Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a set of quality indicators in paediatric and perinatal care in Japan with a modified Delphi method

Backgrounds Few paediatric and perinatal quality indicators (QIs) have been developed in the Japanese setting, and the quality of care is not assured or validated. The aim of this study was to develop QIs in paediatric and perinatal care in Japan using an administrative database and confirm the feasibility and applicability of the indicators using a single-site practice test. Methods We used a RAND-modified Delphi method that integrates evidence review with expert consensus development. QI candidates were generated from clinical practice guidelines (CPGs) available in English or Japanese and existing QIs in nine selected paediatric or perinatal conditions. Consensus building was based on independent panel ratings. The performance of QIs was retrospectively assessed using data from an administrative database at the National Children’s Hospital. Data between April 2018 and March 2019 were used, while data between April 2019 and March 2021 were also used for selected condition, considering the small number of patients. Each QI was calculated as follows: number of times the indicator was met/number of participants×100. Results From the literature review conducted between 2010 and 2020, 124 CPGs and 193 existing indicators were identified to generate QI candidates. Through the consensus-building process, 133 QI candidates were assessed and 79 QIs were accepted. The practice test revealed wide variations in the process-level performance of QIs in four categories: patient safety: median 43.9% (IQR 16.7%–85.6%), general paediatrics: median 98.8% (IQR 84.2%–100%), advanced paediatrics: median 94.4% (IQR 46.0%–100%) and advanced obstetrics: median 80.3% (IQR 59.6%–100%). Conclusions We established 79 QIs for paediatric and perinatal care in Japan using an administrative database that can be applied to hospitals nationwide. The practice test confirmed the measurability of the developed QIs. Benchmarking these QIs will be an attractive approach to improving the quality of care.
HRadNet: A Hierarchical Radiomics-Based Network for Multicenter Breast Cancer Molecular Subtypes Prediction

Breast cancer is a heterogeneous disease, where molecular subtypes of breast cancer are closely related to the treatment and prognosis. Therefore, the goal of this work is to differentiate between luminal and non-luminal subtypes of breast cancer. The hierarchical radiomics network (HRadNet) is proposed for breast cancer molecular subtypes prediction based on dynamic contrast-enhanced magnetic resonance imaging. HRadNet fuses multilayer features with the metadata of images to take advantage of conventional radiomics methods and general convolutional neural networks. A two-stage training mechanism is adopted to improve the generalization capability of the network for multicenter breast cancer data. The ablation study shows the effectiveness of each component of HRadNet. Furthermore, the influence of features from different layers and metadata fusion are also analyzed. It reveals that selecting certain layers of features for a specified domain can make further performance improvements. Experimental results on three data sets from different devices demonstrate the effectiveness of the proposed network. HRadNet also has good performance when transferring to other domains without fine-tuning.
What Controls the Quality of Photodynamical Simulations? Electronic Structure Versus Nonadiabatic Algorithm

The field of nonadiabatic dynamics has matured over the last decade with a range of algorithms and electronic structure methods available at the moment. While the community currently focuses more on developing and benchmarking new nonadiabatic dynamics algorithms, the underlying electronic structure controls the outcome of nonadiabatic simulations. Yet, the electronic-structure sensitivity analysis is typically neglected. In this work, we present a sensitivity analysis of the nonadiabatic dynamics of cyclopropanone to electronic structure methods and nonadiabatic dynamics algorithms. In particular, we compare wave function-based CASSCF, FOMO-CASCI, MS- and XMS-CASPT2, density-functional REKS, and semiempirical MRCI-OM3 electronic structure methods with the Landau–Zener surface hopping, fewest switches surface hopping, and ab initio multiple spawning with informed stochastic selection algorithms. The results clearly demonstrate that the electronic structure choice significantly influences the accuracy of nonadiabatic dynamics for cyclopropanone even when the potential energy surfaces exhibit qualitative and quantitative similarities. Thus, selecting the electronic structure solely on the basis of the mapping of potential energy surfaces can be misleading. Conversely, we observe no discernible differences in the performance of the nonadiabatic dynamics algorithms across the various methods. Based on the above results, we discuss the present-day practice in computational photodynamics.
Synthetic Dual Cysteine-ADP Ribosylated Peptides from the Androgen Receptor are Recognized by the DTX3L/PARP9 Complex

Androgen signaling in prostate cancer cells involves multisite cysteine ADP-ribosylation of the androgen receptor (AR) by PARP7. The AR modification is read by ADP-ribosyl binding macrodomains in PARP9, but the reason that multiple cysteines are modified is unknown. Here, we use synthetic peptides to show that dual ADP-ribosylation of closely spaced cysteines mediates recognition by the DTX3L/PARP9 complex. Mono and dual ADP-ribosylated cysteine peptides were prepared using a novel solid-phase synthetic strategy utilizing a key, Boc-protected, ribofuranosylcysteine building block. This synthetic strategy allowed us to synthesize fluorescently labeled peptides containing a dual ADP-ribosylation motif. It was found that the DTX3L/PARP9 complex recognizes the dual ADP-ribosylated AR peptide (Kd = 80.5 nM) with significantly higher affinity than peptides with a single ADP-ribose. Moreover, oligomerization of the DTX3L/PARP9 complex proved crucial for ADP-ribosyl-peptide interaction since a deletion mutant of the complex that prevents its oligomer formation dramatically reduced peptide binding. Our data show that features of the substrate modification and the reader contribute to the efficiency of the interaction and imply that multivalent interactions are important for AR-DTX3L/PARP9 assembly.
Exploring the intersectionality of stigma and substance use help-seeking behaviours among lesbian, gay, bisexual, transgender, queer, questioning or otherwise gender or sexuality minority (LGBTQ+) individuals in the United States: A scoping review

ABSTRACT Problematic substance use (SU) is a significant issue among LGBTQ+ individuals, but rates of treatment/help-seeking in this population remain low. This review aimed to investigate literature about intersectional stigma of SU and LGBTQ+ identity and its impact on SU help-seeking behaviours in the U.S. Eligible studies from eight-database were included if peer-reviewed, in English, from the U.S., published between 2000 and 2022, focused on SU, stigma, SU help-seeking behaviours, among LGBTQ+ adults. Of 458 search results, 50 underwent full-text review, 12 were included in the final sample. Minority Stress Theory emerged as a relevant theoretical framework. Findings revealed that increased SU as a coping strategy was associated with minority stress. Intersectional stigma negatively impacted SU treatment experience among LGBTQ+ individuals, leading to avoidance of help-seeking or poor treatment outcomes. Patterns of SU and impact of stigma among LGBTQ+ individuals differ, wherein bisexual and transgender individuals reported significantly more treatment barriers and unique stressors. LGBTQ+ individuals reported earlier age of SU onset and were more likely to encounter opportunities for SU. This review highlights the impact of intersectional stigma on SU help-seeking behaviour among LGBTQ+ individuals in the U.S. Recommendations are provided for future clinical practice, research, and policy to better support LGBTQ+ individuals.
IMU Shoulder Angle Estimation: Effects of Sensor-to-Segment Misalignment and Sensor Orientation Error

Accurate shoulder joint angle estimation is crucial for analyzing joint kinematics and kinetics across a spectrum of movement applications including in athletic performance evaluation, injury prevention, and rehabilitation. However, accurate IMU-based shoulder angle estimation is challenging and the specific influence of key error factors on shoulder angle estimation is unclear. We thus propose an analytical model based on quaternions and rotation vectors that decouples and quantifies the effects of two key error factors, namely sensor-to-segment misalignment and sensor orientation estimation error, on shoulder joint rotation error. To validate this model, we conducted experiments involving twenty-five subjects who performed five activities: yoga, golf, swimming, dance, and badminton. Results showed that improving sensor-to-segment misalignment along the segment’s extension/flexion dimension had the most significant impact in reducing the magnitude of shoulder joint rotation error. Specifically, a 1° improvement in thorax and upper arm calibration resulted in a reduction of 0.40° and 0.57° in error magnitude. In comparison, improving IMU heading estimation was only roughly half as effective (0.23° per 1°). This study clarifies the relationship between shoulder angle estimation error and its contributing factors, and identifies effective strategies for improving these error factors. These findings have significant implications for enhancing the accuracy of IMU-based shoulder angle estimation, thereby facilitating advancements in IMU-based upper limb rehabilitation, human-machine interaction, and athletic performance evaluation.
Degradation of Triazole Fungicides by Plant Growth-Promoting Bacteria from Contaminated Agricultural Soil

The widespread application of triazole fungicides (TFs) in agricultural practices can result in the considerable accumulation of active compound residues in the soil and a subsequent negative impact on the soil microbiota and crop health. In this study, we isolated three TF-degrading bacterial strains from contaminated agricultural soils and identified them as Klebsiella sp. , Pseudomonas sp. , and Citrobacter sp. based on analysis of morphological characteristics and 16S rRNA gene sequences. The strains used three common TFs, namely hexaconazole, difenoconazole, and propiconazole, as their only sources of carbon and energy for growth in a liquid mineral salt medium, with high concentrations (~ 500 mg/l) of each TF. In addition to the ability to degrade fungicides, the isolates also exhibited plant growth-promoting characteristics, such as nitrogen fixation, indole acetic acid production, phosphate dissolution, and cellulose degradation. The synergistic combination of three bacterial isolates significantly improved plant growth and development with an increased survival rate (57%), and achieved TF degradation ranging from 85.83 to 96.59% at a concentration of approximately 50 mg/kg of each TF within 45 days in the soil-plant system. Based on these findings, the three strains and their microbial consortium show promise for application in biofertilizers, to improve soil health and facilitate optimal plant growth.
Genomic analysis and characterization of new loci associated with seed protein and oil content in soybeans

Breeding for increased protein without a reduction in oil content in soybeans [Glycine max (L.) Merr. ] is a challenge for soybean breeders but an expected goal. Many efforts have been made to develop new soybean varieties with high yield in combination with desirable protein and/or oil traits. An elite line, R05‐1415, was reported to be high yielding, high protein, and low oil. Several significant quantitative trait loci (QTL) for protein and oil were reported in this line, but many of them were unstable across environments or genetic backgrounds. Thus, a new study under multiple field environments using the Infinium BARCSoySNP6K BeadChips was conducted to detect and confirm stable genomic loci for these traits. Genetic analyses consistently detected a single major genomic locus conveying these two traits with remarkably high phenotypic variation explained (R2), varying between 24.2% and 43.5%. This new genomic locus is located between 25.0 and 26.7 Mb, distant from the previously reported QTL and did not overlap with other commonly reported QTL and the recently cloned gene Glyma.20G085100. Homolog analysis indicated that this QTL did not result from the paracentric chromosome inversion with an adjacent genomic fragment that harbors the reported QTL. The pleiotropic effect of this QTL could be a challenge for improving protein and oil simultaneously; however, a further study of four candidate genes with significant expressions in the seed developmental stages coupled with haplotype analysis may be able to pinpoint causative genes. The functionality and roles of these genes can be determined and characterized, which lay a solid foundation for the improvement of protein and oil content in soybeans.
META4: Semantically-Aligned Generation of Metaphoric Gestures Using Self-Supervised Text and Speech Representation

Image Schemas are repetitive cognitive patterns that influence the way we conceptualize and reason about various concepts present in speech. These patterns are deeply embedded within our cognitive processes and are reflected in our bodily expressions including gestures. Particularly, metaphoric gestures possess essential characteristics and semantic meanings that align with Image Schemas, to visually represent abstract concepts. The shape and form of gestures can convey abstract concepts, such as extending the forearm and hand or tracing a line with hand movements to visually represent the image schema of PATH. Previous behavior generation models have primarily focused on utilizing speech (acoustic features and text) to drive the generation model of virtual agents. They have not considered key semantic information as those carried by Image Schemas to effectively generate metaphoric gestures. To address this limitation, we introduce META4, a deep learning approach that generates metaphoric gestures from both speech and Image Schemas. Our approach has two primary goals: computing Image Schemas from input text to capture the underlying semantic and metaphorical meaning, and generating metaphoric gestures driven by speech and the computed image schemas. Our approach is the first method for generating speech driven metaphoric gestures while leveraging the potential of Image Schemas. We demonstrate the effectiveness of our approach and highlight the importance of both speech and image schemas in modeling metaphoric gestures.
BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings

Sentence embeddings are crucial in measuring semantic similarity. Most recent studies employed large language models (LLMs) to learn sentence embeddings. Existing LLMs mainly adopted autoregressive architecture without explicit backward dependency modeling. Therefore, we examined the effects of backward dependencies in LLMs for semantic similarity measurements. Concretely, we propose a novel model: backward dependency enhanced large language model (BeLLM). It learns sentence embeddings via transforming specific attention layers from uni- to bi-directional. We extensively experiment across various semantic textual similarity (STS) tasks and downstream applications. BeLLM achieves state-of-the-art performance in varying scenarios. It shows that auto-regressive LLMs benefit from backward dependencies for sentence embeddings.
L-WaveBlock: A Novel Feature Extractor Leveraging Wavelets for Generative Adversarial Networks

Generative Adversarial Networks (GANs) have risen to prominence in the field of deep learning, facilitating the generation of realistic data from random noise. The effectiveness of GANs often depends on the quality of feature extraction, a critical aspect of their architecture. This paper introduces L-WaveBlock, a novel and robust feature extractor that leverages the capabilities of the Discrete Wavelet Transform (DWT) with deep learning methodologies. L-WaveBlock is catered to quicken the convergence of GAN generators while simultaneously enhancing their performance. The paper demonstrates the remarkable utility of L-WaveBlock across three datasets, a road satellite imagery dataset, the CelebA dataset and the GoPro dataset, showcasing its ability to ease feature extraction and make it more efficient. By utilizing DWT, L-WaveBlock efficiently captures the intricate details of both structural and textural details, and further partitions feature maps into orthogonal subbands across multiple scales while preserving essential information at the same time. This streamlined process translates into accelerated results, elevating the overall efficiency of GAN-based image generation. Not only does it lead to faster convergence, but also gives competent results on every dataset by employing the L-WaveBlock. The proposed method achieves an Inception Score of 3.6959 and a Structural Similarity Index of 0.4261 on the maps dataset, a Peak Signal-to-Noise Ratio of 29.05 and a Structural Similarity Index of 0.874 on the CelebA dataset. The proposed method performs competently to the state-of-the-art for the image denoising dataset, albeit not better, but still leads to faster convergence than conventional methods. With this, L-WaveBlock emerges as a robust and efficient tool for enhancing GAN-based image generation, demonstrating superior convergence speed and competitive performance across multiple datasets for image resolution, image generation and image denoising.
A higher-order transformation approach to the formalization and analysis of BPMN using graph transformation systems

The Business Process Modeling Notation (BPMN) is a widely used standard notation for defining intra- and inter-organizational workflows. However, the informal description of the BPMN execution semantics leads to different interpretations of BPMN elements and difficulties in checking behavioral properties. In this article, we propose a formalization of the execution semantics of BPMN that, compared to existing approaches, covers more BPMN elements while also facilitating property checking. Our approach is based on a higher-order transformation from BPMN models to graph transformation systems. To show the capabilities of our approach, we implemented it as an open-source web-based tool.
The Impact of Preference Agreement in Reinforcement Learning from Human Feedback: A Case Study in Summarization

Reinforcement Learning from Human Feedback (RLHF) can be used to capture complex and nuanced properties of text generation quality. As a result, the task of text summarization has been identified as a good candidate for this process. In this paper, we explore how preference agreement impacts the efficacy of RLHF for summarization. We show that sampling human preferences to include a range of annotator agreement results in (1) higher accuracy reward models and (2) alters the characteristics of quality captured. We additionally show improvements in downstream generation when using a reward model trained with a range of preference agreements. Our contributions have implications for the design of synthetic datasets as well as the importance of considering quality differentials in comparison-based data.
SciDataFlow: a tool for improving the flow of data through science

Abstract Motivation Managing data and code in open scientific research is complicated by two key problems: large datasets often cannot be stored alongside code in repository platforms like GitHub, and iterative analysis can lead to unnoticed changes to data, increasing the risk that analyses are based on older versions of data. Results SciDataFlow is a fast, concurrent command-line tool paired with a simple Data Manifest specification that streamlines tracking data changes, uploading data to remote repositories, and pulling in all data necessary to reproduce a computational analysis. Availability and implementation SciDataFlow is available at https://github.com/vsbuffalo/scidataflow.
Social Media Bot Detection using Dropout-GAN

Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime. We propose an approach to bot detection using Generative Adversarial Networks (GAN). We discuss how we overcome the issue of mode collapse by utilizing multiple discriminators to train against one generator, while decoupling the discriminator to perform social media bot detection and utilizing the generator for data augmentation. In terms of classification accuracy, our approach outperforms the state-of-the-art techniques in this field. We also show how the generator in the GAN can be used to evade such a classification technique.
Improving Human Legibility in Collaborative Robot Tasks through Augmented Reality and Workspace Preparation

Understanding the intentions of human teammates is critical for safe and effective human-robot interaction. The canonical approach for human-aware robot motion planning is to first predict the human's goal or path, and then construct a robot plan that avoids collision with the human. This method can generate unsafe interactions if the human model and subsequent predictions are inaccurate. In this work, we present an algorithmic approach for both arranging the configuration of objects in a shared human-robot workspace, and projecting ``virtual obstacles'' in augmented reality, optimizing for legibility in a given task. These changes to the workspace result in more legible human behavior, improving robot predictions of human goals, thereby improving task fluency and safety. To evaluate our approach, we propose two user studies involving a collaborative tabletop task with a manipulator robot, and a warehouse navigation task with a mobile robot.
Node-Bound Communities for Partition of Unity Interpolation on Graphs

Graph signal processing benefits significantly from the direct and highly adaptable supplementary techniques offered by partition of unity methods (PUMs) on graphs. In our approach, we demonstrate the generation of a partition of unity solely based on the underlying graph structure, employing an algorithm that relies exclusively on centrality measures and modularity, without requiring the input of the number of subdomains. Subsequently, we integrate PUMs with a local graph basis function (GBF) approximation method to develop cost-effective global interpolation schemes. We also discuss numerical experiments conducted on both synthetic and real datasets to assess the performance of this presented technique.
Conversational AI Threads for Visualizing Multidimensional Datasets

Generative Large Language Models (LLMs) show potential in data analysis, yet their full capabilities remain uncharted. Our work explores the capabilities of LLMs for creating and refining visualizations via conversational interfaces. We used an LLM to conduct a re-analysis of a prior Wizard-of-Oz study examining the use of chatbots for conducting visual analysis. We surfaced the strengths and weaknesses of LLM-driven analytic chatbots, finding that they fell short in supporting progressive visualization refinements. From these findings, we developed AI Threads, a multi-threaded analytic chatbot that enables analysts to proactively manage conversational context and improve the efficacy of its outputs. We evaluate its usability through a crowdsourced study (n=40) and in-depth interviews with expert analysts (n=10). We further demonstrate the capabilities of AI Threads on a dataset outside the LLM's training corpus. Our findings show the potential of LLMs while also surfacing challenges and fruitful avenues for future research.