Flow cytometry for feline lymphoma: a retrospective study about pre-analytical factors possibly affecting the quality of samples

Introduction Flow cytometry (FC) is an increasingly required technique on which veterinary oncologists rely to have an accurate, fast, minimally invasive lymphoma or leukemia diagnosis. FC has been studied and applied with great results in canine oncology, whereas in feline oncology the use of this technique is still to be experienced. This is mainly due to a supposed discomfort in sampling, because of the high prevalence of intra-abdominal lymphomas. The purpose of the present study is to investigate whether any pre-analytical factor might affect the quality of suspected feline lymphoma samples for FC analysis.Methods 97 consecutive samples of suspected feline lymphoma were retrospectively selected from the authors’ institution FC database. The referring veterinarians were recalled and interrogated about several different variables, including signalling, features of the lesion, features of the sampling procedure and the experience of veterinarians performing the sampling. Statistical analyses were performed to assess the possible influence of these variables on the cellularity of the samples and the likelihood of being finally processed for FC.Results None of the investigated variables significantly influenced the quality of the submitted samples, but the needle size, with 21G needles providing the highest cellularity (Table 1). Notably, the samples quality did not vary between peripheral and intra-abdominal lesions. Sample cellularity alone influenced the likelihood of being processed. About a half of the cats required pharmacological restraint. Side effects were reported in one case only (transient swelling after peripheral lymph node sampling).Conclusions FC can be safely applied to cases of suspected feline lymphomas, even for intra-abdominal lesions. 21G needle should be preferred for sampling. This study provides the bases for the spread of this minimally invasive, fast and cost-effective technique in feline medicine

Implication of folate deficiency in CYP2U1 loss of function

International audienceHereditary spastic paraplegias are heterogeneous neurodegenerative disorders. Understanding of their pathogenicmechanisms remains sparse, and therapeutic options are lacking. We characterized a mouse model lacking the Cyp2u1 gene, lossof which is known to be involved in a complex form of these diseases in humans. We showed that this model partially recapitulated the clinical and biochemical phenotypes of patients. Using electron microscopy, lipidomic, and proteomicstudies, we identified vitamin B2 as a substrate of the CYP2U1 enzyme, as well as coenzyme Q, neopterin, and IFN-α levels asputative biomarkers in mice and fluids obtained from the largest series of CYP2U1-mutated patients reported so far. We alsoconfirmed brain calcifications as a potential biomarker in patients. Our results suggest that CYP2U1 deficiency disruptsmitochondrial function and impacts proper neurodevelopment, which could be prevented by folate supplementation in ourmouse model, followed by a neurodegenerative process altering multiple neuronal and extraneuronal tissues

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc