Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Leukocyte Antigen B27‐Negative Axial Spondyloarthritis: What Do We Know?

Axial spondyloarthritis (axSpA) is a chronic, immune‐mediated disease characterized by inflammatory axial skeleton involvement and extra‐musculoskeletal manifestations. The continuum of axSpA ranges from nonradiographic axSpA (nr‐axSpA) to ankylosing spondylitis, also known as radiographic axSpA; the latter is defined by definitive radiographic sacroiliitis. Human leukocyte antigen B27 (HLA‐B27) is a genetic marker strongly associated with axSpA; it aids in the diagnosis of axSpA, and its absence leads to delay in diagnosis. For HLA‐B27‐negative patients, disease pathogenesis is poorly understood, signs and symptoms are frequently underrecognized, and diagnosis and treatment are commonly delayed. The proportion of HLA‐B27‐negative patients may be higher among non‐White patients and those with nr‐axSpA, who can face additional diagnostic challenges related to lack of definitive radiographic sacroiliitis. In this narrative review, we discuss the role of HLA‐B27 in the diagnosis and pathogenesis of axSpA and highlight various pathways and genes that may be related to axSpA pathogenesis in HLA‐B27‐negative patients. We also emphasize the need to characterize gut microbial communities in these patients. Adequate understanding of clinical and pathological features underlying HLA‐B27‐negative patients with axSpA will improve diagnosis, treatment, and outcomes for this complex inflammatory disease.
On Purely Inseparable Extensions of Unbounded Exponent

Let L/K be a field extension of characteristic p ≠ 0. If L/K is purely inseparable and of bounded exponent, then the property that L has a subbasis over K (11, p. 436) is of significance in the theory of higher derivations (11) and in the theory of Hopf algebras (9; 10). In this case, where L/K is of bounded exponent, it has been shown independently in (1; 9; 5) that L/K having a sub-basis is equivalent to the property that L/K is modular (9, p. 401). Our aim in this paper is to extend and apply these properties for L/K purely inseparable and of unbounded exponent. In Theorem 1, we give several conditions on a p-basis of L which are equivalent to the property that L/K has a sub-basis. In Theorem 2, we give a sequence of implications starting with L/K has a sub-basis.
Comparing multi-server queues with finite waiting rooms, I: Same number of servers

We compare two queueing systems with the same number of servers that differ by having stochastically ordered service times and/or interarrival times as well as different waiting room capacities. We establish comparisons for the sequences of actual-arrival and departure epochs, and demonstrate by counterexample that many stochastic comparisons possible with infinite waiting rooms no longer hold with finite waiting rooms.
Towards precision dosing of aripiprazole in children and adolescents with autism spectrum disorder: Linking blood levels to weight gain and effectiveness

Aripiprazole is one of the most commonly prescribed antipsychotic drugs to children and adolescents worldwide, but it is associated with serious side‐effects, including weight gain. This study assessed the population pharmacokinetics of aripiprazole and its active metabolite and investigated the relationship between pharmacokinetic parameters and body mass index (BMI) in children and adolescents with autism spectrum disorder (ASD) and behavioural problems. Secondary outcomes were metabolic, endocrine, extrapyramidal and cardiac side‐effects and drug effectiveness.
Self-reported suicidal behaviours and associated factors in the general population of Latvia (2010–2018)

Background: Latvia has the second highest suicide rate in the European Union – with a total population age-standardized suicide rate of 16.1 per 100,000 inhabitants. Aims: We aimed to assess the prevalence of different types of self-reported suicidal behaviours in Latvia and determine the associated sociodemographic and health-related factors. Methods: This study was based on secondary data obtained from the Health Behaviour Among Latvian Adult Population survey. A representative sample of the general population was used, aged 15 to 64 years in 2010, 2012, 2014; and 15 to 74 years in 2016 and 2018 (n = 16,084). Respondents were asked to report the occurrence of life weariness, death wishes, suicidal ideation, suicidal plans and suicide attempts during the previous year. We assessed socio-demographics and health-related factors associated with suicidality. We performed univariate analysis and constructed stepwise multivariate logistic regression models. Results: In 2010 to 2018, 15.6% of responders reported some type of suicidal behaviour (95% CI [15.1, 16.2]). Sociodemographic factors – including non-cohabitation status and Latvian nationality – were associated with mild (life-weariness and death wishes) and serious (suicidal ideation, plans to commit suicide, suicide attempts) types of behaviour. Older age was associated with mild suicidal behaviours, whereas lower educational levels were associated with serious suicidal behaviours. Diagnosed depression, self-reported depression, self-reported anxiety, stress, low mood, alcohol intake habits with heavy drinking episodes (less than monthly, monthly and weekly), perceived health as average or below average, disuse of primary health services were associated with mild and serious types of suicidal behaviour. Current smoking status and absenteeism were associated with mild suicidal behaviour types. Self-reported insomnia, having at least two somatic diagnoses, occasional smoking status, absenteeism with 11 or more days in the last year, receiving disability pension were associated with serious suicidal behaviour types. Musculoskeletal diseases exhibited preventive effects. Conclusions: Our findings indicate that certain groups of individuals might exhibit greater vulnerability to suicidality.
Common Factor Structure of the Ten Subtest Wechsler Adult Intelligence Scale–Fourth Edition in a Clinical Sample and 15 Subtest Version in the Standardization Sample

Abstract Objective The 10 core subtests of the Wechsler Adult Intelligence Scale-IV (WAIS-IV) suffice to produce the 4 index scores for clinical assessments. Factor analytic studies with the full complement of 15 subtests reveal a 5-factor structure that aligns with Cattell–Horn–Carroll taxonomy of cognitive abilities. The current study investigates the validity of 5-factor structure in a clinical setting with reduced number of 10 subtests. Method Confirmatory factor analytic models were fitted to a clinical neurosciences archival data set (n_Male = 166, n_Female = 155) and to 9 age-group samples of the WAIS-IV standardization data (n = 200 for each group). The clinical and the standardization samples differed as (a) the former comprised scores from patients, aged 16 to 91, with disparate neurological diagnosis whereas the latter was demographically stratified, (b) only the 10 core subtests in the former but all 15 subtests in the latter were administered, and (c) the former had missing data, but the latter was complete. Result Despite empirical constraints to eliciting 5 factors with only 10 indicators, the well-fitting, 5-factor (acquired knowledge, fluid intelligence, short-term memory, visual processing, and processing speed) measurement model evinced metric invariance between the clinical and standardization samples. Conclusion The same cognitive constructs are measured on the same metrics in every sample examined and provide no reason to reject the assumption that the 5 underlying latent abilities of the 15 subtest version in the standardization samples can also be inferred from the 10 subtest version in clinical populations.
H$\alpha$ surface photometry of galaxies in the Virgo cluster - II. Observations with the OHP and Calar Alto 1.2 m telescopes

We present H α line imaging observations of 122 galaxies obtained with the 1.20 m telescopes of the Observatoire de Haute Provence (OHP) and of Calar Alto. The observed galaxies are mostly Virgo cluster members (95), along with 10 objects in the Coma/A1367 supercluster, 6 in the clusters A2197 and A2199, and 11 nearby galaxies taken as fillers. H α +[NII] fluxes and equivalent widths, as well as images of all the detected targets, are presented.
Molecular and Neural Mechanisms of Temperature Preference Rhythm in Drosophila melanogaster

Temperature influences animal physiology and behavior. Animals must set an appropriate body temperature to maintain homeostasis and maximize survival. Mammals set their body temperatures using metabolic and behavioral strategies. The daily fluctuation in body temperature is called the body temperature rhythm (BTR). For example, human body temperature increases during wakefulness and decreases during sleep. BTR is controlled by the circadian clock, is closely linked with metabolism and sleep, and entrains peripheral clocks located in the liver and lungs. However, the underlying mechanisms of BTR are largely unclear. In contrast to mammals, small ectotherms, such as Drosophila, control their body temperatures by choosing appropriate environmental temperatures. The preferred temperature of Drosophila increases during the day and decreases at night; this pattern is referred to as the temperature preference rhythm (TPR). As flies are small ectotherms, their body temperature is close to that of the surrounding environment. Thus, Drosophila TPR produces BTR, which exhibits a pattern similar to that of human BTR. In this review, we summarize the regulatory mechanisms of TPR, including recent studies that describe neuronal circuits relaying ambient temperature information to dorsal neurons (DNs). The neuropeptide diuretic hormone 31 (DH31) and its receptor (DH31R) regulate TPR, and a mammalian homolog of DH31R, the calcitonin receptor (CALCR), also plays an important role in mouse BTR regulation. In addition, both fly TPR and mammalian BTR are separately regulated from another clock output, locomotor activity rhythms. These findings suggest that the fundamental mechanisms of BTR regulation may be conserved between mammals and flies. Furthermore, we discuss the relationships between TPR and other physiological functions, such as sleep. The dissection of the regulatory mechanisms of Drosophila TPR could facilitate an understanding of mammalian BTR and the interaction between BTR and sleep regulation.
DNA methylation and gene expression changes in mouse mammary tissue during successive lactations: part II – the impact of lactation rank

ABSTRACT Mastitis is among the main reasons women cease breastfeeding. In farm animals, mastitis results in significant economic losses and the premature culling of some animals. Nevertheless, the effect of inflammation on the mammary gland is not completely understood. This article discusses the changes to DNA methylation in mouse mammary tissue caused by lipopolysaccharide-induced inflammation after in vivo intramammary challenges and the differences in DNA methylation between 1st and 2nd lactations. Lactation rank induces 981 differential methylations of cytosines (DMCs) in mammary tissue. Inflammation in 1st lactation compared to inflammation in 2nd lactation results in the identification of 964 DMCs. When comparing inflammation in 1st vs. 2nd lactations with previous inflammation history, 2590 DMCs were identified. Moreover, Fluidigm PCR data show changes in the expression of several genes related to mammary function, epigenetic regulation, and the immune response. We show that the epigenetic regulation of two successive physiological lactations is not the same in terms of DNA methylation and that the effect of lactation rank on DNA methylation is stronger than that of the onset of inflammation. The conditions presented here show that few DMCs are shared between comparisons, suggesting a specific epigenetic response depending on lactation rank, the presence of inflammation, and even whether the cells had previously suffered inflammation. In the long term, this information could lead to a better understanding of the epigenetic regulation of lactation in both physiological and pathological conditions. Abbreviations: RRBS, reduced representation bisulphite sequencing; RT-qPCR, real-time quantitative polymerase chain reaction; MEC, mammary epithelial cells; MaSC, mammary stem cell; TSS, transcription start site; TTS, transcription termination site; UTR, untranslated region; SINE, short interspersed nuclear element; LINE, long interspersed nuclear element; CGI, CpG island; DEG, differentially expressed gene; DMC, differentially methylated cytosine; DMR, differentially methylated region; GO term, gene ontology term; MF, molecular function; BP, biological process
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval

Multi-document summarization (MDS) assumes a set of topic-related documents are provided as input. In practice, this document set is not always available; it would need to be retrieved given an information need, i.e. a question or topic statement, a setting we dub"open-domain"MDS. We study this more challenging setting by formalizing the task and bootstrapping it using existing datasets, retrievers and summarizers. Via extensive automatic and human evaluation, we determine: (1) state-of-the-art summarizers suffer large reductions in performance when applied to open-domain MDS, (2) additional training in the open-domain setting can reduce this sensitivity to imperfect retrieval, and (3) summarizers are insensitive to the retrieval of duplicate documents and the order of retrieved documents, but highly sensitive to other errors, like the retrieval of irrelevant documents. Based on our results, we provide practical guidelines to enable future work on open-domain MDS, e.g. how to choose the number of retrieved documents to summarize. Our results suggest that new retrieval and summarization methods and annotated resources for training and evaluation are necessary for further progress in the open-domain setting.
Learning Large-scale Neural Fields via Context Pruned Meta-Learning

We introduce an efficient optimization-based meta-learning technique for large-scale neural field training by realizing significant memory savings through automated online context point selection. This is achieved by focusing each learning step on the subset of data with the highest expected immediate improvement in model quality, resulting in the almost instantaneous modeling of global structure and subsequent refinement of high-frequency details. We further improve the quality of our meta-learned initialization by introducing a bootstrap correction resulting in the minimization of any error introduced by reduced context sets while simultaneously mitigating the well-known myopia of optimization-based meta-learning. Finally, we show how gradient re-scaling at meta-test time allows the learning of extremely high-quality neural fields in significantly shortened optimization procedures. Our framework is model-agnostic, intuitive, straightforward to implement, and shows significant reconstruction improvements for a wide range of signals. We provide an extensive empirical evaluation on nine datasets across multiple multiple modalities, demonstrating state-of-the-art results while providing additional insight through careful analysis of the algorithmic components constituting our method. Code is available at https://github.com/jihoontack/GradNCP
NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution

The capability of video super-resolution (VSR) to synthesize high-resolution (HR) video from ideal datasets has been demonstrated in many works. However, applying the VSR model to real-world video with unknown and complex degradation remains a challenging task. First, existing degradation metrics in most VSR methods are not able to effectively simulate real-world noise and blur. On the contrary, simple combinations of classical degradation are used for real-world noise modeling, which led to the VSR model often being violated by out-of-distribution noise. Second, many SR models focus on noise simulation and transfer. Nevertheless, the sampled noise is monotonous and limited. To address the aforementioned problems, we propose a Negatives augmentation strategy for generalized noise modeling in Video Super-Resolution (NegVSR) task. Specifically, we first propose sequential noise generation toward real-world data to extract practical noise sequences. Then, the degeneration domain is widely expanded by negative augmentation to build up various yet challenging real-world noise sets. We further propose the augmented negative guidance loss to learn robust features among augmented negatives effectively. Extensive experiments on real-world datasets (e.g., VideoLQ and FLIR) show that our method outperforms state-of-the-art methods with clear margins, especially in visual quality. Project page is available at: https://negvsr.github. io/.
DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models

In this study, we aim to extend the capabilities of diffusion-based text-to-image (T2I) generation models by incorporating diverse modalities beyond textual description, such as sketch, box, color palette, and style embedding, within a single model. We thus design a multimodal T2I diffusion model, coined as DiffBlender, by separating the channels of conditions into three types, i.e., image forms, spatial tokens, and non-spatial tokens. The unique architecture of DiffBlender facilitates adding new input modalities, pioneering a scalable framework for conditional image generation. Notably, we achieve this without altering the parameters of the existing generative model, Stable Diffusion, only with updating partial components. Our study establishes new benchmarks in multimodal generation through quantitative and qualitative comparisons with existing conditional generation methods. We demonstrate that DiffBlender faithfully blends all the provided information and showcase its various applications in the detailed image synthesis.
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting

We introduce two new particle-based algorithms for learning latent variable models via marginal maximum likelihood estimation, including one which is entirely tuning-free. Our methods are based on the perspective of marginal maximum likelihood estimation as an optimization problem: namely, as the minimization of a free energy functional. One way to solve this problem is via the discretization of a gradient flow associated with the free energy. We study one such approach, which resembles an extension of Stein variational gradient descent, establishing a descent lemma which guarantees that the free energy decreases at each iteration. This method, and any other obtained as the discretization of the gradient flow, necessarily depends on a learning rate which must be carefully tuned by the practitioner in order to ensure convergence at a suitable rate. With this in mind, we also propose another algorithm for optimizing the free energy which is entirely learning rate free, based on coin betting techniques from convex optimization. We validate the performance of our algorithms across several numerical experiments, including several high-dimensional settings. Our results are competitive with existing particle-based methods, without the need for any hyperparameter tuning.
Instruction Tuning with Lexicons for Zero-Shot Style Classification

Style is used to convey authors' intentions and attitudes. Despite the success of large pre-trained language models on style classification, prior work relies on fine-tuning with labeled examples. Prompting large language models to classify style without fine-tuning is challenging because language styles can be difficult to define. In this study, we investigate the effectiveness of style lexicons as a means for instructing language models how to identify new styles that are unseen during training. Our experiments show that lexicon-based instructions improve transfer zero-shot performance significantly. We will release our code and data.
Delving Deeper into Data Scaling in Masked Image Modeling

Understanding whether self-supervised learning methods can scale with unlimited data is crucial for training large-scale models. In this work, we conduct an empirical study on the scaling capability of masked image modeling (MIM) methods (e.g., MAE) for visual recognition. Unlike most previous works that depend on the widely-used ImageNet dataset, which is manually curated and object-centric, we take a step further and propose to investigate this problem in a more practical setting. Specifically, we utilize the web-collected Coyo-700M dataset. We randomly sample varying numbers of training images from the Coyo dataset and construct a series of sub-datasets, containing 0.5M, 1M, 5M, 10M, and 100M images, for pre-training. Our goal is to investigate how the performance changes on downstream tasks when scaling with different sizes of data and models. The study reveals that: 1) MIM can be viewed as an effective method to improve the model capacity when the scale of the training data is relatively small; 2) Strong reconstruction targets can endow the models with increased capacities on downstream tasks; 3) MIM pre-training is data-agnostic under most scenarios, which means that the strategy of sampling pre-training data is non-critical. We hope these observations could provide valuable insights for future research on MIM.
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification

We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification. We formulate text classification as a neural text retrieval problem where each document is treated as a query, and the system learns the mapping from each query to the relevant class labels by (1) adding prompts to enhance label retrieval, and (2) using retrieved labels to enrich the training set in a self-training loop of contrastive learning. PESCO achieves state-of-the-art performance on four benchmark text classification datasets. On DBpedia, we achieve 98.5% accuracy without any labeled data, which is close to the fully-supervised result. Extensive experiments and analyses show all the components of PESCO are necessary for improving the performance of zero-shot text classification.
ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind

Theory of Mind (ToM), the capacity to comprehend the mental states of distinct individuals, is essential for numerous practical applications. With the development of large language models (LLMs), there is a heated debate about whether they are able to perform ToM tasks. Previous studies have used different tasks and prompts to test the ToM on LLMs and the results are inconsistent: some studies asserted these models are capable of exhibiting ToM, while others suggest the opposite. In this study, We present ToMChallenges, a dataset for comprehensively evaluating the Theory of Mind based on Sally-Anne and Smarties tests with a diverse set of tasks. In addition, we also propose an auto-grader to streamline the answer evaluation process. We tested three models: davinci, turbo, and gpt-4. Our evaluation results and error analyses show that LLMs have inconsistent behaviors across prompts and tasks. Performing the ToM tasks robustly remains a challenge for the LLMs. In addition, our paper wants to raise awareness in evaluating the ToM in LLMs and we want to invite more discussion on how to design the prompts and tasks for ToM tasks that can better access the LLMs’ ability.