Miracle-dz/UniFAR-FTUs huggingface dataset:
Healthy orthorexia, orthorexia nervosa, and personality traits in a community sample in Turkey

Objective Orthorexia nervosa is an eating disorder characterized by a rigid preoccupation with a perceived healthy diet. However, little is known about healthy orthorexia compared to orthorexia nervosa. The current study examined the relationship between healthy orthorexia and orthorexia nervosa and personality traits in a Turkish sample. Methods Three hundred fifty participants from a community sample aged 18-65 were included in the study. Participants completed a sociodemographic data collection form, Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Teruel Orthorexia Scale (TOS), and the 10-Item Personality Inventory (TIPI). Results The frequency of healthy orthorexia was 32.3% and orthorexia nervosa was 10.2%. There were no differences between genders regarding healthy orthorexia and orthorexia nervosa scores. Healthy orthorexia symptoms were negatively correlated with depression and anxiety (r = -0.11 and r = -0.20, respectively, P < .05), while they were positively correlated with agreeableness (r = 0.17), conscientiousness (r = 0.14), and extraversion (r = 0.15). Orthorexia nervosa symptoms were positively associated with anxiety (r = 0.12), depression (r = 0.10), and healthy orthorexia (r = 0.55). Hierarchical regression analyses demonstrated that depression was negatively associated with healthy orthorexia symptoms, explaining 4% of the variance. The agreeableness trait was positively associated with healthy orthorexia symptoms, explaining 4% of the variance. However, no specific personality trait was associated with orthorexia nervosa symptoms. Conclusion To our knowledge, the present study is the first to examine the relationships between personality traits, healthy orthorexia, and orthorexia nervosa symptoms in a Turkish sample. Sociocultural factors may be important for understanding orthorexia nervosa and healthy eating behaviors in this population.
Maternal Postpartum Stress Scale: Translation and validation study of the Chinese version

Objective: This study aimed to translate the Maternal Postpartum Stress Scale (MPSS) into Chinese and validate its psychometric properties in postpartum women. Methods: A total of 406 postpartum women were recruited from 6 hospitals in Nantong, Jiangsu, China. Cronbach's co-efficient, split-half reliability, and test-retest reliability were used to evaluate the reliability of the translated scale. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to evaluate the structural validity of the scale. The Edinburgh Postpartum Depression Scale (EPDS), the anxiety subscale of the Depression-Anxiety-Stress Scale (DASS-21) and the Perceived Stress Scale (PSS-10) were used as calibration scales to measure the correlation of MPSS. Results: The Cronbach's co-efficient of the Chinese version of MPSS was 0.940 and the Cronbach's co-efficient of the three subscales ranged from 0.882 to 0.911. The split-half reliability was 0.825, and the test-retest reliability was 0.912. The content validity index of the scale (S-CVI) was 0.926. A total of three common factors were extracted from the EFA. The CFA validated the explored 3-factor structure, and the indicators fitted well ({chi}2/df = 2.167, comparative fit index = 0.918, tucker-lewis index=0.907, incremental fit index=0.919 and root mean square error of approximation = 0.075). Conclusion: With superb reliability and validity, the Chinese version of MPSS can be used to assess postpartum stress in Chinese women, which is expected to lay a scientific basis for the early identification of postpartum women's stress, thus providing a basis for the development of early individualized interventions. Equally importantly, with specific clinical value and practical significance for postpartum women's physical and mental health, future replication studies of the Chinese version of MPSS should be carried out in diverse samples and clinical populations.
Influence factors on and potential strategies to amplify receptor-mediated nanodrug delivery across the blood–brain barrier

ABSTRACT Introduction A major challenge in treating central nervous system (CNS) disorders is to achieve adequate drug delivery across the blood–brain barrier (BBB). Receptor-mediated nanodrug delivery as a Trojan horse strategy has become an exciting approach. However, these nanodrugs do not accumulate significantly in the brain parenchyma, which greatly limits the therapeutic effect of drugs. Amplifying the efficiency of receptor-mediated nanodrug delivery across the BBB becomes the holy grail in the treatment of CNS disorders. Areas covered In this review, we tend to establish links between dynamic BBB and receptor-mediated nanodrug delivery, starting with the delivery processes across the BBB, describing factors affecting nanodrug delivery efficiency, and summarizing potential strategies that may amplify delivery efficiency. Expert opinion Receptor-mediated nanodrug delivery is a common approach to significantly enhance the efficiency of brain-targeting delivery. As BBB is constantly undergoing changes, it is essential to investigate the impact of diseases on the effectiveness of brain-targeting nanodrug delivery. More critically, there are several barriers to achieving brain-targeting nanodrug delivery in the five stages of receptor-mediated transcytosis (RMT), and the impacts can be conflicting, requiring intricate balance. Further studies are also needed to investigate the material toxicity of nanodrugs to address the issue of clinical translation.
A Comparative Study of Healthy Dietary Patterns for Incident and Fatal Digestive System Cancer

INTRODUCTION: We examined multiple dietary patterns in relation to total digestive system cancer (DSC) incidence and death. METHODS: A total of 213,038 health professionals from the Health Professionals Follow-up Study (1986–2016), the Nurses' Health Study (1986–2018), and the Nurses' Health Study II (1991–2017) with no cancer diagnosis at baseline were analyzed. DSC incidence and death were estimated using time-varying Cox proportional hazards regression models. RESULTS: During up to 32 years of follow-up, 5,724 DSC cases accrued. Adherence to 8 healthy diet patterns was associated with a 7%–13% lower risk of DSC, particularly for digestive tract cancers. An inverse association with gastrointestinal tract cancer was also shown for all pattern scores except Alternate Mediterranean Diet and the healthful Plant-based Diet Index, with hazard ratios between 0.84 and 0.89. Inverse associations were shown for the reversed empirical dietary index for hyperinsulinemia (hazard ratio for 90th vs 10th percentile 0.64, 95% confidence interval [CI] 0.47–0.87) and the empirical dietary index associated with lower inflammation (rEDIP) (0.53, 95% CI 0.39–0.72) for stomach cancer, and for the rEDIP (0.58, 95% CI 0.37–0.92) for small intestine cancer. Among accessory cancers, the Alternate Healthy Eating Index-2010, alternate Mediterranean diet, and diabetes risk reduction diet were associated with a 43%–51% lower risk of liver cancer. The reversed empirical dietary index for hyperinsulinemia, rEDIP, and the Alternate Healthy Eating Index-2010 were inversely associated with the risk of fatal DSC. DISCUSSION: Adherence to healthy diets was associated with a lower risk of incident and fatal DSC, although the magnitude of the association varied slightly among the patterns.
An angiogenesis-related lncRNA signature predicts the immune microenvironment and prognosis of breast cancer

Both angiogenesis and lncRNAs play crucial roles in the development and progression of breast cancer. Considering the unknown association of angiogenesis and lncRNAs in breast cancer, we aim to identify angiogenesis-related lncRNAs (ARLs) and explore their prognostic value. Here, based on analysis of The Cancer Genome Atlas database, the correlation between ARL and the prognosis and immune infiltration landscape of breast cancer were investigated. Eight ARLs (MAFG−DT, AC097478.1, AL357054.4, AL118556.1, SNHG10, MED14OS, OTUD6B−AS1, and CYTOR) were selected to construct the risk model as a prognostic signature. The survival rate of the patients in the high-risk group was lower than that in the low-risk group. The ARL signature was an independent prognostic predictor, and areas under the curve of 1-, 3-, and 5-year survival were 0.745, 0.695, and 0.699, respectively. The prognostic ARLs were associated with the immune infiltration landscape and could indicate the immune status, immune response, tumor mutational burden, and drug sensitivity of patients with breast cancer. Furthermore, qRT-PCR of clinical samples revealed that OTUD6B−AS1 was correlated with prognostic pathological parameters. OTUD6B−AS1 promoted breast cancer cell proliferation, wound healing, migration, invasion, and human umbilical vein endothelial cells tube formation. Mechanistically, OTUD6B−AS1 regulated EMT- and angiogenesis-related molecules. Taken together, we constructed and verified a robust signature of eight ARLs for the prediction of survival in patients with breast cancer, and the characterization of the immune infiltration landscape. Our findings suggest that OTUD6B−AS1 could be a therapeutic target for patients with breast cancer.
Multi-frequency data analysis in AFM by wavelet transform

Interacting cantilevers in AFM experiments generate non-stationary, multi-frequency signals consisting of numerous excited flexural and torsional modes and their harmonics. The analysis of such signals is challenging, requiring special methodological approaches and a powerful mathematical apparatus. The most common approach to the signal analysis is to apply Fourier transform analysis. However, FT gives accurate spectra for stationary signals, and for signals changing their spectral content over time, FT provides only an averaged spectrum. Hence, for non-stationary and rapidly varying signals, such as those from interacting cantilevers, a method that shows the spectral evolution in time is needed. One of the most powerful techniques, allowing detailed time-frequency representation of signals, is the wavelet transform. It is a method of analysis that allows representation of energy associated to the signal at a particular frequency and time, providing correlation between the spectral and temporal features of the signal, unlike FT. This is particularly important in AFM experiments because signals nonlinearities contains valuable information about tip-sample interactions and consequently surfaces properties. The present work is aimed to show the advantages of wavelet transform in comparison with FT using as an example the force curve analysis in dynamic force spectroscopy.
Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study

Objective To evaluate the clinical potential of large language models (LLMs) in ophthalmology using a more robust benchmark than raw examination scores. Materials and methods GPT-3.5 and GPT-4 were trialled on 347 questions before GPT-3.5, GPT-4, PaLM 2, LLaMA, expert ophthalmologists, and doctors in training were trialled on a mock examination of 87 questions. Performance was analysed with respect to question subject and type (first order recall and higher order reasoning). Masked ophthalmologists graded the accuracy, relevance, and overall preference of GPT-3.5 and GPT-4 responses to the same questions. Results The performance of GPT-4 (69%) was superior to GPT-3.5 (48%), LLaMA (32%), and PaLM 2 (56%). GPT-4 compared favourably with expert ophthalmologists (median 76%, range 64-90%), ophthalmology trainees (median 59%, range 57-63%), and unspecialised junior doctors (median 43%, range 41-44%). Low agreement between LLMs and doctors reflected idiosyncratic differences in knowledge and reasoning with overall consistency across subjects and types (p>0.05). All ophthalmologists preferred GPT-4 responses over GPT-3.5 and rated the accuracy and relevance of GPT-4 as higher (p<0.05). Discussion In view of the comparable or superior performance to trainee-grade ophthalmologists and unspecialised junior doctors, state-of-the-art LLMs such as GPT-4 may provide useful medical advice and assistance where access to expert ophthalmologists is limited. Clinical benchmarks provide useful assays of LLM capabilities in healthcare before clinical trials can be designed and conducted. Conclusion LLMs are approaching expert-level knowledge and reasoning skills in ophthalmology. Further research is required to develop and validate clinical applications to improve eye health outcomes.
Statistical Analysis Plan: The Effect of Automated External Defibrillators for Cardiac Arrests in Private Homes

This document describes the statistical analysis plan for the study The Effect of Automated External Defibrillators for Cardiac Arrests in Private Homes. This will be an observational study using prospectively collected data from the CARES registry in the United States. We will include patients with an out-of-hospital cardiac arrest in a private home. The exposure will be application of an AED and the primary outcome will be survival to hospital discharge. Assuming that there is no causal effect of AED application in those with a non-shockable rhythm, we will use a difference-in-difference approach to estimate the causal effect of AED application in those with a shockable rhythm.
Modelling of lossy curved surfaces in the 3‐D frequency‐domain finite‐difference methods

A conformal first‐order or Leontovic surface‐impedance boundary condition (SIBC) for the modelling of fully three‐dimensional (3‐D) lossy curved surfaces in a Cartesian grid is presented for the frequency‐domain finite‐difference (FD) methods. The impedance boundary condition is applied to auxiliary tangential electric and magnetic field components defined at the curved surface. The auxiliary components are subsequently eliminated from the formulation resulting in a modification of the local permeability value at boundary cells, allowing the curved 3‐D surface to be described in terms of Cartesian grid components. The proposed formulation can be applied to model skin‐effect loss in time‐harmonic driven problems. In addition, the impedance matrix can be used as a post‐processor for the eigenmode solver to calculate the wall loss.
Qualitative analysis of kinetic-based models for tumor-immune system interaction

A mathematical model, based on a mesoscopic approach, describing the competition between tumor cells and immune system in terms of kinetic integro-differential equations is presented. Four interacting components are considered, representing, respectively, tumors cells, cells of the host environment, cells of the immune system, and interleukins, which are capable to modify the tumor-immune system interaction and to contribute to destroy tumor cells. The internal state variable (activity) measures the capability of a cell of prevailing in a binary interaction. Under suitable assumptions, a closed set of autonomous ordinary differential equations is then derived by a moment procedure and two three-dimensional reduced systems are obtained in some partial quasi-steady state approximations. Their qualitative analysis is finally performed, with particular attention to equilibria and their stability, bifurcations, and their meaning. Results are obtained on asymptotically autonomous dynamical systems, and also on the occurrence of a particular backward bifurcation.
Point Cloud Model Shape Analysis

Shape analysis of point cloud surface models produces quality results and avoids the pitfalls of working with mesh models. Shape analysis is concerned with understanding the shape of models geometrically, topologically, and relationally. This includes such applications as matching a query shape to a part in a database, grouping shapes by type, segmenting a shape into sub-shapes, and finding complementary shapes. Traditionally, shape analysis methods have operated on solid and surface models of objects, especially tessellated models (i.e., triangular mesh surface models). Recent advances in 3D camera technology has driven demand for automatic shape analysis tools. Devices like the Microsoft Kinect are democratizing 3D sensing and such expansion of what was once an academic and industrial space is making it clear that there is a need for generally-applicable techniques which don’t require expert understanding to use. Unfortunately, mesh model methods require human expertise in order to ensure suitability for processing. Point cloud models, on the other hand, are the natural output of depth cameras and need no human post-processing to render them amenable to analysis. Reed M. Williams, University of Connecticut, 2017 This dissertation demonstrates that it is possible to understand shape from point cloud models in ways that don’t discard the broad mesh model-based shape analysis literature. Instead, I develop an understanding of how to apply a large class of existing mesh model methods directly on point cloud models without global surface reconstruction. Then I show that the results obtained by these point cloud model methods are of a quality on par with those obtained by equivalent mesh methods and can additionally avoid entire classes of mesh-specific problems (e.g., topological errors due to flawed mesh surface reconstruction). I also provide a general improvement to the large “spectral” class of shape signature algorithms on any model type. Point Cloud Model Shape Analysis
On the Calibration of Uncertainty Estimation in LiDAR-Based Semantic Segmentation

The confidence calibration of deep learning-based perception models plays a crucial role in their reliability. Especially in the context of autonomous driving, downstream tasks like prediction and planning depend on accurate confidence estimates. In the point-wise multiclass classification task of sematic segmentation the model naturally has to deal with heavy class imbalances. Due to their underrepresentation, the confidence calibration of classes with smaller instances is challenging but essential, especially for safety reasons. We propose a metric to measure the confidence calibration quality of a semantic segmentation model with respect to individual classes. It utilizes the computation of sparsification curves based on uncertainty estimates. The insights provided by a careful analysis using the proposed metric uncover potential improvements of any semantic segmentation model and allow for a specific improvement of the model's classification and calibration performance.
Experimental Results regarding multiple Machine Learning via Quaternions

This paper presents an experimental study on the application of quaternions in several machine learning algorithms. Quaternion is a mathematical representation of rotation in three-dimensional space, which can be used to represent complex data transformations. In this study, we explore the use of quaternions to represent and classify rotation data, using randomly generated quaternion data and corresponding labels, converting quaternions to rotation matrices, and using them as input features. Based on quaternions and multiple machine learning algorithms, it has shown higher accuracy and significantly improved performance in prediction tasks. Overall, this study provides an empirical basis for exploiting quaternions for machine learning tasks.
Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition: A Systematic Study

Recently, with the advancement of the Internet of Things (IoT), WiFi CSI-based HAR has gained increasing attention from academic and industry communities. By integrating the deep learning technology with CSI-based HAR, researchers achieve state-of-the-art performance without the need of expert knowledge. However, the scarcity of labeled CSI data remains the most prominent challenge when applying deep learning models in the context of CSI-based HAR due to the privacy and incomprehensibility of CSI-based HAR data. On the other hand, SSL has emerged as a promising approach for learning meaningful representations from data without heavy reliance on labeled examples. Therefore, considerable efforts have been made to address the challenge of insufficient data in deep learning by leveraging SSL algorithms. In this paper, we undertake a comprehensive inventory and analysis of the potential held by different categories of SSL algorithms, including those that have been previously studied and those that have not yet been explored, within the field. We provide an in-depth investigation of SSL algorithms in the context of WiFi CSI-based HAR. We evaluate four categories of SSL algorithms using three publicly available CSI HAR datasets, each encompassing different tasks and environmental settings. To ensure relevance to real-world applications, we design performance metrics that align with specific requirements. Furthermore, our experimental findings uncover several limitations and blind spots in existing work, highlighting the barriers that need to be addressed before SSL can be effectively deployed in real-world WiFi-based HAR applications. Our results also serve as a practical guideline for industry practitioners and provide valuable insights for future research endeavors in this field.
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs

GNN-to-MLP distillation aims to utilize knowledge distillation (KD) to learn computationally-efficient multi-layer perceptron (student MLP) on graph data by mimicking the output representations of teacher GNN. Existing methods mainly make the MLP to mimic the GNN predictions over a few class labels. However, the class space may not be expressive enough for covering numerous diverse local graph structures, thus limiting the performance of knowledge transfer from GNN to MLP. To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation. Specifically, we propose a variant of VQ-VAE to learn a structure-aware tokenizer on graph data that can encode each node's local substructure as a discrete code. The discrete codes constitute a codebook as a new graph representation space that is able to identify different local graph structures of nodes with the corresponding code indices. Then, based on the learned codebook, we propose a new distillation target, namely soft code assignments, to directly transfer the structural knowledge of each node from GNN to MLP. The resulting framework VQGraph achieves new state-of-the-art performance on GNN-to-MLP distillation in both transductive and inductive settings across seven graph datasets. We show that VQGraph with better performance infers faster than GNNs by 828x, and also achieves accuracy improvement over GNNs and stand-alone MLPs by 3.90% and 28.05% on average, respectively. Code: https://github.com/YangLing0818/VQGraph.