Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of pulmonary side effects in prolactinoma patients treated with cabergoline.

INTRODUCTION Cabergoline (CAB) is the most used dopamine agonist in the treatment of prolactinomas. Studies related to the treatment of Parkinson's disease have shown that dopamine agonists can lead to fibrotic syndromes affecting the heart and the lung. The aim of this study was to evaluate the possible pulmonary side effects of CAB in prolactinoma patients. MATERIAL AND METHODS Chest X-ray imaging and pulmonary function parameters like forced vital capacity (FVC), total lung capacity (TLC), and diffusion capacity for carbon monoxide (DLCO) were evaluated in 73 prolactinoma patients. The cumulative dose of CAB and the total duration of CAB use were also calculated, and all data were reviewed retrospectively. RESULTS The median cumulative CAB dose was 192 mg, and the median duration of CAB use was 64 months. Only 13 patients (17%) among this cohort had abnormal DLCO results that could be an indirect sign of pulmonary fibrosis. These abnormal DLCO results were found not to be associated with cumulative CAB dose in these 13 patients. CONCLUSIONS CAB appears to be safe in terms of pulmonary functions with a median cumulative dose of 192 mg in prolactinoma patients.
Successful continuance of CDK4/6 inhibitor treatment with palbociclib after abemaciclib-induced hepatotoxicity in breast cancer: a case report

Abemaciclib, a cyclin-dependent kinase 4/6 (CDK4/6) inhibitor, causes severe hepatotoxicity, a severe adverse event associated with the loss of treatment opportunities. We report a case of liver injury (grade 4) during treatment with abemaciclib, in which the patient was switched to palbociclib and successfully treated with this CDK4/6 inhibitor. A 73-year-old woman with bone metastatic breast cancer (hormone-positive, HER2-negative) was treated with abemaciclib, fulvestrant, denosumab, and precipitated calcium carbonate with cholecalciferol and magnesium carbonate (pCCCM). On day 17, the patient developed skin rashes on her trunk and arms. On day 22, abemaciclib and pCCCM were discontinued due to drug eruption. Grade 3 aspartate aminotransferase (AST) and grade 4 alanine aminotransferase (ALT) levels increased on day 29. Therefore, fulvestrant and denosumab were suspended as the causes of severe hepatotoxicity, in addition to the two drugs suspected of causing the skin eruption. On day 43, AST and ALT levels did not improve, and the patient was referred to a hepatologist. The hepatologist diagnosed hepatotoxicity as a drug-induced liver injury through additional tests and interviews. Fulvestrant treatment was resumed on day 78, and palbociclib on day 92, and denosumab and pCCCM on day 134. On day 287, treatment with the CDK4/6 inhibitor was continued without evidence of liver dysfunction. This case suggests that rechallenge with palbociclib after severe liver injury with abemaciclib may allow for continued treatment with CDK4/6 inhibitors.
Presynapses contain distinct actin nanostructures

Bingham et al. use bead-induced isolated presynapses and CRISPR-mediated tagging of endogenous actin in neurons coupled to super-resolution microscopy to reveal that presynapses contain distinct actin nanostructures: a faint mesh at the active zone, intrasynaptic rails, and perisynaptic corrals.
Optimization and construct validity of approaches to preclinical grip strength testing

Grip strength is a robust biomarker showing good reliability and prediction of negative health outcomes. Low grip strength is associated with disability and premature death and is more strongly associated with frailty than chronological age. Accordingly, recent updates to consensus definitions of sarcopenia focus on low grip strength as the primary characteristic as opposed to low muscle mass. Because rodent models are indispensable tools in aging research, scientists have reverse-translated grip testing as a key outcome in the context of sarcopenia. Serendipitous development of preclinical grip testing has resulted in a variety of protocols that have not been extensively examined and compared. Variability, due to motivation, temperament, and other factors such as pain, is inherent in preclinical behavioural assessments. Limited research has focused on standardizing preclinical grip testing, validation of methods against other functional measures, and investigating how preclinical grip data compare to data from humans and whether these tests even measure the same construct. Additionally, prior work has not examined between-day reliability of grip testing in rodents. This work was undertaken to inform rigorous preclinical grip testing.
Inducible Nitric Oxide Synthase Activity Mediates TNF-α-Induced Endothelial Cell Dysfunction.

Inducible nitric oxide synthase (iNOS) and vascular endothelial dysfunction have been implicated in the development and progression of atherosclerosis. This study aimed to elucidate the role of iNOS in vascular endothelial dysfunction. Ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/QTOF-MS) combined with multivariate data analysis was used to characterize the metabolic changes in human umbilical vein endothelial cells (HUVECs) in response to different treatment conditions. In addition, molecular biology techniques were employed to explain the molecular mechanisms underlying the role of iNOS in vascular endothelial dysfunction. Tumor necrosis factor-alpha (TNF-α) enhances the expression of iNOS, TXNIP, and the level of reactive oxygen species (ROS), facilitates the entry of nuclear factor-kappa B (NF-κB) into the nucleus; and promotes injury in HUVECs. iNOS deficiency reversed the TNF-α-mediated pathological changes in HUVECs. Moreover, TNF-α increased the expression of TNFR-2, and the levels of p-IκBα and IL-6 proteins, and CD31, ICAM-1, and VCAM-1 proteins expression which were significantly reduced in HUVECs with iNOS deficiency. In addition, treating HUVECs in the absence or presence of TNF-α or iNOS, respectively, enabled the identification of putative endogenous biomarkers associated with endothelial dysfunction. These biomarkers were involved in critical metabolic pathways, including glycosylphosphatidylinositol-anchor biosynthesis, amino acid metabolism, sphingolipid metabolism, and fatty acid metabolism. iNOS deficiency during vascular endothelial dysfunction may affect the expression of TNFR-2, vascular adhesion factors and the level of ROS via cellular metabolic changes, thereby attenuating vascular endothelial dysfunction.
Effect of food and polymorphisms in SLCO2B1, CYP3A4 and UGT1A4 on pharmacokinetics of abiraterone and its metabolites in Chinese volunteers

Abiraterone acetate, a prodrug of abiraterone (ABI), provides an efficient therapeutic option for metastatic castration‐resistant prostate cancer patients. ABI undergoes extensive metabolism in vivo and is transformed into active metabolites Δ4‐abiraterone and 3‐keto‐5α‐abiraterone as well as inactive metabolites abiraterone sulfate and abiraterone N‐oxide sulfate. We aimed to examine the effect of polymorphisms in SLCO2B1, CYP3A4 and UGT1A4 on the pharmacokinetics of ABI and its metabolites.
Concise Report

Prevalence and clinical correlates of pruritus in patients with systemic sclerosis: an updated analysis of 959 patients Abstract Objectives. One previous study has estimated the prevalence of pruritus in SSc, but that study had important limitations due to a relatively small sample size. The present study updates the analyses of the previous study using a substantially larger patient sample. The objectives were to (i) document the proportion of patients who experience pruritus on most days overall and by disease duration and (ii) identify clinical correlates of pruritus. Methods. Patients from the Canadian Scleroderma Research Group Registry 51 year after registry enrolment were asked on two consecutive annual visits whether they had experienced pruritus in the past month on most days and underwent clinical history and medical examination. Multiple logistic regression was used to assess the association between sociodemographic and clinical variables and pruritus. Results. Among 959 patients, 42.6% reported pruritus. Of 693 patients with data for both visits, 333 (48%) did not report pruritus at either visit, 209 (30%) reported pruritus at both visits and 151 (22%) reported pruritus at one, but not the other, visit. The presence of pruritus was independently associated with greater skin involvement [odds ratio Conclusion. Pruritus is common in SSc across the course of the disease and has small but statistically significant associations with the degree of skin involvement and gastrointestinal system involvement.
Analyze of the Inherent Optical Properties of French Guiana Coastal Waters for Remote Sensing Applications

LHLHHHHLILH Loisel, H. , Meriaux, X. , Poteau, A. , Artigas, L. F. , Lubac, B. , Gardel, A. , Caillaud, J., Lesourd, S, 2009. Analyze of the inherent optical properties of French Guyana coastal waters for remote sensing applications, SI 56 (Proceedings of the 10th International Coastal Symposium), 1532 - 1536. Lisbon, Portugal, ISSN 0749-0258. The development of bio-optical algorithms to assess the spatio-temporal variability of Inherent Optical Properties (IOPs) and biogeochemical related components from space, is conditioned by our knowledge on these IOPs. The objective of this study is to characterize the IOPs and their relationships with biogeochemical parameters for the development of a future algorithm dedicated to the observation of the nearshore and offshore waters of the French Guiana from usual ocean color remote sensors. For that purpose, a set of IOPs and biogeochemical parameters was gathered from field measurements performed during a cruise achieved in the coastal waters of Cayenne in July 2006. The mean values of the mass specific non-algal absorption, (anap(443)/SPM = 0.023? 0.018 m2g"! ), particulate scattering (bp(650)/SPM = 0.36 ? 0.23 m2^1) and backscattering (bbp(532)/SPM = 0.0065 ? 0.0025 m2g l) coefficients, as well as the anap(>.) spectral behaviors, are in good agreement with the fact the investigated waters are dominated by mineral particles. The relatively low particulate backscattering to scattering ratio value for such waters (0.014 ? 0.004) could be explained by the presence of relatively large particles or aggregates with lower refractive index than pure mineral particles. This study shows that the chlorophyll concentration is largely overestimated with the standard OC4v4 algorithm.
Dual fear phenomenon in an eco-epidemiological model with prey aggregation

This study presents a thorough analysis of an eco-epidemiological model that integrates infectious diseases in prey, prey aggregation, and the dual fear effect induced by predators. We establish criteria for determining the existence of equilibrium points, which carry substantial biological significance. We establish the conditions for the occurrence of Hopf, saddle-node, and transcritical bifurcations by employing fear parameters as key bifurcation parameters. Furthermore, through numerical simulations, we demonstrate the occurrence of multiple zero-Hopf (ZH) and saddle-node transcritical (SNTC) bifurcations around the endemic steady states by varying specific key parameters across the two-parametric plane. We demonstrate that the introduction of predator-induced fear, which hinders the growth rate of susceptible prey, can lead to the finite time extinction of an initially stable susceptible prey population. Finally, we discuss management strategies aimed at regulating disease transmission, focusing on fear-based interventions and selective predation via predator attack rate on infectious prey.
OctoPack: Instruction Tuning Code Large Language Models

Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack.
Predicting Resilience with Neural Networks

Resilience engineering studies the ability of a system to survive and recover from disruptive events, which finds applications in several domains. Most studies emphasize resilience metrics to quantify system performance, whereas recent studies propose statistical modeling approaches to project system recovery time after degradation. Moreover, past studies are either performed on data after recovering or limited to idealized trends. Therefore, this paper proposes three alternative neural network (NN) approaches including (i) Artificial Neural Networks, (ii) Recurrent Neural Networks, and (iii) Long-Short Term Memory (LSTM) to model and predict system performance, including negative and positive factors driving resilience to quantify the impact of disruptive events and restorative activities. Goodness-of-fit measures are computed to evaluate the models and compared with a classical statistical model, including mean squared error and adjusted R squared. Our results indicate that NN models outperformed the traditional model on all goodness-of-fit measures. More specifically, LSTMs achieved an over 60\% higher adjusted R squared, and decreased predictive error by 34-fold compared to the traditional method. These results suggest that NN models to predict resilience are both feasible and accurate and may find practical use in many important domains.
A microscopic derivation of Gibbs measures for the 1D focusing quintic nonlinear Schr\"{o}dinger equation

In this work, we obtain a microscopic derivation of Gibbs measures for the focusing quintic nonlinear Schr\"{o}dinger equation (NLS) on $\mathbb{T}$ from many-body quantum Gibbs states. On the quantum many-body level, the quintic nonlinearity corresponds to a three-body interaction. This is a continuation of our previous work. In the aforementioned work, we studied the cubic problem, which corresponds to a two-body interaction on the quantum many-body level. In our setup, we truncate the mass of the classical free field in the classical setting and the rescaled particle number in the quantum setting. Our methods are based on a perturbative expansion previously developed in the work of Fr\"{o}hlich, Knowles, Schlein, and the second author. We prove results both in the time-independent and time-dependent setting. This is the first such known result in the three-body regime. Furthermore, this gives the first microscopic derivation of time-dependent correlation functions for Gibbs measures corresponding to the quintic NLS, as studied in the work of Bourgain.
Learning Abstract Visual Reasoning via Task Decomposition: A Case Study in Raven Progressive Matrices

Learning to perform abstract reasoning often requires decomposing the task in question into intermediate subgoals that are not specified upfront, but need to be autonomously devised by the learner. In Raven Progressive Matrices (RPM), the task is to choose one of the available answers given a context, where both the context and answers are composite images featuring multiple objects in various spatial arrangements. As this high-level goal is the only guidance available, learning to solve RPMs is challenging. In this study, we propose a deep learning architecture based on the transformer blueprint which, rather than directly making the above choice, addresses the subgoal of predicting the visual properties of individual objects and their arrangements. The multidimensional predictions obtained in this way are then directly juxtaposed to choose the answer. We consider a few ways in which the model parses the visual input into tokens and several regimes of masking parts of the input in self-supervised training. In experimental assessment, the models not only outperform state-of-the-art methods but also provide interesting insights and partial explanations about the inference. The design of the method also makes it immune to biases that are known to be present in some RPM benchmarks.
Value-Distributional Model-Based Reinforcement Learning

Quantifying uncertainty about a policy's long-term performance is important to solve sequential decision-making tasks. We study the problem from a model-based Bayesian reinforcement learning perspective, where the goal is to learn the posterior distribution over value functions induced by parameter (epistemic) uncertainty of the Markov decision process. Previous work restricts the analysis to a few moments of the distribution over values or imposes a particular distribution shape, e.g., Gaussians. Inspired by distributional reinforcement learning, we introduce a Bellman operator whose fixed-point is the value distribution function. Based on our theory, we propose Epistemic Quantile-Regression (EQR), a model-based algorithm that learns a value distribution function that can be used for policy optimization. Evaluation across several continuous-control tasks shows performance benefits with respect to established model-based and model-free algorithms.
CupCleaner: A Data Cleaning Approach for Comment Updating

Recently, deep learning-based techniques have shown promising performance on various tasks related to software engineering. For these learning-based approaches to perform well, obtaining high-quality data is one fundamental and crucial issue. The comment updating task is an emerging software engineering task aiming at automatically updating the corresponding comments based on changes in source code. However, datasets for the comment updating tasks are usually crawled from committed versions in open source software repositories such as GitHub, where there is lack of quality control of comments. In this paper, we focus on cleaning existing comment updating datasets with considering some properties of the comment updating process in software development. We propose a semantic and overlapping-aware approach named CupCleaner (Comment UPdating's CLEANER) to achieve this purpose. Specifically, we calculate a score based on semantics and overlapping information of the code and comments. Based on the distribution of the scores, we filter out the data with low scores in the tail of the distribution to get rid of possible unclean data. We first conducted a human evaluation on the noise data and high-quality data identified by CupCleaner. The results show that the human ratings of the noise data identified by CupCleaner are significantly lower. Then, we applied our data cleaning approach to the training and validation sets of three existing comment updating datasets while keeping the test set unchanged. Our experimental results show that even after filtering out over 30\% of the data using CupCleaner, there is still an improvement in all performance metrics. The experimental results on the cleaned test set also suggest that CupCleaner may provide help for constructing datasets for updating-related tasks.
DiffSED: Sound Event Detection with Denoising Diffusion

Sound Event Detection (SED) aims to predict the temporal boundaries of all the events of interest and their class labels, given an unconstrained audio sample. Taking either the split-and-classify (i.e., frame-level) strategy or the more principled event-level modeling approach, all existing methods consider the SED problem from the discriminative learning perspective. In this work, we reformulate the SED problem by taking a generative learning perspective. Specifically, we aim to generate sound temporal boundaries from noisy proposals in a denoising diffusion process, conditioned on a target audio sample. During training, our model learns to reverse the noising process by converting noisy latent queries to the ground-truth versions in the elegant Transformer decoder framework. Doing so enables the model generate accurate event boundaries from even noisy queries during inference. Extensive experiments on the Urban-SED and EPIC-Sounds datasets demonstrate that our model significantly outperforms existing alternatives, with 40+% faster convergence in training. Code: https://github.com/Surrey-UPLab/DiffSED
Neural Categorical Priors for Physics-Based Character Control

Recent advances in learning reusable motion priors have demonstrated their effectiveness in generating naturalistic behaviors. In this paper, we propose a new learning framework in this paradigm for controlling physics-based characters with improved motion quality and diversity over existing methods. The proposed method uses reinforcement learning (RL) to initially track and imitate life-like movements from unstructured motion clips using the discrete information bottleneck, as adopted in the Vector Quantized Variational AutoEncoder (VQ-VAE). This structure compresses the most relevant information from the motion clips into a compact yet informative latent space, i.e., a discrete space over vector quantized codes. By sampling codes in the space from a trained categorical prior distribution, high-quality life-like behaviors can be generated, similar to the usage of VQ-VAE in computer vision. Although this prior distribution can be trained with the supervision of the encoder's output, it follows the original motion clip distribution in the dataset and could lead to imbalanced behaviors in our setting. To address the issue, we further propose a technique named prior shifting to adjust the prior distribution using curiosity-driven RL. The outcome distribution is demonstrated to offer sufficient behavioral diversity and significantly facilitates upper-level policy learning for downstream tasks. We conduct comprehensive experiments using humanoid characters on two challenging downstream tasks, sword-shield striking and two-player boxing game. Our results demonstrate that the proposed framework is capable of controlling the character to perform considerably high-quality movements in terms of behavioral strategies, diversity, and realism. Videos, codes, and data are available at https://tencent-roboticsx.github. io/NCP/.