data deal with challenges because most lack computer science background, such as programming skills, making it difficult to process their data and communicate with data analysts. Over the last few years (2017 to 2020), we assembled interdisciplinary teams of graduate and undergraduate students to develop the Brazilian Python Workshop for Biological Data. These short courses aimed to teach programming skills in a real-world setting. They were offered in Portuguese to facilitate accessibility to both Brazilians and foreigners doing research in Brazil. We accomplished these goals by emphasizing both basic programming skills and foundational concepts, alongside hands-on activities designed with biological datasets. Importantly, we were supported by experienced faculty. Although the first editions were in-person, we reformulated the 2020 edition to an online version due to the Coronavirus Disease 2019 (COVID-19) pandemic. During the online 2020 edition, we taught using a variety of tools to facilitate synchronous and asynchronous communication between participants and organization and to engage participants in activities that promoted their active participation and networking. We used digital notebooks and encouraged students to put into practice shareable and reproducible research. In 2020, we also performed online surveys with participants that helped us to implement real-time improvements and perspectives of future changes based on the students’ feedback. Our workshop comprises a model for future initiatives.

Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative. While machine learning has been highlighted to reduce the limitations of the test, the absence of high-quality curated datasets has prevented strategies development to improve cervical cancer screening. The Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 × 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. This collection has the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of laboratories.

The Collaborations Workshop 2018 (CW18) took place at The School of Mathematics, Cardiff University from the 26th to 28th March 2018. 90 people attended the event to discuss the themes of the workshop: culture change, productivity and sustainability. With a mix of lightning talks, keynotes, Q&A panels, discussions with speed blogging, collaborative ideas sessions, mini-workshops & demos, a social programme and a Hack Day in the agenda, CW18 was a feature-packed and immersive event.

Workshops are used to explore a specific topic, to transfer knowledge, to solve identified problems, or to create something new. In funded research projects and other research endeavours, workshops are the mechanism used to gather the wider project, community, or interested people together around a particular topic. However, natural questions arise: how do we measure the impact of these workshops? Do we know whether they are meeting the goals and objectives we set for them? What indicators should we use? In response to these questions, this paper will outline rules that will improve the measurement of the impact of workshops.