Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing convergent mixed methods design using the Diamond Approach for clinical multiple case studies: an example using data on time to treatment in aneurysmal subarachnoid haemorrhage

Backgrounds and aims: Delays in treatment of aSAH appear common but the causes are not well understood. We explored facilitators and barriers to timely treatment of aSAH. Methods: We used a mixed-methods multiple case study approach including in-depth interviews with stakeholders involved in individual aSAH cases, focusing on events from onset to treatment. Quantitative data were extracted from medical records including date and times. Within-case analysis identified barriers and facilitators in 4 phases (pre-hospital, presentation, transfer, in-hospital), which then being triangulated with the quantified time in each phase to determine significant influencing factors. Finally, we conducted thematic analysis across cases in early (<12h) and delayed (>12h) treatment group using a case-study matrix. Results: Twenty-seven cases (74.1% female) with 89 interviewees were included. Median (IQR) time to treatment was 15.1 (9.0, 24.1) hours. Only 37% of cases had treatment within 12 hours of onset. Qualitative and quantitative data triangulation identified key themes influencing timely treatment of aSAH. Early recognition of aSAH and good coordination during pre-hospital and diagnosis phases, and availability of resources for treatment during in-hospital period were main facilitators for treatment within 12 hours from onset. Lack of recognition of aSAH at onset and lack of resources for immediate in-hospital treatment were major barriers for more delayed treatment. Conclusions: Using a robust mixed-methods approach, we identified the most significant factors affecting more timely treatment within 12 hours from onset of aSAH. The factors are potentially modifiable and may improve timely treatment of aSAH.
Serial evaluation of anti-SARS-CoV-2 IgG antibody and breakthrough infections in BNT162b2 Vaccinated migrant workers from Bangladesh

Background While Bangladesh has started its mass COVID-19 vaccination drive, it is struggling to cover its huge population similar to other low- and middle-income countries due to the lack of vaccine availability. One of the major remittance sources for Bangladesh is its migrant workers who are required to receive mRNA vaccines to return to their jobs. Despite reports of higher efficacy of mRNA vaccine against COVID-19, breakthrough infection cases are arising especially with the emergence of Delta variant. It is highly important to understand the post-vaccination immune response and breakthrough infections in different populations so that the necessity of booster dosage can be assessed properly. Methods We observed post BNT162b2 full vaccination immune response in a small older group (mean age= 59.5+-5.44 years) of migrant workers (n=10) for six months at the Sheikh Hasina National Institute of Burn and Plastic Surgery, Dhaka, Bangladesh. The plasma samples from the participants were collected after 14 days, 2 months, 3 months, 4 months, 5 months, and 6 months of receiving the 2nd dose of the BNT162b2 vaccine. Anti S1 RBD IgG responses were measured as optical density ratios using a commercially available ELISA kit. Results All 10 of the participants were male migrant workers and none of them had a history of previous COVID-19 infection. The median antibody response [IQ1:IQ3] was 9.05 [7.53; 10.0] on day 14 then it increases to 13.6 [12.0; 14.0] at the second month which gradually decreased to a median of 8.63 [8.34; 9.37] on the 6th-month post-vaccination. There were two breakthrough infection cases after receiving the second dose and the antibody responses were highly increased in the following months. Two of the breakthrough cases were diagnosed with mild COVID-19 as the symptom duration was less than 3 days with no respiratory complications and no hospital admission were required. Conclusions The BNT162b2 mRNA vaccine produces a strong immune response that sustains at least 6 months after getting fully vaccinated. But even after getting fully vaccinated people are susceptible to breakthrough infections that are not severe and boost the immune response greatly offering a hybrid immunity from both vaccine and natural infection. Hence, it is still important to fully vaccinate a greater number of people rather than thinking of offering booster dosage to the privileged populations out of the fear of breakthrough cases. If the LMICs can quickly cover at least 80% of their population with usual priority targets (healthcare workers, migrant workers, older people, etc.) then a global risk reduction and pandemic control would be possible that will allow additional variant-specific boosters for targeted populations if evidence support.
Genome‐wide pleiotropy analysis identifies novel blood pressure variants and improves its polygenic risk scores

Systolic and diastolic blood pressure (S/DBP) are highly correlated modifiable risk factors for cardiovascular disease (CVD). We report here a bidirectional Mendelian Randomization (MR) and horizontal pleiotropy analysis of S/DBP summary statistics from the UK Biobank (UKB)‐International Consortium for Blood Pressure (ICBP) (UKB‐ICBP) BP genome‐wide association study and construct a composite genetic risk score (GRS) by including pleiotropic variants. The composite GRS captures greater (1.11–3.26 fold) heritability for BP traits and increases (1.09‐ and 2.01‐fold) Nagelkerke's R2 for hypertension and CVD. We replicated 118 novel BP horizontal pleiotropic variants including 18 novel BP loci using summary statistics from the Million Veteran Program (MVP) study. An additional 219 novel BP signals and 40 novel loci were identified after a meta‐analysis of the UKB‐ICBP and MVP summary statistics but without further independent replication. Our study provides further insight into BP regulation and provides a novel way to construct a GRS by including pleiotropic variants for other complex diseases.
The spatial statistics of structural magnetic resonance images: application to post-acquisition quality assessment of brain MRI images

ABSTRACT This report describes a new quality evaluation method for structural magnetic resonance images (MRI) of the brain. Pixels in MRI images are regarded as regionalized random variables that exhibit distinct and organized geographic patterns. We extract geo-spatial local entropy features and build three separate Gaussian distributed quality models upon them using 250 brain MRI images of different subjects. The MRI images were provided by Alzheimer's disease neuroimaging initiative (ADNI). Image quality of a test image is predicted in a three-step process. In the first step, three separate geo-spatial feature vectors are extracted. The second step standardizes each quality model using corresponding geo-spatial feature vector extracted from the test image. The third step computes image quality by transforming the standardized score to probability. The proposed method was evaluated on images without perceived distortion and images degraded by different levels of motion blur and Rician noise as well as images with different configurations of bias fields. Based on the performance evaluation, our proposed method will be suitable for use in the field of clinical research where quality evaluation is required for the brain MRI images acquired from different MRI scanners and different clinical trial sites before they are fed into automated image processing and image analysis systems.
Velocity dispersion and dynamical mass for 270 galaxy clusters in the Planck PSZ1 catalogue

We present the velocity dispersion and dynamical mass estimates for 270 galaxy clusters included in the ﬁrst Planck Sunyaev- Zeldovich (SZ) source catalogue, the PSZ1. Part of the results presented here were achieved during a two-year observational program, the ITP, developed at the Roque de los Muchachos Observatory (La Palma, Spain). In the ITP we carried out a systematic optical follow-up campaign of all the 212 unidentiﬁed PSZ1 sources in the northern sky that have a declination above − 15 ◦ and are without known counterparts at the time of the publication of the catalogue. We present for the ﬁrst time the velocity dispersion and dynamical mass of 58 of these ITP PSZ1 clusters, plus 35 newly discovered clusters that are not associated with the PSZ1 catalogue. Using Sloan Digital Sky Survey archival data, we extend this sample, including 212 already conﬁrmed PSZ1 clusters in the northern sky. Using a subset of 207 of these galaxy clusters, we constrained the M SZ – M dyn scaling relation, ﬁnding a mass bias of (1 − B ) = 0 . 83 ± 0 . 07(stat) ± 0.02(sys). We show that this value is consistent with other results in the literature that were obtained with di ﬀ erent methods (X-ray, dynamical masses, or weak-lensing mass proxies). This result cannot dissolve the tension between primordial cosmic microwave background anisotropies and cluster number counts in the Ω M – σ 8 plane.
Does Pretraining for Summarization Require Knowledge Transfer?

Pretraining techniques leveraging enormous datasets have driven recent advances in text summarization. While folk explanations suggest that knowledge transfer accounts for pretraining's benefits, little is known about why it works or what makes a pretraining task or dataset suitable. In this paper, we challenge the knowledge transfer story, showing that pretraining on documents consisting of character n-grams selected at random, we can nearly match the performance of models pretrained on real corpora. This work holds the promise of eliminating upstream corpora, which may alleviate some concerns over offensive language, bias, and copyright issues. To see whether the small residual benefit of using real data could be accounted for by the structure of the pretraining task, we design several tasks motivated by a qualitative study of summarization corpora. However, these tasks confer no appreciable benefit, leaving open the possibility of a small role for knowledge transfer.
Evidence of preferential sweeping during snow settling in atmospheric turbulence

Abstract We present a field study of snow settling dynamics based on simultaneous measurements of the atmospheric flow field and snow particle trajectories. Specifically, a super-large-scale particle image velocimetry (SLPIV) system using natural snow particles as tracers is deployed to quantify the velocity field and identify vortex structures in a 22 m $\times$ 39 m field of view centred 18 m above the ground. Simultaneously, we track individual snow particles in a 3 m $\times$ 5 m sample area within the SLPIV using particle tracking velocimetry. The results reveal the direct linkage among vortex structures in atmospheric turbulence, the spatial distribution of snow particle concentration and their settling dynamics. In particular, with snow turbulence interaction at near-critical Stokes number, the settling velocity enhancement of snow particles is multifold, and larger than what has been observed in previous field studies. Super-large-scale particle image velocimetry measurements show a higher concentration of snow particles preferentially located on the downward side of the vortices identified in the atmospheric flow field. Particle tracking velocimetry, performed on high resolution images around the reconstructed vortices, confirms the latter trend and provides statistical evidence of the acceleration of snow particles, as they move toward the downward side of vortices. Overall, the simultaneous multi-scale particle imaging presented here enables us to directly quantify the salient features of preferential sweeping, supporting it as an underlying mechanism of snow settling enhancement in the atmospheric surface layer.
Transcendental Julia Sets of Minimal Hausdorff Dimension

We show the existence of transcendental entire functions f : C → C with Hausdorff-dimension 1 Julia sets, such that every Fatou component of f has infinite inner connectivity. We also show that there exist singleton complementary components of any Fatou component of f , answering a question of [RS19]. Our proof relies on a quasiconformalsurgery approach developed in [BL20].
Unfolding Projection-free SDP Relaxation of Binary Graph Classifier via GDPA Linearization

Algorithm unfolding creates an interpretable and parsimonious neural network architecture by implementing each iteration of a model-based algorithm as a neural layer. However, unfolding a proximal splitting algorithm with a positive semi-definite (PSD) cone projection operator per iteration is expensive, due to the required full matrix eigen-decomposition. In this paper, leveraging a recent linear algebraic theorem called Gershgorin disc perfect alignment (GDPA), we unroll a projection-free algorithm for semi-definite programming relaxation (SDR) of a binary graph classifier, where the PSD cone constraint is replaced by a set of"tightest possible"linear constraints per iteration. As a result, each iteration only requires computing a linear program (LP) and one extreme eigenvector. Inside the unrolled network, we optimize parameters via stochastic gradient descent (SGD) that determine graph edge weights in two ways: i) a metric matrix that computes feature distances, and ii) a sparse weight matrix computed via local linear embedding (LLE). Experimental results show that our unrolled network outperformed pure model-based graph classifiers, and achieved comparable performance to pure data-driven networks but using far fewer parameters.
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization

Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In such a scenario, the topic of a conversation can vary upon progression and the key information for a certain topic is often scattered across multiple utterances of different speakers, which poses challenges to abstractly summarize dialogues. To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task. The proposed contrastive objectives are framed as auxiliary tasks for the primary dialogue summarization task, united via an alternative parameter updating strategy. Extensive experiments on benchmark datasets demonstrate that the proposed simple method significantly outperforms strong baselines and achieves new state-of-the-art performance. The code and trained models are publicly available via \href{https://github.com/Junpliu/ConDigSum}{https://github.com/Junpliu/ConDigSum}.
How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks

The general goal of text simplification (TS) is to reduce text complexity for human consumption. This paper investigates another potential use of neural TS: assisting machines performing natural language processing (NLP) tasks. We evaluate the use of neural TS in two ways: simplifying input texts at prediction time and augmenting data to provide machines with additional information during training. We demonstrate that the latter scenario provides positive effects on machine performance on two separate datasets. In particular, the latter use of TS improves the performances of LSTM (1.82-1.98%) and SpanBERT (0.7-1.3%) extractors on TACRED, a complex, large-scale, real-world relation extraction task. Further, the same setting yields improvements of up to 0.65% matched and 0.62% mismatched accuracies for a BERT text classifier on MNLI, a practical natural language inference dataset.
Automatic Portrait Video Matting via Context Motion Network

Automatic portrait video matting is an under-constrained problem. Most state-of-the-art methods only exploit the semantic information and process each frame individually. Their performance is compromised due to the lack of temporal information between the frames. To solve this problem, we propose the context motion network to leverage semantic information and motion information. To capture the motion information, we estimate the optical flow and design a context-motion updating operator to integrate features between frames recurrently. Our experiments show that our network outperforms state-of-the-art matting methods significantly on the Video240K SD dataset.
Adsorption and photocatalytic properties of NiO nanoparticles synthesized via a thermal decomposition process

NiO nanoparticles (NPs) were synthesized at different annealing temperatures via a thermal decomposition process and characterized using X-ray diffraction, scanning electron microscopy, and UV-vis spectroscopy. The NiO NPs prepared at higher annealing temperature (400 °C) were shown excellent adsorption and photocatalytic activity toward textile dyes reactive black 5 (RB-5) and methylene blue (MB). About 87.2% of RB-5 in 60 min and 70.2% of MB in 5 h was removed using NiO NPs synthesized at 400 °C. The photocatalytic degradation of MB was found to increase with an increase in the annealing temperature of the catalyst. Moreover, the kinetic study revealed that the adsorption and photocatalytic activity of NiO NPs followed the second and first-order kinetics, respectively. The enhanced performance of NiO NPs toward dye removal might be related to its optical and structural properties.
Bootstrapped Meta-Learning

Meta-learning empowers artificial intelligence to increase its efficiency by learning how to learn. Unlocking this potential involves overcoming a challenging meta-optimisation problem. We propose an algorithm that tackles this problem by letting the meta-learner teach itself. The algorithm first bootstraps a target from the meta-learner, then optimises the meta-learner by minimising the distance to that target under a chosen (pseudo-)metric. Focusing on meta-learning with gradients, we establish conditions that guarantee performance improvements and show that the metric can control meta-optimisation. Meanwhile, the bootstrapping mechanism can extend the effective meta-learning horizon without requiring backpropagation through all updates. We achieve a new state-of-the art for model-free agents on the Atari ALE benchmark and demonstrate that it yields both performance and efficiency gains in multi-task meta-learning. Finally, we explore how bootstrapping opens up new possibilities and find that it can meta-learn efficient exploration in an epsilon-greedy Q-learning agent, without backpropagating through the update rule.
Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation

Event camera has offered promising alternative for visual perception, especially in high speed and high dynamic range scenes. Recently, many deep learning methods have shown great success in providing model-free solutions to many event-based problems, such as optical flow estimation. However, existing deep learning methods did not address the importance of temporal information well from the perspective of architecture design and cannot effectively extract spatio-temporal features. Another line of research that utilizes Spiking Neural Network suffers from training issues for deeper architecture. To address these points, a novel input representation is proposed that captures the events temporal distribution for signal enhancement. Moreover, we introduce a spatio-temporal recurrent encoding-decoding neural network architecture for event-based optical flow estimation, which utilizes Convolutional Gated Recurrent Units to extract feature maps from a series of event images. Besides, our architecture allows some traditional frame-based core modules, such as correlation layer and iterative residual refine scheme, to be incorporated. The network is end-to-end trained with self-supervised learning on the Multi-Vehicle Stereo Event Camera dataset. We have shown that it outperforms all the existing state-of-the-art methods by a large margin.
Interaction Models and Generalized Score Matching for Compositional Data

Applications such as the analysis of microbiome data have led to renewed interest in statistical methods for compositional data, i.e., multivariate data in the form of probability vectors that contain relative proportions. In particular, there is considerable interest in modeling interactions among such relative proportions. To this end we propose a class of exponential family models that accommodate general patterns of pairwise interaction while being supported on the probability simplex. Special cases include the family of Dirichlet distributions as well as Aitchison's additive logistic normal distributions. Generally, the distributions we consider have a density that features a difficult to compute normalizing constant. To circumvent this issue, we design effective estimation methods based on generalized versions of score matching. A high-dimensional analysis of our estimation methods shows that the simplex domain is handled as efficiently as previously studied full-dimensional domains.
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution

Multi-label text classification is a challenging task because it requires capturing label dependencies. It becomes even more challenging when class distribution is long-tailed. Resampling and re-weighting are common approaches used for addressing the class imbalance problem, however, they are not effective when there is label dependency besides class imbalance because they result in oversampling of common labels. Here, we introduce the application of balancing loss functions for multi-label text classification. We perform experiments on a general domain dataset with 90 labels (Reuters-21578) and a domain-specific dataset from PubMed with 18211 labels. We find that a distribution-balanced loss function, which inherently addresses both the class imbalance and label linkage problems, outperforms commonly used loss functions. Distribution balancing methods have been successfully used in the image recognition field. Here, we show their effectiveness in natural language processing. Source code is available at https://github.com/blessu/BalancedLossNLP.
Heterogeneous Graph Neural Networks for Keyphrase Generation

The encoder–decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not. However, relying solely on the source document can result in generating uncontrollable and inaccurate absent keyphrases. To address these problems, we propose a novel graph-based method that can capture explicit knowledge from related references. Our model first retrieves some document-keyphrases pairs similar to the source document from a pre-defined index as references. Then a heterogeneous graph is constructed to capture relations with different levels of granularity of the source document and its retrieved references. To guide the decoding process, a hierarchical attention and copy mechanism is introduced, which directly copies appropriate words from both source document and its references based on their relevance and significance. The experimental results on multiple KG benchmarks show that the proposed model achieves significant improvements against other baseline models, especially with regard to the absent keyphrase prediction.