Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnostic accuracy of apex-pulse deficit for detecting atrial fibrillation

Background: Screening for asymptomatic atrial fibrillation (AF) can identify patients at risk of stroke and help initiate treatment. Apex-pulse deficit, the difference between apex beat rate and peripheral pulse rate, has been described as a clinical sign to identify AF. However, the accuracy of this measure to identify AF has not been studied before. Aims: The primary aim of this study was to determine the sensitivity and specificity of apex-pulse deficit more than 10, measured over 1 min, to identify AF using 12-lead electrocardiogram (ECG) as the gold standard. Methods: This was a prospective cross-sectional study. Subjects were those above 30 years of age with known AF (cases) or not in AF (controls). Apex-pulse deficit was measured in each of them and correlated with rhythm detected in 12-lead ECG. Results: A total of 70 patients were studied, 35 cases and 35 controls. Apex-pulse deficit was significantly larger for cases as compared to controls and was a good discriminant to identify AF. Receiver operating characteristic curve analysis showed an area under the curve of 0.86. With a cutoff of 10, sensitivity and specificity to identify AF were 62.8% and 85.7%, respectively. Using a cutoff of 5 increased the sensitivity to 80%. Counting over 30 s was significantly less accurate than counting over one full minute. Conclusion: Apex-pulse deficit is a low-cost method to identify AF and may be useful for screening. A cutoff of 5 may enhance the sensitivity of measurement as compared to the traditional cutoff of 10.
Precision medicine in the diagnosis and treatment of male lower urinary tract symptoms suggestive of benign prostatic hyperplasia

Male lower urinary tract symptoms (LUTSs) are highly prevalent in men and the incidence increases with aging. The pathophysiology of male LUTSs might be bladder outlet dysfunctions such as bladder neck (BN) dysfunction, benign prostatic obstruction, and poor relaxation of external sphincter and bladder dysfunctions such as detrusor overactivity (DO), detrusor underactivity, DO, and inadequate contractility. Male LUTSs include voiding and storage symptoms, and precision diagnosis should not be done based on the symptoms alone. Videourodynamic study provides a thorough look at the bladder and bladder outlet and can clearly demonstrate the underlying pathophysiology when the initial medication fails to relieve LUTS. Medical treatment should be given based on the underlying pathophysiology of LUTS, and surgical intervention to remove prostate should only be performed when a definite bladder outlet obstruction due to prostatic obstruction has been confirmed by invasive urodynamic study.
Bioinformatic Analysis Reveals an Immune/Inflammatory-Related Risk Signature for Oral Cavity Squamous Cell Carcinoma

High-throughput gene expression profiling has recently emerged as a promising technique that provides insight into cancer subtype classification and improved prediction of prognoses. Immune/inflammatory-related mRNAs may potentially enrich genes to allow researchers to better illustrate cancer microenvironments. Oral cavity squamous cell carcinoma (OC-SCC) exhibits high morbidity and poor prognosis compared to that of other types of head and neck squamous cell carcinoma (HNSCC), and these differences may be partially due to differences within the tumor microenvironments. Based on this, we designed an immune-related signature to improve the prognostic prediction of OC-SCC. A cohort of 314 OC-SCC samples possessing whole genome expression data that were sourced from The Cancer Genome Atlas (TCGA) database was included for discovery. The GSE41613 database was used for validation. A risk score was established using immune/inflammatory signatures acquired from the training dataset. Principal components analysis, GO analysis, and gene set enrichment analysis were used to explore the bioinformatic implications. When grouped by the dichotomized risk score based on the signature, this classifier could successfully discriminate patients with distinct prognoses within the training and validation cohorts (P < 0.05 in both cohorts) and within different clinicopathological subgroups. Similar somatic mutation patterns were observed between high and low risk score groups, and different copy number variation patterns were also identified. Further bioinformatic analyses suggested that the lower risk score group was significantly correlated with immune/inflammatory-related biological processes, while the higher risk score group was highly associated with cell cycle-related processes. The analysis indicated that the risk score was a robust predictor of patient survival, and its functional annotation was well established. Therefore, this bioinformatic-based immune-related signature suggested that the microenvironment of OC-SCC could distinguish among patients with different underlying biological processes and clinical outcomes, and the use of this signature may shed light on future OC-SCC classification and therapeutic design.
Ubiquitin carboxyl‐terminal hydrolase L1 promotes hypoxia‐inducible factor 1‐dependent tumor cell malignancy in spheroid models

Hypoxia‐inducible factor 1 (HIF‐1) is a critical heterodimeric transcription factor for tumor malignancy. Recently, ubiquitin carboxyl‐terminal hydrolase L1 (UCHL1) has been reported to function as a deubiquitinating enzyme for the stabilization of its α subunit (HIF‐1α). In the present study, we showed that UCHL1 inhibition can be an effective therapeutic strategy against HIF‐1‐dependent tumor malignancy. In 2D monolayer culture, a UCHL1 inhibitor suppressed HIF activity and decreased the transcription of HIF downstream genes by inhibiting the UCHL1‐mediated accumulation of HIF‐1α. Phenotypically, UCHL1 inhibition remarkably blocked cell migration. In 3D spheroid culture models, ectopic expression of UCHL1 significantly upregulated malignancy‐related factors such as solidity, volume, as well as viable cell number in an HIF‐1α‐dependent manner. Conversely, inhibition of the UCHL1‐HIF‐1 pathway downregulated these malignancy‐related factors and also abolished UCHL1‐mediated cell proliferation and invasiveness. Finally, inhibition of UCHL1 promoted HIF‐1α degradation and lowered the expression of HIF‐1 target genes in the 3D model, as also observed in 2D monolayer culture. Our research indicates that the UCHL1‐HIF‐1 pathway plays a crucial role in tumor malignancy, making it a promising therapeutic target for cancer chemotherapy.
Graph Theory Approach to the Vulnerability of Transportation Networks

Nowadays, transport is the basis for the functioning of national, continental, and global economies. Thus, many governments recognize it as a critical element in ensuring the daily existence of societies in their countries. Those responsible for the proper operation of the transport sector must have the right tools to model, analyze, and optimize its elements. One of the most critical problems is the need to prevent bottlenecks in transport networks. Thus, the main aim of the article was to define the parameters characterizing the transportation network vulnerability and select algorithms to support their search. The parameters proposed are based on characteristics related to domination in graph theory. The domination, edge-domination concepts, and related topics, such as bondage-connected and weighted bondage-connected numbers, were applied as the tools for searching and identifying the bottlenecks in transportation networks. Furthermore, the algorithms for finding the minimal dominating set and minimal (maximal) weighted dominating sets are proposed. This way, the exemplary academic transportation network was analyzed in two cases: stationary and dynamic. Some conclusions are presented. The main one is the fact that the methods given in this article are universal and applicable to both small and large-scale networks. Moreover, the approach can support the dynamic analysis of bottlenecks in transport networks.
A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization

Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.
Exploring Travelers' Characteristics Affecting their Intention to Shift to Bike-Sharing Systems due to a Sophisticated Mobile App

Many cities have already installed bike-sharing systems for several years now, but especially in recent years with the rise of micro-mobility, many efforts are being made worldwide to improve the operation of these systems. Technology has an essential role to play in the success of micro-mobility schemes, including bike-sharing systems. In this paper, it is examined if a state-of-the-art mobile application (app) can contribute to increasing the usage levels of such a system. It is also seeking to identify groups of travelers, who are more likely to be affected by the sophisticated app. With this aim, a questionnaire survey was designed and addressed to the users of the bike-sharing system of the city of Thessaloniki, Greece, as well as to other residents of the city. Through a descriptive analysis, the most useful services that an app can provide are identified. Most importantly, two different types of predictive models (i.e., classification tree and binary logit model) were applied in order to identify groups of users who are more likely to shift to or to use the bike-sharing system due to the sophisticated app. The results of the two predictive models confirm that people of younger ages and those who are not currently users of the system are those most likely to be attracted to the system due to such an app. Other factors, such as car usage frequency, education, and income also appeared to have slight impact on travelers’ intention to use the system more often due to the app.
Verbal Working Memory Load Dissociates Common Indices of the Numerical Distance Effect: Implications for the Study of Numerical Cognition

In four experiments, we explore the role that verbal WM plays in numerical comparison. Experiment 1 demonstrates that verbal WM load differentially impacts the two most common variants of numerical comparison tasks, evidenced by distinct modulation of the size of the numerical distance effect (NDE). Specifically, when comparing one Arabic digit to a standard, the size of the NDE increases as a function of increased verbal WM load; however, when comparing two simultaneously presented Arabic digits, the size of the NDE decreases (and here is eliminated) as a function of an increased verbal WM load. Experiment 2, using the same task structure but different stimuli (physical size judgments), provides support for the notion that this pattern of results is unique to tasks employing numerical stimuli. Experiment 3 demonstrates that the patterns observed in Experiment 1 are not an artifact of the stimulus pairs used. Experiment 4 provides evidence that the differing pattern of results observed between Experiment 1 and Experiment 2 are due to differences in stimuli (numerical vs. non-numerical) rather than to other differences between tasks. These results are discussed in terms of current theories of numerical comparison.
Scale‐adaptive Structure‐preserving Texture Filtering

This paper proposes a scale‐adaptive filtering method to improve the performance of structure‐preserving texture filtering for image smoothing. With classical texture filters, it usually is challenging to smooth texture at multiple scales while preserving salient structures in an image. We address this issue in the concept of adaptive bilateral filtering, where the scales of Gaussian range kernels are allowed to vary from pixel to pixel. Based on direction‐wise statistics, our method distinguishes texture from structure effectively, identifies appropriate scope around a pixel to be smoothed and thus infers an optimal smoothing scale for it. Filtering an image with varying‐scale kernels, the image is smoothed according to the distribution of texture adaptively. With commendable experimental results, we show that, needing less iterations, our proposed scheme boosts texture filtering performance in terms of preserving the geometric structures of multiple scales even after aggressive smoothing of the original image.
Reservoir Computing with Complex Cellular Automata

Reservoir computing (RC) is a computational framework in which a dynamical system, known as the reservoir, casts a temporal input signal to a high-dimensional space, and a trainable readout layer creates the output signal by extracting salient features from the reservoir. Several researchers have experimented with using the dynamical behavior of elementary cellular automaton (CA) rules as reservoirs. CA reservoirs have the potential to reduce the size, weight and power (SWaP) required to perform complex computation by orders of magnitude compared with traditional RC implementations. The research described in this paper expands this approach to CA rules with larger neighborhoods and/or more states, which are termed complex, as opposed to the elementary rules. Results show that some of these non-elementary cellular automaton rules outperform the best elementary rules at the standard benchmark five-bit memory task, requiring half the reservoir size to produce comparable results. This research is relevant to the design of simple, small, and low-power systems capable of performing complex computation.
Fenton-Wilkinson Order Statistics and German Tanks: A Case Study of an Orienteering Relay Race

Ordinal regression falls between discrete-valued classification and continuous-valued regression. Ordinal target variables can be associated with ranked random variables. These random variables are known as order statistics and they are closely related to ordinal regression. However, the challenge of using order statistics for ordinal regression prediction is finding a suitable parent distribution. In this work, we provide a case study of a real-world orienteering relay race by viewing it as a random process. For this process, we show that accurate order statistical ordinal regression predictions of final team rankings, or places, can be obtained by assuming a lognormal distribution of individual leg times. Moreover, we apply Fenton-Wilkinson approximations to intermediate changeover times alongside an estimator for the total number of teams as in the notorious German tank problem. The purpose of this work is, in part, to spark interest in studying the applicability of order statistics in ordinal regression problems.
Learning Explicit Smoothing Kernels for Joint Image Filtering

Smoothing noises while preserving strong edges in images is an important problem in image processing. Image smoothing filters can be either explicit (based on local weighted average) or implicit (based on global optimization). Implicit methods are usually time‐consuming and cannot be applied to joint image filtering tasks, i.e., leveraging the structural information of a guidance image to filter a target image. Previous deep learning based image smoothing filters are all implicit and unavailable for joint filtering. In this paper, we propose to learn explicit guidance feature maps as well as offset maps from the guidance image and smoothing parameter that can be utilized to smooth the input itself or to filter images in other target domains. We design a deep convolutional neural network consisting of a fully‐convolution block for guidance and offset maps extraction together with a stacked spatially varying deformable convolution block for joint image filtering. Our models can approximate several representative image smoothing filters with high accuracy comparable to state‐of‐the‐art methods, and serve as general tools for other joint image filtering tasks, such as color interpolation, depth map upsampling, saliency map upsampling, flash/non‐flash image denoising and RGB/NIR image denoising.
Reducing Interference Improves the Memorization of Multiplication Facts in Case of Hypersensitivity to Interference

Hypersensitivity to interference (HYSTI) is a situation in which a person has a severe difficulty in memorizing verbal items that are similar to each other. This may result in induced dyscalculia: HYSTI was shown to correlate with a difficulty in learning the multiplication table, presumably because the multiplication table, which is memorized verbally, has much similarity between the items ("six times seven equals forty two", "six times eight equals forty eight", etc.). Here, we show causal evidence that HYSTI disrupts the memorization of multiplication facts. We report DL, a woman with HYSTI who had extremely poor knowledge of the multiplication table. To examine whether her multiplication difficulty resulted from HYSTI, we tested whether she could learn multiplication facts when interference was reduced. In a series of merely 12 short sessions over a period of 4 weeks, DL rehearsed 16 multiplication facts – four facts per week. When the 4 facts in a given week were similar to each other, DL’s learning was poor. Conversely, when the 4 facts in a given week were dissimilar from each other, DL learned them quickly and easily. The effect of similarity was observed during the training period and persisted at least two months after the end of training. These results provide the first causal evidence that HYSTI impairs the learning or retrieval of arithmetic facts. From a pedagogical perspective, our findings may call for re-considering how multiplication facts should be taught in elementary school.
Human Tumor–Derived Matrix Improves the Predictability of Head and Neck Cancer Drug Testing

In vitro cancer drug testing carries a low predictive value. We developed the human leiomyoma–derived matrix “Myogel” to better mimic the human tumor microenvironment (TME). We hypothesized that Myogel could provide an appropriate microenvironment for cancer cells, thereby allowing more in vivo–relevant drug testing. We screened 19 anticancer compounds, targeting the epidermal growth factor receptor (EGFR), MEK, and PI3K/mTOR on 12 head and neck squamous cell carcinoma (HNSCC) cell lines cultured on plastic, mouse sarcoma–derived Matrigel (MSDM), and Myogel. We applied a high-throughput drug screening assay under five different culturing conditions: cells in two-dimensional (2D) plastic wells and on top or embedded in Matrigel or Myogel. We then compared the efficacy of the anticancer compounds to the response rates of 19 HNSCC monotherapy clinical trials. Cancer cells on top of Myogel responded less to EGFR and MEK inhibitors compared to cells cultured on plastic or Matrigel. However, we found a similar response to the PI3K/mTOR inhibitors under all culturing conditions. Cells grown on Myogel more closely resembled the response rates reported in EGFR-inhibitor monotherapy clinical trials. Our findings suggest that a human tumor matrix improves the predictability of in vitro anticancer drug testing compared to current 2D and MSDM methods.
Reduced Levels of Cerebrospinal Fluid/Plasma Aβ40 as an Early Biomarker for Cerebral Amyloid Angiopathy in RTg-DI Rats

The accumulation of fibrillar amyloid β-protein (Aβ) in blood vessels of the brain, the condition known as cerebral amyloid angiopathy (CAA), is a common small vessel disease that promotes cognitive impairment and is strongly associated with Alzheimer’s disease. Presently, the clinical diagnosis of this condition relies on neuroimaging markers largely associated with cerebral macro/microbleeds. However, these are markers of late-stage disease detected after extensive cerebral vascular amyloid accumulation has become chronic. Recently, we generated a novel transgenic rat model of CAA (rTg-DI) that recapitulates multiple aspects of human CAA disease with the progressive accumulation of cerebral vascular amyloid, largely composed of Aβ40, and the consistent emergence of subsequent microbleeds. Here, we investigated the levels of Aβ40 in the cerebrospinal fluid (CSF) and plasma of rTg-DI rats as CAA progressed from inception to late stage disease. The levels of Aβ40 in CSF and plasma precipitously dropped at the early onset of CAA accumulation at three months of age and continued to decrease with the progression of disease. Notably, the reduction in CSF/plasma Aβ40 levels preceded the emergence of cerebral microbleeds, which first occurred at about six months of age, as detected by in vivo magnetic resonance imaging and histological staining of brain tissue. These findings support the concept that reduced CSF/plasma levels of Aβ40 could serve as a biomarker for early stage CAA disease prior to the onset of cerebral microbleeds for future therapeutic intervention.
Association between Circulation Indole-3-Acetic Acid Levels and Stem Cell Factor in Maintenance Hemodialysis Patients: A Cross-Sectional Study

Protein-bound uremic toxin is a cardiovascular (CV) risk factor for patients with end-stage renal disease. Indole-3-acetic acid (IAA) was found to be associated with CV disease but the detailed pathophysiology remains unknown. Moreover, mitogen-activated protein kinase (MAPK) signaling cascades play an important role in the pathogenesis of CV disease. Thus, we explored the association between circulating IAA levels and forty MAPK cascade associated proteins in patients undergoing hemodialysis (HD). Circulating total form IAA was quantified by mass spectrometry and forty MAPK cascade associated proteins by a proximity extension assay in 331 prevalent HD patients. Accounting for multiple testing, and in multivariable-adjusted linear regression models, circulating total form IAA levels were positively associated with stem cell factor (β coefficient 0.13, 95% confidence interval 0.04 to 0.21, p = 0.004). A bioinformatics approach using the search tool for interactions of chemicals (STITCH) tool provided information that IAA may be involved in the regulation of cell proliferation, hematopoietic cells, and the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway. The knowledge gained here can be generalized, thereby impacting the non-traditional CV risk factors in patients with kidney disease. Further in vitro work is necessary to validate the translation of the mechanistic pathways.