bluuebunny/crossref_metadata_2025 huggingface dataset:
TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
Authors: Xu Liyan , Xu Kang , Qin Yinchuan , Li Yixuan , Huang Xingting , Lin Zhicheng , Ye Ning , Ji Xuechun
Published: August 2022

Anomaly detection on time series data has been successfully used in power grid operation and maintenance, flow detection, fault diagnosis, and other applications. However, anomalies in time series often lack strict definitions and labels, and existing methods often suffer from the need for rigid hypotheses, the inability to handle high-dimensional data, and highly time-consuming calculation costs. Generative Adversarial Networks (GANs) can learn the distribution pattern of normal data, detecting anomalies by comparing the reconstructed normal data with the original data. However, it is difficult for GANs to extract contextual information from time series data. In this paper, we propose a new method, Transformer-based GAN for Anomaly Detection of Time Series Data (TGAN-AD), The transformer-based generators of TGAN-AD can extract contextual features of time series data to prompt the performance. TGAN-AD’s discriminator can also assist in determining abnormal data. Anomaly scores are calculated through both the generator and the discriminator. We have conducted comprehensive experiments on three public datasets. Experimental results show that our TGAN-AD has better performance in anomaly detection than the state-of-the-art anomaly detection techniques, with the highest Recall and F1 values on all datasets. Our experiments also demonstrate the high efficiency of the model and the optimal choice of hyperparameters.
Machine Learning in Recognition of Basic Pulmonary Pathologies
Authors: Płudowski Jakub , Mulawka Jan
Published: August 2022

Nowadays, during the diagnosis process, the doctor is able to obtain access to much information describing the patient’s condition using appropriate tools. However, there are always two sides to the coin. The doctor has certain limitations regarding the amount of data they can process at once. Information technology comes to the rescue, which with the help of computers is able to quickly and effectively separate important information from redundant information and support the doctor in making a diagnosis. In this work, a decision-making system was created to diagnose common lung pathologies in digital radiography images. Here, we consider four basic pulmonary diseases: pneumothorax, pneumonia, pulmonary consolidation, and lung lesions. Our objective is to develop a new automatic detection method of lung pathologies on chest X-ray radiographs using python programming language and its libraries. The approach uses solutions in the field of artificial intelligence, such as deep learning, convolutional neural network and segmentation to make a diagnosis that aims to help the radiologist at work. In the first sections, this work describes the fundamentals of the present form of diagnosis, a proposal to improve this process, the method of operation of the algorithms used, data acquisition, segmentation and processing methods. Then, the results of the operation of four different models and their implementation in a practical window program were presented. The best model, which detects pulmonary consolidation, achieves accuracy higher than 91%, which is a satisfactory result because they are not intended to replace radiologists but to improve their work. In the future, this type of program can be further developed by adding models that recognize other conditions.
Multi-Population Differential Evolution Algorithm with Uniform Local Search
Authors: Tan Xujie , Shin Seong-Yoon , Shin Kwang-Seong , Wang Guangxing
Published: August 2022

Differential evolution (DE) is a very effective stochastic optimization algorithm based on population for solving various real-world problems. The quality of solutions to these problems is mainly determined by the combination of mutation strategies and their parameters in DE. However, in the process of solving these problems, the population diversity and local search ability will gradually deteriorate. Therefore, we propose a multi-population differential evolution (MUDE) algorithm with a uniform local search to balance exploitation and exploration. With MUDE, the population is divided into multiple subpopulations with different population sizes, which perform different mutation strategies according to the evolution ratio, i.e., DE/rand/1, DE/current-to-rand/1, and DE/current-to-pbest/1. To improve the diversity of the population, the information is migrated between subpopulations by the soft-island model. Furthermore, the local search ability is improved by way of the uniform local search. As a result, the proposed MUDE maintains exploitation and exploration capabilities throughout the process. MUDE is extensively evaluated on 25 functions of the CEC 2005 benchmark. The comparison results show that the MUDE algorithm is very competitive with other DE variants and optimization algorithms in generating efficient solutions.
Comparison of Functional Connectivity Analysis Methods in Alzheimer’s Disease
Authors: Chauhan Nishant , Choi Byung-Jae
Published: August 2022

This paper intends to present a comparative review of functional connectivity (FC) analysis methods and their computational methodologies measured through functional magnetic resonance imaging (fMRI). The fMRI technique has been established as a powerful tool for identifying and visualizing the active brain areas in response to certain stimuli and tasks. FC is a metric for the interaction between various brain regions. The synchronization of the functional activity between non-adjacent brain regions is reflected in FC, and changes in FC occur earlier than changes in the physical brain structure. The functionally active brain area can be identified by detecting signal changes caused by blood oxygen levels during the corresponding neuronal activity. The fMRI technique can assess these physiological signals, which can be utilized for further study and research. FC is therefore crucial in identifying a variety of brain disorders, including Alzheimer’s (AD). AD is a neurodegenerative disease that primarily affects the elderly, and previous studies have reported that patients with AD seem to have impaired FC between different brain areas. Henceforth, AD patients’ clinical diagnosis and prediction depend significantly on the practical and precise classification of symptoms using fMRI. We have first reviewed the existing FC analysis methods, such as model/seed-based methods and data-driven methods, and further compared them based on the reduced FC observed in AD patients compared to normal controls (NC). The goal is to provide an overview of the benefits, challenges, and limitations of FC analysis methods in the context of medical imaging for AD.
Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments
Authors: Mishra Ram Krishn , Raj Harshit , Urolagin Siddhaling , Jothi J. Angel Arul , Nawaz Nishad
Published: August 2022

The tourism industry has experienced fast and sustainable growth over the years in the economic sector. The data available online on the ever-growing tourism sector must be given importance as it provides crucial economic insights, which can be helpful for consumers and governments. Natural language processing (NLP) techniques have traditionally been used to tackle the issues of structuring of unprocessed data, and the representation of the data in a knowledge-based system. NLP is able to capture the full richness of the text by extracting the entity and relationship from the processed data, which is gathered from various social media platforms, webpages, blogs, and other online sources, while successfully taking into consideration the semantics of the text. With the purpose of detecting connections between tourism and economy, the research aims to present a visual representation of the refined data using knowledge graphs. In this research, the data has been gathered from Twitter using keyword extraction techniques with an emphasis on tourism and economy. The research uses TextBlob to convert the tweets to numeric vector representations and further uses clustering techniques to group similar entities. A cluster-wise knowledge graph has been constructed, which comprises a large number of relationships among various factors, that visualize entities and their relationships connecting tourism and economy.
Deep Learning in Design of Semi-Automated 3D Printed Chainmail with Pre-Programmed Directional Functions for Hand Exoskeleton
Authors: Rojek Izabela , Kopowski Jakub , Kotlarz Piotr , Dorożyński Janusz , Dostatni Ewa , Mikołajewski Dariusz
Published: August 2022

The aim of this paper is to refine a scientific solution to the problem of automated or semi-automated efficient and practical design of 3D printed chainmails of exoskeletons with pre-programmed properties (variable stiffness/flexibility depending on direction) reflecting individual user needs, including different types and degrees of deficit. We demonstrate this with the example of using chainmail in a hand exoskeleton, where 3D printed chainmail components can be arranged in a single-layer structure with adjustable one- or two-way bending modulus. The novelty of the proposed approach consists in combining the use of real data from research on the exoskeleton of the hand, new methods of their analysis using deep neural networks, with a clear and scalable design of a 3D printed fabric product that can be personalized (mechanical parameters such as stiffness and bend angles in various directions) to the needs and goals of therapy in a particular patient. So far, this approach is unique, having no equivalent in the literature. This paves the way for a wider implementation of adaptive chainmails based on machine learning, more efficient for more complex chainmail designs.
Icing Time Prediction Model of Pavement Based on an Improved SVR Model with Response Surface Approach
Authors: Shangguan Lingxiao , Yin Yunfei , Zhang Qingtao , Liu Qun , Xie Wei , Dong Zejiao
Published: August 2022

Pavement icing imposes a great threat to driving safety and impacts the efficiency of the road transportation system in cold regions. This has attracted research predicting pavement icing time to solve the problems brought about by icing. Different models have been proposed in the past decades to predict pavement icing, within which support vector regression (SVR) is a widely used algorithm for calibrating highly nonlinear relationships. This paper presents a hybrid improved SVR algorithm to predict the time of pavement icing with an enhancement operation by response surface method (RSM) and particle swarm optimization (PSO). RSM is used to increase the number of input data collected onsite. Based on that, the optimal SVR model is established by optimizing the kernel function parameters and penalty coefficient with the particle swarm optimization (PSO) algorithm. The hybrid improved SVR is compared with SVR, PSO-SVR, and RSM-PSO for coefficient of determination (R2), mean absolute error, mean absolute percentage error, and root mean square error to check the effectiveness of PSO and RSM in optimizing SVR. The results show that the combination of two methods in the hybrid improved algorithm has a better optimization capability with R2 of 0.9655 and 0.9318 in a train set and test set, respectively, which outperforms PSO-SVR, RSM-SVR, and SVR. In addition, the R2 of the hybrid improved SVR and PSO-SVR both reach the optimal fitness value approximately at the iteration of 20, which suggests that convergence capacity remains relatively constant with the predictive accuracy being improved.
A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
Authors: Yang Guangcan , Li Jiayang , He Yunhua , Xiao Ke , Xin Yang , Zhu Hongliang , Li Chen
Published: August 2022

Location-based services (LBSs) facilitate people’s lives; location-based service providers (LBSPs) usually outsource services to third parties to provide better services. However, the third party is a dishonest entity that might return incorrect or incomplete query results under the consideration of saving storage space and computation resources. In this paper, we propose a security-enhanced query result verification scheme (SEQRVS) for the outsourced data in a LBS. Specifically, while retaining fine-grained query result verification, we improve the construction process of verification objects to enhance the security of the outsourced data. To prevent the third party from deducing the knowledge of the outsourced data stored in itself (statistically), our scheme designs a novel storage structure to enhance the ability of privacy preservation for the outsourced data. Furthermore, based on the secure keyword search and query result verification mode proposed in our scheme, the user cannot only verify the correctness and completeness of the query result but also achieve consistency verification by the blockchain. Finally, the security analysis and extensive simulation results show the security and practicality of the proposed scheme.
Pathological Voice Detection Using Joint Subsapce Transfer Learning
Authors: Zhang Yihua , Qian Jinyang , Zhang Xiaojun , Xu Yishen , Tao Zhi
Published: August 2022

A pathological voice detection system is designed to detect pathological characteristics of vocal cords from speech. Such systems are particularly susceptible to domain mismatch where the training and testing data come from the source and target domains, respectively. Due to the difference in speech disease etiology, recording environment, and device, etc., the feature distributions of source and target domain are quite different. Meanwhile, considering the high costs of annotating labels, it is hard to acquire labeled data in the target domain. This paper attempts to formulate cross-domain pathological voice detection as an unsupervised domain adaptation problem. Joint subspace transfer learning (JSTL) aims to find a projection matrix to transform source and target domain data into a common space. The maximum mean discrepancy function is used to measure the divergence across databases. Intra-class and inter-class distance act as regularization to guarantee the maximum separability between different classes. A graph matrix is constructed to help transfer knowledge from the relevant source data to the target data. Three popular pathological voice databases were selected in this paper. For six cross-database experiments, the accuracy of the method proposed increased by up to 15%. For different voice categories, the category of structural voice showed the most significant increase, nearly 20%.
Train Occupancy Time of a Railway Section and the Combined Occupancy Method for Capacity Assessment
Authors: Chen Angyang , Chen Junhua , Zhang Xingchen
Published: August 2022

Accurate and convenient assessment of a section’s capacity is an essential problem in the operation of high-speed railway companies. The concept of occupancy time is widely used in the field, but there is still a lack of quantitative analysis to support follow-up studies and application. In this research, the calculation method for one train, two trains and multiple trains is discussed. The occupancy time of the two trains was divided into three cases according to the relationship between them, and the calculation formulas are given. For the calculation of multiple trains, the circumstances were too intricate to be classified. To solve this problem, the concept of a continuous train group was proposed. The three properties of the continuous train group were analyzed and proved by mathematical derivation to provide theoretical support to transform a multi-train problem into many two-train problems. Finally, a combined occupancy method for capacity assessment was formed and applied to the Beijing–Shanghai high-speed railway. A series of numerical cases were designed to evaluate the performance compared with the UIC406 method. The results demonstrate that the combined occupancy method can better assess the capacity and is more convenient to implement.
Varroa Control by Means of a Hyperthermic Device
Authors: Porporato Marco , Manino Aulo , Cuttini Davide , Lorenzon Simone , Ciaudano Silvia , Parodi Valerio
Published: August 2022

Hyperthermia is the use of heat to control Varroa destructor. Various apparatuses have been proposed to effectively apply heat and recently the Bee Ethic system was developed in Italy. The Bee Ethic system is a technological hive consisting of a set of heated frames and an electronic control unit. Trials were carried out in the years 2018, 2020 and 2021 to compare colony strength and mite infestation in heat-treated and untreated control bee-hives. In addition, the influence of repeated heat treatments on the development of bee colonies and mite populations was verified by means of a mathematical model. Both in apiary and in silica results show that hyperthermia can be effectively used for V. destructor control within an IPM approach, even in the presence of substantial re-infestation phenomena.
Complete Blood Cell Detection and Counting Based on Deep Neural Networks
Authors: Lee Shin-Jye , Chen Pei-Yun , Lin Jeng-Wei
Published: August 2022

Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep learning algorithms has been successfully applied in many different visual tasks. In this paper, we propose a deep neural network-based architecture to accurately detect and count blood cells on blood smear images. A public BCCD (Blood Cell Count and Detection) dataset is used for the performance evaluation of our architecture. It is not uncommon that blood smear images are in low resolution, and blood cells on them are blurry and overlapping. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. With different settings in the proposed architecture, five models are constructed herein. We compare their performance on red blood cells (RBC), white blood cells (WBC), and platelet detection and deeply investigate the factors related to their performance. The experiment results show that our models can recognize blood cells accurately when blood cells are not heavily overlapping.
Optimal Time–Jerk Trajectory Planning for Delta Parallel Robot Based on Improved Butterfly Optimization Algorithm
Authors: Wu Pu , Wang Zongyan , Jing Hongxiang , Zhao Pengfei
Published: August 2022

In this paper, a multi-objective integrated trajectory planning method based on an improved butterfly optimization algorithm (IBOA) is proposed, to improve the dynamic performance of the Delta parallel pickup robot in high-speed pick-and-place processes. The main objective of the present study is to improve dynamic positioning accuracy and running stability at high speeds and high accelerations. On the one hand, the intention is to ensure smooth motions using the trajectory planning method, and on the other hand to improve the picking efficiency. To this end, the pick-and-place trajectory of the robot is constructed by using NURBS curves in Cartesian space. Taking the time and jerk as the optimization objectives, a trajectory optimization method based on the improved butterfly optimization algorithm (IBOA) is proposed. The IBOA is based on the butterfly optimization algorithm (BOA); a circle chaotic sequence is introduced to replace the random initial population of the original BOA, and the fractional differential is used to improve the convergence speed of the BOA. Then, the problem of parallel segment deformation of the optimized trajectory is solved. Finally, a three-degrees-of-freedom Delta robot is used to evaluate the performance of the prosed algorithm. The obtained results show that, compared with other optimization algorithms, IBOA reduces the optimization time by 16.2%, and the maximum jerk is reduced by 87.6%. The results are better than the optimization results of other algorithms by 14.1% and 27.2%. The robot motion simulation results show that IBOA can effectively reduce the vibration acceleration of the end platform.
Coherent Backscattering by Large Ice Crystals of Irregular Shapes in Cirrus Clouds
Authors: Kustova Natalia , Konoshonkin Alexander , Shishko Victor , Timofeev Dmitry , Borovoi Anatoli , Wang Zhenzhu
Published: August 2022

All elements of the scattering matrix have been numerically studied for particles of irregular shapes whose size is much larger than incident wavelength. The calculations are performed in the physical optics approximation for a particle size of 20 μm at a wavelength of 0.532 μm. Here the scattered intensity reveals the backscattering coherent peak. It is shown that the polarization elements of the matrix reveal the surges within the backscattering peak. The angular width of the surges does not practically depend on particle shape, but depends on the particle size. It is shown that these surges are created by interference between the conjugate scattered waves propagating in the inverse directions. The results obtained are of interest for interpretation of lidar measurements in cirrus clouds.
Arctan-Based Family of Distributions: Properties, Survival Regression, Bayesian Analysis and Applications
Authors: Kharazmi Omid , Alizadeh Morad , Contreras-Reyes Javier E. , Haghbin Hossein
Published: August 2022

In this paper, a new class of the continuous distributions is established via compounding the arctangent function with a generalized log-logistic class of distributions. Some structural properties of the suggested model such as distribution function, hazard function, quantile function, asymptotics and a useful expansion for the new class are given in a general setting. Two special cases of this new class are considered by employing Weibull and normal distributions as the parent distribution. Further, we derive a survival regression model based on a sub-model with Weibull parent distribution and then estimate the parameters of the proposed regression model making use of Bayesian and frequentist approaches. We consider seven loss functions, namely the squared error, modified squared error, weighted squared error, K-loss, linear exponential, general entropy, and precautionary loss functions for Bayesian discussion. Bayesian numerical results include a Bayes estimator, associated posterior risk, credible and highest posterior density intervals are provided. In order to explore the consistency property of the maximum likelihood estimators, a simulation study is presented via Monte Carlo procedure. The parameters of two sub-models are estimated with maximum likelihood and the usefulness of these sub-models and a proposed survival regression model is examined by means of three real datasets.
Co-Graft of Acellular Dermal Matrix and Split Thickness Skin Graft—A New Reconstructive Surgical Method in the Treatment of Hidradenitis Suppurativa
Authors: Gierek Marcin , Łabuś Wojciech , Słaboń Anna , Ziółkowska Karolina , Ochała-Gierek Gabriela , Kitala Diana , Szyluk Karol , Niemiec Paweł
Published: August 2022

Hidradenitis suppurativa is a chronic disease that significantly reduces patients’ quality of life. Patients are chronically treated with systemic therapies, which are often ineffective. Surgical treatment for severe cases of hidradenitis suppurativa is one option for affected patients. Surgical treatment has its limitations, and wound closure may be particularly problematic. This requires the use of reconstructive techniques. The methods of choice for wound closure are split-thickness skin grafts or local flaps reconstructions. However, each method has its limitations. This is a presentation of a new reconstructive surgical method in hidradenitis suppurativa surgery: the use of a co-graft of Acellular dermal matrix and split thickness skin graft as a novel method in wound closure after wide excisions, based on two cases. The results of this method are very promising: we achieved very fast wound closure with good aesthetic results regarding scar formation. In this paper, we used several examinations: laser speckle analysis, cutometer tests, and health-related quality of life (QoL) questionnaire to check the clinical impact of this method. Our initial results are very encouraging. ADM with STSG as a co-graft could be widely used in reconstructive surgery. This is a preliminary study, which should be continued in further, extended research.
Recent Developments and Current Applications of Organic Nanomaterials in Cartilage Repair
Authors: Wei Zhanqi , Zhang Ganlin , Cao Qing , Zhao Tianhao , Bian Yixin , Zhu Wei , Weng Xisheng
Published: August 2022

Regeneration of cartilage is difficult due to the unique microstructure, unique multizone organization, and avascular nature of cartilage tissue. The development of nanomaterials and nanofabrication technologies holds great promise for the repair and regeneration of injured or degenerated cartilage tissue. Nanomaterials have structural components smaller than 100 nm in at least one dimension and exhibit unique properties due to their nanoscale structure and high specific surface area. The unique properties of nanomaterials include, but are not limited to, increased chemical reactivity, mechanical strength, degradability, and biocompatibility. As an emerging nanomaterial, organic nanocomposites can mimic natural cartilage in terms of microstructure, physicochemical, mechanical, and biological properties. The integration of organic nanomaterials is expected to develop scaffolds that better mimic the extracellular matrix (ECM) environment of cartilage to enhance scaffold-cell interactions and improve the functionality of engineered tissue constructs. Next-generation hydrogel technology and bioprinting can be used not only for healing cartilage injury areas but also for extensive osteoarthritic degenerative changes within the joint. Although more challenges need to be solved before they can be translated into full-fledged commercial products, nano-organic composites remain very promising candidates for the future development of cartilage tissue engineering.
Distance-Based Phylogenetic Placement with Statistical Support
Authors: Hasan Navid Bin , Balaban Metin , Biswas Avijit , Bayzid Md. Shamsuzzoha , Mirarab Siavash
Published: August 2022

Phylogenetic identification of unknown sequences by placing them on a tree is routinely attempted in modern ecological studies. Such placements are often obtained from incomplete and noisy data, making it essential to augment the results with some notion of uncertainty. While the standard likelihood-based methods designed for placement naturally provide such measures of uncertainty, the newer and more scalable distance-based methods lack this crucial feature. Here, we adopt several parametric and nonparametric sampling methods for measuring the support of phylogenetic placements that have been obtained with the use of distances. Comparing the alternative strategies, we conclude that nonparametric bootstrapping is more accurate than the alternatives. We go on to show how bootstrapping can be performed efficiently using a linear algebraic formulation that makes it up to 30 times faster and implement this optimized version as part of the distance-based placement software APPLES. By examining a wide range of applications, we show that the relative accuracy of maximum likelihood (ML) support values as compared to distance-based methods depends on the application and the dataset. ML is advantageous for fragmentary queries, while distance-based support values are more accurate for full-length and multi-gene datasets. With the quantification of uncertainty, our work fills a crucial gap that prevents the broader adoption of distance-based placement tools.
Behavioural Responses of Defended and Undefended Prey to Their Predator—A Case Study of Rotifera
Authors: Parry Victor , Schlägel Ulrike E. , Tiedemann Ralph , Weithoff Guntram
Published: August 2022

Predation is a strong species interaction causing severe harm or death to prey. Thus, prey species have evolved various defence strategies to minimize predation risk, which may be immediate (e.g., a change in behaviour) or transgenerational (morphological defence structures). We studied the behaviour of two strains of a rotiferan prey (Brachionus calyciflorus) that differ in their ability to develop morphological defences in response to their predator Asplanchna brightwellii. Using video analysis, we tested: (a) if two strains differ in their response to predator presence and predator cues when both are undefended; (b) whether defended individuals respond to live predators or their cues; and (c) if the morphological defence (large spines) per se has an effect on the swimming behaviour. We found a clear increase in swimming speed for both undefended strains in predator presence. However, the defended specimens responded neither to the predator presence nor to their cues, showing that they behave indifferently to their predator when they are defended. We did not detect an effect of the spines on the swimming behaviour. Our study demonstrates a complex plastic behaviour of the prey, not only in the presence of their predator, but also with respect to their defence status.
Assessing the Effectiveness of Correlative Ecological Niche Model Temporal Projection through Floristic Data
Authors: Dolci David , Peruzzi Lorenzo
Published: August 2022

Correlative ecological niche modelling (ENM) is a method widely used to study the geographic distribution of species. In recent decades, it has become a leading approach for evaluating the most likely impacts of changing climate. When used to predict future distributions, ENM applications involve transferring models calibrated with modern environmental data to future conditions, usually derived from Global Climate Models (GCMs). The number of algorithms and software packages available to estimate distributions is quite high. To experimentally assess the effectiveness of correlative ENM temporal projection, we evaluated the transferability of models produced using 12 different algorithms on historical and modern data. In particular, we compared predictions generated using historical data and projected to the modern climate (simulating a “future” condition) with predictions generated using modern distribution and climate data. The models produced with the 12 ENM algorithms were evaluated in geographic (range size and coherence of predictions) and environmental space (Schoener’s D index). None of the algorithms shows an overall superior capability to correctly predict future distributions. On the contrary, a few algorithms revealed an inadequate predictive ability. Finally, we provide hints that can be used as guideline to plan further studies based on the adopted general workflow, useful for all studies involving future projections.
Translocation of Distinct Alpha Synuclein Species from the Nucleus to Neuronal Processes during Neuronal Differentiation
Authors: Pieger Katharina , Schmitt Verena , Gauer Carina , Gießl Nadja , Prots Iryna , Winner Beate , Winkler Jürgen , Brandstätter Johann Helmut , Xiang Wei
Published: August 2022

Alpha synuclein (aSyn) and its aggregation are crucial for the neurodegeneration of Parkinson’s disease (PD). aSyn was initially described in the nucleus and presynaptic nerve terminals. However, the biology of nuclear aSyn and the link of aSyn between subcellular compartments are less understood. Current knowledge suggests the existence of various aSyn species with distinct structural and biochemical properties. Here, we identified a C-terminal-targeting aSyn antibody (Nu-aSyn-C), which has a high immunoaffinity towards aSyn in the nucleus. Comparing the Nu-aSyn-C antibody to aSyn antibodies developed against phosphorylated or aggregated forms, we observed that nuclear aSyn differs from cytosolic aSyn by an increased phosphorylation and assembly level in proliferating cells. Employing Nu-aSyn-C, we characterized aSyn distribution during neuronal differentiation in midbrain dopaminergic neurons (mDANs) derived from human-induced pluripotent stem cells (hiPSCs) and Lund human mesencephalic cells, and in primary rat hippocampal neurons. We detected a specific translocation pattern of aSyn during neuronal differentiation from the nucleus to the soma and finally to neuronal processes. Interestingly, a remarkable shift of Nu-aSyn-C-positive species towards neurites was detected in hiPSC mDANs from a PD patient carrying aSyn gene duplication. Together, our results reveal distinct nuclear and cytosolic aSyn species that redistribute during neuronal differentiation—a process that is altered in PD-derived neurons.
The Protective Effects of Neurotrophins and MicroRNA in Diabetic Retinopathy, Nephropathy and Heart Failure via Regulating Endothelial Function
Authors: Shityakov Sergey , Nagai Michiaki , Ergün Süleyman , Braunger Barbara M. , Förster Carola Y.
Published: August 2022

Diabetes mellitus is a common disease affecting more than 537 million adults worldwide. The microvascular complications that occur during the course of the disease are widespread and affect a variety of organ systems in the body. Diabetic retinopathy is one of the most common long-term complications, which include, amongst others, endothelial dysfunction, and thus, alterations in the blood-retinal barrier (BRB). This particularly restrictive physiological barrier is important for maintaining the neuroretina as a privileged site in the body by controlling the inflow and outflow of fluid, nutrients, metabolic end products, ions, and proteins. In addition, people with diabetic retinopathy (DR) have been shown to be at increased risk for systemic vascular complications, including subclinical and clinical stroke, coronary heart disease, heart failure, and nephropathy. DR is, therefore, considered an independent predictor of heart failure. In the present review, the effects of diabetes on the retina, heart, and kidneys are described. In addition, a putative common microRNA signature in diabetic retinopathy, nephropathy, and heart failure is discussed, which may be used in the future as a biomarker to better monitor disease progression. Finally, the use of miRNA, targeted neurotrophin delivery, and nanoparticles as novel therapeutic strategies is highlighted.
PAGln, an Atrial Fibrillation-Linked Gut Microbial Metabolite, Acts as a Promoter of Atrial Myocyte Injury
Authors: Fang Chen , Zuo Kun , Jiao Kaicheng , Zhu Xiaoming , Fu Yuan , Zhong Jiuchang , Xu Li , Yang Xinchun
Published: August 2022

Phenylacetylglutamine (PAGln), a gut microbiota (GM)-derived metabolite, is associated with cardiovascular disease. Studies have shown that disordered GM participated in the progression of atrial fibrillation (AF), but the relationship between PAGln and AF is unclear. This study investigated the characteristics of PAGln in AF patients and its impact on atrial myocytes. Based on our previous metagenomic data, the relative abundance of porA, a critical bacterial enzyme for PAGln synthesis, exhibited an increased tendency in AF. In an independent cohort consisting of 42 controls without AF and 92 AF patients, plasma PAGln levels were higher in AF patients than in controls (p < 0.001) by immunoassay. Notably, PAGln exerted a predictive potential of AF with an AUC of 0.774 (p < 0.001), and a predictive model constructed based on the PAGln and Taiwan AF score further improved the predictive potential. Furthermore, a positive correlation was determined between PAGln and LA diameter. Subsequently, the effect of PAGln intervention was examined on HL-1 cells in vitro, revealing that PAGln increased apoptosis, reactive oxygen species (ROS) production, CaMKII and RyR2 activation and decreased cell viability. In conclusion, increased PAGln was associated with AF, and PAGln might contribute to the AF pathogenesis by promoting oxidative stress and apoptosis in atrial myocytes.
Association of N-Acetyl Asparagine with QTc in Diabetes: A Metabolomics Study
Authors: Gravina Giacomo , Moey Melissa Y. Y. , Prifti Edi , Ichou Farid , Bourron Olivier , Balse Elise , Badillini Fabio , Funck-Brentano Christian , Salem Joe-Elie
Published: August 2022

Changes in the cardio-metabolomics profile and hormonal status have been associated with long QT syndrome, sudden cardiac death and increased mortality. The mechanisms underlying QTc duration are not fully understood. Therefore, an identification of novel markers that complement the diagnosis in these patients is needed. In the present study, we performed untargeted metabolomics on the sera of diabetic patients at a high risk of cardiovascular disease, followed up for 2.55 [2.34–2.88] years (NCT02431234), with the aim of identifying the metabolomic changes associated with QTc. We used independent weighted gene correlation network analysis (WGCNA) to explore the association between metabolites clusters and QTc at T1 (baseline) and T2 (follow up). The overlap of the highly correlated modules at T1 and T2 identified N-Acetyl asparagine as the only metabolite in common, which was involved with the urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine. This analysis was confirmed by applying mixed models, further highlighting its association with QTc. In the current study, we were able to identify a metabolite associated with QTc in diabetic patients at two chronological time points, suggesting a previously unrecognized potential role of N-Acetyl asparagine in diabetic patients suffering from long QTc.
Hypergravity Attenuates Reactivity in Primary Murine Astrocytes
Authors: Lichterfeld Yannick , Kalinski Laura , Schunk Sarah , Schmakeit Theresa , Feles Sebastian , Frett Timo , Herrmann Harald , Hemmersbach Ruth , Liemersdorf Christian
Published: August 2022

Neuronal activity is the key modulator of nearly every aspect of behavior, affecting cognition, learning, and memory as well as motion. Hence, disturbances of the transmission of synaptic signals are the main cause of many neurological disorders. Lesions to nervous tissues are associated with phenotypic changes mediated by astrocytes becoming reactive. Reactive astrocytes form the basis of astrogliosis and glial scar formation. Astrocyte reactivity is often targeted to inhibit axon dystrophy and thus promote neuronal regeneration. Here, we aim to understand the impact of gravitational loading induced by hypergravity to potentially modify key features of astrocyte reactivity. We exposed primary murine astrocytes as a model system closely resembling the in vivo reactivity phenotype on custom-built centrifuges for cultivation as well as for live-cell imaging under hypergravity conditions in a physiological range (2g and 10g). We revealed spreading rates, migration velocities, and stellation to be diminished under 2g hypergravity. In contrast, proliferation and apoptosis rates were not affected. In particular, hypergravity attenuated reactivity induction. We observed cytoskeletal remodeling of actin filaments and microtubules under hypergravity. Hence, the reorganization of these key elements of cell structure demonstrates that fundamental mechanisms on shape and mobility of astrocytes are affected due to altered gravity conditions. In future experiments, potential target molecules for pharmacological interventions that attenuate astrocytic reactivity will be investigated. The ultimate goal is to enhance neuronal regeneration for novel therapeutic approaches.
Recent Advances in Fluorescent Methods for Polyamine Detection and the Polyamine Suppressing Strategy in Tumor Treatment
Authors: Lu Bingli , Wang Lingyun , Ran Xueguang , Tang Hao , Cao Derong
Published: August 2022

The biogenic aliphatic polyamines (spermine, spermidine, and putrescine) are responsible for numerous cell functions, including cell proliferation, the stabilization of nucleic acid conformations, cell division, homeostasis, gene expression, and protein synthesis in living organisms. The change of polyamine concentrations in the urine or blood is usually related to the presence of malignant tumors and is regarded as a biomarker for the early diagnosis of cancer. Therefore, the detection of polyamine levels in physiological fluids can provide valuable information in terms of cancer diagnosis and in monitoring therapeutic effects. In this review, we summarize the recent advances in fluorescent methods for polyamine detection (supramolecular fluorescent sensing systems, fluorescent probes based on the chromophore reaction, fluorescent small molecules, and fluorescent nanoparticles). In addition, tumor polyamine-suppressing strategies (such as polyamine conjugate, polyamine analogs, combinations that target multiple components, spermine-responsive supramolecular chemotherapy, a combination of polyamine consumption and photodynamic therapy, etc.) are highlighted. We hope that this review promotes the development of more efficient polyamine detection methods and provides a comprehensive understanding of polyamine-based tumor suppressor strategies.
Rapid and Visual RPA-Cas12a Fluorescence Assay for Accurate Detection of Dermatophytes in Cats and Dogs
Authors: Wang Liyang , Fu Jinyu , Cai Guang , Cheng Xiyu , Zhang Di , Shi Shuobo , Zhang Yueping
Published: August 2022

Dermatophytosis, an infectious disease caused by several fungi, can affect the hair, nails, and/or superficial layers of the skin and is of global significance. The most common dermatophytes in cats and dogs are Microsporum canis and Trichophyton mentagrophytes. Wood’s lamp examination, microscopic identification, and fungal culture are the conventional clinical diagnostic methods, while PCR (Polymerase Chain Reaction) and qPCR (Quantitative PCR) are playing an increasingly important role in the identification of dermatophytes. However, none of these methods could be applied to point-of-care testing (POCT). The recent development of the CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) based diagnostic platform promises a rapid, accurate, and portable diagnostic tool. In this paper, we present a Cas12a-fluorescence assay to detect and differentiate the main dermatophytes in clinical samples with high specificity and sensitivity. The Cas12a-based assay was performed with a combination of recombinase polymerase amplification (RPA). The results could be directly visualized by naked eyes under blue light, and all tested samples were consistent with fungal culture and sequencing results. Compared with traditional methods, the RPA-Cas12a-fluorescence assay requires less time (about 30 min) and less complicated equipment, and the visual changes can be clearly observed with naked eyes, which is suitable for on-site clinical diagnosis.
State-of-the-Art Smart and Intelligent Nanobiosensors for SARS-CoV-2 Diagnosis
Authors: Thapa Sushma , Singh Kshitij RB , Verma Ranjana , Singh Jay , Singh Ravindra Pratap
Published: August 2022

The novel coronavirus appeared to be a milder infection initially, but the unexpected outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), commonly called COVID-19, was transmitted all over the world in late 2019 and caused a pandemic. Human health has been disastrously affected by SARS-CoV-2, which is still evolving and causing more serious concerns, leading to the innumerable loss of lives. Thus, this review provides an outline of SARS-CoV-2, of the traditional tools to diagnose SARS-CoV-2, and of the role of emerging nanomaterials with unique properties for fabricating biosensor devices to diagnose SARS-CoV-2. Smart and intelligent nanomaterial-enabled biosensors (nanobiosensors) have already proven their utility for the diagnosis of several viral infections, as various detection strategies based on nanobiosensor devices are already present, and several other methods are also being investigated by researchers for the determination of SARS-CoV-2 disease; however, considerably more is undetermined and yet to be explored. Hence, this review highlights the utility of various nanobiosensor devices for SARS-CoV-2 determination. Further, it also emphasizes the future outlook of nanobiosensing technologies for SARS-CoV-2 diagnosis.
Development and Practical Application of Glucose Biosensor Based on Dendritic Gold Nanostructures Modified by Conducting Polymers
Authors: German Natalija , Popov Anton , Ramanavicius Arunas , Ramanaviciene Almira
Published: August 2022

In this study, graphite rod (GR) electrodes were electrochemically modified by dendritic gold nanostructures (DGNs) followed by immobilization of glucose oxidase (GOx) in the presence of mediator phenazine methosulfate (PMS). Modified with polyaniline (PANI) or polypyrrole (Ppy), GOx/DGNs/GR electrodes were used in glucose biosensor design. Different electrochemical methods were applied for the registration of glucose concentration, and constant potential amperometry (CPA) was chosen as the best one. PANI and Ppy layers synthesized enzymatically on the GOx/DGNs/GR electrodes extended the linear glucose determination range, the width of which depended on the duration of PANI- and Ppy-layers formation. Enzymatically formed polypyrrole was determined as the most suitable polymer for the modification and formation of the glucose biosensor instead of polyaniline, because it was 1.35 times more sensitive and had a 2.57 times lower limit of detection (LOD). The developed glucose biosensor based on the Ppy/GOx/DGNs/GR electrode was characterized by appropriate sensitivity (59.4 μA mM−1 cm−2), low LOD (0.070 mmol L−1), wide linear glucose determination range (up to 19.9 mmol L−1), good repeatability (8.01%), and appropriate storage stability (33 days). The performance of the developed glucose biosensor was tested in biological samples and beverages.
A Gene Circuit Combining the Endogenous I-E Type CRISPR-Cas System and a Light Sensor to Produce Poly-β-Hydroxybutyric Acid Efficiently
Authors: Li Xiaomeng , Jiang Wei , Qi Qingsheng , Liang Quanfeng
Published: August 2022

‘Metabolic burden,’ which arises when introducing exogenic synthesizing pathways into a host strain, remains a challenging issue in metabolic engineering. Redirecting metabolic flux from cell growth to product synthesis at an appropriate culture timepoint is ideal for resolving this issue. In this report, we introduce optogenetics—which is capable of precise temporal and spatial control—as a genetic switch, accompanied by the endogenous type I-E CRISPRi system in Escherichia coli (E. coli) to generate a metabolic platform that redirects metabolic flux. Poly-β-hydroxybutyric acid (PHB) production was taken as an example to demonstrate the performance of this platform. A two-to-three-fold increase in PHB content was observed under green light when compared with the production of PHB under red light, confirming the regulatory activity of this platform and its potential to redirect metabolic flux to synthesize target products.