bluuebunny/crossref_metadata_2025 huggingface dataset:
Cold Plasma Technology in Food Packaging
Authors: Perera Kalpani Y. , Prendeville Jack , Jaiswal Amit K. , Jaiswal Swarna
Published: December 2022

Cold plasma (CP) is an effective strategy to alter the limitations of biopolymer materials for food packaging applications. Biopolymers such as polysaccharides and proteins are known to be sustainable materials with excellent film-forming properties. Bio-based films can be used as an alternative to traditional plastic packaging. There are limitations to biopolymer packaging materials such as hydrophobicity, poor barrier, and thermos-mechanical properties. For this reason, biopolymers must be modified to create a packaging material with the desired applicability. CP is an effective method to enhance the functionality and interfacial features of biopolymers. It etches the film surface allowing for better adhesion between various polymer layers while also improving ink printability. CP facilitates adhesion between two or more hydrophobic materials, resulting in significantly better water vapour permeability (WVP) properties. The sputtering of ionic species by CP results in cross-linkage reactions which improve the mechanical properties of films (tensile strength (TS) and elongation at break (EAB)). Cross-linkage reactions are reported to be responsible for the improved thermal stability of CP-treated biopolymers. CP treatment is known to decrease oxygen permeability (OP) in protein-based biopolymers. CP can also enable the blending of polymers with specific antimicrobial substances to develop active packaging materials. In this review article, we have presented an overview of the recent advancements of CP in the food packaging application. Furthermore, the influence of CP on the properties of packaging materials, and recent advancements in the modification of polymeric food packaging materials have been discussed.
Asymmetrical Gaussian Potential Effects on Strongly Coupled Magnetopolaron Properties in Triangular Confinement Potential Quantum Wells
Authors: Ge Jun , Han Shuang , Miao Xiujuan , Sun Yong , Xiao Jinglin
Published: December 2022

In this research, the existence of an asymmetrical Gaussian confinement potential (AGCP) along the quantum well (QW) growth direction and of a parabolic potential perpendicular to the polar coordinate direction were considered. The magnetic field and temperature properties of the longitudinal optical (LO)-phonon mean number, ground-state energy (GSE), ground-state binding energy (GSBE) and vibrational frequency (VF) of strongly coupled magnetopolarons in triangular confinement potential QWs (TCPQWs) were investigated according to the quantum statistical theory as well as the linear combination operator and unitary transformation methods. We obtained analytical expressions for the GSE, GSBE, VF and LO-phonon mean number as functions of the applied magnetic field, temperature, AGCP barrier height, AGCP range, polar coordinate system’s polar angle and polar coordinate system’s confinement strength. It was demonstrated by the calculated numerical results that the GSE, GSBE, VF and LO-phonon mean number varied with the related physical quantities. The obtained theoretical results are expected to provide a reference for future research on polarons.
Facile Preparation, Microstructure and Dielectric Properties of La(Cr0.2Mn0.2Fe0.2Co0.2Ni0.2)O3 Perovskite High-Entropy Ceramics
Authors: Tian Zhifeng , Zhang Ying , Zhang Junzhan , Shi Peng
Published: December 2022

Preparation and properties of La(Cr0.2Mn0.2Fe0.2Co0.2Ni0.2)O3 high-entropy ceramics are investigated. La(Cr0.2Mn0.2Fe0.2Co0.2Ni0.2)O3 high-entropy ceramics are prepared by a traditional two-step solid-state reaction method in air. La(Cr0.2Mn0.2Fe0.2Co0.2Ni0.2)O3 single-phase powders are synthesized by calcining the mixed oxides at 1000 °C for 20 h. The high-entropy ceramics are sintered at 1350–1650 °C in a muffle furnace for 4 h by using the above powders. The phase compositions of the high-entropy ceramics at different temperatures are characterized by X-ray diffraction (XRD) with Cu Kα radiation. A field-emission scanning electron microscope with energy-dispersive spectroscopy (EDS) is used to observe the microstructures and analyze the elemental distributions. The hardness and dielectric properties are measured and discussed.
Smart Design of Cz-Ge Crystal Growth Furnace and Process
Authors: Dropka Natasha , Tang Xia , Chappa Gagan Kumar , Holena Martin
Published: December 2022

The aim of this study was to evaluate the potential of the machine learning technique of decision trees to understand the relationships among furnace design, process parameters, crystal quality, and yield in the case of the Czochralski growth of germanium. The ultimate goal was to provide the range of optimal values of 13 input parameters and the ranking of their importance in relation to their impact on three output parameters relevant to process economy and crystal quality. Training data were provided by CFD modelling. The variety of data was ensured by the Design of Experiments method. The results showed that the process parameters, particularly the pulling rate, had a substantially greater impact on the crystal quality and yield than the design parameters of the furnace hot zone. Of the latter, only the crucible size, the axial position of the side heater, and the material properties of the radiation shield were relevant.
Laser Irradiation Behavior of Carbon Fiber Epoxy Resin Composites with Laminar Structure
Authors: Yang Yu , Tian Xinchun , Ma Zhuang , Liu Hanyang , Gao Hong , Tian Weizhi , Liu Xiaoyu , Zhou Zhigang , Rogachev Alexandr. A. , Gao Lihong
Published: December 2022

Laser attracts more attention and it could cause material failure by its high energy. Carbon fiber resin composites are widely used in aerospace vehicles experiencing dramatic damage to their surface if exposed to high-energy laser irradiation. The available studies on irradiation behavior are mainly focused on pulsed lasers and bulk composites, and investigations of thin laminar structures under continuous-wave laser irradiation have rarely been reported. In this study, the damage behavior of laminar carbon fiber epoxy resin composites (CFE composites) was studied. Using a threshold model of resin pyrolysis, CFE composite is observed to be damaged at 0.18 s when irradiated at 100 W/cm2, and if the laser power density is increased to 200 W/cm2 for 2 s, no resin remains on the fiber surface, which is now completely exposed. With an increase in power density and irradiation time, the ablation rate always shows an upward trend: the ablation region expands and the separation of layers in the interior appears, which can reach 0.01156 g/s when irradiated at 100 W/cm2 for 5 s. The damage mechanism of CFE composite was also revealed by the temperature evolution data, thermogravimetric analysis, and composition change.
Triggers and Coping Strategies for Fear of Cancer Recurrence in Cancer Survivors: A Qualitative Study
Authors: Zhang Xu , Sun Di , Wang Zhiwen , Qin Nan
Published: December 2022

Background: Fear of cancer recurrence (FCR) has been demonstrated to be one of the most frequently reported unmet psychological needs among cancer survivors. The aim of this study was to explore and describe the potential triggers and coping strategies for FCR in Chinese cancer survivors. Methods: The study process was conducted using an interpretive phenomenological research method, and Chinese cancer survivors were interviewed face-to-face in a semi-structured interview, using purposive sampling combined with a maximum variance sampling strategy, and the interviews were transcribed, organized, and analyzed by applying Giorgi analysis with the help of NVivo11 software. Results: A total of 10 participants, 4 males and 6 females, were interviewed. Three themes emerged in terms of potential triggers for FCR: (1) intrusive thoughts; (2) disease symptoms; and (3) awaiting medical examination. Two themes regarding positive coping and avoidance coping emerged with regard to coping strategies adopted by cancer survivors when experiencing FCR. Under these 2 themes were 5 sub-themes: (1) seeking medical support; (2) self-health management; (3) spiritual coping; (4) unaccompanied toleration; and (5) attention shifting. Conclusion: FCR as the most common psychological problem for cancer survivors, and it should be given more attention. Early identification and precise intervention for potential triggers of FCR may prevent the emergence and development of FCR. The guidance toward and cultivation of positive coping strategies when cancer survivors experience FCR could be an important direction in future nursing education.
New Morphological and Molecular Data Reveal an Underestimation of Species Diversity of Mites of the Genus Geckobia (Acariformes: Pterygosomatidae) in India
Authors: Fajfer Monika , Karanth Praveen
Published: December 2022

Mites of the genus Geckobia (Acariformes: Pterygosomatidae) are permanent and highly specialised ectoparasites of geckos (Gekkota). We conducted a local study on Geckobia mites associated with the geckos of the family Gekkonidae found mainly in the territory of the Indian Institute of Science’s campus (Bangalore, India). In total, we examined 208 lizards belonging to two genera: Hemidactylus and Cnemaspis. We assessed the prevalence of the mites and identified the preferred site for their infestation. We extended the standard morphological identification of the mite species by using DNA barcode markers, partial sequences of the mitochondrial cytochrome c oxidase subunit I (COI) gene and nuclear ribosomal gene sequences: 18S rRNA and hypervariable region D2 of nuclear 28S rRNA. We checked the suitability of COI and nuclear (D2 of 28S rRNA) markers for species delimitations and identification purposes of the genus. The distance- and phylogeny-based approaches were applied: (i) to test the presence of a barcoding gap, we used the automated barcoding gap discovery tool (ABGD) and investigated intra- and interspecific genetic distances, and (ii) to reconstruct evolutionary relationships within the species, we performed maximum likelihood (ML) and Bayesian inference with Markov-Chain Monte Carlo (BI) analyses. As a result, we described five new species—Geckobia gigantea sp. n., G. treutleri sp. n., G. unica sp. n. and G. brevicephala sp. n.—from four Hemidactylus species: H. giganteus, H. treutleri, H. parvimaculatus and H. frenatus, respectively, and G. mysoriensis sp. n. from Cnemaspis mysoriensis. Additionally, we found three already described species: Geckobia indica Hirst, 1917 on H. treutleri (new host), Geckobia bataviensis Vitzhum, 1926 on H. parvimaculatus (new host) and H. frenatus (new locality) and Geckobia phillipinensis Lawrence, 1953 on H. frenatus (new locality). The diagnoses of G. indica and G. phillipinensis were improved and supplemented by descriptions of the males and juveniles. Both topologies of the BI and ML phylogenetic trees, as well as genetic distances, supported the species boundaries in the mite population shown by the morphological data. Hemidactylus frenatus was the most infected gecko species (61% prevalence), with the highest number of mite species (three spp.). The scale-mite richness was higher than expected; therefore, further research is required to evaluate the true diversity of Geckobia mites.
Genetic Diversity of Two Globally Invasive Snails in Asia and Americas in Relation with Agricultural Habitats and Climate Factors
Authors: Zhao Benliang , Luo Mingzhu , Zhang Jiaen , Liu Yiliang , Deng Zhixin , Gong Xin
Published: December 2022

The successful establishment of invasive populations is closely linked to environmental factors. It is unclear whether coexisting species in the native area follow the same genetic pattern in the invaded continents under the local climate factors. Two coexisting morphologically similar snails (Pomacea canaliculata and P. maculata), native to tropical and sub-tropical South America, have become invasive species for agriculture production and wetland conservation across five continents over 40 years. We analyzed the correlation between the genetic diversity of the two snails and the climate factors or habitat changes. Based on the 962 sequences from the invaded continents and South America, the nucleotide diversity in the agricultural habitat was low for P. canaliculata, whereas it was high for P. maculata, compared with that in the non-agricultural habitat. The two snails showed a divided population structure among the five continents. The P. canaliculata population in the invaded continents has remained stable, whereas the P. maculata population expanded suddenly. Seven main haplotype networks and two ancestral haplotypes (Pc3, Pm1) were found in the P. canaliculata and P. maculata populations. The haplotypes of the two snails were related to local climate factors. The overall fixation index of P. canaliculata and P. maculata was 0.2657 and 0.3097 between the invaded continents and South America. The population expansion of the two snails fitted the isolation-by-distance model. We discovered nine new sequences from the sampling locations. Overall, the genetic diversity and genetic differentiation of the two invasive snails were closely related to geographic separation, agricultural habitat, and climate factors.
Development of an In-House EphA2 ELISA for Human Serum and Measurement of Circulating Levels of EphA2 in Hypertensive Patients with Renal Dysfunction
Authors: Murakoshi Maki , Iwasawa Takumi , Koshida Takeo , Suzuki Yusuke , Gohda Tomohito , Kato Kazunori
Published: December 2022

Identifying novel biomarkers of kidney function in patients with chronic kidney disease (CKD) has strong clinical value as current measures have limitations. This study aims to develop and validate a sensitive and specific ephrin type-A receptor 2 (EphA2) enzyme-linked immunosorbent assay (ELISA) for human serum, and determine whether its results correlate with traditional renal measures in patients with hypertension. The novel ELISA of the current study was validated and used to measure circulating EphA2 levels in 80 hypertensive patients with and without kidney function decline (eGFR less than 60 mL/min/1.73 m2). Validation of the EphA2 ELISA showed good recovery (87%) and linearity (103%) and no cross-reactivity with other Eph receptors. Patients with kidney function decline had lower diastolic blood pressure, and higher UPCR and EphA2 than those without kidney function decline. The association of age and eGFR with EphA2 was maintained in the stepwise multiple regression analysis. In a multivariate logistic model, EphA2 was associated with a lower eGFR (<60 mL/min/1.73 m2) after adjustment for age, sex, and UPCR. High circulating EphA2 levels have potential application as a clinical biomarker for the presence of CKD in patients with hypertension.
Molecular Method Is Essential to Identify Asymptomatic Malaria Reservoirs: A Successful Experience in the Malaria Elimination Program in Iran
Authors: Shamseddin Jebreil , Ghanbarnejad Amin , Zakeri Abdoljabbar , Abedi Farshid , Khojasteh Shaghayegh , Turki Habibollah
Published: December 2022

Background: The accurate diagnosis of malaria cases, especially asymptotic and low-parasitemia patients, using robust molecular methods (nested-PCR) have been emphasized. The goal of this study was to detect active cases of malaria in areas with a history of local malaria transmission focusing on the use of molecular tools to ensure that the malaria elimination program has been implemented successfully. Methods: In this cross-sectional study, 816 blood samples were taken from immigrants and local residents of malaria-endemic areas in Hormozgan province, Iran. In order to identify asymptomatic malaria parasite reservoirs, the samples were examined using microscopic, RDT, and nested-PCR techniques. Results: About twelve positive asymptomatic malaria cases were identified when the molecular method (nested-PCR) was used. The positivity rates among immigrants and local residents were 2.07% and 0.93%, respectively. No positive cases were detected using microscopic and RDT methods. Conclusions: The finding of the research emphasize that in addition to microscopy and RDTs methods, sensitive molecular tools as a standard and essential strategy are needed in the diagnosis and detection of asymptomatic parasite reservoir.
Clinical, Sonographic, and Hysteroscopic Features of Endometrial Carcinoma Diagnosed after Hysterectomy in Patients with a Preoperative Diagnosis of Atypical Hyperplasia: A Single-Center Retrospective Study
Authors: Pace Luca , Actis Silvia , Mancarella Matteo , Novara Lorenzo , Mariani Luca , Perrini Gaetano , Govone Francesca , Testi Alessandra , Campisi Paola , Ferrero Annamaria , Biglia Nicoletta
Published: December 2022

Background: atypical endometrial hyperplasia (AEH) is a precancerous condition implying a high risk of concurrent endometrial cancer (EC), which might be occult and only diagnosed at postoperative histopathological examination after hysterectomy. Our study aimed to investigate potential differences in preoperative clinical, sonographic, and hysteroscopic characteristics in patients with AEH and postoperative diagnosis of EC. Methods: a retrospective single-center study was carried out on a case series of 80 women with AEH undergoing diagnostic workup, including ultrasonography and hysteroscopy, with subsequent hysterectomy. Women with AEH confirmed at the histopathological examination were compared with patients with a postoperative diagnosis of EC. Results: in our population, EC was diagnosed in 53 women, whereas the preoperative diagnosis of AEH was confirmed in 27 cases. At ultrasonography, women with occult EC showed greater endometrial thickness (20.3 mm vs. 10.3 mm, p 0.001) and size of the endocavitary lesion (maximum diameter 25.2 mm vs. 10.6 mm, p 0.001), and a higher prevalence of irregular endometrial-myometrial junction (40.5% vs. 6.7%, p 0.022) and endouterine vascularization at color Doppler (64.2% vs. 34.6%, p 0.017). At hysteroscopy, patients with occult EC showed a higher prevalence of necrosis (44.2% vs. 4.2%, p 0.001) and atypical vessels (70.6% vs. 33.3%, p 0.003), whereas true AEH mainly presented as a protruding intracavitary lesion (77.8% vs. 50.9%, p 0.029). In EC, subjective assessment by the operator was more frequently indicative of cancer (80.0% vs. 12.5%). No difference was found for clinical variables. Conclusions: occult EC in AEH may exhibit some differences in ultrasonographic and hysteroscopic patterns of presentation compared with real AEH, which could prompt a more significant suspect for the possible presence of concurrent EC at preoperative diagnostic workup.
Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs
Authors: Alfonso-Francia Gendry , Pedraza-Ortega Jesus Carlos , Badillo-Fernández Mariana , Toledano-Ayala Manuel , Aceves-Fernandez Marco Antonio , Rodriguez-Resendiz Juvenal , Ko Seok-Bum , Tovar-Arriaga Saul
Published: December 2022

Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder–Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.
Biomarkers in Pulmonary Arterial Hypertension
Authors: Hojda Silvana Elena , Chis Irina Camelia , Clichici Simona
Published: December 2022

Pulmonary arterial hypertension (PAH) is a severe medical condition characterized by elevated pulmonary vascular resistance (PVR), right ventricular (RV) failure, and death in the absence of appropriate treatment. The progression and prognosis are strictly related to the etiology, biochemical parameters, and treatment response. The gold-standard test remains right-sided heart catheterization, but dynamic monitoring of systolic pressure in the pulmonary artery is performed using echocardiography. However, simple and easily accessible non-invasive assays are also required in order to monitor this pathology. In addition, research in this area is in continuous development. In recent years, more and more biomarkers have been studied and included in clinical guidelines. These biomarkers can be categorized based on their associations with inflammation, endothelial cell dysfunction, cardiac fibrosis, oxidative stress, and metabolic disorders. Moreover, biomarkers can be easily detected in blood and urine and correlated with disease severity, playing an important role in diagnosis, prognosis, and disease progression.
A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images
Authors: Mostafa Fatma A. , Elrefaei Lamiaa A. , Fouda Mostafa M. , Hossam Aya
Published: December 2022

Thoracic diseases refer to disorders that affect the lungs, heart, and other parts of the rib cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis, cardiomegaly, and fracture. Millions of people die every year from thoracic diseases. Therefore, early detection of these diseases is essential and can save many lives. Earlier, only highly experienced radiologists examined thoracic diseases, but recent developments in image processing and deep learning techniques are opening the door for the automated detection of these diseases. In this paper, we present a comprehensive review including: types of thoracic diseases; examination types of thoracic images; image pre-processing; models of deep learning applied to the detection of thoracic diseases (e.g., pneumonia, COVID-19, edema, fibrosis, tuberculosis, chronic obstructive pulmonary disease (COPD), and lung cancer); transfer learning background knowledge; ensemble learning; and future initiatives for improving the efficacy of deep learning models in applications that detect thoracic diseases. Through this survey paper, researchers may be able to gain an overall and systematic knowledge of deep learning applications in medical thoracic images. The review investigates a performance comparison of various models and a comparison of various datasets.
Recent Advances and Potential Future Applications of MALDI-TOF Mass Spectrometry for Identification of Helminths
Authors: Sy Issa , Conrad Lucie , Becker Sören L.
Published: December 2022

Helminth infections caused by nematodes, trematodes, and cestodes are major neglected tropical diseases and of great medical and veterinary relevance. At present, diagnosis of helminthic diseases is mainly based on microscopic observation of different parasite stages, but microscopy is associated with limited diagnostic accuracy. Against this background, recent studies described matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry as a potential, innovative tool for helminth identification and differentiation. MALDI-TOF mass spectrometry is based on the analysis of spectra profiles generated from protein extracts of a given pathogen. It requires an available spectra database containing reference spectra, also called main spectra profiles (MSPs), which are generated from well characterized specimens. At present, however, there are no commercially available databases for helminth identification using this approach. In this narrative review, we summarize recent developments and published studies between January 2019 and September 2022 that report on the use of MALDI-TOF mass spectrometry for helminths. Current challenges and future research needs are identified and briefly discussed.
ECG and Biomarker Profile in Patients with Acute Heart Failure: A Pilot Study
Authors: Chetran Adriana , Costache Alexandru Dan , Ciongradi Carmen Iulia , Duca Stefania Teodora , Mitu Ovidiu , Sorodoc Victorita , Cianga Corina Maria , Tuchilus Cristina , Mitu Ivona , Mitea Raluca Daria , Badescu Minerva Codruta , Afrasanie Irina , Huzum Bogdan , Moisa Stefana Maria , Prepeliuc Cristian Sorin , Roca Mihai , Costache Irina Iuliana
Published: December 2022

Background: Biomarkers, electrocardiogram (ECG) and Holter ECG are basic, accessible and feasible cardiac investigations. The combination of their results may lead to a more complex predictive model that may improve the clinical approach in acute heart failure (AHF). The main objective was to investigate which ECG parameters are correlated with the usual cardiac biomarkers (prohormone N-terminal proBNP, high-sensitive cardiac troponin I) in patients with acute heart failure, in a population from Romania. The relationship between certain ECG parameters and cardiac biomarkers may support future research on their combined prognostic value. Methods: In this prospective case-control study were included 49 patients with acute heart failure and 31 participants in the control group. For all patients we measured levels of prohormone N-terminal proBNP (NT-proBNP), high-sensitive cardiac troponin I (hs-cTnI) and MB isoenzyme of creatine phosphokinase (CK-MB) and evaluated the 12-lead ECG and 24 h Holter monitoring. Complete clinical and paraclinical evaluation was performed. Results: NT-proBNP level was significantly higher in patients with AHF (p < 0.001). In patients with AHF, NT-proBNP correlated with cQTi (p = 0.027), pathological Q wave (p = 0.029), complex premature ventricular contractions (PVCs) (p = 0.034) and ventricular tachycardia (p = 0.048). Hs-cTnI and CK-MB were correlated with ST-segment modification (p = 0.038; p = 0.018) and hs-cTnI alone with complex PVCs (p = 0.031). Conclusions: The statistical relationships found between cardiac biomarkers and ECG patterns support the added value of ECG in the diagnosis of AHF. We emphasize the importance of proper ECG analysis of more subtle parameters that can easily be missed. As a non-invasive technique, ECG can be used in the outpatient setting as a warning signal, announcing the acute decompensation of HF. In addition, the information provided by the ECG complements the biomarker results, supporting the diagnosis of AHF in cases of dyspnea of uncertain etiology. Further studies are needed to confirm long-term prognosis in a multi-marker approach.
Diffusion Tensor and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Correlate with Molecular Markers of Inflammation in the Synovium
Authors: Tripathi Deepak , Awasthi Rishi , Agarwal Vikas , Agrawal Vinita , Rathore Ram Kishore Singh , Sharma Kusum , Pandey Chandra Mani , Gupta Rakesh Kumar
Published: December 2022

Objectives: It is difficult to capture the severity of synovial inflammation on imaging. Herein we hypothesize that diffusion tensor imaging (DTI) derived metrics may delineate the aggregation of the inflammatory cells and expression of inflammatory cytokines and dynamic contrast-enhanced (DCE) imaging may provide information regarding vascularity in the inflamed synovium. Patients and methods: Patients with knee arthritis (>3-months duration) underwent conventional (T2-weighted fast spin echo and spin echo T1-weighted images) as well as DTI and DCE MRI and thereafter arthroscopic guided synovial biopsy. DCE and DTI metrics were extracted from the masks of the segments of the inflamed synovium which enhanced on post-contrast T1-weighted MRI. These metrics were correlated with immunohistochemistry (IHC) parameters of inflammation on synovium. Statistical analysis: Pearson’s correlation was performed to study the relationship between DTI- and DCE-derived metrics, IHC parameters, and post-contrast signal intensity. Linear regression model was used to predict the values of IHC parameters using various DTI and DCE derived metrics as predictors. Results: There were 80 patients (52 male) with mean age 39.78 years and mean disease duration 19.82 months. Nineteen patients had tuberculosis and the rest had chronic undifferentiated monoarthritis (n = 31), undifferentiated spondyloarthropathy (n = 14), rheumatoid arthritis (n = 6), osteoarthritis (n = 4), reactive arthritis (n = 3), ankylosing spondylitis (n = 2), and juvenile idiopathic arthritis (n = 1). Fractional anisotropy (FA), a metric of DTI, had significant correlation with number of immune cells (r = 0.87, p < 0.01) infiltrating into the synovium and cytokines (IL-1β, r = 0.55, p < 0.01; TNF-α, r = 0.42, p < 0.01) in all patients and also in each group of patients and adhesion molecule expressed on these cells in all patients (CD54, r = 0.51, p < 0.01). DCE parameters significantly correlated with CD34 (blood flow, r = 0.78, p < 0.01; blood volume, r = 0.76, p < 0.01) in each group of patients, a marker of neo-angiogenesis. FA was the best predictor of infiltrating inflammatory cells, adhesion molecule and proinflammatory cytokines. Amongst the DCE parameters, blood volume, was best predictor of CD34. Conclusion: DTI and DCE metrics capture cellular and molecular markers of synovial inflammation in patients with chronic inflammatory arthritis.
The Role of Active or Passive Drainage after Evacuation of Chronic Subdural Hematoma: An Analysis of Two Randomized Controlled Trials (cSDH-Drain-Trial and TOSCAN Trial)
Authors: Ebel Florian , Greuter Ladina , Lutz Katharina , Häni Levin , Fandino Javier , Guzman Raphael , Mariani Luigi , Beck Jürgen , Raabe Andreas , Z’Graggen Werner J. , Schucht Philippe , Soleman Jehuda
Published: December 2022

The evacuation of a chronic subdural hematoma (cSDH) is one of the most common procedures in neurosurgery. The aim of this study was to assess the influence of drainage suction in the surgical treatment of cSDH on the recurrence rate. Post hoc analysis was conducted on two randomized controlled trials (cSDH-Drain-Trial and TOSCAN trial) stratifying a total of 581 patients into active or passive drain type. Of the 581 patients, 359 (61.8%) and 220 (37.9%) were stratified into the active and passive drainage groups, respectively. The reoperation rate following postoperative recurrence was 23.1% and 14.1% in the active and passive drainage groups, respectively (p < 0.011). After propensity score matching, the differences in recurrence rate remained significant (26.6% versus 15.6%, p = 0.012). However, the functional outcome (mRS) at 6–12 months did not differ significantly (median [IQR]) between the 2 groups (passive drainage group 0.00 [0.00, 2.00], active drainage group 1.00 [0.00, 2.00], p = 0.431). Mortality was comparable between the groups (passive drainage group 12 (5.5%), active drainage group 20 (5.6%), p = 0.968). In the univariate analysis, active drainage, short (<48 h) duration of drainage, and early (<48 h) postoperative mobilization were significantly associated with a higher recurrence rate. However, the multivariate logistic regression model could not confirm that any of these parameters were significantly associated with recurrence. Our post hoc analysis proposes that using a passive instead of an active drain might be associated with a reduced recurrence rate after evacuation of a cSDH. We suggest gathering further evidence by means of a randomized controlled trial.
Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature
Authors: Donisi Leandro , Cesarelli Giuseppe , Pisani Noemi , Ponsiglione Alfonso , Ricciardi Carlo , Capodaglio Edda
Published: December 2022

Physical ergonomics has established itself as a valid strategy for monitoring potential disorders related, for example, to working activities. Recently, in the field of physical ergonomics, several studies have also shown potential for improvement in experimental methods of ergonomic analysis, through the combined use of artificial intelligence, and wearable sensors. In this regard, this review intends to provide a first account of the investigations carried out using these combined methods, considering the period up to 2021. The method that combines the information obtained on the worker through physical sensors (IMU, accelerometer, gyroscope, etc.) or biopotential sensors (EMG, EEG, EKG/ECG), with the analysis through artificial intelligence systems (machine learning or deep learning), offers interesting perspectives from both diagnostic, prognostic, and preventive points of view. In particular, the signals, obtained from wearable sensors for the recognition and categorization of the postural and biomechanical load of the worker, can be processed to formulate interesting algorithms for applications in the preventive field (especially with respect to musculoskeletal disorders), and with high statistical power. For Ergonomics, but also for Occupational Medicine, these applications improve the knowledge of the limits of the human organism, helping in the definition of sustainability thresholds, and in the ergonomic design of environments, tools, and work organization. The growth prospects for this research area are the refinement of the procedures for the detection and processing of signals; the expansion of the study to assisted working methods (assistive robots, exoskeletons), and to categories of workers suffering from pathologies or disabilities; as well as the development of risk assessment systems that exceed those currently used in ergonomics in precision and agility.
The Effects of Greek Orthodox Christian Fasting during Holy Week on Body Composition and Cardiometabolic Parameters in Overweight Adults
Authors: Georgakouli Kalliopi , Siamata Fotini , Draganidis Dimitrios , Tsimeas Panagiotis , Papanikolaou Konstantinos , Batrakoulis Alexios , Gatsas Athanasios , Poulios Athanasios , Syrou Niki , Deli Chariklia K. , Fatouros Ioannis G. , Jamurtas Athanasios Z.
Published: December 2022

This study investigated whether Greek Orthodox Christian fasting during Holy Week can change body composition and cardiometabolic parameters in overweight individuals, and whether these changes are maintained one week after fasting cessation (FC). Body composition and physiological and biochemical parameters were measured before, immediately after (n = 23) and one week after FC (subgroup of n = 10). Fasting resulted in decreased body weight, waist circumference, waist-to-hip ratio, body mass index and total body fat, as well as blood glucose, total cholesterol and low-density lipoprotein cholesterol levels. Nutrition analysis showed a decreased protein and saturated fat intake during fasting. FC (n = 10) resulted in a decreased carbohydrate intake and increased protein and cholesterol intake compared to fasting. Fasting resulted in decreased blood glucose, total cholesterol and LDL cholesterol levels but returned to pre-fasting levels after FC. Greek Orthodox Christian fasting during Holy Week is beneficial for body composition and some aspects of cardiometabolic health. However, these favourable changes are not maintained one week following fasting.
Development of a Chemically Modified Electrode with Magnetic Molecularly Imprinted Polymer (MagMIP) for 17-β-Estradiol Determination in Water Samples
Authors: da Silva Daniela Nunes , Pereira Arnaldo César
Published: December 2022

The present work consisted of the development of an electrode based on carbon paste modified with magnetic molecularly imprinted polymer (CPE-MagMIP) for 17-β-estradiol (E2) detection. The incorporation of magnetic material (MagMIP) improved sensor performance, an increase of over 317%. The proposed method resulted in a linear response range from 0.5 to 14.0 μM, and the detection limit (LOD) and quantification limit (LOQ) were equal to 0.13 and 0.44 μM, respectively. Under optimized conditions, the developed sensor obtained satisfactory parameters in E2 determination in water samples, demonstrating selectivity, accuracy, and precision, making it a promising method for monitoring E2 in environmental samples.
Privacy-Enhanced Federated Learning: A Restrictively Self-Sampled and Data-Perturbed Local Differential Privacy Method
Authors: Zhao Jianzhe , Yang Mengbo , Zhang Ronglin , Song Wuganjing , Zheng Jiali , Feng Jingran , Matwin Stan
Published: December 2022

As a popular distributed learning framework, federated learning (FL) enables clients to conduct cooperative training without sharing data, thus having higher security and enjoying benefits in processing large-scale, high-dimensional data. However, by sharing parameters in the federated learning process, the attacker can still obtain private information from the sensitive data of participants by reverse parsing. Local differential privacy (LDP) has recently worked well in preserving privacy for federated learning. However, it faces the inherent problem of balancing privacy, model performance, and algorithm efficiency. In this paper, we propose a novel privacy-enhanced federated learning framework (Optimal LDP-FL) which achieves local differential privacy protection by the client self-sampling and data perturbation mechanisms. We theoretically analyze the relationship between the model accuracy and client self-sampling probability. Restrictive client self-sampling technology is proposed which eliminates the randomness of the self-sampling probability settings in existing studies and improves the utilization of the federated system. A novel, efficiency-optimized LDP data perturbation mechanism (Adaptive-Harmony) is also proposed, which allows an adaptive parameter range to reduce variance and improve model accuracy. Comprehensive experiments on the MNIST and Fashion MNIST datasets show that the proposed method can significantly reduce computational and communication costs with the same level of privacy and model utility.
A Lightweight CNN and Class Weight Balancing on Chest X-ray Images for COVID-19 Detection
Authors: Alduaiji Noha , Algarni Abeer , Abdalaha Hamza Saadia , Abdel Azim Gamil , Hamam Habib
Published: December 2022

In many locations, reverse transcription polymerase chain reaction (RT-PCR) tests are used to identify COVID-19. It could take more than 48 h. It is a key factor in its seriousness and quick spread. Images from chest X-rays are utilized to diagnose COVID-19. Which generally deals with the issue of imbalanced classification. The purpose of this paper is to improve CNN’s capacity to display Chest X-ray pictures when there is a class imbalance. CNN Training has come to an end while chastening the classes for using more examples. Additionally, the training data set uses data augmentation. The achievement of the suggested method is assessed on an image’s two data sets of chest X-rays. The suggested model’s efficiency was analyzed using criteria like accuracy, specificity, sensitivity, and F1 score. The suggested method attained an accuracy of 94% worst, 97% average, and 100% best cases, respectively, and an F1-score of 96% worst, 98% average and 100% best cases, respectively.
Hybrid Attention-Based 3D Object Detection with Differential Point Clouds
Authors: Han Guangjie , Zhu Yintian , Liao Lyuchao , Yao Huiwen , Zhao Zhaolin , Zheng Qi
Published: December 2022

Object detection based on point clouds has been widely used for autonomous driving, although how to improve its detection accuracy remains a significant challenge. Foreground points are more critical for 3D object detection than background points; however, most current detection frameworks cannot effectively preserve foreground points. Therefore, this work proposes a hybrid attention-based 3D object detection method with differential point clouds, which we name HA-RCNN. The method differentiates the foreground points from the background ones to preserve the critical information of foreground points. Extensive experiments conducted on the KITTI dataset show that the model outperforms the state-of-the-art methods, especially in recognizing large objects such as cars and cyclists.
Deep Learning-Based Modulation Recognition for Low Signal-to-Noise Ratio Environments
Authors: He Peng , Zhang Yang , Yang Xinyue , Xiao Xiao , Wang Haolin , Zhang Rongsheng
Published: December 2022

Automatic modulation classification (AMC), which plays a significant role in wireless communication, can recognize the modulation type of the received signal without large amounts of transmitted data and parameter information. Supported by deep learning, which is a powerful tool for functional expression and feature extraction, the development of AMC can be greatly promoted. In this paper, we propose a deep learning-based modulation classification method with 2D time-frequency signal representation. In our proposed method, signals which have been received are first analyzed by time-frequency based on continuous wavelet transform (CWT). Then, CWT images of received signals are obtained and input to the deep learning model for classifying. We create a new CWT image dataset including 12 modulation types of signals under various signal-to-noise ratio (SNR) environment to verify the effectiveness of the proposed method. The experimental results demonstrate that our proposed method can reach to a high classification accuracy over the SNR of −11 dB.
Design Principle of RF Stealth Anti-Sorting Signal Based on Multi-Dimensional Compound Modulation with Pseudo-Center Width Agility
Authors: Jia Jinwei , Han Zhuangzhi , Liu Limin , Xie Hui , Lv Meng
Published: December 2022

Anti-sorting signal design is an important direction of radio frequency (RF) stealth signal design. The RF stealth signal design is based on the anti-sorting signal design principle, which is essentially the failure principle of the radar signal sorting algorithm. Cluster pre-sorting, the key to radar signal sorting, has the advantages of fast sorting, simultaneous sorting of multiple sources, and greatly reduced computational pressure of the main sorting. However, a unified and widely applicable cluster-sorting failure principle guiding the anti-sorting signal design has not been formally reported in RF stealth anti-sorting signal design. In this paper, the principles of the data field-based K-means clustering algorithm and the fuzzy C-means clustering algorithm are first studied. Aiming at the key step of data similarity measurement in the clustering algorithm, the failure principle of cluster sorting based on pseudo-center wide-agile multi-dimensional compound modulation is proposed. This principle can correctly guide the design of the anti-clustering sorting signal, so it is also called the design principle of the RF stealth anti-sorting signal based on pseudo-center wide-agile multi-dimensional compound modulation. The correctness of the principle is proved by formula derivation, signal simulation, and a sorting experiment. Through a signal comparison simulation with random interference pulse anti-sorting signals, it is strongly proved that the anti-sorting performance of signals designed under the guidance of the anti-clustering signal design principle proposed in this paper is stronger than that of random interference pulse signals. This study provides theoretical support for designing RF stealth anti-sorting signals. Using the signal design principle proposed in this paper, the anti-sorting performance of the RF stealth signal is improved by 10%. The principle of signal design helps to improve design efficiency.
Comparing the Robustness of Humans and Deep Neural Networks on Facial Expression Recognition
Authors: Lévêque Lucie , Villoteau François , Sampaio Emmanuel V. B. , Perreira Da Silva Matthieu , Le Callet Patrick
Published: December 2022

Emotion recognition, and more particularly facial expression recognition (FER), has been extensively used for various applications (e.g., human–computer interactions). The ability to automatically recognize facial expressions has been facilitated with recent progress in the fields of computer vision and artificial intelligence. Nonetheless, FER algorithms still seem to face difficulties with image degradations due to real-life conditions (e.g., because of image compression or transmission). In this paper, we propose to investigate the impact of different distortion configurations on a large number of images of faces on human performance, thanks to the conduct of a crowdsourcing experiment. We further compare human performance with two open-source FER algorithms. Results show that, overall, models are more sensitive to distortions than humans—even when fine-tuned. Furthermore, we broach the subject of annotation errors and bias which exist in several well-established datasets, and suggest approaches to improve the latter.
Spectral Data Analysis for Forgery Detection in Official Documents: A Network-Based Approach
Authors: Al-Ameri Mohammed Abdulbasit Ali , Ciylan Bunyamin , Mahmood Basim
Published: December 2022

Despite the huge advances in digital communications in the last decade, physical documents are still the most common media for information transfer, especially in the official context. However, the readily available document processing devices and techniques (printers, scanners, etc.) facilitate the illegal manipulation or imitation of original documents by forgers. Therefore, verification of the authenticity and detection of forgery is of paramount importance to all agencies receiving printed documents. We suggest an unsupervised forgery detection framework that can distinguish whether a document is forged based on the spectroscopy of the document’s ink. The spectra of the tested documents inks (original and questioned) were obtained using laser-induced breakdown spectroscopy (LIBS) technology. Then, a correlation matrix of the spectra was calculated for both the original and questioned documents together, which were then transformed into an adjacency matrix aiming at converting it into a weighted network under the concept of graph theory. Clustering algorithms were then applied to the network to determine the number of clusters. The proposed approach was tested under a variety of scenarios and different types of printers (e.g., inkjet, laser, and photocopiers) as well as different kinds of papers. The findings show that the proposed approach provided a high rate of accuracy in identifying forged documents and a high detection speed. It also provides a visual output that is easily interpretable to the non-expert, which provides great flexibility for real-world application.