Miracle-dz/UniFAR-FTUs huggingface dataset:
Usefulness of Impella support in different clinical settings in cardiogenic shock

BACKGROUND The Impella pump has emerged as a promising tool in patients with cardiogenic shock (CS). Despite its attractive properties, there are scarce data on the specific clinical setting and the potential role of Impella devices in CS patients from routine clinical practice. METHODS This is an observational, retrospective, single center, cohort study. All consecutive patients with diagnosis of CS and undergoing support with Impella 2.5®, Impella CP® or Impella 5.0® from April 2015 to December 2020 were included. Baseline characteristics, management and outcomes were assessed according to CS severity, age and cause of CS. Main outcome measured was in-hospital mortality. RESULTS A total of 50 patients were included (median age: 59.3 ± 10 years). The most common cause of CS was acute coronary syndrome (ACS) (68%), followed by decompensation of previous cardiomyopathy (22%). A total of 13 patients (26%) had profound CS. Most patients (54%) improved pulmonary congestion at 48 h after Impella support. A total of 19 patients (38%) presented significant bleeding. In-hospital mortality was 42%. Among patients with profound CS (n = 13), five patients were previously supported with venoarterial extracorporeal membrane oxygenation. A total of eight patients (61.5%) died during the admission, and no patient achieved ventricular recovery. Older patients (≥ 67 years, n = 10) had more comorbidities and the highest mortality (70%). Among patients with ACS (n = 34), 35.3% of patients had profound CS; and in most cases (52.9%), Impella support was performed as a bridge to recovery. In contrast, only one patient from the decompensated cardiomyopathy group (n = 11) presented with profound CS. In 90.9% of these cases, Impella support was used as a bridge to cardiac transplantation. There were no cases of death. CONCLUSIONS In this cohort of real-life CS patients, Impella devices were used in different settings, with different clinical profiles and management. Despite a significant rate of complications, mortality was acceptable and lower than those observed in other series.
A method for assaying peptide: N-glycanase/N-glycanase 1 activities in crude extracts using an N-glycosylated cyclopeptide

Abstract Cytosolic peptide: N-glycanase (PNGase; NGLY1), an enzyme responsible for de-glycosylation of N-glycans on glycoproteins, is known to play pivotal roles in a variety of biological processes. In 2012, NGLY1 deficiency, a rare genetic disorder, was reported and since then, more than 100 patients have now been identified worldwide. Patients with this disease exhibit several common symptoms that are caused by the dysfunction of NGLY1. However, correlation between the severity of patient symptoms and the extent of the reduction in NGLY1 activity in these patients remains to be clarified, mainly due to the absence of a facile quantitative assay system for this enzyme, especially in a crude extract as an enzyme source. In this study, a quantitative, non-radioisotope (RI)-based assay method for measuring recombinant NGLY1 activity was established using a BODIPY-labeled asialoglycopeptide (BODIPY-ASGP) derived from hen eggs. With this assay, the activities of 27 recombinant NGLY1 mutants that are associated with the deficiency were examined. It was found that the activities of three (R469X, R458fs and H494fs) out of the 27 recombinant mutant proteins were 30–70% of the activities of wild-type NGLY1. We further developed a method for measuring endogenous NGLY1 activity in crude extracts derived from cultured cells, patients’ fibroblasts, iPS cells or peripheral blood mononuclear cells (PBMCs), using a glycosylated cyclopeptide (GCP) that exhibited resistance to the endogenous proteases in the extract. Our methods will not only provide new insights into the molecular mechanism responsible for this disease but also promises to be applicable for its diagnosis.
Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer

Background: At present, radical total mesorectal excision after neoadjuvant chemoradiotherapy is crucial for locally advanced rectal cancer. Therefore, the use of histopathological images analysis technology to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer is of great significance for the subsequent treatment of patients. Methods: In this study, we propose a new pathological images analysis method based on multi-instance learning to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Specifically, we proposed a gated attention normalization mechanism based on the multilayer perceptron, which accelerates the convergence of stochastic gradient descent optimization and can speed up the training process. We also proposed a bilinear attention multi-scale feature fusion mechanism, which organically fuses the global features of the larger receptive fields and the detailed features of the smaller receptive fields and alleviates the problem of pathological images context information loss caused by block sampling. At the same time, we also designed a weighted loss function to alleviate the problem of imbalance between cancerous instances and normal instances. Results: We evaluated our method on a locally advanced rectal cancer dataset containing 150 whole slide images. In addition, to verify our method’s generalization performance, we also tested on two publicly available datasets, Camelyon16 and MSKCC. The results show that the AUC values of our method on the Camelyon16 and MSKCC datasets reach 0.9337 and 0.9091, respectively. Conclusion: Our method has outstanding performance and advantages in predicting the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Clinical and Translational Impact Statement—This study aims to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer to assist clinicians quickly diagnose and formulate personalized treatment plans for patients.
Am I truly monolingual? Exploring foreign language experiences in monolinguals

Monolingualism has typically been understood as a homogeneous phenomenon. The linguistic experiences of monolinguals are usually overlooked when analysing the impact of foreign language experiences on language processing and cognitive functioning. In this study, we analyse the linguistic experiences of 962 English-speaking individuals from the United Kingdom (UK) who identified as monolinguals. Through an online survey, we found that more than 80% of these monolinguals had learned at least one foreign language, dialect, or type of jargon. More than half of this 80% of monolinguals also used languages they had learned at some point in their lives. Moreover, nearly 40% of all the studied monolinguals confirmed that they had been passively exposed to foreign languages or dialects in their environment; approximately a fourth of these monolinguals who declared exposure to at least one foreign language (or dialect) confirmed that they also used these languages. Furthermore, activities that involved passive use of languages (i.e., activities that require reading or listening but do not require speaking or writing; e.g., watching TV) were occasionally carried out in foreign languages: around 26% of these monolinguals confirmed the passive use of more than one language. Lastly, around 58% of monolinguals who had visited one or more non-English-speaking countries declared the active use of foreign languages during their stay(s). These results suggest that the linguistic experiences of monolinguals from the UK often include exposure to and use of foreign languages. Moreover, these results show the need to consider the specificity of the monolingual language experience when analysing the impact of foreign languages on cognitive functioning, as differences in the language experiences of bilinguals also have divergent impacts on cognition. Lastly, monolingual experiences are different from bilingual experiences; therefore, existing questionnaires that evaluate language experiences should be adapted to capture the particular linguistic experiences of monolinguals.
A “Valve‐Closing” Starvation Strategy for Amplification of Tumor‐Specific Chemotherapy

Starvation‐dependent differential stress sensitization effect between normal and tumor cells provides a potentially promising strategy to amplify chemotherapy effects and reduce side effects. However, the conventional starvation approaches such as glucose oxidase (Gox)‐induced glucose depletion and nanomedicine‐enabled vascular embolism usually suffer from aggravated tumor hypoxia, systemic toxicity, and unpredictable metabolic syndrome. Herein, a novel “valve‐closing” starvation strategy is developed to amplify the chemotherapy effects via closing the “valve” of glucose transported into tumor cells, which is accomplished by a glucose transporters 1 (GLUT1, valve of glucose uptake) inhibitor (Genistein, Gen) and chemotherapeutic agent (Curcumin, Cur) coloaded hybrid organosilica‐micelles nanomedicine (designated as (Gen + Cur)@FOS) with controllable stability. In vitro and in vivo results demonstrate that (Gen + Cur)@FOS can effectively reduce glucose/adenosine triphosphate levels in tumor cells by inhibiting GLUT1 expression (i.e., “valve‐closing”) to induce the starvation of tumor cells, thus weakening the resistance of tumor cells to apoptosis caused by chemotherapy, and consequently contributing to the remarkably improved antitumor efficiency and minimized side effects based on the stress sensitization effect mediated by GLUT1 inhibition‐induced starvation. This “valve‐closing” starvation strategy provides a promising paradigm for the development of novel nanotherapeutics with amplified chemotherapy effect.
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL

In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from skill discovery to reward shaping. Recently, learning the Laplacian representation has been framed as the optimization of a temporally-contrastive objective to overcome its computational limitations in large (or continuous) state spaces. However, this approach requires uniform access to all states in the state space, overlooking the exploration problem that emerges during the representation learning process. In this work, we propose an alternative method that is able to recover, in a non-uniform-prior setting, the expressiveness and the desired properties of the Laplacian representation. We do so by combining the representation learning with a skill-based covering policy, which provides a better training distribution to extend and refine the representation. We also show that a simple augmentation of the representation objective with the learned temporal abstractions improves dynamics-awareness and helps exploration. We find that our method succeeds as an alternative to the Laplacian in the non-uniform setting and scales to challenging continuous control environments. Finally, even if our method is not optimized for skill discovery, the learned skills can successfully solve difficult continuous navigation tasks with sparse rewards, where standard skill discovery approaches are no so effective.
One-Bit Compressive Sensing: Can We Go Deep and Blind?

One-bit compressive sensing is concerned with the accurate recovery of an underlying sparse signal of interest from its one-bit noisy measurements. The conventional signal recovery approaches for this problem are mainly developed based on the assumption that an exact knowledge of the sensing matrix is available. In this work, however, we present a novel data-driven and model-based methodology that achieves blind recovery; i.e., signal recovery without requiring the knowledge of the sensing matrix. To this end, we make use of the deep unfolding technique and develop a model-driven deep neural architecture which is designed for this specific task. The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements. In addition, due to the incorporation of the domain knowledge and the mathematical model of the system into the proposed deep architecture, the resulting network benefits from enhanced interpretability, has a very small number of trainable parameters, and requires very small number of training samples, as compared to the commonly used black-box deep neural network alternatives for the problem at hand.
Learning from All Vehicles

In this paper, we present a system to train driving policies from experiences collected not just from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of other agents to create more diverse driving scenarios without collecting additional data. The main difficulty in learning from other vehicles is that there is no sensor information. We use a set of supervisory tasks to learn an intermediate representation that is invariant to the viewpoint of the controlling vehicle. This not only provides a richer signal at training time but also allows more complex reasoning during inference. Learning how all vehicles drive helps predict their behavior at test time and can avoid collisions. We evaluate this system in closed-loop driving simulations. Our system outperforms all prior methods on the public CARLA Leaderboard by a wide margin, improving driving score by 25 and route completion rate by 24 points.
Healthy Twitter discussions? Time will tell

Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important ﬁeld of research within social studies. With the rapid development of social media, and the increasing amount of available information and sources, rigorous manual analysis of such discourses has become un-feasible. Many approaches tackle the issue by studying the semantic and syntactic properties of discussions following a supervised approach, for example using natural language processing on a dataset labeled for abusive, fake or bot-generated content. Solutions based on the existence of a ground truth are limited to those domains which may have ground truth. However, within the context of misinformation, it may be dif-ﬁcult or even impossible to assign labels to instances. In this context, we consider the use of temporal dynamic patterns as an indicator of discussion health. Working in a domain for which ground truth was unavailable at the time (early COVID-19 pandemic discussions) we explore the characterization of discussions based on the the volume and time of contributions. First we explore the types of discussions in an unsupervised manner, and then characterize these types using the concept of ephemerality, which we formalize. In the end, we discuss the potential use of our ephemerality deﬁnition for labeling online discourses based on how desirable, healthy and constructive they are.
Milliarcsecond Astrometry for the Galilean Moons Using Stellar Occultations

A stellar occultation occurs when a Solar System object passes in front of a star for an observer. This technique allows the sizes and shapes of the occulting body to be determined with kilometer precision. In addition, this technique constrains the occulting body’s positions, albedos, densities, and so on. In the context of the Galilean moons, these events can provide their best ground-based astrometry, with uncertainties in the order of 1 mas (∼3 km at Jupiter’s distance during opposition). We organized campaigns and successfully observed a stellar occultation by Io (JI) in 2021, one by Ganymede (JIII) in 2020, and one by Europa (JII) in 2019, with stations in North and South America. We also re-analyzed two previously published events: one by Europa in 2016 and another by Ganymede in 2017. We then fit the known 3D shape of the occulting satellite and determine its center of figure. This resulted in astrometric positions with uncertainties in the milliarcsecond level. The positions obtained from these stellar occultations can be used together with dynamical models to ensure highly accurate orbits of the Galilean moons. These orbits can help when planning future space probes aiming at the Jovian system, such as JUICE by ESA and Europa Clipper by NASA. They also allow more efficient planning of flyby maneuvers.
Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue

Background: Machine learning techniques have been widely used and demonstrate promising performance in many software security tasks such as software vulnerability prediction. However, the class ratio within software vulnerability datasets is often highly imbalanced (since the percentage of observed vulnerability is usually very low). Goal: To help security practitioners address software security data class imbalanced issues and further help build better prediction models with resampled datasets. Method: We introduce an approach called Dazzle which is an optimized version of conditional Wasserstein Generative Adversarial Networks with gradient penalty (cWGAN-GP). Dazzle explores the architecture hyperparameters of cWGAN-GP with a novel optimizer called Bayesian Optimization. We use Dazzle to generate minority class samples to resample the original imbalanced training dataset. Results: We evaluate Dazzle with three software security datasets, i.e., Moodle vulnerable files, Ambari bug reports, and JavaScript function code. We show that Dazzle is practical to use and demonstrates promising improvement over existing state-of-the-art oversampling techniques such as SMOTE (e.g., with an average of about 60% improvement rate over SMOTE in recall among all datasets). Conclusion: Based on this study, we would suggest the use of optimized GANs as an alternative method for security vulnerability data class imbalanced issues.
A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection

Deep convolutional neural networks have shown remarkable results on multiple detection tasks. Despite the significant progress, the performance of such detectors are often assessed in public benchmarks under non-realistic conditions. Specifically, impact of conventional distortions and processing operations such as compression, noise, and enhancement are not sufficiently studied. This paper proposes a rigorous framework to assess performance of learning-based detectors in more realistic situations. An illustrative example is shown under deepfake detection context. Inspired by the assessment results, a data augmentation strategy based on natural image degradation process is designed, which significantly improves the generalization ability of two deepfake detectors.
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation

With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress. However, it remains a great challenge to accurately extract disparities from real-world image pairs taken by consumer-level devices like smartphones, due to practical complicating factors such as thin structures, non-ideal rectification, camera module inconsistencies and various hard-case scenes. In this paper, we propose a set of innovative designs to tackle the problem of practical stereo matching: 1) to better recover fine depth details, we design a hierarchical network with recurrent refinement to update disparities in a coarse-to-fine manner, as well as a stacked cascaded architecture for inference; 2) we propose an adaptive group correlation layer to mitigate the impact of erroneous rectification; 3) we introduce a new synthetic dataset with special attention to difficult cases for better generalizing to real-world scenes. Our results not only rank 1st on both Middlebury and ETH3D benchmarks, outperforming existing state-of-the-art methods by a notable margin, but also exhibit high-quality details for real-life photos, which clearly demonstrates the efficacy of our contributions.
Star Formation in the Central Molecular Zone of the Milky Way

The Central Molecular Zone (CMZ) is a ring-like accumulation of molecular gas in the innermost few hundred parsecs of the Milky Way, generated by the inward transport of matter driven by the Galactic bar. The CMZ is the most extreme star-forming environment in the Galaxy. The unique combination of large-scale dynamics and extreme interstellar medium conditions, characterised by high densities, temperatures, pressures, turbulent motions, and strong magnetic fields, make the CMZ an ideal region for testing current star and planet formation theories. We review the recent observational and theoretical advances in the field, and combine these to draw a comprehensive, multi-scale and multi-physics picture of the cycle of matter and energy in the context of star formation in the closest galactic nucleus.
A Machine Learning Generative Method for Automating Antenna Design and Optimization

To facilitate the antenna design with the aid of computer, one of the practices in consumer electronic industry is to model and optimize antenna performances with a simplified antenna geometric scheme. The ease of handling multi-dimensional optimization problems and the less dependence on the engineers' knowledge and experience are the key to achieve the popularity of simulation-driven antenna design and optimization for the industry. In this paper, we introduce a flexible geometric scheme with the concept of mesh network that can form any arbitrary shape by connecting different nodes. For such problems with high dimensional parameters, we propose a machine learning based generative method to assist the searching of optimal solutions. It consists of discriminators and generators. The discriminators are used to predict the performance of geometric models, and the generators to create new candidates that will pass the discriminators. Moreover, an evolutionary criterion approach is proposed for further improving the efficiency of our method. Finally, not only optimal solutions can be found, but also the well trained generators can be used to automate future antenna design and optimization. For a dual resonance antenna design, our proposed method is better than the other mature algorithms.
Potential destination prediction for low predictability individuals based on knowledge graph

Individuals with low predictability are common due to observation limitation, which shows random movement patterns and makes it diﬃcult to perform mobility prediction (e.g., destination prediction). In this paper, we develop a new knowledge graph-based framework (PDPFKG) for destination prediction of low predictability travelers, especially those potential destinations a traveler never visits in history, by considering trip association relationships between them. We ﬁrst construct a trip knowledge graph (TKG) to model the trip scenario by entities (e.g., travelers, destinations and time information) and their relationships, in which we introduce the concept of private relationship for complexity reduction. Then a modiﬁed knowledge graph embedding algorithm is implemented to optimize the overall graph representation. Based on the trip knowledge graph embedding model (TKGEM), the possible ranking of individuals’ unobserved destinations to be chosen in the future can be obtained by calculating triples’ distance. Empirically. PDPFKG is tested using an anonymous vehicular dataset from 138 intersections equipped with video-based vehicle detection systems in Xuancheng city, China. The results show that (i) the proposed method signiﬁcantly outperforms baseline methods, and (ii) the results show strong consistency with human behavioural pattern of choosing potential destinations. Finally, we provide a comprehensive discussion and respond to the innovative points of the methodology.