bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Dual-Channel Single-Polarization PCF: Single-Polarization PCF-Based Plasmonic Biosensor for Multi-Analyte Detection
Authors: Azman Mohd Fahmi Bin , Mashrafi Md. , Haider Firoz , Ahmed Rajib , Aoni Rifat Ahmmed , Junayed Md , Wong Wei Ru , Mahdiraji Ghafour Amouzad , Adikan Faisal Rafiq Mahamd 
Published: January 2024

Abstract
A miniaturized biosensor capable of detecting multiple analytes is proposed with real-time measuring and high sensitivity which are crucial for future sensing devices. We introduce here a dual-channel multi-analyte single polarization (DCSP) photonic crystal fiber (PCF) sensor. The DCSP sensor was fabricated using the stack-and-draw technique to show the sensor practical feasibility. The DCSP offers maximum wavelength and amplitude sensitivities of 11,000 nm/RIU and 807 RIU− 1, respectively in both channels for the analyte refractive index (RI) of na=1.33 to 1.41. The wavelength sensitivities are improved in this case by using (DCSP) sensors, which aid in the detection of multi-analyte, high-sensitivity, and real-time measurements. The DCSP sensor can be a suitable candidate for biosensing where the sample can be in a micro dimension due to having high sensing response. Moreover, the sensor works only at single polarization with lower confinement loss and better wavelength resolution, enhancing the previous works. These results indicate the potential use of the proposed DCSP sensors to improve future sensing technology and sensor's performance in the detection of diseases diagnostic and liquid detection and so on.
Mother trees of common ash (Fraxinus excelsior) disperse different sets of mycobiome through their samaras
Authors: Long Feng , Doonan James Michael , Nielsen Lene Rostgaard , Kjaer Erik Dahl , Kosawang Chatchai 
Published: January 2024

Abstract
Objective
Endophytic mycobiomes are present in all studied plant compartments, including fruits and seeds, but a transmission of the mycobiome between generations is largely unknown. Our objectives were to examine mycobiome transfer via seed wings (samaras) of European ash (Fraxinus excelsior), and to test whether these mycobiomes differ among trees. To achieve this, we used ITS1-based amplicon sequencing and two genotypes of F. excelsior as a model to compare the mycobiome of mother trees and their samaras.
Results
We profiled the mycobiome of 57 seed stalks and seed wings (samaras) collected from two genotypes of F. excelsior using three ramets of each genotype. Alpha diversity indices (Observed OTUs and ACE) suggested a higher richness of the mycobiome associated with seed wing than seed stalk within each genotype. However, there was neither significant differences in diversity between the mycobiomes from the two tissue types nor the two genotypes. PERMANOVA analysis revealed significant differences in the mycobiome composition between seed wings, but not between seed stalks, of the two genotypes. Our results suggest that Fraxinus excelsior mother trees disperse different sets of mycobiomes with their samaras, which may be important for germination and seedling establishment – especially in the light of ash dieback.
Detection of Alzheimer and Mild Cognitive Impairment Patients by Poincare and Entropy Methods based on Electroencephalography Signals
Authors: Aslan Umut , Akşahin Mehmet Feyzi 
Published: January 2024

Abstract
Alzheimer's disease (AD) is characterized by deficits in cognition, behavior, and intellectual functioning, and Mild Cognitive Impairment (MCI) refers to individuals whose cognitive impairment deviates from what is expected for their age but does not significantly interfere with daily activities. Because there is no treatment for AD, early prediction of AD can be helpful to reducing the progression of this disease. This study examines the Electroencephalography (EEG) signal of 3 distinct groups including AD, MCI, and healthy individuals. Recognizing the non-stationary nature of EEG signals, two nonlinear approaches, Poincare and Entropy, are employed for meaningful feature extraction. To extract features from EEG signal, data should segmented into epochs and for each one, feature extraction approaches are implemented. The obtained features are given to machine learning algorithms to classify the subjects. Extensive experiments were conducted to analyze the features comprehensively The results demonstrate that, our proposed method surpasses previous studies in terms of accuracy, sensitivity, and specificity, indicating its effectiveness in classifying individuals with AD, MCI, and those without cognitive impairment.
Identifying causes and associated factors of stillbirths using autopsy of the fetus and placenta
Authors: Sade Eliel Kedar , Lantsberg Daniel , Sar-el Moriel Tagar , Gefen Sheizaf , Gafner Michal , Katorza Eldad 
Published: January 2024

Abstract

Purpose: The study aimed to evaluate the causes of death and associated factors in cases of stillbirth, using post-mortem examination and applying a rigorous, evidence-based holistic approach.
Methods: Our retrospective observational study included cases of autopsy following stillbirth that occurred at our tertiary medical center during a period of 8 years. Detailed up-to-date criteria that incorporate clinical reports, medical history, prenatal imaging, and histopathological findingswere used to evaluate the cause of death and associated factors.
Results: After applying our proposed holistic approach, 138 cases of stillbirth were classified into eight categories based on the causes of death. A definitive cause of death was observed in 100 (72%) cases, while 38 (28%) cases were considered unexplained. The leading cause of death was placental lesions (n=39 ,28%) with maternal vascular malperfusion (MVM) lesions being the most common (54%). Ascending infection was the second most common cause of fetal death (n=24, 17%) and was often seen in the setting of preterm labor and cervical insufficiency.
Conclusion: The largest category of cause of death was attributed to placental pathology. Using rigorous detailed up-to-date criteria that incorporate pathological and clinical factors may help in objectively classifying the cause of death.
Experimental investigation on microstructure fractal characteristics of low-temperature oxidation of gas-bearing coal
Authors: Li Shuaikui , Li Zhenrong , Qin Yujin , Jia Dongxu , Jia Kangteng , Tian Fuchao 
Published: January 2024

Abstract
In order to study the multi-field coupling mechanism of gas and coal spontaneous combustion, low-temperature nitrogen adsorption and SEM were applied to carry out microstructure testing experiments on oxidation of gas-bearing coal in this paper. And a pore fractal calculation model incorporating pore size distribution was established. The effects of low-temperature oxidation and gas ad/de-sorption on the change of coal pore structure were investigated. The results showed that the pore volume of coal oxidized and gas-bearing coal oxidized were decreased by 1.805 and 2.232×10− 3 cm3·g− 1 in all pore size distributions, where the micropores and macropores were converted to mesopores after oxidation of coal and macropores were formed by destruction of micropores and mesopores after oxidization of gas-bearing coal; coal oxidation and gas-bearing coal oxidation specific surface area decreased by 2.264 and 2.320 m2·g− 1, respectively, and the modifying effect of coal pore surface was stronger in the latter than in the former; the irregularity of the surface morphology increased after oxidation of the coal, and a large number of micropores were connected to form microcracks, while the surface morphology of the pores of the oxidized gas-bearing coal tended to be more regular compared with the oxidation of the non-gas-bearing coal; a pore fractal model based on the characteristics of the pore diameter distribution was established, and Dfb is 2.14–2.23, and the coupled action of gas desorption and oxidation on pore modification is stronger than the oxidation. The gas storage state of gas-bearing coal and the distribution of desorbead gas in goaf were affected by change of pore structure, so that the oxidation of gas-bearing coal can be weakened and the risk of spontaneous combustion of gas-bearing coal can be declined, which can provide a theoretical basis for the judgment of the risk of spontaneous combustion of gas-bearing coal in goaf.
PSO-UFS: A Novel Approach to Univariate Feature Selection Using Particle Swarm Optimization
Authors: Bouchene Mohammed Mehdi 
Published: January 2024

Abstract

Univariate Feature Selection (UFS) traditionally involves a labor-intensive process of trial-and-error, necessitating the selection of scoring functions and the determination of feature numbers. These choices can inadvertently affect both the performance and interpretability of the model. To address this challenge, we introduce Particle Swarm Optimization for Univariate Feature Selection (PSO-UFS), an innovative method that automates these crucial decisions. PSO-UFS leverages the power of Particle Swarm Optimization (PSO) to autonomously identify the optimal scoring function and feature subset that maximize a machine learning algorithm's performance metric. Our empirical evaluations across multiple datasets demonstrate that PSO-UFS significantly outperforms traditional UFS in various performance metrics, including accuracy, precision, recall, and F1-score. Importantly, PSO-UFS generates more interpretable feature subsets, thereby enhancing the model's comprehensibility. This advancement paves the way for broader applications in real-world scenarios where feature reduction and interpretability are paramount.
PRMT6-mediated Transcriptional Activation of YTHDF2 Promotes Glioblastoma Migration, Invasion, and EMT via the Wnt–β-Catenin Pathway
Authors: Yu Peng , Xu Tutu , Ma Wenmeng , Fang Xiang , Bao Yue , Xu Chengran , Huang Jinhai , Sun Yongqing , Li Guangyu 
Published: January 2024

Abstract

Background
Protein arginine methyltransferase 6 (PRMT6) plays a crucial role in various pathophysiological processes and diseases. Our current research finds PRMT6 upregulated in glioblastoma multiforme (GBM), enhancing tumor malignancy. Yet, the specifics of PRMT6's regulatory processes and potential molecular mechanisms in GBM remain largely unexplored.
Methods
PRMT6's expression and prognostic significance in GBM were assessed using glioma public databases, immunohistochemistry (IHC), and immunoblotting. Scratch and Transwell assays examined GBM cell migration and invasion. Immunoblotting evaluated the expression of epithelial-mesenchymal transition (EMT) and Wnt-β-catenin pathway-related proteins. Dual-luciferase reporter assays and ChIP-qPCR assessed the regulatory relationship between PRMT6 and YTHDF2. An in situ tumor model in nude mice evaluated in vivo conditions.
Results
Bioinformatics analysis indicates high expression of PRMT6 and YTHDF2 in GBM, correlating with poor prognosis. Functional experiments show PRMT6 and YTHDF2 promote GBM migration, invasion, and EMT. Mechanistic experiments reveal PRMT6 and CDK9 co-regulate YTHDF2 expression. YTHDF2 binds and promotes the degradation of negative regulators APC and GSK3β mRNA of the Wnt-β-catenin pathway, activating it and consequently enhancing GBM malignancy.
Conclusions
Our results demonstrate the PRMT6-YTHDF2-Wnt-β-Catenin axis promotes GBM migration, invasion, and EMT in vitro and in vivo, potentially serving as a therapeutic target for GBM.
Characteristics of Emergency Department Visits Among Older Adults With Hearing Difficultly During the COVID19 Pandemic in the US
Authors: yu juebo 
Published: January 2024

Abstract
Objective
Hearing difficultly(HD) may be associated with an increased risk for emergency department(ED) visits among older adults.The COVID-19 pandemic has negatively affected adult healthy status. However, less is known about the characteristics of ED visits in older adults with hearing difficulty during the COVID-19 pandemic.this study is to assess the association between self-reported HD severity and ED visits during the COVID-19 pandemic.
Methods
This population-based cross-sectional study used self-reported hearing difficulty and characteristics for respondents aged 65 years or older from 3 cycles of National Health Interview Survey from 2020 to 2022. Data were analyzed from February 23, 2023, to March 22, 2023.The main outcome was respondent-reported emergency department visits(including visited hospital emergency room and/or visited urgent care ) in the past 12 months. Generalized linear models were used to adjust for differences between the groups, and the associations between HD status and visit characteristics were expressed as rate ratios.
Results
The COVID19 Pandemic period, common reasons for HD visits included chronic pain (82.8%),fragile (77.9%),trouble falling/staying asleep (73.2%),hypertension (67.4%) ,and arthritis (60.1%)which were 1.5 times as likely compared with non-SRHD visits ( chronic pain: adjusted rate ratio [ARR], 1.64[95% CI, 1.44 to 1.93]; fragile: ARR, 1.57 [95% CI, 1.16 to 1.87]; trouble falling/staying asleep:ARR, 1.51[95% CI, 1.21 to 1.82]; hypertension: ARR, 1.01[95% CI, 0.92 to 1.23]; arthritis:ARR, 1.39[95% CI, 1.31 to 1.57]
Conclusions
In this cross-sectional study,older adults with hearing impairments were more likely to seek emergency visits for chronic pain,fragile,trouble falling/staying asleep, hypertension and arthritis than those without hearing impairments during the COVID-19 pandemic.
Joint Offloading Decision and Trajectory Optimization for Multi-UAV-Assisted Mobile Edge Computing System
Authors: Qiu Bin , Li Xueli , Li Xian 
Published: January 2024

Abstract
Multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has recently emerged as a cost-effective solution to provide computational services to Internet of Thing (IOT) terminal devices (TDs) in the limited or no available infrastructures. However, irrational trajectory of UAVs and offloading decision by TDs contribute to increased delay and energy consumption in the UAV-MEC system. In this paper, we present a two-stage optimization algorithm that jointly considers computation-offloading decision and trajectory to minimize the weighted sum of delay and energy consumption in the multi-UAV-assisted MEC system. Specifically, a Stackelberg Game-based computation offloading (SG-CO) algorithm is designed to obtain the optimal offloading decision for TDs. Furthermore, under the given TDs offloading decision, we develop the Simulated Annealing-Beetle Antennae Search (SA-BAS) algorithm to achieve the optimal trajectory for UAVs, in which SA-BAS algorithm accelerates convergence and enhances global optimization performance. Extensive simulation analysis demonstrates that the proposed scheme outperforms the benchmark schemes, showcasing its effectiveness in minimizing the weight sum of delay and energy consumption.
Collaborative Task Processing for Multiple Edge Nodes Based on Service Placement
Authors: Shi Lei , Feng Shilong , Ji Rui , Xu Juan , Ding Xu 
Published: January 2024

Abstract

With the rapid development of the Internet of Things (IoT), Mobile Edge Com-puting (MEC) technology is shifting computational and storage capabilities fromcentralized clouds to the network edge to meet the low-latency demands ofnumerous emerging applications. However, ensuring quality of service (QoS) formobile users becomes challenging in dense, decentralized wireless communica-tion environments and with limited MEC server storage capacity. Against thisbackground, this paper proposes a collaborative task processing model for mul-tiple ENs based on service placement and formulates a MINLP optimizationproblem aimed at minimizing system latency and cost. To address this problem,we introduce an online optimization algorithm (OPDA) based on the Lyapunovframework which operates in real-time without the need to predict future infor-mation. Subsequently, we decompose the long-term optimization problem into aseries of one-time slot problems and design a two-stage one-time slot optimiza-tion algorithm to obtain an approximate optimal solution. Specifically, we usethe Lagrange multiplier approach to solve the resource allocation problem fortasks and the matching theory to solve the offloading decision and service place-ment problem for tasks. Simulation results show that our algorithm can achievenear-optimal latency performance while satisfying long-term cost constraints.
DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI
Authors: Chatterjee Soumick , Sarasaen Chompunuch , Rose Georg , Nürnberger Andreas , Speck Oliver 
Published: January 2024

Abstract

Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for interventions to visualise movements or changes of the target organ. However, such MRI acquisitions with high temporal resolution suffer from limited spatial resolution - also known as the spatio-temporal trade-off of dynamic MRI. Several approaches, including deep learning based super-resolution approaches by treating each time-point as individual volumes. This research addresses this issue by creating a deep learning model which attempts to learn both spatial and temporal relationships. A modified 3D UNet model, DDoS-UNet, is proposed - which takes the low-resolution volume of the current time-point along with a prior image volume. Initially, the network is supplied with a static high-resolution planning scan as the prior image along with the low-resolution input to super-resolve the first time-point. Then it continues step-wise by using the super-resolved time-points as the prior image while super-resolving the subsequent time-points. The model performance was tested with 3D dynamic data that was undersampled to different in-plane levels and achieved an average SSIM value of 0.951±0.017 while reconstructing only 4% of the k-space - which could result in a theoretical acceleration factor of 25. The proposed approach can be used to reduce the required scan-time while achieving high spatial resolution - consequently alleviating the spatio-temporal trade-off of dynamic MRI, by incorporating prior knowledge of spatio-temporal information from the available high resolution planning scan and the existing temporal redundancy of time-series images into the network model.
Biodegradation of Kitchen Waste mediated by Fungal Enzyme cocktail
Authors: Singh Muskaan , Dutta Upma 
Published: January 2024

Abstract
The non-edible portions of food that are wasted during handling, processing, or after consumption are referred to as "kitchen waste." Due to its high moisture concentration, kitchen waste is difficult to handle using conventional methods, such as incineration. However, it is crucial to find an alternative to securely dispose of organic waste since it is a major source of pollution. Efficient decomposition of food waste may be achieved by a variety of microorganisms utilizing cellulose, proteins, lipids, and carbohydrates to fulfil their energy needs. Therefore, it is possible to accelerate the biodegradation of kitchen trash by establishing consortia of these microbes. The present study adopted a novel approach relating to hydrolysis of food waste by biological treatment using hydrolytic enzymes produced by fungal isolates Aspergillus niger MA6, Fusarium solani MA7 and Trichoderma asperellum MA8. The screening of the isolates was done by qualitative assays using specific media viz. tributyrin agar, skim milk agar, cellulose congo red agar, and starch agar as substrates. Futhermore, compatibility of selected isolates was checked followed by production of consortia by submerged fermentation. A 10mL of consortium of MA6, MA7 and MA8 was tested in 450 g of kitchen waste blended with sawdust in different trials with varied pH (5.7, 6.5 and 7.5) and temperature (28ºC, 30°C and 40ºC) to estimate the weight reduction (%) and the study showed that approximately 90.80% weight reduction could be achieved at pH 5.7 and at 30ºC using enzymes produced by these potential fungi. The physiochemical properties of the untreated and biologically treated kitchen waste were compared to ascertain the changes occurring due to degradation. The current approach is anticipated to become an economically attractive and environmental friendly solution for the disposal of substantial kitchen waste causing various environmental issues.
Sublinear Algorithms for Several Geometric Optimization (With Outliers) Problems
Authors: DING HU 
Published: January 2024

Abstract

In this paper, we study several important geometric optimization problems arising in machine learning. First, we revisit the Minimum Enclosing Ball (MEB) problem in Rd. The problem has been extensively studied before, but real-world machine learning tasks often need to handle large-scale datasets so that we cannot even afford linear time algorithms. We introduce the notion of stability for MEB, which is natural and easy to understand. Roughly speaking, an instance of MEB is stable, if the radius of the resulting ball cannot be significantly reduced by removing a small fraction of the input points. Under the stability assumption, we present two sampling algorithms for computing radius-approximate MEB with sample complexities independent of the number of input points n. In particular, the second algorithm has the sample complexity even independent of the dimensionality d. We also consider the general case without the stability assumption. We present a hybrid algorithm that can output either a radius-approximate MEB or a covering-approximate MEB. Our algorithm improves the running time and the number of passes for the previous sublinear MEB algorithms. Our method relies on two novel techniques, the Uniform-Adaptive Sampling method and Sandwich Lemma. Furthermore, we observe that these two techniques can be generalized to design sublinear time algorithms for a range of geometric optimization problems with outliers in high dimensions, including MEB with outliers, one-class and two-class linear SVMs with outliers, k-center clustering with outliers, and flat fitting with outliers.