bluuebunny/crossref_metadata_2025 huggingface dataset:
Musculoskeletal Diseases as the Most Prevalent Component of Multimorbidity: A Population-Based Study
Authors: Rajovic Nina , Zagorac Slavisa , Cirkovic Andja , Matejic Bojana , Jeremic Danilo , Tasic Radica , Cumic Jelena , Masic Srdjan , Grupkovic Jovana , Mitrovic Vekoslav , Milic Natasa , Gluscevic Boris
Published: May 2024

Background/Objectives: Due to their high frequency, common risk factors, and similar pathogenic mechanisms, musculoskeletal disorders (MSDs) are more likely to occur with other chronic illnesses, making them a “component disorder“ of multimorbidity. Our objective was to assess the prevalence of multimorbidity and to identify the most common clusters of diagnosis within multimorbidity states, with the primary hypothesis that the most common clusters of multimorbidity are MSDs. Methods: The current study employed data from a population-based 2019 European Health Interview Survey (EHIS). Multimorbidity was defined as a ≥2 diagnosis from the list of 17 chronic non-communicable diseases, and to define clusters, the statistical method of hierarchical cluster analysis (HCA) was performed. Results: Out of 13,178 respondents, multimorbidity was present among 4398 (33.4%). The HCA method yielded six multimorbidity clusters representing the most common diagnoses. The primary multimorbidity cluster, which was prevalent among both genders, age groups, incomes per capita, and statistical regions, consisted of three diagnoses: (1) lower spine deformity or other chronic back problem (back pain), (2) cervical deformity or other chronic problem with the cervical spine, and (3) osteoarthritis. Conclusions: Given the influence of musculoskeletal disorders on multimorbidity, it is imperative to implement appropriate measures to assist patients in relieving the physical discomfort and pain they endure. Public health information, programs, and campaigns should be utilized to promote a healthy lifestyle. Policymakers should prioritize the prevention of MSDs by encouraging increased physical activity and a healthy diet, as well as focusing on improving functional abilities.
Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis
Authors: Kenyon Courtney R. , Pietri Milagros Pereyra , Rosenthal Julie L. , Arsanjani Reza , Ayoub Chadi
Published: May 2024

A 69-year-old female presented with symptomatic atrial fibrillation. Cardiac amyloidosis was suspected due to an artificial intelligence clinical tool applied to the presenting electrocardiogram predicting a high probability for amyloidosis, and the subsequent unexpected finding of left atrial appendage thrombus reinforced this clinical suspicion. This facilitated an early diagnosis by the biopsy of AL cardiac amyloidosis and the prompt initiation of targeted therapy. This case highlights the utilization of an AI clinical tool and its impact on clinical care, particularly for the early detection of a rare and difficult to diagnose condition where early therapy is critical.
Targeted Liposomal Drug Delivery: Overview of the Current Applications and Challenges
Authors: Gatto Matthew S. , Johnson McNeely P. , Najahi-Missaoui Wided
Published: May 2024

In drug development, it is not uncommon that an active substance exhibits efficacy in vitro but lacks the ability to specifically reach its target in vivo. As a result, targeted drug delivery has become a primary focus in the pharmaceutical sciences. Since the approval of Doxil® in 1995, liposomes have emerged as a leading nanoparticle in targeted drug delivery. Their low immunogenicity, high versatility, and well-documented efficacy have led to their clinical use against a wide variety of diseases. That being said, every disease is accompanied by a unique set of physiological conditions, and each liposomal product must be formulated with this consideration. There are a multitude of different targeting techniques for liposomes that can be employed depending on the application. Passive techniques such as PEGylation or the enhanced permeation and retention effect can improve general pharmacokinetics, while active techniques such as conjugating targeting molecules to the liposome surface may bring even further specificity. This review aims to summarize the current strategies for targeted liposomes in the treatment of diseases.
Overcoming Resistance to Temozolomide in Glioblastoma: A Scoping Review of Preclinical and Clinical Data
Authors: Smerdi Dimitra , Moutafi Myrto , Kotsantis Ioannis , Stavrinou Lampis C. , Psyrri Amanda
Published: May 2024

Glioblastoma (GB) is the most common and most aggressive primary brain tumor in adults, with an overall survival almost 14.6 months. Optimal resection followed by combined temozolomide chemotherapy and radiotherapy, also known as Stupp protocol, remains the standard of treatment; nevertheless, resistance to temozolomide, which can be obtained throughout many molecular pathways, is still an unsurpassed obstacle. Several factors influence the efficacy of temozolomide, including the involvement of other DNA repair systems, aberrant signaling pathways, autophagy, epigenetic modifications, microRNAs, and extracellular vesicle production. The blood–brain barrier, which serves as both a physical and biochemical obstacle, the tumor microenvironment’s pro-cancerogenic and immunosuppressive nature, and tumor-specific characteristics such as volume and antigen expression, are the subject of ongoing investigation. In this review, preclinical and clinical data about temozolomide resistance acquisition and possible ways to overcome chemoresistance, or to treat gliomas without restoration of chemosensitinity, are evaluated and presented. The objective is to offer a thorough examination of the clinically significant molecular mechanisms and their intricate interrelationships, with the aim of enhancing understanding to combat resistance to TMZ more effectively.
Evolving Concepts of the SCORE System: Subtracting Cholesterol from Risk Estimation: A Way for a Healthy Longevity?
Authors: Natale Francesco , Franzese Rosa , Marotta Luigi , Mollo Noemi , Solimene Achille , Luisi Ettore , Gentile Carmine , Loffredo Francesco S. , Golino Paolo , Cimmino Giovanni
Published: May 2024

The role of cholesterol, mainly low-density lipoproteins (LDL-C), as a causal risk factor for atherosclerotic cardiovascular disease (ASCVD) is now established and accepted by the international scientific community. Based on this evidence, the European and American guidelines recommend early risk stratification and “rapid” achievement of the suggested target according to the risk estimation to reduce the number of major cardiovascular events. Prolonged exposure over the years to high levels of LDL-C is one of the determining factors in the development and progression of atherosclerotic plaque, on which the action of conventional risk factors (cigarette smoking, excess weight, sedentary lifestyle, arterial hypertension, diabetes mellitus) as well as non-conventional risk factors (gut microbiota, hyperuricemia, inflammation), alone or in combination, favors the destabilization of the atherosclerotic lesion with rupture/fissuration/ulceration and consequent formation of intravascular thrombosis, which leads to the acute clinical manifestations of acute coronary syndromes. In the current clinical practice, there is a growing number of cases that, although extremely common, are emblematic of the concept of long-term exposure to the risk factor (LDL hypercholesterolemia), which, not adequately controlled and in combination with other risk factors, has favored the onset of major cardiovascular events. The triple concept of “go lower, start earlier and keep longer!” should be applied in current clinical practice at any level of prevention. In the present manuscript, we will review the current evidence and documents supporting the causal role of LDL-C in determining ASCVD and whether it is time to remove it from any score.
Enhanced Photocatalytic Activity of V2C MXene-Coupled ZnO Porous Nanosheets with Increased Surface Area and Effective Charge Transfer
Authors: Zhou Weibing , Sun Lilong , Li Kang , Tian Shouqin
Published: May 2024

Photocatalysis performs excellently when degrading organic pollutants, but the photocatalytic degradation rate is not high for most photocatalysts due to their narrow sunlight adsorption range and high recombination rate of electron hole pairs. Herein, we use V2C-MXene with a wide sunlight adsorption range to couple ZnO porous nanosheets and form ZnO/MXene hybrids using a facile electrostatic self-assembly method. The ZnO/MXene hybrids acquired demonstrated improved photochemical efficiency in breaking down methylene blue (MB) when contrasted with porous ZnO nanosheets. The degradation rate of MB reached 99.8% under UV irradiation for 120 min after the ZnO/MXene hybrid formation, while 38.6% was attained by the ZnO porous nanosheets. Moreover, photodegradation rate constants (k) were calculated as 3.05 × 10−3 and 5.42 × 10−2 min−1 for ZnO porous nanosheets and ZnO/MXene hybrids, respectively, indicating that the photodegradation performance was enhanced by 17.8 times after the modification of V2C. This was probably because the modification of V2C can increase the specific surface area to provide more sites for MB adsorption, widen the sunlight adsorption range to produce good photothermal effect, and facilitate the transfer of photogenerated carriers in ZnO to promote the reaction of more photogenerated carriers with MB. Hence, this work offers a simple approach to creating effective photocatalysts for breaking down organic contaminants.
Research on Alloy Design and Process Optimization of Al–Mg–Zn-Cu-Based Aluminum Alloy Sheets for Automobiles with Secured Formability and Bake-Hardenability
Authors: Joo GyeongSeok , Choi SeungGyu , Jung YoungKil , Kim SeHoon , Shin JaeHyuck
Published: May 2024

In this study, the compositional design of high-formability, high-bake-hardening Al–Mg–Zn-Cu-based aluminum alloys was carried out, and process conditions were established to secure mechanical properties under harsh conditions for Al–Mg–Zn-Cu-based alloys. Using JMatPro13.0 for precipitation phase simulation, the optimal pre-aging temperature and time of the design composition were selected. Through the introduction of pre-aging, it was confirmed that no over-aging phenomena occurred, even after bake-hardening, and it was confirmed that it could have mechanical properties similar to those of test specimens subjected to traditional heat treatment. Through DSC (Differential Scanning Calorimetry) and TEM (Transmission Electron Microscope) analyses, it was found that pre-aging provided sufficient thermal stability to the GP (Guinier–Preston) zone and facilitated transformation to the η’-phase. In addition, it was confirmed that, even under bake-hardening conditions, coarsening of the precipitation phase was prevented and number density was increased, thereby contributing to improvements in the mechanical properties. The designed alloy plate was evaluated as having excellent anisotropy properties through n-value and r¯-value calculations, and it was confirmed that a similar level of formability was secured through FLC (Forming Limit Curve) comparison with commercial plates.
Effect of the Addition of WO3 on the Structure and Luminescent Properties of ZnO-B2O3:Eu3+ Glass
Authors: Yordanova Aneliya , Aleksandrov Lyubomir , Milanova Margarita , Iordanova Reni , Petrova Petia , Nedyalkov Nikolay
Published: May 2024

Glasses with the compositions in mol % of 50ZnO:(50 − x)B2O3:0.5Eu2O3:xWO3, x = 0, 1, 3, 5 and 10 were obtained by applying the melt-quenching method and investigated by Raman spectroscopy, DSC analysis and photoluminescence (PL) spectroscopy. Raman spectra revealed that tungstate ions incorporate into the base zinc borate glass as tetrahedral [WO4]2− groups, and octahedral [WØ4O2]2− species with four bridging and two non-bridging oxygen atoms. There are also metaborate, [BØ2O]− and pyroborate units, [B2O5]4−, in the glass networks. The glasses are characterized by good transmission in the visible region, at about 80%. Photoluminescence (PL) spectra evidenced that WO3 is an appropriate constituent for the modification of zinc borate glass structure and for enhancing the Eu3+ luminescent intensity. The most intense luminescence peak observed, at 612 nm, suggests that the glasses are potential materials for red emission.
Maclura cochinchinensis (Lour.) Corner Heartwood Extracts Containing Resveratrol and Oxyresveratrol Inhibit Melanogenesis in B16F10 Melanoma Cells
Authors: Promden Worrawat , Chanvorachote Pithi , Viriyabancha Wittawat , Sintupachee Siriluk , De-Eknamkul Wanchai
Published: May 2024

This study aimed to isolate and purify resveratrol and oxyresveratrol from the heartwoods of Maclura cochinchinensis, and to evaluate their inhibitory effects on melanogenesis in B16F10 murine melanoma cells. A methanol maceration process yielded a crude extract comprising 24.86% of the initial mass, which was subsequently analyzed through HPTLC, HPLC, and LC-MS/MS. These analyses revealed the presence of resveratrol and oxyresveratrol at concentrations of 4.32 mg/g and 33.6 mg/g in the extract, respectively. Initial purification employing food-grade silica gel column chromatography separated the extract into two fractions: FA, exhibiting potent inhibition of both tyrosinase activity and melanogenesis, and FM, showing no such inhibitory activity. Further purification processes led to the isolation of fractions Y11 and Gn12 with enhanced concentrations of resveratrol (94.9 and 110.21 mg/g, respectively) and fractions Gn15 and Gn16 with elevated levels of oxyresveratrol (321.93 and 274.59 mg/g, respectively), all of which significantly reduced melanin synthesis. These outcomes affirm the substantial presence of resveratrol and oxyresveratrol in the heartwood of M. cochinchinensis, indicating their promising role as natural agents for skin lightening.
A NIR-Fluorochrome for Live Cell Dual Emission and Lifetime Tracking from the First Plasma Membrane Interaction to Subcellular and Extracellular Locales
Authors: Booth Eden , Garre Massimiliano , Wu Dan , Daly Harrison C. , O’Shea Donal F.
Published: May 2024

Molecular probes with the ability to differentiate between subcellular variations in acidity levels remain important for the investigation of dynamic cellular processes and functions. In this context, a series of cyclic peptide and PEG bio-conjugated dual near-infrared emissive BF2-azadipyrromethene fluorophores with maxima emissions at 720 nm (at pH > 6) and 790 nm (at pH < 5) have been developed and their aqueous solution photophysical properties determined. Their inter-converting emissions and fluorescence lifetime characteristics were exploited to track their spatial and temporal progression from first contact with the plasma membrane to subcellular locales to their release within extracellular vesicles. A pH-dependent reversible phenolate/phenol interconversion on the fluorophore controlled the dynamic changes in dual emission responses and corresponding lifetime changes. Live-cell confocal microscopy experiments in the metastatic breast cancer cell line MDA-MB-231 confirmed the usability of the dual emissive properties for imaging over prolonged periods. All three derivatives performed as probes capable of real-time continuous imaging of fundamental cellular processes such as plasma membrane interaction, tracking endocytosis, lysosomal/large acidic vesicle accumulation, and efflux within extracellular vesicles without perturbing cellular function. Furthermore, fluorescence lifetime imaging microscopy provided valuable insights regarding fluorophore progression through intracellular microenvironments over time. Overall, the unique photophysical properties of these fluorophores show excellent potential for their use as information-rich probes.
Engineering Electron Transfer Pathway of Cytochrome P450s
Authors: He Jingting , Liu Xin , Li Chun
Published: May 2024

Cytochrome P450s (P450s), a superfamily of heme-containing enzymes, existed in animals, plants, and microorganisms. P450s can catalyze various regional and stereoselective oxidation reactions, which are widely used in natural product biosynthesis, drug metabolism, and biotechnology. In a typical catalytic cycle, P450s use redox proteins or domains to mediate electron transfer from NAD(P)H to heme iron. Therefore, the main factors determining the catalytic efficiency of P450s include not only the P450s themselves but also their redox-partners and electron transfer pathways. In this review, the electron transfer pathway engineering strategies of the P450s catalytic system are reviewed from four aspects: cofactor regeneration, selection of redox-partners, P450s and redox-partner engineering, and electrochemically or photochemically driven electron transfer.
Effect of Dielectric Constant on the Zeta Potential of Spherical Electric Double Layers
Authors: Qamhieh Khawla
Published: May 2024

Zeta potential refers to the electrokinetic potential present in colloidal systems, exerting significant influence on the diverse properties of nano-drug delivery systems. The impact of the dielectric constant on the zeta potential and charge inversion of highly charged colloidal particles immersed in a variety of solvents spanning from polar, such as water, to nonpolar solvents and in the presence of multivalent salts was investigated through primitive Monte Carlo (MC) model simulations. Zeta potential, ξ, is decreased with the decreasing dielectric constant of the solvent and upon further increase in the salinity and the valency of the salt. At elevated levels of salt, the colloidal particles become overcharged in all solvents. As a result, their apparent charge becomes opposite in sign to the stoichiometric charge. This reversal of charge intensifies until reaching a saturation point with further increase in salinity.
Recent Advances in Amphipathic Peptidomimetics as Antimicrobial Agents to Combat Drug Resistance
Authors: Su Ma , Su Yongxiang
Published: May 2024

The development of antimicrobial drugs with novel structures and clear mechanisms of action that are active against drug-resistant bacteria has become an urgent need of safeguarding human health due to the rise of bacterial drug resistance. The discovery of AMPs and the development of amphipathic peptidomimetics have lay the foundation for novel antimicrobial agents to combat drug resistance due to their overall strong antimicrobial activities and unique membrane-active mechanisms. To break the limitation of AMPs, researchers have invested in great endeavors through various approaches in the past years. This review summarized the recent advances including the development of antibacterial small molecule peptidomimetics and peptide-mimic cationic oligomers/polymers, as well as mechanism-of-action studies. As this exciting interdisciplinary field is continuously expanding and growing, we hope this review will benefit researchers in the rational design of novel antimicrobial peptidomimetics in the future.
Validation of an Analytical Method of 3′,4′,5-Trihydroxy-3-Methoxy-6,7-Methylenedioxyflavone 4′-Glucuronide for Standardization of Spinacia oleracea
Authors: Son Yun Gon , Jung Juyoung , Lee Dong Kun , Park Sang Won , Kim Jeong Yoon , Kim Hyun Joon
Published: May 2024

Spinach (Spinacia oleracea) is one of the most famous vegetables worldwide, rich in essential metabolites for various health benefits. It is a valuable plant source that has the potential to be a nutraceutical. This study aimed to evaluate the single characteristic marker compound to establish the validation of HPLC-DAD methods applied to the development of a nutraceutical using spinach samples. Six metabolites (1–6) were identified from the spinach samples such as freeze-dried spinach (FDS) and spinach extract concentrate (SEC) by LC-Q-TOF/MS analysis. Among the six metabolites, 3′,4′,5-trihydroxy-3-methoxy-6,7-methylenedioxyflavone 4′-glucuronide (TMG) was selected as a marker compound due to its highest abundance and high selectivity. The specificity, accuracy, linearity, precision, repeatability, limit of detection (LOD), and limit of quantification (LOQ) of TMG in the spinach samples (FDS and SEC) were validated according to AOAC international guideline. The specificity was confirmed by monitoring the well separation of the marker compound from other compounds of spinach samples in the base peak intensity (BPI) and ultraviolet (UV) chromatogram. The calibration curve of TMG (15.625~500 μg/mL) had reasonable linearity (R2 = 0.999) considered with LOD and LOQ values, respectively. Recovery rate of TMG was 93–101% for FDS and 90–95% for SEC. The precision was less than 3 and 6% in the intraday and interday. As a result, the HPLC-DAD validation method of TMG in the spinach samples (FDS and SEC) was first established with AOAC and KFDA regulations for approving functional ingredients in functional foods.
A Bulk Oxygen Vacancy Dominating WO3−x Photocatalyst for Carbamazepine Degradation
Authors: Guo Weiqing , Wei Qianhui , Li Gangrong , Wei Feng , Hu Zhuofeng
Published: May 2024

Creating oxygen vacancy in tungsten trioxide (WO3) has been considered as an effective strategy to improve the photocatalytic performance for degrading organic pollutants. In this study, oxygen vacancies were introduced into WO3 by thermal treatment under Ar atmosphere and their proportion was changed by setting different treatment times. WO3−x samples show better photoelectric properties and photocatalytic degradation performance for carbamazepine (CBZ) than an oxygen-vacancy-free sample, and WO3−x with the optimal proportion of oxygen vacancies is obtained by thermal treatment for 3 h in 550 °C. Furthermore, it discovers that the surface oxygen vacancies on WO3−x would be recovered when it is exposed to air, resulting in a bulk oxygen vacancy dominating WO3−x (bulk-WO3−x). The bulk-WO3−x exhibited much higher degradation efficiency for CBZ than WO3−x with both surface and bulk oxygen vacancies. The mechanism study shows bulk-WO3−x mainly degrades the CBZ by producing OH radicals and superoxide radicals, while oxygen-vacancy-free sample mainly oxidizes the CBZ by the photoexcited hole, which requires the CBZ to be adsorbed on the surface for degradation. The radical generated by bulk-WO3−x exhibits stronger oxidizing capacity by migrating to the solution for CBZ degradation. In summary, the influence of oxygen vacancy on photocatalytic degradation performance depends on both the proportion and location distribution and could lie in the optimization of the photodegradation mechanism. The results of this study could potentially broaden our understanding of the role of oxygen vacancies and provide optimal directions and methods for oxygen vacancy regulation for photocatalysts.
Effects of Delaying the Storage of ‘Hass’ Avocados under a Controlled Atmosphere on Skin Color, Bioactive Compounds and Antioxidant Capacity
Authors: Olivares Daniela , Ulloa Pablo A. , Vergara Cristina , Hernández Ignacia , García-Rojas Miguel Ángel , Campos-Vargas Reinaldo , Pedreschi Romina , Defilippi Bruno G.
Published: May 2024

During ripening, ‘Hass’ avocado skin changes from green to purple/black. Low-temperature storage with a controlled atmosphere (CA) is the most widely used method for avocado storage; however, few studies have simulated this technology and considered the days of regular air (RA) storage prior to CA storage. Herein, the effect of delaying the storage of ‘Hass’ avocado (>30% dry matter) in a CA was examined. Long-term storage conditions (5 °C for 50 days) corresponded to (i) regular air storage (RA), (ii) CA (4 kPa O2 and 6 kPa CO2) and (iii) 10 days in RA + 40 days in a CA and (iv) 20 days in RA + 30 days in a CA. Evaluations were performed during storage and at the ready-to-eat (RTE) stage. Skin color remained unchanged during storage, but at the RTE stage, more color development was observed for fruits stored under CA conditions, as these fruits were purple/black (>50%). At the RTE stage, the anthocyanin content increased, and compared to fruit under RA, fruit under a CA contained a five-fold greater content. A 20-day delay between harvest and CA storage increased the fruit softening rate and skin color development after cold storage, reducing the effectiveness of CA as a postharvest technology for extending storage life.
Robust Point Cloud Registration for Aircraft Engine Pipeline Systems
Authors: Liu Yusong , Wang Zhihai , Huang Jichuan , Zhang Liyan
Published: May 2024

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line–Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine’s piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data.
The Force–Frequency Characteristics of Quartz Wafers under a Cantilever Beam Structure
Authors: Shen Junquan , Chen Chin-Yin , Wu Cheng-Yi , Cheng Jiguang , Chao Min-Chiang , Zhou Qiang , Lu Congda
Published: May 2024

This study investigated the force–frequency characteristics of quartz wafers inside a cantilever beam frame. Firstly, the force–frequency coefficient formula of quartz wafers with fixed ends under axial force was analyzed. Firstly, the formula for the force–frequency coefficient of quartz wafers with fixed ends under axial force was analyzed. A force–frequency coefficient formula suitable for cantilever beam structures was derived by considering the changes in surface stress and stiffness of quartz wafers with fixed ends and one end under force on the other. Subsequently, the formula’s accuracy was verified by experiments, and the accuracy was more than 92%. In addition, strain simulation analysis was performed on three different shapes of quartz wafers, and experimental verification was carried out on two of them. The results revealed that trapezoidal quartz wafers and cantilever beam structures exhibited superior stress distribution to rectangular chips. Furthermore, by positioning electrodes at various locations on the surface of the quartz chip, it was observed that, as the electrodes moved closer to the fixed end, the force–frequency coefficient of the rectangular quartz chip increased, along with an increase in chip strain under the cantilever structure. In summary, this study provides a new approach for designing cantilever quartz resonator sensors in the future.
Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy
Authors: Xing Yunzhao , Song Bolin , Crouthamel Michelle , Chen Xiaotian , Goss Sandra , Wang Li , Shen Jie
Published: May 2024

Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD.
RB-GAT: A Text Classification Model Based on RoBERTa-BiGRU with Graph ATtention Network
Authors: Lv Shaoqing , Dong Jungang , Wang Chichi , Wang Xuanhong , Bao Zhiqiang
Published: May 2024

With the development of deep learning, several graph neural network (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges when capturing contextual text information within a document sequence. To address this, a novel text classification model, RB-GAT, is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on five benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, 80.32%, 90.84%, and 95.67% on Ohsumed, R8, MR, 20NG and R52, respectively, which is superior to the existing nine text classification approaches.
Position-Aware Indoor Human Activity Recognition Using Multisensors Embedded in Smartphones
Authors: Wang Xiaoqing , Wang Yue , Wu Jiaxuan
Published: May 2024

Composite indoor human activity recognition is very important in elderly health monitoring and is more difficult than identifying individual human movements. This article proposes a sensor-based human indoor activity recognition method that integrates indoor positioning. Convolutional neural networks are used to extract spatial information contained in geomagnetic sensors and ambient light sensors, while transform encoders are used to extract temporal motion features collected by gyroscopes and accelerometers. We established an indoor activity recognition model with a multimodal feature fusion structure. In order to explore the possibility of using only smartphones to complete the above tasks, we collected and established a multisensor indoor activity dataset. Extensive experiments verified the effectiveness of the proposed method. Compared with algorithms that do not consider the location information, our method has a 13.65% improvement in recognition accuracy.
A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
Authors: Luan Yuchen , Sun Fukun , Zhou Jiaen
Published: May 2024

Satellite fog computing (SFC) achieves computation, caching, and other functionalities through collaboration among fog nodes. Satellites can provide real-time and reliable satellite-to-ground fusion services by pre-caching content that users may request in advance. However, due to the high-speed mobility of satellites, the complexity of user-access conditions poses a new challenge in selecting optimal caching locations and improving caching efficiency. Motivated by this, in this paper, we propose a real-time caching scheme based on a Double Deep Q-Network (Double DQN). The overarching objective is to enhance the cache hit rate. The simulation results demonstrate that the algorithm proposed in this paper improves the data hit rate by approximately 13% compared to methods without reinforcement learning assistance.
Early-Stage Fault Diagnosis of Motor Bearing Based on Kurtosis Weighting and Fusion of Current–Vibration Signals
Authors: Zhang Bingye , Li Haibo , Kong Weiyi , Fu Minjie , Ma Jien
Published: May 2024

To solve the problem of a low signal-to-noise ratio of fault signals and the difficulty in effectively and accurately identifying the fault state in the early stage of motor bearing fault occurrence, this paper proposes an early fault diagnosis method for bearings based on the Differential Local Mean Decomposition (DLMD) and fusion of current–vibration signals. This method uses DLMD to decompose the current signal and vibration signal, respectively, and weights the decomposed product function (PF) according to the kurtosis value to reconstruct the signal, and then fuses the reconstructed signals to obtain the current–vibration fusion signal after normalization, and then analyzes the fusion signal spectrally through the Hilbert envelope spectrum. Finally, the fusion signal is analyzed by the Hilbert envelope spectrum, and a clear fault characteristic frequency is obtained. The experimental results demonstrate that compared to traditional bearing fault diagnosis methods, the proposed method significantly improves the signal-to-noise ratio of fault signals, effectively enhances the sensitivity of early-stage fault detection in motor bearings, and improves the accuracy of fault identification.
Train Station Pedestrian Monitoring Pilot Study Using an Artificial Intelligence Approach
Authors: Garcia Gonzalo , Velastin Sergio A. , Lastra Nicolas , Ramirez Heilym , Seriani Sebastian , Farias Gonzalo
Published: May 2024

Pedestrian monitoring in crowded areas like train stations has an important impact in the overall operation and management of those public spaces. An organized distribution of the different elements located inside a station will contribute not only to the safety of all passengers but will also allow for a more efficient process of the regular activities including entering/leaving the station, boarding/alighting from trains, and waiting. This improved distribution only comes by obtaining sufficiently accurate information on passengers’ positions, and their derivatives like speeds, densities, traffic flow. The work described here addresses this need by using an artificial intelligence approach based on computational vision and convolutional neural networks. From the available videos taken regularly at subways stations, two methods are tested. One is based on tracking each person’s bounding box from which filtered 3D kinematics are derived, including position, velocity and density. Another infers the pose and activity that a person has by analyzing its main body key points. Measurements of these quantities would enable a sensible and efficient design of inner spaces in places like railway and subway stations.
Reliability of Obstacle-Crossing Parameters during Overground Walking in Young Adults
Authors: Chardon Matthias , Barbieri Fabio Augusto , Petit Pascal , Vuillerme Nicolas
Published: May 2024

We aimed to evaluate the intra-session relative and absolute reliability of obstacle-crossing parameters during overground walking in young adults, and to determine the number of trials required to ensure reliable assessment. We analysed data from 43 young male adults who were instructed to walk at a self-selected velocity on a pathway and to step over an obstacle (height = 15 cm; width = 80 cm, thickness = 2 cm) three times. Spatial–temporal gait parameters of the approaching and crossing phases (i.e., before and after the obstacle) and obstacle clearance parameters (i.e., vertical and horizontal distance between the foot and the obstacle during crossing) were computed using a three-dimensional motion analysis system. Intraclass correlation coefficients were used to compute the relative reliability, while standard error of measurement and minimal detectable change were used to assess the absolute reliability for all possible combinations between trials. Results showed that most spatial–temporal gait parameters and obstacle clearance parameters are reliable using the average of three trials. However, the mean of the second and third trials ensures the best relative and absolute reliabilities of most obstacle-crossing parameters. Further works are needed to generalize these results in more realistic conditions and in other populations.
A Comparative Review on Enhancing Visual Simultaneous Localization and Mapping with Deep Semantic Segmentation
Authors: Liu Xiwen , He Yong , Li Jue , Yan Rui , Li Xiaoyu , Huang Hui
Published: May 2024

Visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded performance in dynamic environments featuring mobile objects. Recent research in deep learning led to notable progress in semantic segmentation, which involves assigning semantic labels to image pixels. The integration of semantic segmentation into VSLAM can effectively differentiate between static and dynamic elements in intricate scenes. This paper provided a comprehensive comparative review on leveraging semantic segmentation to improve major components of VSLAM, including visual odometry, loop closure detection, and environmental mapping. Key principles and methods for both traditional VSLAM and deep semantic segmentation were introduced. This paper presented an overview and comparative analysis of the technical implementations of semantic integration across various modules of the VSLAM pipeline. Furthermore, it examined the features and potential use cases associated with the fusion of VSLAM and semantics. It was found that the existing VSLAM model continued to face challenges related to computational complexity. Promising future research directions were identified, including efficient model design, multimodal fusion, online adaptation, dynamic scene reconstruction, and end-to-end joint optimization. This review shed light on the emerging paradigm of semantic VSLAM and how deep learning-enabled semantic reasoning could unlock new capabilities for autonomous intelligent systems to operate reliably in the real world.
Stretchable Sensor Materials Applicable to Radiofrequency Coil Design in Magnetic Resonance Imaging: A Review
Authors: Vazquez Rigoberto , Motovilova Elizaveta , Winkler Simone Angela
Published: May 2024

Wearable sensors are rapidly gaining influence in the diagnostics, monitoring, and treatment of disease, thereby improving patient outcomes. In this review, we aim to explore how these advances can be applied to magnetic resonance imaging (MRI). We begin by (i) introducing limitations in current flexible/stretchable RF coils and then move to the broader field of flexible sensor technology to identify translatable technologies. To this goal, we discuss (ii) emerging materials currently used for sensor substrates, (iii) stretchable conductive materials, (iv) pairing and matching of conductors with substrates, and (v) implementation of lumped elements such as capacitors. Applicable (vi) fabrication methods are presented, and the review concludes with a brief commentary on (vii) the implementation of the discussed sensor technologies in MRI coil applications. The main takeaway of our research is that a large body of work has led to exciting new sensor innovations allowing for stretchable wearables, but further exploration of materials and manufacturing techniques remains necessary, especially when applied to MRI diagnostics.
Cleaned Meta Pseudo Labels-Based Pet Behavior Recognition Using Time-Series Sensor Data
Authors: Go Junhyeok , Moon Nammee
Published: May 2024

With the increasing number of households owning pets, the importance of sensor data for recognizing pet behavior has grown significantly. However, challenges arise due to the costs and reliability issues associated with data collection. This paper proposes a method for classifying pet behavior using cleaned meta pseudo labels to overcome these issues. The data for this study were collected using wearable devices equipped with accelerometers, gyroscopes, and magnetometers, and pet behaviors were classified into five categories. Utilizing this data, we analyzed the impact of the quantity of labeled data on accuracy and further enhanced the learning process by integrating an additional Distance Loss. This method effectively improves the learning process by removing noise from unlabeled data. Experimental results demonstrated that while the conventional supervised learning method achieved an accuracy of 82.9%, the existing meta pseudo labels method showed an accuracy of 86.2%, and the cleaned meta pseudo labels method proposed in this study surpassed these with an accuracy of 88.3%. These results hold significant implications for the development of pet monitoring systems, and the approach of this paper provides an effective solution for recognizing and classifying pet behavior in environments with insufficient labels.
An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression
Authors: Rong Zhiming , Li Yuxiong , Wu Li , Zhang Chong , Li Jialin
Published: May 2024

Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability.
COVID-19 Vaccine Motivation and Hesitancy among a Sample of African American, Afro-Caribbean, and African Respondents in the United States
Authors: Elbers Shauna K. , Vaughan Denise A. , Tiruneh Yordanos M.
Published: May 2024

Understanding the motivations and decisions behind COVID-19 vaccine acceptance is crucial for designing targeted public health interventions to address vaccine hesitancy. We conducted a qualitative analysis to explore COVID-19 vaccine acceptance among diverse ethnic subgroups of Black Americans in the United States. This study investigates the 2021–2022 responses of 79 African American, Afro-Caribbean, and African respondents over the age of 18 in Washington State and Texas. Respondents were asked “Do you plan to get the COVID-19 vaccination?” Qualitative responses were analyzed by content category and ethnic subgroup. Of the 79 responses, 60 expressed favorable perceptions, 16 expressed unfavorable perceptions, and 3 expressed neutral perceptions. Dominant categories among participants in favor of the vaccine included personal health (26), concern for health of family/or community members (13), and desire to protect others (11). Among the 42 vaccinated African American respondents, the primary motivation was personal health (20). The 12 unvaccinated African American respondents cited fear of side effects as their dominate motivation. Caribbean respondents cited family or elders as motivation for their decision. African respondents were nearly unanimous in taking the vaccine (13/16), citing trust in health care, protecting friends and family, and personal health as reasons. Community and personal relationships were critical decision-making factors in accepting the COVID-19 vaccine, with African Americans having the strongest hesitancy.
Fasting before Intra-Gastric Dosing with Antigen Improves Intestinal Humoral Responses in Syrian Hamsters
Authors: Wood Liam , Hughes Jaime , Trussell Mark , Bishop Anne L. , Griffin Ruth
Published: May 2024

Oral vaccines, unlike injected, induce intestinal secretory immunoglobulin A (sIgA) mimicking our natural defense against gut pathogens. We previously observed sIgA responses after administering the Clostridioides difficile colonisation factor CD0873 orally in enteric capsules to hamsters. Enteric-coated capsules are designed to resist dissolution in the stomach and disintegrate only at the higher pH of the small intestine. However, the variable responses between animals led us to speculate suboptimal transit of antigens to the small intestine. The rate of gastric emptying is a controlling factor in the passage of oral drugs for subsequent availability in the small intestine for absorption. Whilst in humans, food delays gastric emptying, in rats, capsules can empty quicker from fed stomachs than from fasted. To test in hamsters if fasting improves the delivery of antigens to the small intestine, as inferred from the immune responses generated, 24 animals were dosed intragastrically with enteric capsules containing recombinant CD0873. Twelve hamsters were fasted for 12 h prior to each dose and the other 12 fed. Significantly higher sIgA titres, with significantly greater bacterial-adherence-blocking activity, were detected in small intestinal lavages in the fasted group. We conclude that fasting in hamsters improves intestinal delivery leading to more robust responses.
Étude critique de la charte d’inféodation plaçant le duc Henri III de Limbourg dans la vassalité du duc Henri Ier de Brabant en 1191
Authors: Richartz Michel
Published: May 2024

Très souvent utilisé dans l’historiographie brabançonne et limbour-geoise, l’acte de 1191 concernant l’inféodation des biens d’Henri III, duc de Limbourg à Henri I er , duc de Brabant présente nombre de contradictions et d’in-certitudes qui nous portent à croire qu’on est en présence d’un document faux fabriqué à la fin du XIII e siècle.
Energy extractivism and water dispossession by contamination: an essay of fracking in mexican legal order
Authors: Jacobo-Marín Daniel
Published: May 2024

The article analyzes the extraction of hydrocarbons through fracking in Mexico. Energy extractivism is discussed as the hegemonic model of production and consumption of fossil fuels and, in particular, as a neoliberal prototype of energy management. The water-energy relationship is examined from a critical perspective, highlighting the granting of water rights for the stimulation of unconventional reservoirs. It is documented that the explicit regulation of fracking intervened in the allocation of hydraulic uses and the order of priority of uses. In any case, these issues sharpen competition for water on lands of collective social property. It is concluded that fracking constitutes a mechanism of water dispossession by contamination.