Miracle-dz/UniFAR-FTUs huggingface dataset:
CD8+ Regulatory T Cells Induced by Lipopolysaccharide Improve Mouse Endotoxin Shock

Abstract Sepsis is a systemic inflammatory disease caused by a bacterial infection that leads to severe mortality, especially in elderly patients, because of an excessive immune response and impaired regulatory functions. Antibiotic treatment is widely accepted as the first-line therapy for sepsis; however, its excessive use has led to the emergence of multidrug-resistant bacteria in patients with sepsis. Therefore, immunotherapy may be effective in treating sepsis. Although CD8+ regulatory T cells (Tregs) are known to have immunomodulatory effects in various inflammatory diseases, their role during sepsis remains unclear. In this study, we investigated the role of CD8+ Tregs in an LPS-induced endotoxic shock model in young (8–12 wk old) and aged (18–20 mo old) mice. The adoptive transfer of CD8+ Tregs into LPS-treated young mice improved the survival rate of LPS-induced endotoxic shock. Moreover, the number of CD8+ Tregs in LPS-treated young mice increased through the induction of IL-15 produced by CD11c+ cells. In contrast, LPS-treated aged mice showed a reduced induction of CD8+ Tregs owing to the limited production of IL-15. Furthermore, CD8+ Tregs induced by treatment with the rIL-15/IL-15Rα complex prevented LPS-induced body wight loss and tissue injury in aged mice. In this study, to our knowledge, the induction of CD8+ Tregs as novel immunotherapy or adjuvant therapy for endotoxic shock might reduce the uncontrolled immune response and ultimately improve the outcomes of endotoxic shock.
Motivated Categories: Social Structures Shape the Construction of Social Categories Through Attentional Mechanisms

Social categorization is often framed as the antecedent to stereotyping, with perceivers rationally sorting the social world on the basis of perceptually salient categories before applying biased or motivated beliefs about those categories. Here, we instead suggest that the construction of social categories by individuals is itself subject to motivational influences, such that perceivers will attend to a given dimension of social categorization (e.g., race or gender) insofar as doing so fits within their motivations. Drawing from classic conceptualizations of social structure as the interplay of schemas and resources, we focus on how the motivations for shared schemas and for material benefits or resources may shape attention to social category dimensions. We outline the potential cognitive mechanisms through which these motivations may act on attention, before discussing the implications of this model for individual differences, conceptualizations of social categorization as rational information reduction, and prejudice reduction. Public Abstract Social categories like race and gender often give rise to stereotypes and prejudice, and a great deal of research has focused on how motivations influence these biased beliefs. Here, we focus on potential biases in how these categories are even formed in the first place, suggesting that motivations can influence the very categories people use to group others. We propose that motivations to share schemas with other people and to gain resources shape people’s attention to dimensions like race, gender, and age in different contexts. Specifically, people will pay attention to dimensions to the degree that the conclusions produced from using those dimensions align with their motivations. Overall, we suggest that simply examining the downstream effects of social categorization like stereotyping and prejudice is not enough, and that research should look earlier in the process at how and when we form the categories on which those stereotypes are based.
HEY1-NCOA2 expression modulates chondrogenic differentiation and induces mesenchymal chondrosarcoma in mice

Mesenchymal chondrosarcoma affects adolescents and young adults, and most cases usually have the HEY1::NCOA2 fusion gene. However, the functional role of HEY1-NCOA2 in the development and progression of mesenchymal chondrosarcoma remains largely unknown. This study aimed to clarify the functional role of HEY1-NCOA2 in transformation of the cell of origin and induction of typical biphasic morphology of mesenchymal chondrosarcoma. We generated a mouse model for mesenchymal chondrosarcoma by introducing HEY1-NCOA2 into mouse embryonic superficial zone (eSZ) followed by subcutaneous transplantation into nude mice. HEY1-NCOA2 expression in eSZ cells successfully induced subcutaneous tumors in 68.9% of recipients, showing biphasic morphologies and expression of Sox9, a master regulator of chondrogenic differentiation. ChIP sequencing analyses indicated frequent interaction between HEY1-NCOA2 binding peaks and active enhancers. Runx2, which is important for differentiation and proliferation of the chondrocytic lineage, is invariably expressed in mouse mesenchymal chondrosarcoma, and interaction between HEY1-NCOA2 and Runx2 is observed using NCOA2 C-terminal domains. Although Runx2 knockout resulted in significant delay in tumor onset, it also induced aggressive growth of immature small round cells. Runx3, which is also expressed in mesenchymal chondrosarcoma and interacts with HEY1-NCOA2, replaced the DNA-binding property of Runx2 only in part. Treatment with the HDAC inhibitor panobinostat suppressed tumor growth both in vitro and in vivo, abrogating expression of genes downstream of HEY1-NCOA2 and Runx2. In conclusion, HEY1::NCOA2 expression modulates the transcriptional program in chondrogenic differentiation, affecting cartilage-specific transcription factor functions.
Insertion of ISPa1635 in ISCR1 Creates a Hybrid Promoter for blaPER-1 Resulting in Resistance to Novel β-lactam/β-lactamase Inhibitor Combinations and Cefiderocol

Eleven blaPER-1-positive Pseudomonas aeruginosa clinical isolates showed variable susceptibility to ceftazidime-avibactam (CZA). The genetic contexts of blaPER-1 were identical (ISCR1-blaPER-1-gst) except for the ST697 isolate HS204 (ISCR1-ISPa1635-blaPER-1-gst). ABSTRACT Eleven blaPER-1-positive Pseudomonas aeruginosa clinical isolates showed variable susceptibility to ceftazidime-avibactam (CZA). The genetic contexts of blaPER-1 were identical (ISCR1-blaPER-1-gst) except for the ST697 isolate HS204 (ISCR1-ISPa1635-blaPER-1-gst). The insertion of ISPa1635 in ISCR1 upstream of blaPER-1 created a hybrid promoter, which elevated the blaPER-1 transcription level and resulted in increased resistance to CZA, ceftolozane-tazobactam, cefepime-zidebactam, and cefiderocol. Diversity in the promoter activity of blaPER-1 partially explains the variable susceptibility to CZA in PER-producing isolates.
Diabetes and prediabetes in children with cystic fibrosis

Purpose of review Glucose metabolism alterations in cystic fibrosis range from the classic cystic fibrosis-related diabetes (CFRD) to forms of glucose intolerance and prediabetes. The aim of the present work is to review the most up-to-date novelties in terms of CFRD diagnosis and therapy. This review is timely and relevant because it allows an update for the early and correct classification of glucose abnormalities in cystic fibrosis and because it favours an appropriate therapeutic approach. Recent findings Confirm that Oral Glucose Tolerance Test is still the diagnostic gold standard despite the advent of continuous glucose monitoring (CGM) systems; this latter is spreading very rapidly, however, to date, there is still no strong evidence to hypothesize the use of CGM for diagnostic purposes. CGM has indeed proven to be very useful in managing and guiding CFRD therapy. Summary Tailored and personalized insulin therapy is still the recommended therapy for children and adolescents with CFRD, although nutritional intervention and oral hypoglycaemic treatment are equally important and efficacious. Finally CFTR modulators have allowed the increase of the life expectancy of cystic fibrosis patients and have proven effective not only in improving the pulmonary function and the nutritional status but also the glucose control.
Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentation

Zero-shot instance segmentation aims to detect and precisely segment objects of unseen categories without any training samples. Since the model is trained on seen categories, there is a strong bias that the model tends to classify all the objects into seen categories. Besides, there is a natural confusion between background and novel objects that have never shown up in training. These two challenges make novel objects hard to be raised in the final instance segmentation results. It is desired to rescue novel objects from background and dominated seen categories. To this end, we propose D2Zero with Semantic-Promoted Debiasing and Background Disambiguation to enhance the performance of Zero-shot instance segmentation. Semantic-promoted debiasing utilizes inter-class semantic relationships to involve unseen categories in visual feature training and learns an input-conditional classifier to conduct dynamical classification based on the input image. Background disambiguation produces image-adaptive background representation to avoid mistaking novel objects for background. Extensive experiments show that we significantly outperform previous state-of-the-art methods by a large margin, e.g., 16.86% improvement on COCO.
Causality-Aided Trade-Off Analysis for Machine Learning Fairness

There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understanding of the trade-offs among factors considered in the ML pipeline when fairness-improving methods are applied. This understanding is essential for developers to make informed decisions regarding the provision of fair ML services. Nonetheless, it is extremely difficult to analyze the trade-offs when there are multiple fairness parameters and other crucial metrics involved, coupled, and even in conflict with one another. This paper uses causality analysis as a principled method for analyzing trade-offs between fairness parameters and other crucial metrics in ML pipelines. To practically and effectively conduct causality analysis, we propose a set of domain-specific optimizations to facilitate accurate causal discovery and a unified, novel interface for trade-off analysis based on well-established causal inference methods. We conduct a comprehensive empirical study using three real-world datasets on a collection of widely-used fairness-improving techniques. Our study obtains actionable suggestions for users and developers of fair ML. We further demonstrate the versatile usage of our approach in selecting the optimal fairness-improving method, paving the way for more ethical and socially responsible AI technologies.
Primordial or Secondary? Testing Models of Debris Disk Gas with ALMA

The origin and evolution of gas in debris disks are still not well understood. Secondary gas production from cometary material or a primordial origin have been proposed. So far, observations have mostly concentrated on CO, with only a few C observations available. We overview the C and CO content of debris disk gas and test state-of-the-art models. We use new and archival Atacama Large Millimeter/submillimeter Array (ALMA) observations of CO and C i emission, complemented by C ii data from Herschel, for a sample of 14 debris disks. This expands the number of disks with ALMA measurements of both CO and C i by 10 disks. We present new detections of C i emission toward three disks: HD 21997, HD 121191, and HD 121617. We use a simple disk model to derive gas masses and column densities. We find that current state-of-the-art models of secondary gas production overpredict the C0 content of debris disk gas. This does not rule out a secondary origin, but might indicate that the models require an additional C removal process. Alternatively, the gas might be produced in transient events rather than a steady-state collisional cascade. We also test a primordial gas origin by comparing our results to a simplified thermochemical model. This yields promising results, but more detailed work is required before a conclusion can be reached. Our work demonstrates that the combination of C and CO data is a powerful tool to advance our understanding of debris disk gas.
The Case Against Explainability

As artificial intelligence (AI) becomes more prevalent there is a growing demand from regulators to accompany decisions made by such systems with explanations. However, a persistent gap exists between the need to execute a meaningful right to explanation vs. the ability of Machine Learning systems to deliver on such a legal requirement. The regulatory appeal towards"a right to explanation"of AI systems can be attributed to the significant role of explanations, part of the notion called reason-giving, in law. Therefore, in this work we examine reason-giving's purposes in law to analyze whether reasons provided by end-user Explainability can adequately fulfill them. We find that reason-giving's legal purposes include: (a) making a better and more just decision, (b) facilitating due-process, (c) authenticating human agency, and (d) enhancing the decision makers' authority. Using this methodology, we demonstrate end-user Explainabilty's inadequacy to fulfil reason-giving's role in law, given reason-giving's functions rely on its impact over a human decision maker. Thus, end-user Explainability fails, or is unsuitable, to fulfil the first, second and third legal function. In contrast we find that end-user Explainability excels in the fourth function, a quality which raises serious risks considering recent end-user Explainability research trends, Large Language Models' capabilities, and the ability to manipulate end-users by both humans and machines. Hence, we suggest that in some cases the right to explanation of AI systems could bring more harm than good to end users. Accordingly, this study carries some important policy ramifications, as it calls upon regulators and Machine Learning practitioners to reconsider the widespread pursuit of end-user Explainability and a right to explanation of AI systems.
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent

Algorithmic stability is an important notion that has proven powerful for deriving generalization bounds for practical algorithms. The last decade has witnessed an increasing number of stability bounds for different algorithms applied on different classes of loss functions. While these bounds have illuminated various properties of optimization algorithms, the analysis of each case typically required a different proof technique with significantly different mathematical tools. In this study, we make a novel connection between learning theory and applied probability and introduce a unified guideline for proving Wasserstein stability bounds for stochastic optimization algorithms. We illustrate our approach on stochastic gradient descent (SGD) and we obtain time-uniform stability bounds (i.e., the bound does not increase with the number of iterations) for strongly convex losses and non-convex losses with additive noise, where we recover similar results to the prior art or extend them to more general cases by using a single proof technique. Our approach is flexible and can be generalizable to other popular optimizers, as it mainly requires developing Lyapunov functions, which are often readily available in the literature. It also illustrates that ergodicity is an important component for obtaining time-uniform bounds -- which might not be achieved for convex or non-convex losses unless additional noise is injected to the iterates. Finally, we slightly stretch our analysis technique and prove time-uniform bounds for SGD under convex and non-convex losses (without additional additive noise), which, to our knowledge, is novel.
Simply typed convertibility is TOWER-complete even for safe lambda-terms

We consider the following decision problem: given two simply typed $\lambda$-terms, are they $\beta$-convertible? Equivalently, do they have the same normal form? It is famously non-elementary, but the precise complexity - namely TOWER-complete - is lesser known. One goal of this short paper is to popularize this fact. Our original contribution is to show that the problem stays TOWER-complete when the two input terms belong to Blum and Ong's safe $\lambda$-calculus, a fragment of the simply typed $\lambda$-calculus arising from the study of higher-order recursion schemes. Previously, the best known lower bound for this safe $\beta$-convertibility problem was PSPACE-hardness. Our proof proceeds by reduction from the star-free expression equivalence problem, taking inspiration from the author's work with Pradic on"implicit automata in typed $\lambda$-calculi". These results also hold for $\beta\eta$-convertibility.
Rethinking Semi-supervised Learning with Language Models

Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled data to improve model performance in downstream natural language processing (NLP) tasks. Currently, there are two popular approaches to make use of unlabelled data: Self-training (ST) and Task-adaptive pre-training (TAPT). ST uses a teacher model to assign pseudo-labels to the unlabelled data, while TAPT continues pre-training on the unlabelled data before fine-tuning. To the best of our knowledge, the effectiveness of TAPT in SSL tasks has not been systematically studied, and no previous work has directly compared TAPT and ST in terms of their ability to utilize the pool of unlabelled data. In this paper, we provide an extensive empirical study comparing five state-of-the-art ST approaches and TAPT across various NLP tasks and data sizes, including in- and out-of-domain settings. Surprisingly, we find that TAPT is a strong and more robust SSL learner, even when using just a few hundred unlabelled samples or in the presence of domain shifts, compared to more sophisticated ST approaches, and tends to bring greater improvements in SSL than in fully-supervised settings. Our further analysis demonstrates the risks of using ST approaches when the size of labelled or unlabelled data is small or when domain shifts exist. We offer a fresh perspective for future SSL research, suggesting the use of unsupervised pre-training objectives over dependency on pseudo labels.
Automated stance detection in complex topics and small languages: the challenging case of immigration in polarizing news media

Automated stance detection and related machine learning methods can provide useful insights for media monitoring and academic research. Many of these approaches require annotated training datasets, which limits their applicability for languages where these may not be readily available. This paper explores the applicability of large language models for automated stance detection in a challenging scenario, involving a morphologically complex, lower-resource language, and a socio-culturally complex topic, immigration. If the approach works in this case, it can be expected to perform as well or better in less demanding scenarios. We annotate a large set of pro and anti-immigration examples, and compare the performance of multiple language models as supervised learners. We also probe the usability of ChatGPT as an instructable zero-shot classifier for the same task. Supervised achieves acceptable performance, and ChatGPT yields similar accuracy. This is promising as a potentially simpler and cheaper alternative for text classification tasks, including in lower-resource languages. We further use the best-performing model to investigate diachronic trends over seven years in two corpora of Estonian mainstream and right-wing populist news sources, demonstrating the applicability of the approach for news analytics and media monitoring settings, and discuss correspondences between stance changes and real-world events.
Detection of Thermal Sunyaev–Zel’dovich Effect in the Circumgalactic Medium of Low-mass Galaxies—A Surprising Pattern in Self-similarity and Baryon Sufficiency

We report on the measurement of the thermal Sunyaev–Zel’dovich (tSZ) effect in the circumgalactic medium (CGM) of 641,923 galaxies with M ⋆ = 109.8–11.3 M ⊙ at z < 0.5, pushing the exploration of the tSZ effect to lower-mass galaxies compared to those in previous studies. We cross-correlate the galaxy catalog of the Wide-field Infrared Survey Explorer and SuperCOSMOS with Compton-y maps derived from the combined data of the Atacama Cosmology Telescope and Planck. We improve on the data analysis methods (correcting for cosmic infrared background and Galactic dust, masking galaxy clusters and radio sources, stacking, and employing aperture photometry), as well as modeling (taking into account beam smearing, the “two-halo” term, and any zero-point offset). We constrain the thermal pressure in the CGM of M ⋆ = 1010.6–11.3 M ⊙ galaxies for a generalized Navarro–Frenk–White profile and provide upper limits for M ⋆ = 109.8–10.6 M ⊙ galaxies. The relation between M 500 (obtained from an empirical M ⋆–M 200 relation and a concentration factor) and Y˜R500sph (a measure of the thermal energy within R 500) is >2σ steeper than the self-similarity relation and the deviation from the same that has been reported previously in higher-mass halos. We calculate the baryon fraction of the galaxies, f b , assuming the CGM to be at the virial temperature that is derived from M 200. The baryon fraction f b exhibits a nonmonotonic trend with mass, with M ⋆ = 1010.9–11.2 M ⊙ galaxies being baryon-sufficient.
Open-Domain Event Graph Induction for Mitigating Framing Bias

Researchers have proposed various information extraction (IE) techniques to convert news articles into structured knowledge for news understanding. However, none of the existing methods have explicitly addressed the issue of framing bias that is inherent in news articles. We argue that studying and identifying framing bias is a crucial step towards trustworthy event understanding. We propose a novel task, neutral event graph induction, to address this problem. An event graph is a network of events and their temporal relations. Our task aims to induce such structural knowledge with minimal framing bias in an open domain. We propose a three-step framework to induce a neutral event graph from multiple input sources. The process starts by inducing an event graph from each input source, then merging them into one merged event graph, and lastly using a Graph Convolutional Network to remove event nodes with biased connotations. We demonstrate the effectiveness of our framework through the use of graph prediction metrics and bias-focused metrics.
Coordinate-Update Algorithms can Efficiently Detect Infeasible Optimization Problems

Coordinate update/descent algorithms are widely used in large-scale optimization due to their low per-iteration cost and scalability, but their behavior on infeasible or misspecified problems has not been much studied compared to the algorithms that use full updates. For coordinate-update methods to be as widely adopted to the extent so that they can be used as engines of general-purpose solvers, it is necessary to also understand their behavior under pathological problem instances. In this work, we show that the normalized iterates of randomized coordinate-update fixed-point iterations (RC-FPI) converge to the infimal displacement vector and use this result to design an efficient infeasibility detection method. We then extend the analysis to the setup where the coordinates are defined by non-orthonormal basis using the Friedrichs angle and then apply the machinery to decentralized optimization problems.