Miracle-dz/UniFAR-FTUs huggingface dataset:
Anatomical predictors for suture-based closure of the patent foramen ovale: A multicenter experience.

BACKGROUND NobleStitch EL is a novel suture-based technique used for patent foramen ovale (PFO) closure and an alternative to traditional double-disc devices without the need for antithrombotic therapy. However, successful closure rates are still unknown, and certain anatomies may be unfavorable for successful closure. AIMS We assessed the efficacy of the NobleStitch EL and sought to identify patient-related anatomical features associated with successful suture-based closure. METHODS We included 55 patients who underwent PFO closure with the NobleStitch EL in The Netherlands and Switzerland. Successful closure was defined as residual right-to-left shunt grade ≤1 with Valsalva maneuver at a cardiac ultrasound. Predefined possible anatomical determinants for effective closure included PFO length, atrial septal aneurysm, PFO entry- and exit diameter. RESULTS Successful closure was achieved in 33 patients (60%). The PFO length was shorter in patients with successful closure compared to unsuccessful closure with a median length of 9.6 mm (IQR 8.0-15.0) versus 13.3 mm (IQR 11.4-18.6) on preprocedural ultrasound (p = 0.041) and 9.9 mm (IQR 8.0-13.1) versus 12.5 mm (IQR 9.7-15.4) on angiography (p = 0.049). Additionally, the PFO exit diameter and PFO volume were smaller in patients with successful closure than unsuccessful closure, with a mean diameter of 7.0 ± 3.1 mm versus 9.5 ± 3.8 mm (p = 0.015) and a median volume of 381 mm3 (IQR 286-894) versus 985 mm3 (IQR 572-1550) (p = 0.016). CONCLUSION In our study cohort, the successful PFO closure rate using NobleStitch EL was relatively low (60%). With this alternative procedure, patients with a small PFO driven by a short PFO tunnel length and small exit diameter seem to be eligible for successful suture-based closure.
Abnormal activation of NF‐κB and MAPK signaling pathways affect osimertinib resistance and influence the recruitment of myeloid‐derived suppressor cells to shape the immunosuppressive tumor immune microenvironment

Osimertinib is the first‐line treatment for patients with epidermal growth factor receptor (EGFR) mutations, but the treatment options after drug resistance are limited. Previous studies have suggested that EGFR is in an immunosuppressive tumor immune microenvironment (TIME). However, the evolution of TIME after osimertinib resistance and whether this resistance can be overcome by targeting TIME needs to be further investigated.
Nonlinear Ultrasound Simulation Based on Full-Wave Model and Comparisons with KZK

The linear acoustic wave equation has been widely used for modeling the ultrasound propagation through biological tissues. However, the nonlinear effect can’t be neglected when transmitting an ultrasound beam of high frequency or high amplitude. The commonly used nonlinear models for ultrasound imaging simulation include the KZK and the Full-wave model. This article firstly solves the full-wave equation numerically by using the Finite Difference Time Domain (FDTD) method. Experiments are then conducted to verify the accuracy of the numerical solution. A plane-wave imaging algorithm is consequently designed based on the full-wave simulation. The plane-wave imaging simulation based on the full-wave model can get the similar imaging quality for the simulation based on the KZK model with focused beam scanning mode. Simulation results prove that compared with KZK (CREANUIS) which is limited by the paraxial approximation, more flexible excitation setup and more feedback information can be acquired in the simulation based on the full-wave model and the FDTD. Thus it’s helpful to better study nonlinear ultrasound.
Application of Nonlinear Models with Random Coefficients to Growth Data

SUMMARY The application of nonlinear random coefficient models to the analysis of growth curve data is described. The approach is further developed for the estimation of mean growth curves and their variability from mark-recapture data when the age of an animal at first capture is unknown, but the time between successive captures is known. These methods are of wide applicability as shown by the analysis of data on the growth of emus (Dromaius novaehollandiae), noisy scrub-birds (Atrichornis clamosus), and whelks (Dicathais aegaota). Simulations of a mark-recapture experiment on the rock (spiny) lobster (Panulirus argus) in Mexico, using a known growth curve, showed the effectiveness of the approach in estimating both the population's growth curve and the variability in individual growth.
The landscape of BRCA1 and BRCA2 large rearrangements in an international cohort of over 20 000 ovarian tumors identified using next‐generation sequencing

Approximately half of ovarian tumors have defects within the homologous recombination repair pathway. Tumors carrying pathogenic variants (PVs) in BRCA1/BRCA2 are more likely to respond to poly‐ADP ribose polymerase (PARP) inhibitor treatment. Large rearrangements (LRs) are a challenging class of variants to identify and characterize in tumor specimens and may therefore be underreported. This study describes the prevalence of pathogenic BRCA1/BRCA2 LRs in ovarian tumors and discusses the importance of their identification using a comprehensive testing strategy.
Biodegradable CuMoO4 Nanodots with Multienzyme Activities for Multimodal Treatment of Tumor

Due to their complexity and variability, tumors need to be treated with multimodal combined therapy, which requires the development of therapeutic agents that can provide multimodal therapeutic effects. Herein, CuMoO4 nanodots smaller than 10 nm that can be prepared by simple hydrothermal method are reported. These nanodots can be well dispersed in water and have good biosafety and biodegradability. Further studies show that these nanodots also present multienzyme activities, such as catalase, peroxidase and glutathione peroxidase. In addition, CuMoO4 nanodots exhibit high photothermal conversion efficiency (41%) under 1064 nm near‐infrared laser irradiation. In vitro and in vivo experimental results indicate that CuMoO4 nanodots can effectively inhibit the instinctive regulation of tumor cells to oxidative stress, provide sustained treatment to achieve photothermal synergistic ferroptosis, and trigger immune responses to immunogenic cell death. It is worth mentioning that the CuMoO4 nanodots also cause cuproptosis of tumor cells. This study provides a promising nanoplatform for multimodal combined therapy of cancer.
The role of genetic selection and climatic factors in the dispersal of anatomically modern humans out of Africa

Significance We analyze the functional and spatiotemporal properties of 57 hard sweeps inferred in ancient human genomes to reconstruct human evolution during the poorly understood Out of Africa migration. Our analyses reveal a previously unsuspected extended period of genetic adaptation lasting ~30,000 y, potentially in Arabia or surrounding regions, prior to a rapid dispersal across the rest of Eurasia as far as Australia. Functional targets include multiple interacting loci involved in fat storage, neural development, skin physiology, and cilia function, with associations with multiple modern Western diseases. Similar adaptive signatures are also evident in introgressed archaic hominin loci and modern Arctic human groups, indicating that cold environments were a prominent historical selection pressure that potentially facilitated the successful peopling of Eurasia.
Possible Detection of the Progenitor of the Type II Supernova SN 2023ixf

Stellar evolution theory predicts multiple pathways to the explosive deaths of stars as supernovae. Locating and characterizing the progenitors of well-studied supernovae is important to constrain the theory and to justify and design future surveys to improve on progenitor detections. Here we report the serendipitous preexplosion imaging, by the Hubble Space Telescope, of SN 2023ixf, one of the nearest extragalactic supernovae ever discovered, in the galaxy M101. The extremely red color and absolute magnitude MF814W=−5.11−0.47+0.65 mag suggest that the progenitor was a red supergiant. Comparison with stellar evolutionary isochrones suggests it is within the relatively low initial mass range of ∼8–10 M ⊙ and that there is likely a lot of dust present at the supernova site.
Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors

ChatGPT has stimulated the research boom in the field of large language models. In this paper, we assess the capabilities of ChatGPT from four perspectives including Performance, Evaluation Criteria, Robustness and Error Types. Specifically, we first evaluate ChatGPT's performance on 17 datasets with 14 IE sub-tasks under the zero-shot, few-shot and chain-of-thought scenarios, and find a huge performance gap between ChatGPT and SOTA results. Next, we rethink this gap and propose a soft-matching strategy for evaluation to more accurately reflect ChatGPT's performance. Then, we analyze the robustness of ChatGPT on 14 IE sub-tasks, and find that: 1) ChatGPT rarely outputs invalid responses; 2) Irrelevant context and long-tail target types greatly affect ChatGPT's performance; 3) ChatGPT cannot understand well the subject-object relationships in RE task. Finally, we analyze the errors of ChatGPT, and find that"unannotated spans"is the most dominant error type. This raises concerns about the quality of annotated data, and indicates the possibility of annotating data with ChatGPT. The data and code are released at Github site.
Contrastive Training of Complex-Valued Autoencoders for Object Discovery

Current state-of-the-art object-centric models use slots and attention-based routing for binding. However, this class of models has several conceptual limitations: the number of slots is hardwired; all slots have equal capacity; training has high computational cost; there are no object-level relational factors within slots. Synchrony-based models in principle can address these limitations by using complex-valued activations which store binding information in their phase components. However, working examples of such synchrony-based models have been developed only very recently, and are still limited to toy grayscale datasets and simultaneous storage of less than three objects in practice. Here we introduce architectural modifications and a novel contrastive learning method that greatly improve the state-of-the-art synchrony-based model. For the first time, we obtain a class of synchrony-based models capable of discovering objects in an unsupervised manner in multi-object color datasets and simultaneously representing more than three objects
HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge. Under such observation, we tend to investigate the feasibility of a memory-friendly model with strong generalization capability that could boost the performance of HTC without prior statistics or label semantics. In this paper, we propose Hierarchy-aware Tree Isomorphism Network (HiTIN) to enhance the text representations with only syntactic information of the label hierarchy. Specifically, we convert the label hierarchy into an unweighted tree structure, termed coding tree, with the guidance of structural entropy. Then we design a structure encoder to incorporate hierarchy-aware information in the coding tree into text representations. Besides the text encoder, HiTIN only contains a few multi-layer perceptions and linear transformations, which greatly saves memory. We conduct experiments on three commonly used datasets and the results demonstrate that HiTIN could achieve better test performance and less memory consumption than state-of-the-art (SOTA) methods.
Iteratively Improving Biomedical Entity Linking and Event Extraction via Hard Expectation-Maximization

Biomedical entity linking and event extraction are two crucial tasks to support text understanding and retrieval in the biomedical domain. These two tasks intrinsically benefit each other: entity linking disambiguates the biomedical concepts by referring to external knowledge bases and the domain knowledge further provides additional clues to understand and extract the biological processes, while event extraction identifies a key trigger and entities involved to describe each biological process which also captures the structural context to better disambiguate the biomedical entities. However, previous research typically solves these two tasks separately or in a pipeline, leading to error propagation. What's more, it's even more challenging to solve these two tasks together as there is no existing dataset that contains annotations for both tasks. To solve these challenges, we propose joint biomedical entity linking and event extraction by regarding the event structures and entity references in knowledge bases as latent variables and updating the two task-specific models in a hard Expectation-Maximization (EM) fashion: (1) predicting the missing variables for each partially annotated dataset based on the current two task-specific models, and (2) updating the parameters of each model on the corresponding pseudo completed dataset. Experimental results on two benchmark datasets: Genia 2011 for event extraction and BC4GO for entity linking, show that our joint framework significantly improves the model for each individual task and outperforms the strong baselines for both tasks. We will make the code and model checkpoints publicly available once the paper is accepted.
Separation of dust emission from the cosmic infrared background in Herschel observations with wavelet phase harmonics

The low-brightness dust emission at high Galactic latitudes is of interest with respect to studying the interplay among the physical processes involved in shaping the structure of the interstellar medium (ISM), as well as in statistical characterizations of the dust emission as a foreground to the cosmic microwave background (CMB). Progress in this avenue of research has been hampered by the difficulty related to separating the dust emission from the cosmic infrared background (CIB). We demonstrate that the dust and CIB may be effectively separated based on their different structure on the sky and we use the separation to characterize the structure of diffuse dust emission on angular scales, where the CIB is a significant component in terms of power. We used scattering transform statistics, wavelet phase harmonics (WPH) to perform a statistical component separation using Herschel SPIRE observations. This component separation is done only from observational data using non-Gaussian properties as a lever arm and is done at a single $250\ frequency. This method, which we validated on mock data, gives us access to non-Gaussian statistics of the interstellar dust and an output dust map that is essentially free from CIB contamination. Our statistical modeling characterizes the non-Gaussian structure of the diffuse ISM down to the smallest scales observed by Herschel. We recovered the power law shape of the dust power spectrum up to $k = 2 \ arcmin $, where the dust signal represents $2 $ of the total power. Going beyond the standard power spectra analysis, we show that the non-Gaussian properties of the dust emission are not scale-invariant. The output dust map reveals coherent structures at the smallest scales, which had been hidden by the CIB anisotropies. This aspect opens up new observational perspectives on the formation of structure in the diffuse ISM, which we discuss here in reference to a previous work. We have succeeded in performing a statistical separation from the observational data at a single frequency by using non-Gaussian statistics.
Identifying Informational Sources in News Articles

News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annotated dataset, to date, of informational sources used in news writing. We show that our dataset can be used to train high-performing models for information detection and source attribution. We further introduce a novel task, source prediction, to study the compositionality of sources in news articles. We show good performance on this task, which we argue is an important proof for narrative science exploring the internal structure of news articles and aiding in planning-based language generation, and an important step towards a source-recommendation system to aid journalists.
Stock and market index prediction using Informer network

Applications of deep learning in financial market prediction has attracted huge attention from investors and researchers. In particular, intra-day prediction at the minute scale, the dramatically fluctuating volume and stock prices within short time periods have posed a great challenge for the convergence of networks result. Informer is a more novel network, improved on Transformer with smaller computational complexity, longer prediction length and global time stamp features. We have designed three experiments to compare Informer with the commonly used networks LSTM, Transformer and BERT on 1-minute and 5-minute frequencies for four different stocks/ market indices. The prediction results are measured by three evaluation criteria: MAE, RMSE and MAPE. Informer has obtained best performance among all the networks on every dataset. Network without the global time stamp mechanism has significantly lower prediction effect compared to the complete Informer; it is evident that this mechanism grants the time series to the characteristics and substantially improves the prediction accuracy of the networks. Finally, transfer learning capability experiment is conducted, Informer also achieves a good performance. Informer has good robustness and improved performance in market prediction, which can be exactly adapted to real trading.
From Characters to Words: Hierarchical Pre-trained Language Model for Open-vocabulary Language Understanding

Current state-of-the-art models for natural language understanding require a preprocessing step to convert raw text into discrete tokens. This process known as tokenization relies on a pre-built vocabulary of words or sub-word morphemes. This fixed vocabulary limits the model’s robustness to spelling errors and its capacity to adapt to new domains. In this work, we introduce a novel open-vocabulary language model that adopts a hierarchical two-level approach: one at the word level and another at the sequence level. Concretely, we design an intra-word module that uses a shallow Transformer architecture to learn word representations from their characters, and a deep inter-word Transformer module that contextualizes each word representation by attending to the entire word sequence. Our model thus directly operates on character sequences with explicit awareness of word boundaries, but without biased sub-word or word-level vocabulary. Experiments on various downstream tasks show that our method outperforms strong baselines. We also demonstrate that our hierarchical model is robust to textual corruption and domain shift.
GRACE: Discriminator-Guided Chain-of-Thought Reasoning

In the context of multi-step reasoning, e.g., with chain-of-thought, language models (LMs) can easily assign a high likelihood to incorrect steps. As a result, decoding strategies that optimize for solution likelihood often yield incorrect solutions. To address this issue, we propose Guiding chain-of-thought ReAsoning with a CorrectnEss Discriminator (GRACE), a stepwise decoding approach that steers the decoding process towards producing correct reasoning steps. GRACE employs a discriminator trained with a contrastive loss over correct and incorrect steps, which is used during decoding to score next-step candidates based on their correctness. Importantly, GRACE only requires sampling from the LM, without the need for LM training or fine-tuning. Using models from FLAN-T5 and LLaMA families, we evaluate GRACE over four math and two symbolic reasoning tasks, where it exhibits substantial performance gains compared to greedy decoding, verifiers, and self-consistency in most settings. When further combined with self-consistency, GRACE outperforms all the baselines by sizeable margins. Human and LLM evaluations over GSM8K show that GRACE not only improves the final answer accuracy but also the correctness of the intermediate reasoning. Our implementation can be accessed at \url{https://github.com/mukhal/grace}.
Topic-Guided Self-Introduction Generation for Social Media Users

Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests. While most prior work profiles users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user's tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework; it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user's history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling.
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods

We establish the first mathematically rigorous link between Bayesian, variational Bayesian, and ensemble methods. A key step towards this it to reformulate the non-convex optimisation problem typically encountered in deep learning as a convex optimisation in the space of probability measures. On a technical level, our contribution amounts to studying generalised variational inference through the lense of Wasserstein gradient flows. The result is a unified theory of various seemingly disconnected approaches that are commonly used for uncertainty quantification in deep learning -- including deep ensembles and (variational) Bayesian methods. This offers a fresh perspective on the reasons behind the success of deep ensembles over procedures based on parameterised variational inference, and allows the derivation of new ensembling schemes with convergence guarantees. We showcase this by proposing a family of interacting deep ensembles with direct parallels to the interactions of particle systems in thermodynamics, and use our theory to prove the convergence of these algorithms to a well-defined global minimiser on the space of probability measures.