bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemical Composition and In Vitro and In Silico Antileishmanial Evaluation of the Essential Oil from Croton linearis Jacq. Stems
Authors: García-Díaz Jesús , Escalona-Arranz Julio César , Ochoa-Pacheco Ania , Santos Sócrates Golzio Dos , González-Fernández Rosalia , Rojas-Vargas Julio Alberto , Monzote Lianet , Setzer William N.
Published: November 2022

Croton linearis Jacq. is an aromatic shrub that has been utilized in traditional medicine in the Bahamas, Jamaica, and Cuba. Recent studies have revealed the antiprotozoal potential of its leaves. The present work is aimed to identify the volatile constituents of essential oil from the stems of C. linearis (CLS-EO) and evaluate its in vitro antileishmanial activity. In addition, an in silico study of the molecular interactions was performed using molecular docking. A gas chromatographic–mass spectrometric analysis of CLS-EO identified 1,8-cineole (27.8%), α-pinene (11.1%), cis-sabinene (8.1%), p-cymene (5.7%), α-terpineol (4.4%), epi-γ-eudesmol (4.2%), linalool (3.9%), and terpinen-4-ol (2.6%) as major constituents. The evaluation of antileishmanial activity showed that CLS-EO has good activity on both parasite forms (IC50Promastigote = 21.4 ± 0.1 μg/mL; IC50Amastigote = 18.9 ± 0.3 μg/mL), with a CC50 of 49.0 ± 5.0 μg/mL on peritoneal macrophages from BALB/c mice (selectivity index = 2 and 3 using the promastigote and amastigote results). Molecular docking showed good binding of epi-γ-eudesmol with different target enzymes of Leishmania. This study is the first report of the chemical composition and anti-Leishmania evaluation of CLS-EO. These findings provide support for further studies of the antileishmanial effect of this product.
Systemic Antibiotics as an Adjunct to Subgingival Debridement: A Network Meta-Analysis
Authors: Kherul Anuwar Ainol Haniza , Saub Roslan , Safii Syarida Hasnur , Ab-Murat Norintan , Mohd Taib Mohd Syukri , Mamikutty Rokiah , Ng Chiu Wan
Published: November 2022

This review aimed to evaluate the effectiveness of systemic antibiotics as adjunctive treatment to subgingival debridement in patients with periodontitis. Randomized controlled trials were included that assessed the effectiveness of systemic antibiotics in improving periodontal status, indicated by clinical attachment gain level, probable pocket depth reduction, and bleeding on probing reduction of patients with any form of periodontitis at any follow-up time. Network meta-analyses with a frequentist model using random effects was employed to synthesize the data. The relative effects were reported as mean difference with a 95% confidence interval. Subsequently, all treatments were ranked based on their P-scores. A total of 30 randomized controlled trials were included in this network meta-analyses. Minimally important clinical differences were observed following the adjunctive use of satranidazole, metronidazole, and clindamycin for clinical attachment gain level and probable pocket depth reduction. For bleeding on probing reduction, minimally important clinical differences were observed following the adjunctive use of metronidazole and a combination of amoxycillin and metronidazole. However, the network estimates were supported by evidence with certainty ranging from very low to high. Therefore, the findings of this network meta-analyses should be interpreted with caution. Moreover, the use of these antibiotics adjunct to subgingival debridement should be weighed against possible harm to avoid overuse and inappropriate use of these antibiotics in patients with periodontitis.
Comprehensive Characterization of Chemical Composition and Antioxidant Activity of Lignan-Rich Coniferous Knotwood Extractives
Authors: Ul’yanovskii Nikolay V. , Onuchina Aleksandra A. , Faleva Anna V. , Gorbova Natalia S. , Kosyakov Dmitry S.
Published: November 2022

A knotwood of coniferous trees containing large amounts of polyphenolic extractives is considered a promising industrial-scale source of lignans possessing antioxidant properties and other bioactivities. The present study is aimed at a detailed characterization of the chemical composition and antioxidant activity of lignan-rich extractives obtained from the knotwood of the Norway spruce, Scotch pine, Siberian fir, and Siberian larch growing in the European North of Russia as a region with a highly developed forest industry. To achieve this, a comprehensive approach based on a combination of two-dimensional NMR spectroscopy with high-performance liquid chromatography—high-resolution Orbitrap mass spectrometry, and the determination of antioxidant activity by the three complementary methods were proposed. The studied knotwood samples contained from 3.9 to 17% of extractive substances and were comparable to Trolox’s antioxidant activity in the single-electron transfer processes and superoxide radical scavenging, which is associated with the predominance of polyphenolic compounds. The latter was represented by 12 tentatively identified monolignans and 27 oligolignans containing 3–5 phenylpropane units in their structure. The extracts were characterized by an identical set of lignans and differed only in the ratios of their individual compounds. Other components of the knotwood were flavonoids taxifolin, quercetin (Siberian larch), and three stilbenes (pinosylvin, its methyl ester, and pterostilbene), which were identified in the Scotch pine extractives. Sesquiterpene juvabione and its derivatives were found in extracts of Siberian larch knotwood.
Antioxidant and Hepatoprotective Effect of a Nutritional Supplement with Silymarin Phytosome, Choline Chloride, l-Cystine, Artichoke, and Vitamin E in Dogs
Authors: Giannetto Claudia , Arfuso Francesca , Giudice Elisabetta , Rizzo Maria , Piccione Giuseppe , Mhalhel Kamel , Levanti Maria
Published: November 2022

Phytotherapy has been promoted for the treatment of liver diseases in dogs. The interest in identifying the antioxidant/hepatoprotective potential of various plants is increasing. Every 10 days for 30 days, forty dogs were subjected to blood sampling and hepatic ultrasound assessment. Clinically healthy dogs (group A) and dogs with liver enzyme and ultrasound hepatic aspects and sizes outside the physiological range (group B) were divided into two subgroups. Every day for 30 days, one subgroup received Epacare pet + pasta® and the other received a placebo. Differences due to groups were observed in dROM, SHp, AST and LDH serum concentrations. The administration of Epacarepet + pasta® for 30 days had an influence on the serum concentration of dROMs, SHp, AST, ALT, ALP, LDH, and urea. The application of paired Student’s t-test showed a decrease in the longitudinal and transverse liver axis size. In conclusion, feed supplementation with Epacare pet + pasta® had a beneficial effect on the antioxidant status and liver enzymes in animals with liver enzymatic alterations and in healthy dogs.
Palmitic Acid Induced a Long-Lasting Lipotoxic Insult in Human Retinal Pigment Epithelial Cells, which Is Partially Counteracted by TRAIL
Authors: Sergi Domenico , Zauli Enrico , Casciano Fabio , Secchiero Paola , Zauli Giorgio , Fields Matteo , Melloni Elisabetta
Published: November 2022

Hyperglycaemia and increased circulating saturated fatty acids are key metabolic features of type 2 diabetes mellitus (T2DM) that contribute to diabetic retinopathy pathogenesis. Contrarily, tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) has been shown to improve or prevent T2DM. This study aimed at investigating the effect of TRAIL in an in vitro model of human retinal pigment epithelium: the ARPE-19 cell line, treated with palmitic acid (PA) in the presence of high glucose concentration. PA caused a drop in cellular metabolic activity and cell viability as well as an increase in apoptosis rates, which were paralleled by an upregulation of reactive oxygen species (ROS) generation as well as mitochondrial fragmentation. Despite ARPE-19 cells expressing TRAIL-R2 at the cell surface, TRAIL failed to counteract the cytotoxic effects of PA. However, when TRAIL was used alongside PA and then removed or used alone following PA challenge, it partially attenuated PA-induced lipotoxicity. This effect of TRAIL appeared to rely upon the modulation of inflammation and ROS production. Thus, TRAIL exerted a trophic effect on ARPE-19 cells, which became evident only when the lipotoxic insult was removed. Nevertheless, whether recombinant TRAIL might have a therapeutic potential for the treatment of diabetic retinopathy requires further investigation.
Nrf2 and Oxidative Stress: A General Overview of Mechanisms and Implications in Human Disease
Authors: Ngo Vy , Duennwald Martin L.
Published: November 2022

Organisms are continually exposed to exogenous and endogenous sources of reactive oxygen species (ROS) and other oxidants that have both beneficial and deleterious effects on the cell. ROS have important roles in a wide range of physiological processes; however, high ROS levels are associated with oxidative stress and disease progression. Oxidative stress has been implicated in nearly all major human diseases, from neurogenerative diseases and neuropsychiatric disorders to cardiovascular disease, diabetes, and cancer. Antioxidant defence systems have evolved as a means of protection against oxidative stress, with the transcription factor Nrf2 as the key regulator. Nrf2 is responsible for regulating an extensive panel of antioxidant enzymes involved in the detoxification and elimination of oxidative stress and has been extensively studied in the disease contexts. This review aims to provide the reader with a general overview of oxidative stress and Nrf2, including basic mechanisms of Nrf2 activation and regulation, and implications in various major human diseases.
Trans-(±)-Kusunokinin Binding to AKR1B1 Inhibits Oxidative Stress and Proteins Involved in Migration in Aggressive Breast Cancer
Authors: Tanawattanasuntorn Tanotnon , Rattanaburee Thidarath , Thongpanchang Tienthong , Graidist Potchanapond
Published: November 2022

Synthetic trans-(±)-kusunokinin ((±)KU), a potential anticancer substance, was revealed to have an inhibitory effect on breast cancer. According to the computational modeling prediction, AKR1B1, an oxidative stress and cancer migration protein, could be a target protein of trans-(−)-kusunokinin. In this study, we determined the binding of (±)KU and AKR1B1 on triple-negative breast and non-serous ovarian cancers. We found that (±)KU exhibited a cytotoxic effect that was significantly stronger than zopolrestat (ZP) and epalrestat (EP) (known AKR1B1 inhibitors) on breast and ovarian cancer cells. (±)KU inhibited aldose reductase activity that was stronger than trans-(−)-arctiin ((−)AR) but weaker than ZP and EP. Interestingly, (±)KU stabilized AKR1B1 on SKOV3 and Hs578T cells after being heated at 60 and 75 °C, respectively. (±)KU decreased malondialdehyde (MDA), an oxidative stress marker, on Hs578T cells in a dose-dependent manner and the suppression was stronger than EP. Furthermore, (±)KU downregulated AKR1B1 and its downstream proteins, including PKC-δ, NF-κB, AKT, Nrf2, COX2, Twist2 and N-cadherin and up-regulated E-cadherin. (±)KU showed an inhibitory effect on AKR1B1 and its downstream proteins, similar to siRNA–AKR1B1. Interestingly, the combination of siRNA–AKR1B1 with EP or (±)KU showed a greater effect on the suppression of AKR1B1, N-cadherin, E-cadherin and NF-κB than single treatments. Taken together, we concluded that (±)KU-bound AKR1B1 leads to the attenuation of cellular oxidative stress, as well as the aggressiveness of breast cancer cell migration.
Contribution of Non-Coding RNAs to Anticancer Effects of Dietary Polyphenols: Chlorogenic Acid, Curcumin, Epigallocatechin-3-Gallate, Genistein, Quercetin and Resveratrol
Authors: Hayakawa Sumio , Ohishi Tomokazu , Oishi Yumiko , Isemura Mamoru , Miyoshi Noriyuki
Published: November 2022

Growing evidence has been accumulated to show the anticancer effects of daily consumption of polyphenols. These dietary polyphenols include chlorogenic acid, curcumin, epigallocatechin-3-O-gallate, genistein, quercetin, and resveratrol. These polyphenols have similar chemical and biological properties in that they can act as antioxidants and exert the anticancer effects via cell signaling pathways involving their reactive oxygen species (ROS)-scavenging activity. These polyphenols may also act as pro-oxidants under certain conditions, especially at high concentrations. Epigenetic modifications, including dysregulation of noncoding RNAs (ncRNAs) such as microRNAs, long noncoding RNAs, and circular RNAs are now known to be involved in the anticancer effects of polyphenols. These polyphenols can modulate the expression/activity of the component molecules in ROS-scavenger-triggered anticancer pathways (RSTAPs) by increasing the expression of tumor-suppressive ncRNAs and decreasing the expression of oncogenic ncRNAs in general. Multiple ncRNAs are similarly modulated by multiple polyphenols. Many of the targets of ncRNAs affected by these polyphenols are components of RSTAPs. Therefore, ncRNA modulation may enhance the anticancer effects of polyphenols via RSTAPs in an additive or synergistic manner, although other mechanisms may be operating as well.
Neuroprotective Effect of Stearidonic Acid on Amyloid β-Induced Neurotoxicity in Rat Hippocampal Cells
Authors: Li Yueru , Lai Wencong , Zheng Chen , Babu Jeganathan Ramesh , Xue Changhu , Ai Qinghui , Huggins Kevin W.
Published: November 2022

Dietary intake of omega-3 fatty acids found in fish has been reported to reduce the risk of Alzheimer’s Disease (AD). Stearidonic acid (SDA), a plant-based omega-3 fatty acid, has been targeted as a potential surrogate for fish-based fatty acids. However, its role in neuronal degeneration is unknown. This study was designed to evaluate effects of SDA on Amyloid-β(A-β)-induced neurotoxicity in rat hippocampal cells. Results showed that SDA effectively converted to eicosapentaenoic acid (EPA) in hippocampal cells. Aβ-induced apoptosis in H19-7 cells was protected by SDA pretreatment as evidenced by its regulation on the expression of relevant pro- and anti-apoptotic genes, as well as the inhibition on caspase activation. SDA also protected H19-7 cells from Aβ-induced oxidative stress by regulating the expression of relevant pro- and anti-oxidative genes, as well as the improvement in activity of catalase. As for Aβ/LPS-induced neuronal inflammation, SDA pretreatment reduced the release of IL-1β and TNFα. Further, we found that the anti-Aβ effect of SDA involves its inhibition on the expression of amyloid precursor protein and the regulation on MAPK signaling. These results demonstrated that SDAs have neuroprotective effect in Aβ-induced H19-7 hippocampal cells. This beneficial effect of SDA was attributed to its antiapoptotic, antioxidant, and anti-inflammatory properties.
Pterostilbene Ameliorates Fumonisin B1-Induced Cytotoxic Effect by Interfering in the Activation of JAK/STAT Pathway
Authors: Jin Jian , Shan Yiyi , Zhang Liangliang , Wu Zhengchang , Wu Shenglong , Sun Mingan , Bao Wenbin
Published: November 2022

Fumonisin B1 (FB1) is a mycotoxin that poses a great threat to agricultural production and the health of humans and animals. Pterostilbene (PTE) is a natural plant polyphenolic compound with good anti-inflammatory, antioxidant and cell regeneration effects, yet its effectiveness in treating FB1-induced cytotoxicity remains to be explored. In this study, we used porcine alveolar macrophages (3D4/21) as a model to characterize the cytotoxicity induced by FB1, and to investigate the potential alleviating effect of PTE on FB1-induced cytotoxicity. We demonstrate that FB1 induces cytotoxicity, apoptosis, pro-inflammatory cytokine production and mitochondrial damage, which can be largely recovered by PTE treatment, suggesting the promising application of PTE to treat FB1-induced damage. Mechanistically, FB1 activates the JAK/STAT signaling pathway, while PTE attenuates FB1-induced cytotoxicity through the inhibition of key JAK/STAT genes such as JAK2 and STAT3. Overall, our study characterized the molecular mechanism for FB1-induced cytotoxicity and found PTE to be a promising component which can alleviate FB1-induced cytotoxicity by interfering in the activation of JAK/STAT pathway.
Intuition and Symmetries in Electromagnetism: Eigenstates of Four Antennas
Authors: Bilel Hamdi , Taoufik Aguili
Published: November 2022

Symmetries play an essential role in the field of physics. In this paper, we examine the relationship between the eigen-amplitudes of four (2 × 2) symmetrical antennas and the symmetry of the amplitudes of their sources (excitations) using mirroring effects. In our case, we find that changing mirrors using symmetry is identical to the point group theory. By exploiting the symmetry problem, we can show the advantage of reducing the size of the analysis domain, at least by a factor of two or more (2, 4, and 8…etc.) (depending on the problem). Several simulation examples have been developed by the MoM-GEC and HFSS to validate this approach.
Discovering Irregularities from Computer Networks by Topological Mapping
Authors: Hamid Khalid , Waseem Iqbal Muhammad , Abbas Qaiser , Arif Muhammad , Brezulianu Adrian , Geman Oana
Published: November 2022

Any number that can be uniquely identified and varied by a graph is known as a graph invariant. This paper will talk about three unique variations of bridge networks, sierpinski networks, honeycomb, and hexagonal networks, with great capability of forecast in the field of software engineering, arithmetic, physics, drug store, informatics, and chemistry in setting with physical and chemical properties. Irregularity sombor invariant is newly introduced and has various expectation characteristics for various variations of bridge graphs or other networks, as mentioned. First, find the irregularities in the networks with the help of the Irregularity sombor index. This will be performed in a step by step procedure. The study will take an existing network, associate it with a graph after finding their vertices and edges, then solve the topology of a graph of a network. Graphical results demonstrate the upper and lower bounds and irregularities of certain networks, and mathematical results are used for modeling purposes. The review settled the topologies of graphs/networks of seven distinct sorts with an Irregularity sombor index. These concluded outcomes can be utilized for the demonstration and modeling of computer networks such as local area networks, Metropolitan area networks, Wide area networks, memory interconnection networks, processor interconnection networks, the spine of the internet, and different networks/designs of Personal computers, power generation networks, mobile base station and chemical compound amalgamation and so on.
Effects of Rehabilitation Exercise with Blood Flow Restriction after Anterior Cruciate Ligament Reconstruction
Authors: Jung Won-Sang , Kim Seung-Hwan , Nam Sang-Seok , Kim Jeong-Weon , Moon Hwang-Woon
Published: November 2022

The present study examined the effects of a rehabilitation exercise program with blood flow restriction (BFR) vs. a general rehabilitation exercise program in patients who have undergone anterior cruciate ligament (ACL) reconstruction. Among a total of 24 patients, the general rehabilitation exercise group (GRE, n = 12) and blood flow restriction group (BFR, n = 12) were assigned the same. The GRE group conducted a general rehabilitation exercise, and the BFR group conducted a BFR exercise along with the general rehabilitation exercise. All participants performed the rehabilitation exercise program session for 60 min three times a week for 12 weeks under supervision. All dependent parameters (Lysholm score and International Knee Documentation Committee (IKDC) subjective score, muscle activity, isokinetic muscular function, Y-balance test) were evaluated before and after the rehabilitation exercise program. GRE improved the Lysholm score, IKDC subjective score and Y balance test (posterior-medial, posterior-lateral) (p < 0.05). Moreover, BFR was effective in improving the Lysholm score and IKDC subjective score and muscle activity (e.g., vastus medialis oblique during isokinetic contraction and rectus femoris during isometric contraction), isokinetic function (e.g., peak torque and total work) and Y-balance test (e.g., anterior, posterior medial, posterior lateral) (p < 0.05). Our study confirmed that a rehabilitation exercise program with BFR after ACL reconstruction is a more effective rehabilitation modality for improving muscle activity during muscle contraction and muscle function compared with GRE. Therefore, it is recommended to use BFR as an effective rehabilitation program for rapid recovery after ACL reconstruction.
Underground Disposal of Coal Gangue Backfill in China
Authors: Song Weijian , Zhang Jixiong , Li Meng , Yan Hao , Zhou Nan , Yao Yinan , Guo Yaben
Published: November 2022

China’s total coal production in 2021 exceeded 4.13 billion tons, 52% of the world’s total. Coal gangue, a solid waste of coal mining accounts for 15–20% of coal production, when directly discharged on the ground surface as waste heaps, it occupies large areas of land and cause environmental pollution. This paper summarizes the existing gangue backfilling methods, their working principles, efficiency, and application status. The methods that are meeting Middle and Western China’s mining demands are discussed in detail. The state-of-the-art technologies that can realize high-efficiency, centralized, and large-scale underground backfilling of coal gangue are analyzed. This paper shows that the industrial implementation of these technologies can increase the current maximum disposal capacity of coal gangue by three times, reaching five million tons per year. The equipment innovation and automation are analyzed, and the environmental effect of coal gangue backfilling is discussed. This review offers inspirations and guidelines for coal gangue disposal and the environmental hazard reduction of coal mining.
Molecular and Phytochemical Characterizations of Cichorium intybus L. in Diverse Ecogeographical Regions of Kashmir Himalaya
Authors: Malik Bisma , Dar Fayaz Ahmad , Pirzadah Tanveer Bilal , Zari Ali , Zari Talal A. , Alharby Hesham F. , Hakeem Khalid Rehman , Rehman Reiaz Ul
Published: November 2022

Cichorium intybus L. (chicory) is an important medicinal plant with significant economic potential and has recently gained rapid momentum in the functional food sector. In the present study, soil chemistry, phytochemical, and molecular diversity were assessed for 50 accessions of chicory collected from diverse agro-climatic zones. In total, 64 common metabolites were identified from the leaves of 7 chicory accessions collected from different altitudes and among them, the predominant metabolites included methyl commate B (6.3–10.14%), gamma sitosterol (2.79–9.3%), and 9, 12, 15-octadecatrienoic-acid (2.55–8.42%). Three terpenoid compounds, viz., betulin, kolavelool and betulinaldehyde, were observed at high altitudes (1790, 1901, and 2172 m) and not observed at low altitudes. Among these compounds, betulin had the highest concentration with an average value of 23.53% followed by kolavelool with 7.37% and betulinaldehyde with 7.21%. For molecular diversity analysis, 12 ISSR primers were selected for PCR amplification and 86 bands were generated with an overall polymorphism percentage of 67.44%. The observed Nei’s genetic diversity (H) and Shannon’s information indices (I) were highest for the Pulwama (CIN-PU) group of accessions (H = 0.222 ± 0.018; I = 0.343 ± 0.027) and lowest for the Baramulla (CIN-BM) group of accessions (H = 0.115 ± 0.019; I = 0.173 ± 0.028). The Analysis of Molecular Variance (AMOVA) analysis revealed 56% variation existing within the groups and 44% among the groups of chicory accessions. This study shows that chicory populations vary considerably in terms of their molecular and phytochemical composition as a function of their geographic location. Furthermore, this study demonstrates that chicory phytochemical and molecular diversity are significantly influenced by altitude, soil chemistry, and growing conditions. Using metabolomics and altitudinal variation, cluster analysis showed that geographic origin was correlated with diversity patterns.
Performance Analysis of Magnetorheological Porous Fabric Composite
Authors: Yan Hua , Wu Lifan , Li Pingyang , Li Xuan , Zhao Junxin , Dong Xiaomin
Published: November 2022

In this paper, magnetorheological (MR) porous fabric composites were prepared by using non-woven fabrics pretreated with lithium stearate. Due to the porous structure of non-woven fabric and the good stability of lithium stearate, the mechanical properties and sedimentation stability of the composite have been improved. The viscosity, shear stress and dynamic viscoelasticity of different samples were analyzed by rheological properties tests. The results indicate that the mechanical properties of the composite samples containing lithium stearate are improved compared with the MRF (magnetorheological fluid) sample, and its sedimentation stability under non-working conditions is also improved. However, with the increase of lithium stearate content, the improving effect of material performance gradually decreased. The experimental results show that when the magnetic flux density is 0.31 T, the shear stress of the MR porous fabric composite with 1 wt% lithium stearate is about 27 kPa, which increases by 51.1% compared with the MRF sample.
Cortical Activation in Response to Speech Differs between Prelingually Deafened Cochlear Implant Users with Good or Poor Speech-in-Noise Understanding: An fNIRS Study
Authors: Levin Michal , Balberg Michal , Zaltz Yael
Published: November 2022

Cochlear implant (CI) users with prelingual deafness (hearing impairment started before language development was completed) show variable speech-in-noise (SIN) understanding. The present study aimed to assess cortical activation patterns to speech-in-quiet (SIQ) and SIN in prelingual CI users and compared to individuals with normal hearing (NH), using functional Near-Infrared Spectroscopy (fNIRS). Participants included 15 NH who listened to natural speech, 15 NH who listened via 8-channel noise-excited vocoder, and 14 prelingual CI users. fNIRS data were collected in a block design that included three conditions: SIQ, SIN in a signal-to-noise ratio of 0 dB, and noise. Speech reception thresholds in noise (SRTn) were also assessed. Results revealed different patterns of activation between the NH and CI participants in channels covering mainly the right and left middle temporal gyrus (MTG), depending on the SRTn of the CI users. Specifically, while the NH group showed large response to SIQ and SIN in the MTG areas, prelingual CI users with poor SRTn showed significantly smaller response to SIQ, and inversed response (a reduction in activation) to SIN in the same brain areas. These novel findings support the notion that the MTG can serve as a neural marker for speech understanding in CI patients.
Planning the Aesthetics of the Mandibular Angles in Orthognathic Surgery: Traditional vs. Virtual 3D Articulators
Authors: Di Blasio Chiara , Di Blasio Marco , Vaienti Benedetta , Di Francesco Fabrizio , Lanza Alessandro , Minervini Giuseppe , Segù Marzia , Di Blasio Alberto
Published: November 2022

The surgical correction of dento-maxillofacial deformities for aesthetic reasons is increasingly required. In order to obtain the results that our patients expect, it is necessary to very thoroughly evaluate every aesthetic detail of the face, because an optimal result comes from the correction of all parameters. The region of the mandibular angles is very important in determining the final aesthetics but is at the same time difficult to predict. Traditional programming based on model surgery is inadequate in this regard compared to that based on CT (Computed Tomography) reconstruction. The authors explain the reasons for this and demonstrate the problems by reporting a typical clinical case.
LASSO-Cox Modeling of Survival Using High-Resolution CT-Based Radiomic Features in a Cohort of COVID-19 Patients and Its Generalizability to Standard Image Reconstruction
Authors: Paolani Giulia , Spagnoli Lorenzo , Morrone Maria Francesca , Santoro Miriam , Coppola Francesca , Strolin Silvia , Golfieri Rita , Strigari Lidia
Published: November 2022

Background: Few studies have focused on predicting the overall survival (OS) of patients affected by SARS-CoV-2 (i.e., COVID-19) using radiomic features (RFs) extracted from computer tomography (CT) images. Reconstruction of CT scans might potentially affect the values of RFs. Methods: Out of 435 patients, 239 had the scans reconstructed with a single modality, and hence, were used for training/testing, and 196 were reconstructed with two modalities were used as validation to evaluate RFs robustness to reconstruction. During training, the dataset was split into train/test using a 70/30 proportion, randomizing the procedure 100 times to obtain 100 different models. In all cases, RFs were normalized using the z-score and then given as input into a Cox proportional-hazards model regularized with the Least Absolute Shrinkage and Selection Operator (LASSO-Cox), used for feature selection and developing a robust model. The RFs retained multiple times in the models were also included in a final LASSO-Cox for developing the predictive model. Thus, we conducted sensitivity analysis increasing the number of retained RFs with an occurrence cut-off from 11% to 60%. The Bayesian information criterion (BIC) was used to identify the cut-off to build the optimal model. Results: The best BIC value indicated 45% as the optimal occurrence cut-off, resulting in five RFs used for generating the final LASSO-Cox. All the Kaplan-Meier curves of training and validation datasets were statistically significant in identifying patients with good and poor prognoses, irrespective of CT reconstruction. Conclusions: The final LASSO-Cox model maintained its predictive ability for predicting the OS in COVID-19 patients irrespective of CT reconstruction algorithms.
Robust Classification Model for Diabetic Retinopathy Based on the Contrastive Learning Method with a Convolutional Neural Network
Authors: Feng Xinxing , Zhang Shuai , Xu Long , Huang Xin , Chen Yanyan
Published: November 2022

Diabetic retinopathy is one of the most common microvascular complications of diabetes. Early detection and treatment can effectively reduce the risk. Hence, a robust computer-aided diagnosis model is important. Based on the labeled fundus images, we build a binary classification model based on ResNet-18 and transfer learning and, more importantly, improve the robustness of the model through supervised contrastive learning. The model is tested with different learning rates and data augmentation methods. The standard deviations of the multiple test results decrease from 4.11 to 0.15 for different learning rates and from 1.53 to 0.18 for different data augmentation methods. In addition, the supervised contrastive learning method also improves the average accuracy of the model, which increases from 80.7% to 86.5%.
Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network
Authors: Wu Ming-Yu , Wu Yan , Yuan Xin-Yi , Chen Zhi-Hua , Wu Wei-Tao , Aubry Nadine
Published: November 2022

We propose a steady-state aerodynamic data-driven method to predict the incompressible flow around airfoils of NACA (National Advisory Committee for Aeronautics) 0012-series. Using the Signed Distance Function (SDF) to parameterize the geometric and flow condition setups, the prediction core of the method is constructed essentially by a consecutive framework of a convolutional neural network (CNN) and a deconvolutional neural network (DCNN). Impact of training parameters on the behavior of the proposed CNN-DCNN model is studied, so that appropriate learning rate, mini-batch size, and random deactivation rate are specified. Tested by “unseen” airfoil geometries and far-field velocities, it is found that the prediction process is three orders of magnitudes faster than a corresponding Computational Fluid Dynamics (CFD) simulation, while relative errors are maintained lower than 1% on most of the sample points. The proposed model manages to capture the essential dynamics of the flow field, as its predictions correspond reasonably with the reconstructed field by proper orthogonal decomposition (POD). The performance and accuracy of the proposed model indicate that the deep learning-based approach has great potential as a robust predictive tool for aerodynamic design and optimization.
Behavior and Damage Characterization of Impulsively Loaded Cross-Laminated Timber Panels
Authors: Flores Noel R. , Gentry Russell , Stewart Lauren K.
Published: November 2022

This research bridges the gap between the quasi-static and high-strain-rate loading regimes in cross-laminated timber (CLT) by investigating two areas that have remained unstudied or elusive, i.e., rolling shear failure of CLT under impulsive, blast-like loading and intermediate strain rates in CLT. To study the conditions that would promote shear modes of failure, a novel, highly adaptable center-point testing system and methodology were developed that permitted the application of impulsive loading to undamaged CLT panels in a highly controlled and repeatable manner. The loading condition and low span-to-depth ratio (6.40 ≤ L:h ≤ 6.55) CLT were selected to encourage the development of shear modes of failure. Changes to the rotational rigidity at the boundary conditions allowed for the empirical simulation of realistic boundary conditions. Digital Image Correlation (DIC) and load cell data were used to identify failure modes following loss in resistance in the specimens. Overall, the experiment was successful in consistently eliciting shear modes of failure and providing damage characterization in impulsively loaded CLT. Shear modes of failure resulted in the dramatic loss of resistance in all specimens tested. Strain-rate enhancement in the dynamic apparent flexural stiffness of CLT of 1.3 to 7.2 times was observed. Lower levels of damage were observed in specimens with higher levels of boundary-condition rotational rigidity.
Numerical Modeling of Quasi-Brittle Materials Using a Phase-Field Regularized Cohesive Zone Model with Optimal Softening Law
Authors: Xu Xunqian , Wu Tongxin , Qian Guangyao , Kang Fengyi , Patrick Ganhouegnon Eric , Shi Wenkang
Published: November 2022

In this paper, we propose an approach combining optimal softening laws and a phase-field regularized cohesive zone model (PF-CZM) for modeling the fracture and damage properties of quasi-brittle materials accurately. In this method, the optimal softening law is determined by comparing the predicted results with experimental data in the framework of the PF-CZM; three typical softening laws are considered. The PF-CZM with a length scale is used to model crack initiation and propagation without considering the mesh bias. We first investigate the mechanical responses and crack propagations of different concrete beams based on the above approach; the predicted results are compared with the data from conventional methods and experiments. The results indicate that the mechanical properties of concrete beams with the optimal softening law are better than the data reported in the literature. Further validation indicates that once the optimal softening law is determined, it is stable for the same group of materials. Moreover, we demonstrate that the PF-CZM can naturally predict and reproduce the critical notch offset and fracture transition process of three-point bending concrete beams and the fracture features of typical double-notched concrete beams, such as the interaction between two notches objectively, together with the changes of limit load capacity.
In-Depth Evaluation of Association between Crash and Hand Arthritis via Naturalistic Driving Study
Authors: Almannaa Mohammed H. , Bareiss Max G. , Riexinger Luke E. , Guo Feng
Published: November 2022

Severe arthritis can limit a driver’s range of motion and increase their crash risk. The high prevalence of arthritis among the US driver population, especially among senior drivers, makes it a public safety concern. In this study, we evaluate the impact of arthritis on driving behavior and crash risk using the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS), which collected continuous driving data through data acquisition systems installed on participant’s vehicles. A detailed questionnaire survey was administered on demographic, health conditions, and personality information at the time of recruitment. The dataset includes 3563 participants. Among them, 78 drivers were identified to have severe arthritis, and they contributed to 414 out of 1641 crashes. We systematically evaluated the impact of severe arthritis on crash risk, secondary task engagement, and fitness-to-drive metrics. The results show there is a significant relationship between arthritis and crash risk, with an odds ratio of 1.99 with adjustment for age effects, which indicates that individuals with arthritis are twice as likely to be involved in a crash. There is no statistically significant association between arthritis and secondary task engagement, as well as the sensation-seeking scores, a personality trait.
Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare
Authors: Yaqoob Muhammad Mateen , Nazir Muhammad , Yousafzai Abdullah , Khan Muhammad Amir , Shaikh Asad Ali , Algarni Abeer D. , Elmannai Hela
Published: November 2022

Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized information about the patient and this data has serious privacy concerns. To overcome data privacy issues, several data protection laws are proposed internationally. These privacy laws created a huge problem for techniques used in traditional machine learning. We propose a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease in this paper. The proposed technique improves the prediction accuracy, classification error, and communication efficiency as compared to the state-of-the-art federated learning algorithms on the real-world heart disease dataset.
The Power Zeghdoudi Distribution: Properties, Estimation, and Applications to Real Right-Censored Data
Authors: Aidi Khaoula , Al-Omari Amer Ibrahim , Alsultan Rehab
Published: November 2022

A new two-parameter power Zeghdoudi distribution (PZD) is suggested as a modification of the Zeghdoudi distribution using the power transformation method. As a result, the PZD may have increasing, decreasing, and unimodal probability density function and decreasing mean residual life function. In addition, other properties are presented, such as moments, order statistics, reliability measures, Bonferroni and Lorenz curves, Gini index, stochastic ordering, mean and median deviations, and quantile function. Following this, a section is devoted to the related model parameters which are estimated using the maximum likelihood estimation method, the weighted least squares and least squares methods, the maximum product of spacing method, the Cramer–von Mises method, and the right-tail and left-tail Anderson–Darling methods, and the Nikulin–Rao–Robson test statistic is considered. A simulation study is conducted to assess these methods and to investigate the distribution properties with right-censored data. The applicability of the proposed model is studied based on three real data sets of failure times, bladder cancer patients, and glass fiber data with a comparison with such competitors as the gamma, xgamma, Lomax, Darna, power Darna, power Lindley, and exponentiated power Lindley models. According to several established criteria, the comparative findings are overwhelmingly favorable to the suggested model.
Optimization Strategy for Modal Test Measurement Points of Large-Span Steel Beams Based on Improved Particle Swarm Optimization Algorithm with Random Weights
Authors: Zhao Jian , Wang Kun , Wu Di , Huang Qin , Yu Ming
Published: November 2022

In order to obtain better vibration response data and improve the accuracy of results in large-span steel beam modal tests, this paper proposes an optimization strategy for the arrangement of measurement points on large-span steel beams. First, an optimized arrangement of large-span steel beam measurement points was proposed based on an improved particle swarm optimization algorithm; the test function verified the superiority of the improved algorithm. Secondly, the deck of a steel tube truss girder bridge (STTGB) was taken as the research object; the computational modal analysis method was adopted to obtain the computational modal results of the bridge deck. In addition, measurement points were arranged on the bridge deck according to the uniform distribution method and the proposed optimization algorithm, and modal tests were conducted. Finally, the modal parameters of the bridge deck based on the two arrangement methods were obtained and compared to the best arrangement method for the STTGB deck. The results show that the proposed method has good efficiency in the optimal arrangement of the bridge deck measurement points and the obtained modal parameters have high accuracy. Therefore, this paper has important guiding significance for the study of structural dynamic characteristics using the distribution method based on an optimization algorithm.
Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
Authors: Huang Weichao , Zhang Ganggang , Jiao Shangbin , Wang Jing
Published: November 2022

Aiming at the poor effect of traditional denoising algorithms on image enhancement with strong noise, an image denoising algorithm based on improved whale optimization algorithm and parameter adaptive array stochastic resonance is proposed in the paper. In this algorithm, through dimensionality reduction scanning, coding, modulation and other processing, the noise-containing gray image becomes a one-dimensional aperiodic binary pulse amplitude modulation signal suitable for a bistable stochastic resonance model. Then, the traditional whale optimization algorithm is improved in the initial solution distribution, global search ability and population diversity generalization. The improved whale optimization algorithm is applied to select the parameters of the stochastic resonance, which effectively improves the parameters self-adaptive of the array stochastic resonance model. Finally, the denoised image is obtained by demodulating, decoding and anti-scanning the stochastic resonance output. The experimental results show that compared with the array stochastic resonance method with fixed parameters and the classical image denoising method, the algorithm proposed in this paper has better performance in terms of visual effect and peak signal-to-noise ratio index, which proves the advantages and effective application of the method in image denoising.
mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm
Authors: Hao Zhanjun , Wang Ruidong , Dang Xiaochao , Yan Hao , Peng Jianxiang
Published: November 2022

Millimeter-wave SAR (Synthetic Aperture Radar) imaging is widely studied as a common means of RF (Radio Frequency) imaging, but there are problems of the ghost image in Sparsely-Sampled cases and the projection of multiple targets at different distances. Therefore, a robust imaging algorithm based on the Analytic Fourier Transform is proposed, which is named mmSight. First, the original data are windowed with Blackman window to take multiple distance planes into account; then, the Analytic Fourier Transform that can effectively suppress the ghost image under Sparsely-Sampled is used for imaging; finally, the results are filtered using a Mean Filter to remove spatial noise. The experimental results show that the proposed imaging algorithm in this paper, relative to other algorithms, can image common Fully-Sampled single target, hidden target, and multiple targets at the same distance, and solve the ghost image problem of single target in the case of Sparsely-Sampled, as well as the projection problem of multiple targets at different distances; the Image Entropy of the mmSight is 4.6157 and is on average 0.3372 lower than that of other algorithms. Compared with other algorithms, the sidelobe and noise of the Point Spread Function are suppressed, so the quality of the image obtained from imaging is better than that of other algorithms.
Usefulness of Potentially Probiotic L. lactis Isolates from Polish Fermented Cow Milk for the Production of Cottage Cheese
Authors: Pytka Monika , Kordowska-Wiater Monika , Wajs Justyna , Glibowski Paweł , Sajnaga Ewa
Published: November 2022

Lactococcus lactis bacteria are used as starter cultures in cottage cheese and fermented milk production due to their acidification and contribution in the creation of the characteristic sensory features. The aim of the research was to carry out isolation, genetic identification, and verification of the probiotic properties of selected Lactococcus lactis isolates obtained from Polish fermented cow milk and apply the best strains to produce cottage cheese with good rheological and sensory characteristics. The isolates obtained were identified morphologically, biochemically, and with the use of 16S rRNA gene sequencing. After pre-screening two of the tested Lactococcus lactis strains, A13 and A14 were observed to be most tolerant to high NaCl concentrations and bile salts and to acidify milk the most. We confirmed the activity of A13 and A14 against such pathogenic strains as Escherichia coli, Salmonella enterica ssp. enterica, Salmonella enterica ssp. enterica sv. anatum, Staphylococcus aureus, and Enterococcus faecalis. As a potential industrial starter culture with probiotic potential, the selected Lactococcus lactis A13 and A14 strains produced cottage cheese quickly with good sensory (colour, smell, taste, texture) and rheological (viscosity, elasticity) properties.
Analysis of Ground Motion Intensity Measures and Selection Techniques for Estimating Building Response
Authors: Aquib Tariq A. , Sivasubramonian Jayalakshmi , Mai P. Martin
Published: November 2022

The structural response of buildings to earthquake shaking is of critical importance for seismic design purposes. Research on the relationship between earthquake ground motion intensity, building response, and seismic risk is ongoing, but not yet fully conclusive. Often, probability demand models rely on one ground motion intensity measure (IM) to predict the engineering demand parameter (EDP). The engineering community has suggested several IMs to account for different ground motion characteristics, but there is no single optimal IM. For this study, we compile a comprehensive list of IMs and their characteristics to assist engineers in making an informed decision. We discuss the ground motion selection process used for dynamic analysis of structural systems. For illustration, we compute building responses of 2D frames with different natural period subjected to more than 3500 recorded earthquake ground motions. Using our analysis, we examine the effects of different structural characteristics and seismological parameters on EDP-IM relationships by applying multi-regression models and statistical inter-model comparisons. As such, our results support and augment previous studies and suggest further improvements on the relationship between EDP and IM in terms of efficiency and sufficiency. Finally, we provide guidance on future approaches to the selection of both optimal intensity measures and ground motions using newer techniques.
Special Issue on New Insights into Atmospheric Chemistry and Climate
Authors: Ielpo Pierina
Published: November 2022

Atmospheric gases and aerosols affect air quality and play an important role in the Earth’s climate system [...]