711 research outputs found

    Models of classroom assessment for course-based research experiences

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    Course-based research pedagogy involves positioning students as contributors to authentic research projects as part of an engaging educational experience that promotes their learning and persistence in science. To develop a model for assessing and grading students engaged in this type of learning experience, the assessment aims and practices of a community of experienced course-based research instructors were collected and analyzed. This approach defines four aims of course-based research assessment—(1) Assessing Laboratory Work and Scientific Thinking; (2) Evaluating Mastery of Concepts, Quantitative Thinking and Skills; (3) Appraising Forms of Scientific Communication; and (4) Metacognition of Learning—along with a set of practices for each aim. These aims and practices of assessment were then integrated with previously developed models of course-based research instruction to reveal an assessment program in which instructors provide extensive feedback to support productive student engagement in research while grading those aspects of research that are necessary for the student to succeed. Assessment conducted in this way delicately balances the need to facilitate students’ ongoing research with the requirement of a final grade without undercutting the important aims of a CRE education

    Ultrasonographic non-radiographic erosions could predict the efficacy of belimumab in articular systemic lupus erythematosus

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    : The aim of this study is to characterise lupus-related arthritis and assess if the presence of ultrasound-detected erosions could be associated with belimumab in the treatment of systemic lupus erythematosus (SLE) articular manifestations. We performed a spontaneous, monocentric, retrospective, and observational study. We enrolled patients affected by SLE with articular involvement treated with belimumab. We excluded patients with positive rheumatoid factor (RF) or anti-citrullinated peptide antibody (ACPA), Jaccoud's arthropathy, and radiographic erosions. Patients were assessed at baseline, 3, and 6 months. We collected laboratory and clinical data from electronic records. Joint disease activity was assessed using disease activity score on 28 joints based on C-reactive protein (DAS28-CRP), swollen and tender joints count. All patients underwent an ultrasound examination of the wrist, metacarpophalangeal, proximal interphalangeal, and metatarsal-phalangeal joints before the initiation of treatment with belimumab. We performed Student's T-test and Mann-Whitney's U-test to assess the difference between means and Fisher's exact test to assess difference in proportions, and linear univariate regression to investigate predictors of disease activity. We enrolled 23 patients (female 82.6%, mean age of 50.65 ± 14.1 years). Seven patients (30.4%) presented bone erosions at baseline. Patients with bone erosions were generally older (61 ± 16.1 vs 46.13 ± 10.7 years, p = 0.016), more frequently male (42.8 vs 6.2%, p = 0.03), with higher baseline CRP levels (10.29 ± 11.6 vs 2.25 ± 3.1 mg/L, p = 0.015) and C4 levels (0.19 ± 0.17 vs 0.1 ± 0.04 g/L, p = 0.05). After 6 months of treatment with belimumab, patients without erosions improved their DAS28-CRP significantly (2.95 ± 0.89 vs 2.26 ± 0.48, p = 0.01), while patients with erosions did not (3.6 ± 0.79 vs 3.2 ± 0.95, p = 0.413). DAS28-CRP did not differ between the two groups at baseline, while it was significantly lower at the other two time points in patients without erosions. The majority of patients achieved remission at 6 months follow-up based on DAS28-CRP criteria (73.9%), with a significant difference between patients with and without erosions (42.8 vs 87.5%, p = 0.045). The presence of articular ultrasound-detected erosions could be predictive of a decreased efficacy of belimumab in the articular manifestations of SLE. A possible explanation is a rheumatoid-like articular phenotype, despite the lack of ACPA-positivity and radiologic erosions. However, due to the small sample population, larger cohorts are needed to assess the possible predictive role of this finding

    Supplementary Material for: Small Fiber Neuropathy Triggered by COVID-19 Vaccination: Association with FGFR3 Autoantibodies and Improvement during Intravenous Immunoglobulin Treatment

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    Multiple case series have demonstrated the emergence of small fiber neuropathy following acute coronavirus disease 2019 (COVID-19) infections. Further, one large case supports that the COVID-19 vaccine has been reported to result in small fiber neuropathy. We report a case of a patient with confirmed small fiber neuropathy post-COVID-19 vaccination with positive FGFR3 antibodies. The effect of intravenous immunoglobulin (IVIG) has been recently explored for treatment of presumed autoimmune small fiber neuropathy. To our knowledge, this is the first published case report of COVID vaccination-induced FGFR3-associated small fiber neuropathy improving in the context of IVIG administration as demonstrated by normalization of small fiber density measured by skin biopsy accompanied by marked improvement in the patient’s symptoms

    Toward Automatically Completing GitHub Workflows

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    Continuous integration and delivery (CI/CD) are nowadays at the core of software development. Their benefits come at the cost of setting up and maintaining the CI/CD pipeline, which requires knowledge and skills often orthogonal to those entailed in other software-related tasks. While several recommender systems have been proposed to support developers across a variety of tasks, little automated support is available when it comes to setting up and maintaining CI/CD pipelines. We present GH-WCOM (GitHub Workflow COMpletion), a Transformer-based approach supporting developers in writing a specific type of CI/CD pipelines, namely GitHub workflows. To deal with such a task, we designed an abstraction process to help the learning of the transformer while still making GH-WCOM able to recommend very peculiar workflow elements such as tool options and scripting elements. Our empirical study shows that GH-WCOM provides up to 34.23% correct predictions, and the model's confidence is a reliable proxy for the recommendations' correctness likelihood

    Automatically Generating Dockerfiles via Deep Learning: Challenges and Promises

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    Containerization allows developers to define the execution environment in which their software needs to be installed. Docker is the leading platform in this field, and developers that use it are required to write a Dockerfile for their software. Writing Dockerfiles is far from trivial, especially when the system has unusual requirements for its execution environment. Despite several tools exist to support developers in writing Dockerfiles, none of them is able to generate entire Dockerfiles from scratch given a high-level specification of the requirements of the execution environment. In this paper, we present a study in which we aim at understanding to what extent Deep Learning (DL), which has been proven successful for other coding tasks, can be used for this specific coding task. We preliminarily defined a structured natural language specification for Dockerfile requirements and a methodology that we use to automatically infer the requirements from the largest dataset of Dockerfiles currently available. We used the obtained dataset, with 670,982 instances, to train and test a Text-to-Text Transfer Transformer (T5) model, following the current state-of-the-art procedure for coding tasks, to automatically generate Dockerfiles from the structured specifications. The results of our evaluation show that T5 performs similarly to the more trivial IR-based baselines we considered. We also report the open challenges associated with the application of deep learning in the context of Dockerfile generation

    Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?

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    Upon evolving their software, organizations and individual developers have to spend a substantial effort to pay back technical debt, i.e., the fact that software is released in a shape not as good as it should be, e.g., in terms of functionality, reliability, or maintainability. This paper empirically investigates the extent to which technical debt can be automatically paid back by neural-based generative models, and in particular models exploiting different strategies for pre-training and fine-tuning. We start by extracting a dateset of 5,039 Self-Admitted Technical Debt (SATD) removals from 595 open-source projects. SATD refers to technical debt instances documented (e.g., via code comments) by developers. We use this dataset to experiment with seven different generative deep learning (DL) model configurations. Specifically, we compare transformers pre-trained and fine-tuned with different combinations of training objectives, including the fixing of generic code changes, SATD removals, and SATD-comment prompt tuning. Also, we investigate the applicability in this context of a recently-available Large Language Model (LLM)-based chat bot. Results of our study indicate that the automated repayment of SATD is a challenging task, with the best model we experimented with able to automatically fix ~2% to 8% of test instances, depending on the number of attempts it is allowed to make. Given the limited size of the fine-tuning dataset (~5k instances), the model's pre-training plays a fundamental role in boosting performance. Also, the ability to remove SATD steadily drops if the comment documenting the SATD is not provided as input to the model. Finally, we found general-purpose LLMs to not be a competitive approach for addressing SATD

    Nuevos registros de garrapatas (Acari: Ixodidae) asociadas a roedores sigmodontinos en Chubut, Argentina

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    Se reportan nuevos hallazgos de garrapatas obtenidas de roedores capturados en sitios cercanos a Cholila (42º31‟S, 71º27‟O, provincia del Chubut (Argentina). Se registraron las siguientes asociaciones parásito-hospedador: Oligoryzomys longicaudatus: 1 larva de Amblyomma tigrinum, 1 hembra de Ixodes sigelos, 2 ninfas de I. sigelos; Reithrodon auritus: 1 hembra de I. sigelos; Loxodontomys micropus: 1 ninfa de I. sigelos; Chelemys macronyx: 7 larvas de I. sigelos. Ixodes sigelos no ha sido reportada previamente en C. macronyx, así como tampoco A. tigrinum sobre O. longicaudatus. Estos resultados brindan nueva evidencia del rol fundamental que cumplen los roedores en el ciclo biológico de algunas garrapatas presentes en la Argentina.We report new findings of ticks from rodents trapped at different sites near Cholila (42º31‟S, 71º27‟W), Chubut Province (Argentina). The following host-parasite associations were recorded: Oligoryzomys longicaudatus: 1 larva of Amblyomma tigrinum, 1 female of Ixodes sigelos, 2 nymphs of I. sigelos; Reithrodon auritus: 1 female of I. sigelos; Loxodontomys micropus: 1 nymph of I. sigelos; Chelemys macronyx: 7 larvae of I. sigelos. Ixodes sigelos has not been previously reported on C. macronyx as well as not A. tigrinum on O. longicaudatus. These results provide new evidence about the fundamental role of rodents in the life cycle of some ticks present in Argentina.Asociación Parasitológica ArgentinaFacultad de Ciencias Naturales y Muse

    Chapitre 7. Comment se fabrique un parti politique : communistes et paysans en Sicile entre 1943 et 1948

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    Le 3 juillet 1943, les Alliés débarquaient sur la côte méridionale de la Sicile. En quarante jours d’intenses combats, ils prirent le contrôle de l’île. Ils exclurent les autorités fascistes et mirent en place des nouvelles équipes municipales, en faisant appel de préférence aux notables, aux classes supérieures et au clergé, en tout cas à ceux de leurs membres qui s’étaient tenus à l’écart durant le fascisme. Avec beaucoup de défiance, ils autorisèrent la reprise de la vie politique et des p..

    Instructional Models for Course-Based Research Experience (CRE) Teaching

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    The course-based research experience (CRE) with its documented educational benefits is increasingly being implemented in science, technology, engineering, and mathematics education. This article reports on a study that was done over a period of 3 years to explicate the instructional processes involved in teaching an undergraduate CRE. One hundred and two instructors from the established and large multi-institutional SEA-PHAGES program were surveyed for their understanding of the aims and practices of CRE teaching. This was followed by large-scale feedback sessions with the cohort of instructors at the annual SEA Faculty Meeting and subsequently with a small focus group of expert CRE instructors. Using a qualitative content analysis approach, the survey data were analyzed for the aims of inquiry instruction and pedagogical practices used to achieve these goals. The results characterize CRE inquiry teaching as involving three instructional models: 1) being a scientist and generating data; 2) teaching procedural knowledge; and 3) fostering project ownership. Each of these models is explicated and visualized in terms of the specific pedagogical practices and their relationships. The models present a complex picture of the ways in which CRE instruction is conducted on a daily basis and can inform instructors and institutions new to CRE teaching
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