Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of health-related quality of life across the spectrum of connective tissue diseases using latent profile analysis: results from the LEAP cohort

Abstract Objectives Poor health-related quality of life (HRQoL) is well recognized in patients with CTD. We hypothesized that subgroups of patients across the spectrum of CTD experience different HRQoL patterns and aimed to determine patient-level characteristics associated with these different subgroups. Methods Using the eight continuous domains of the Medical Outcomes Study 36-item Short Form (SF-36) questionnaire we performed data-driven clustering to derive latent profiles (LPs) of patients with distinct HRQoL patterns. Multivariable ordinal logistic regression was used to determine patient-level characteristics associated with each HRQoL subgroup identified. Results A total of 309 CTD patients completed the SF-36 questionnaire. The most impaired SF-36 domains in each disease group were vitality, general health and bodily pain. The physical component of the SF-36 was consistently more impaired compared with the mental component, with similar scores across disease groups. Three LPs were identified with poor [n = 89 (29%)], average [n = 190 (61.4%)] and excellent [n = 30 (9.7%)] HRQoL. LPs were not associated with diagnostic grouping or autoantibody profiles. Black background [odds ratio (OR) 0.22 (95% CI 0.08, 0.63)], Indo-Asian background [OR 0.39 (95% CI 0.19, 0.78)], concomitant fibromyalgia [OR 0.40 (95% CI 0.20, 0.78)], sicca symptoms [OR 0.56 (95% CI 0.32, 0.98)] and multimorbidity [Charlson Comorbidity Index; OR 0.81 (95% CI 0.67, 0.97)] were associated with the ‘poor’ HRQoL LP. Conclusion Distinct HRQoL subgroups exist that are not primarily driven by a specific diagnosis or autoantibody profiles. We identified a number of key demographic and clinical factors associated with poor HRQoL. These factors need to be addressed across the whole CTD spectrum as part of a holistic management approach aimed at improving overall patient outcomes.
Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation

Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals’ feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.
Reduction of the source size of gas-puff z-pinch plasmas using an inductive pulsed power system

A small scale inductive pulsed power (IPP) generator with copper wire fuses as an opening switch has been applied to an argon gas-puff z-pinch plasma which can be used as a point source for x-ray lithography. The IPP generator with fuses supplies a driving current of 60 kA in 400 ns to the z-pinch plasma. Without fuses, the generator serves as a conventional fast bank (FB) system which supplies z-pinch current of 60 kA in 4.5 μs. Characteristics of the z-pinch implosions in IPP and FB systems are investigated and compared. The result obtained shows that the intensity of soft x rays emitted from hot spots in both systems is decreased gradually with longer delay time, which is the time difference between energizing a gas-puff actuator and a primary current trigger. Dual axis pinhole imagery shows that the hot spot size is 0.8 mm in diameter in both systems. However, the average radial displacement of hot spots is 0.9 mm in the FB system and it is reduced to 0.4 mm in the IPP system. Optical framing camera i.
Macrophage-Colony-Stimulating Factor Receptor Enhances Prostate Cancer Cell Growth and Aggressiveness In Vitro and In Vivo and Increases Osteopontin Expression

Prostate cancer is a major public health concern and one of the most prevalent forms of cancer worldwide. The definition of altered signaling pathways implicated in this complex disease is thus essential. In this context, abnormal expression of the receptor of Macrophage Colony-Stimulating Factor-1 (M-CSF or CSF-1) has been described in prostate cancer cells. Yet, outcomes of this expression remain unknown. Using mouse and human prostate cancer cell lines, this study has investigated the functionality of the wild-type CSF-1 receptor in prostate tumor cells and identified molecular mechanisms underlying its ligand-induced activation. Here, we showed that upon CSF-1 binding, the receptor autophosphorylates and activates multiple signaling pathways in prostate tumor cells. Biological experiments demonstrated that the CSF-1R/CSF-1 axis conferred significant advantages in cell growth and cell invasion in vitro. Mouse xenograft experiments showed that CSF-1R expression promoted the aggressiveness of prostate tumor cells. In particular, we demonstrated that the ligand-activated CSF-1R increased the expression of spp1 transcript encoding for osteopontin, a key player in cancer development and metastasis. Therefore, this study highlights that the CSF-1 receptor is fully functional in a prostate cancer cell and may be a potential therapeutic target for the treatment of prostate cancer.
Iron-Dome: Securing IoT Networked Systems at Runtime by Network and Device Characteristics to Confine Malware Epidemics

The rapid growth of IoT networks presents an enlarged "attack space" for the adversary and poses significant security risks on a large scale. A single device in a network that is compromised under the influence of a malware attack, has the potential to spread malware across the network. This leads to a plethora of attacks, including DoS and ceasing the network functionality. Given the scale of IoT networks and the connectivity among the devices, mere detection and quarantining of malware in IoT networks does not limit the propagation of malware in IoT networks. This work proposes an integrated defense, termed as "IRON-DOME", comprising of (1) an on-device application analyzer: Image-based Malware detector that utilizes grayscale images of executables, (2) Device dynamic behavior analysis: Reliable extraction and dynamic analysis of malware Hardware Performance Counter (HPC) values; and (3) Device communication trait analyzer: Uses network packet data analysis to confine and propagate malware in the IoT network. The proposed solution yields: (1) a runtime malware detection accuracy of 93% within 19 ns, (2) is resource and power efficient; it consumes 30% fewer resources and 40% less power than state-of-the art defense techniques.
Stereolithography 3D Printed Resin Pressure Enclosures Applied in the Marine Environment

To date, pressure enclosures for the marine environment lack modular options that are low-cost and accessible. This paper explores stereolithography (SLA) 3D printing using resin for the creation of customizable underwater pressure enclosures. The material properties of the SLA prints are critical to accurately evaluating the materials’ performance in an underwater environment. For this project, SLA resin formulas made by Siraya Tech, selected due to their cost and availability, were assessed. The ASTM D695-15 standard was followed to find their compressive properties. From there, a case study was conducted using SLA printed pressure enclosures to examine their performance. The enclosures’ 3D models in Autodesk Fusion 360 were analyzed using Finite Element Analysis (FEA) and were validated through physical testing of the enclosure within a pressure tank. Qualitative testing of the enclosures was also conducted by observing the physical changes of the sample after extended sun and water exposure.
On the Validity of Edgeworth and Saddlepoint Approximations

In the classical theory of Edgeworth expansion for the sample mean, it is typically assumed that the sampling distribution is either lattice valued or is sufficiently smooth to satisfy Cramer? s regularity condition. However, applications of Edgeworth expansions to problems involving the bootstrap require regularity conditions which fail into the poorly understood grey area between these two cases. In the past, a limited amount of theory has been developed to take care of this problem, but it is restricted to the special case of the "classical" bootstrap, where the resample size is equal to the sample size and the resampling probabilities are all identical, In the present paper we extend this theory by developing Edgeworth expansions for general discrete distributions where the number of atoms is either fixed or increasing with sample size at an arbitrary rate. Implications of this result for the Lugannani-Rice tail area approximation are discussed, and it is established that this approximation is a large deviation formula in the present context. Our results shed light on much older work about the validity of Edgeworth expansions in the absence of Cramer? s condition, despite being motivated by very recent developments in bootstrap theory, for example, to contexts where the sample size and resample size are different or where the resampling probabilities are unequal.
Effect of Segment Types on Characterization of Soft Sensing Textile Actuators for Soft Wearable Robots

The use of textiles in soft robotics is gaining popularity because of the advantages textiles offer over other materials in terms of weight, conformability, and ease of manufacture. The purpose of this research is to examine the stitching process used to construct fabric-based pneumatic bending actuators as well as the effect of segment types on the actuators’ properties when used in soft robotic glove applications. To impart bending motion to actuators, two techniques have been used: asymmetry between weave and weft knit fabric layers and mechanical anisotropy between these two textiles. The impacts of various segment types on the actuators’ grip force and bending angle were investigated further. According to experiments, segmenting the actuator with a sewing technique increases the bending angle. It was discovered that actuators with high anisotropy differences in their fabric combinations have high gripping forces. Textile-based capacitive strain sensors are also added to selected segmented actuator types, which possess desirable properties such as increased grip force, increased bending angle, and reduced radial expansion. The sensors were used to demonstrate the controllability of a soft robotic glove using a closed-loop system. Finally, we demonstrated that actuators integrated into a soft wearable glove are capable of grasping a variety of items and performing various grasp types.
Optimal Routh approximations for continuous-time systems

A new combination of Routh stability criterion and integral squared error (ISE) criterion approach is proposed for the linear model reduction of high-order dynamic systems. The method consists of a two-step computational scheme. In the first step, the Routh approximation method is used to reduce the order of the denominator polynomial of the system transfer function. In the second step, the Routh table is used to derive a set of optimal coefficients of the numerator polynomial of reduced model such that the ISE between the unit step responses of the original and simplified system is reduced to a minimum. The advantages of the proposed method are that it does not actually evaluate the system time response in the step of minimizing the ISE, and the reduced model is stable if the original system is stable.
In Silico Study of the Interactions of Anle138b Isomer, an Inhibitor of Amyloid Aggregation, with Partner Proteins

Herein, we aimed to highlight current “gaps” in the understanding of the potential interactions between the Anle138b isomer ligand, a promising agent for clinical research, and the intrinsically disordered alpha-synuclein protein. The presence of extensive unstructured areas in alpha-synuclein determines its existence in the cell of partner proteins, including the cyclophilin A chaperone, which prevents the aggregation of alpha-synuclein molecules that are destructive to cell life. Using flexible and cascaded molecular docking techniques, we aimed to expand our understanding of the molecular architecture of the protein complex between alpha-synuclein, cyclophilin A and the Anle138b isomer ligand. We demonstrated the possibility of intricate complex formation under cellular conditions and revealed that the main interactions that stabilize the complex are hydrophobic and involve hydrogen.
Kinematic Analysis of Multi-Section Opposite Magnetic Catheter Robots With Solution Multiplicity

Magnetic catheter robots have great potential for application in minimally invasive surgical interventions that require submillimeter soft catheters to be inserted into the body. Compared to other medical continuum robots, the kinematic characteristics of the magnetic continuum robot are required to be improved, including operating dexterity. The opposite magnetic continuum robot (OMCR), which has oppositely-magnetized magnets on the robot, is capable of deforming in large and high-order curvatures and shows improved dexterity. However, unstable kinematic behaviors emerge owing to solution multiplicity and affect the actual operation. For example, snapping, which indicates the sudden jump from one state to another, induces danger for medical operations. In addition, the motion sequence of the external mobile magnet affects the final state of OMCR. Therefore, in this study, we first analyze the reason for these emerging behaviors by understanding the solution multiplicity of an OMCR. We then modified the OMCR kinematic model with instability analysis and additional constraints, to predict the critical point for snapping and avoid other unexpected behavior for path planning. Herein, our study aims to analyze the stability of the OMCR with relatively unstable configurations in their actuation space to ensure stable and dexterous motions for medical applications. Note to Practitioners—The motivation of this work is to analyze, predict, and avoid the unexpected behaviors of the medical magnetic catheter robots, and ensure stable and dexterous operation. Submillimeter magnetic catheter robots have received considerable attention in the past ten years owing to their small size and dexterous motion. They have great potential for minimally invasive surgical interventions that require the navigation of “tighter corners” inside the human body. However, there may exist multiple local solutions under an external field, especially when the robot deforms in high-order curvature for dexterous operation. This situation induces unexpected behaviors and danger for medical use, such as a sudden jump from one state to another. Herein, we analyze the solution evolution as the external field varies and predict the unexpected behaviors using a modified kinematic model with instability analysis. Preliminary experiments demonstrated the effectiveness of the modified model, which can predict unexpected behaviors, and evaluate and enhance the stability during path planning. This method can be implemented for the dexterous and stable operation of magnetic catheter robots, which is significant for medical applications.
The Kepler asteroseismic investigation

The NASA Kepler mission for studies of extra-solar planets, with expected launch early in 2009, will provide a large set of excellent data for asteroseismology. Here we provide a brief presentation of the mission and discuss some aspects of the expected results of the asteroseismic investigations and the organization of the effort in the Kepler Asteroseismic Science Consortium (KASC).
ECHARPE mechanical design

ECHARPE spectrograph - Espectrógrafo ECHelle de Alta Resolução para o telescópio Perkin-Elmer - is being designed at LNA - Laboratório Nacional de Astrofísica, Brazil - to be mounted on 1.60 meter telescope at Pico dos Dias Observatory, Brazil. It will offer a spectral resolution of R ~ 50000, in the interval 390-900 nm and in a single exposition. It will be a fiber fed, bench spectrograph with two channels: blue and red, fed by two optical fibers (object, sky or calibration) with aperture of 1.5 or 2.0 arcseconds. This paper reports on technical characteristics of the spectrograph mechanical design and presents a new developed mounting system for echelle grating and collimator and relay mirrors, which allows linear and rotational adjustments in all degrees of freedom without using springs.
Fatigue in Patients with Idiopathic/Isolated REM Sleep Behavior Disorder

Introduction: Fatigue is one of the most common and disabling symptoms of Parkinson’s Disease (PD). The occurrence and clinical features of fatigue in patients with prodromal PD remain largely elusive. This study aimed to investigate the prevalence and clinical characteristics of fatigue in patients with idiopathic/isolated REM sleep behavior disorders (iRBD). Methods: A total of 97 polysomnography-confirmed iRBD patients were enrolled in this study. A comprehensive neurological assessment (including motor and non-motor assessment) was performed. Fatigue was assessed using the Fatigue Severity Scale (FSS). Motor and non-motor characteristics were compared between iRBD patients with and without fatigue. Logistic regression was used to identify the factors associated with fatigue. Results: The prevalence of fatigue was 35.05%. Compared to the non-fatigue patients, patients with fatigue had higher non-motor symptom scale (NMSS) score (p = 0.009), higher Hamilton Depression Rating Scale (HAMD) score (p = 0.002), and a higher prevalence of orthostatic hypotension (p = 0.021). Multivariate regression analysis showed that depression (OR 4.17, 95% CI 1.13–15.49, p = 0.033) and orthostatic hypotension (OR 2.80, 95% CI 1.09–7.18, p = 0.032) were significantly associated with fatigue in iRBD patients. Additionally, both NMSS (rs = 0.310, p = 0.002) and HAMD (rs = 0.385, p < 0.001) scores were mildly correlated with fatigue severity. Conclusion: Our study showed that fatigue is common in patients with iRBD. In addition, depression and orthostatic hypotension were independently associated with fatigue in iRBD patients.
Local Field Potential-Guided Contact Selection Using Chronically Implanted Sensing Devices for Deep Brain Stimulation in Parkinson’s Disease

Intra- and perioperatively recorded local field potential (LFP) activity of the nucleus subthalamicus (STN) has been suggested to guide contact selection in patients undergoing deep brain stimulation (DBS) for Parkinson’s disease (PD). Despite the invention of sensing capacities in chronically implanted devices, a comprehensible algorithm that enables contact selection using such recordings is still lacking. We evaluated a fully automated algorithm that uses the weighted average of bipolar recordings to determine effective monopolar contacts based on elevated activity in the beta band. LFPs from 14 hemispheres in seven PD patients with newly implanted directional DBS leads of the STN were recorded. First, the algorithm determined the stimulation level with the highest beta activity. Based on the prior determined level, the directional contact with the highest beta activity was chosen in the second step. The mean clinical efficacy of the contacts chosen using the algorithm did not statistically differ from the mean clinical efficacy of standard contact selection as performed in clinical routine. All recording sites were projected into MNI standard space to investigate the feasibility of the algorithm with respect to the anatomical boundaries of the STN. We conclude that the proposed algorithm is a first step towards LFP-based contact selection in STN-DBS for PD using chronically implanted devices.
A 2.47 μJ/sample QR-Decomposition-based Extreme Learning Machine Engine Supporting Online Class Incremental Learning for ECG-based User Identification

In the Industrial Internet of Things (IIoT) applications, smart devices generate massive data during monitoring machine status. Meanwhile, authorized access to these IIoT data and machines becomes crucial where biometrics comes in handy [1]. Among all bio-signals, Electrocardiography (ECG) signal has two key advantages: 1) ECG signals indicates electrical activity of the heart, providing intrinsic proof of liveness. 2) ECG signals cannot be easily eavesdropped without physical contact [2]. These anti-spoofing features make ECG an attractive biometric modality in the IIoT scenario. One challenge in such user identification system is how to identify a “newly enrolled user” efficiently without retraining the entire system as shown in Fig. 1. That is, the classifier of the system should “incrementally” learn the new authorized users without “forgetting” the previously learned/trained ones, called “online class incremental learning (O-CIL). ” It involves both “online learning (OL)” and “class incremental learning (CIL). ” However, most back-propagation-based neural networks (BP-NN) suffers from the notorious “catastrophic forgetting,” which results in severe accuracy degradation due to the recency learning bias towards new classes [3]. To deal with the issue of catastrophic forgetting, the scalable extreme learning machine (S-ELM) [4] provides an analytical solution by online updating the classifier with recursive least squares (RLS). However, the matrix-inversion operations of conventional RLS are very computation-intensive, which is not suitable for direct VLSI implementation. Moreover, most existing ELM chips only support inferencing [5], lacking the O-CIL capability.
A Fast Mesh-Growing Algorithm for Manifold Surface Reconstruction

AbstractIn a previous paper these authors presented a new mesh-growing approach based on the Gabriel 2 – Simplex (G2S) criterion. If compared with the Cocone family and the Ball Pivoting methods, G2S demonstrated to be competitive in terms of tessellation rate, quality of the generated triangles and defectiveness produced when the surface to be reconstructed was locally flat. Nonetheless, its major limitation was that, in the presence of a mesh which was locally non – flat or which was not sufficiently sampled, the method was less robust and holes and non – manifold vertices were generated. In order to overcome these limitations, in this paper, the performance of the G2S mesh- growing method is fully improved in terms of robustness. The performances of the new version of the G2S approach (in the following Robust G2S) has been compared with that of the old one, and that of the Cocone family and the Ball Pivoting methods in the tessellation of some benchmark point clouds and artificially noised test cases.