bluuebunny/crossref_metadata_2025 huggingface dataset:
The Activation of Nrf2/HO-1 by 8-Epi-7-deoxyloganic Acid Attenuates Inflammatory Symptoms through the Suppression of the MAPK/NF-κB Signaling Cascade in In Vitro and In Vivo Models
Authors: Simu Shakina Yesmin , Alam Md Badrul , Kim Sun Yeou
Published: September 2022

In this study, we examined the ameliorative effects of 8-epi-7-deoxyloganic acid (DLA), an iridoid glycoside, on oxidative stress and inflammation in both LPS-stimulated macrophages and mice with carrageenan-induced inflammation. DLA decreased oxidative stress through the up-regulation of heme oxygenase-1 (HO-1) via the activation of nuclear factor erythroid 2-related factor 2 (Nrf2), leading to the suppression of reactive oxygen species (ROS) and nitric oxide generation (NO). In addition, DLA inhibited the activation of mitogen-activated protein kinases (MAPKs) and the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway, resulting in a decreased production of the proinflammatory cytokines tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) and -6 (IL-6), as well as of monocyte chemoattractant protein-1 (MCP-1). In addition, DLA effectively inhibited the generation of nitric oxide (NO) and prostaglandin E2 (PGE2) by inhibiting the expression of the upstream genes inducible nitric oxidase (iNOS) and cyclooxygenase-2 (COX-2). DLA demonstrated powerful anti-inflammatory and antioxidant properties and thus appears as an intriguing prospective therapeutic treatment.
Oxidative Stress Is a Potential Cost of Synchronous Nesting in Olive Ridley Sea Turtles
Authors: Arango B. Gabriela , Ensminger David C. , Moreno-Santillán Diana Daniela , Harfush-Meléndez Martha , López-Reyes Elpidio Marcelino , Marmolejo-Valencia José Alejandro , Merchant-Larios Horacio , Crocker Daniel E. , Vázquez-Medina José Pablo
Published: September 2022

Olive ridley sea turtles, Lepidochelys olivacea, exhibit a polymorphic reproductive behavior, nesting solitarily or in mass aggregations termed “arribadas”, where thousands of individuals nest synchronously. Arribada nesting provides fitness benefits including mate finding during nearshore aggregations and predator satiation at the time of hatching, but it is unknown if such benefits come with a physiological cost. We used plasma metabolite profiling, stable isotope analysis, biochemical and endocrine assays to test whether metabolic parameters differ between nesting modes, and if arribada nesting is associated with increased levels of oxidative damage compared to solitary nesting. Arribada nesters were bigger and had higher circulating thyroid hormone levels than solitary nesters. Similarly, pathways related to phospholipid and amino acid metabolism, catabolic processes, and antioxidant defense were enriched in individuals nesting in arribada. Stable isotope signatures in skin samples showed differences in feeding zones with arribada nesters likely feeding on benthic and potentially more productive grounds. Arribada nesters had increased levels of plasma lipid peroxidation and protein oxidation products compared to solitary nesters. These results suggest that metabolic profiles differ between nesting modes and that oxidative stress is a trade-off for the fitness benefits associated with arribada nesting.
Effect of Resveratrol Supplementation on Intestinal Oxidative Stress, Immunity and Gut Microbiota in Weaned Piglets Challenged with Deoxynivalenol
Authors: Qiu Yueqin , Nie Xinzhi , Yang Jun , Wang Li , Zhu Cui , Yang Xuefen , Jiang Zongyong
Published: September 2022

(1) Background: Deoxynivalenol (DON) is a general mycotoxin that induces severe intestinal barrier injury in humans and animals. Resveratrol (RES) efficiently exerts anti-inflammatory and antioxidant effects. However, the information regarding RES protecting against DON-induced oxidative stress and intestinal inflammation in piglets is limited. (2) Methods: A total of 64 weaned piglets (Duroc × (Landrace × Yorkshire), 21-d-old, barrow) were randomly allocated to four groups (eight replicate pens per group, each pen containing two piglets) for 28 d. The piglets were fed a control diet (CON) or the CON diet supplemented with 300 mg RES/kg diet (RES group), 3.8 mg DON/kg diet (DON) or both (DON+RES) in a 2 × 2 factorial design. (3) Compared with unsupplemented DON-challenged piglets, RES supplementation in DON-challenged piglets increased ileal villus height and the abundance of ileal SOD1, GCLC and PG1-5 transcripts and Muc2 protein (p < 0.05), while decreasing the mRNA and proteins expression of ileal IL-1β, IL-6 and TNF-α, and malondialdehyde (MDA) levels in plasma and ileum in DON-challenged piglets (p < 0.05). Moreover, the abundances of class Bacilli, order Lactobacillales, family Lactobacillaceae and species Lactobacillus gasseri were increased in DON-challenged piglets fed a RES-supplemented diet compared with those in DON-challenged piglets(p ≤ 0.05). (4) Conclusions: our results indicated that RES supplementation in DON-challenged piglets efficiently attenuated intestinal inflammation and oxidative stress and improved gut microbiota, thereby alleviating DON-induced intestinal barrier injury.
Chronic Effects of Carbamazepine, Progesterone and Their Mixtures at Environmentally Relevant Concentrations on Biochemical Markers of Zebrafish (Danio rerio)
Authors: Ács András , Liang Xinyue , Bock Illés , Griffitts Jeffrey , Ivánovics Bence , Vásárhelyi Erna , Ferincz Árpád , Pirger Zsolt , Urbányi Béla , Csenki Zsolt
Published: September 2022

The impact of pharmaceuticals on non-target organisms in the environment is of increasing concern and study. Pharmaceuticals and other pollutants are often present as mixtures in an environmental compartment. Studies on the toxicological implications of these drugs on fish, particularly as mixtures at environmentally relevant concentrations, are very limited. Thus, this study aimed to evaluate the chronic effects of the anticonvulsant drug carbamazepine (CBZ) and progesterone (P4) at environmentally relevant concentrations, individually and in binary mixtures, applying a suite of biomarkers at the molecular level in zebrafish (Danio rerio). The effects on biotransformation enzymes 7-ethoxyresorufin O-deethylase (EROD) and glutathione-S-transferase (GST), antioxidant enzymes catalase (CAT), superoxide dismutase (SOD), glutathione peroxidases (GPxSe and GPxTOT), and glutathione reductase (GR), and markers of damage, such as DNA strand breaks (DNAsb), lactate dehydrogenase (LDH), lipid peroxidation (LPO), and vitellogenin-like proteins (VTG), were evaluated. Analyses of the biochemical markers indicated that a synergistic dose-ratio-dependent effect of CBZ and P4 in zebrafish occurs after chronic exposure regarding VTG, biotransformation enzymes (EROD, GST), and oxidative stress marker (DNAsb). The results suggest a synergistic effect regarding VTG, thus indicating a high risk to the reproductive success of fish if these pharmaceuticals co-occur.
Special Issue on Musculoskeletal Research: Biomechanics and Biomaterials for the Treatment of Orthopedic Diseases
Authors: Welke Bastian , Seehaus Frank
Published: September 2022

Musculoskeletal research deals with the effects of the orthopedic treatment of pathologies on the biomechanics of the affected areas and on the musculoskeletal system [...]
Low-Profile VHF Antenna Based on Quarter-Mode Substrate-Integrated Waveguide Structure
Authors: Chung Jae-Young , Yih Geown
Published: September 2022

A traditional whip antenna is suitable for terrestrial wireless communication due to its omnidirectional radiation pattern which is capable of transmitting and receiving signals from all directions. However, the length of a whip antenna is long for the very high frequency (VHF) band applications; for example, the ideal length is 80 cm at 90 MHz, which restrains the mobility when mounted on a vehicle. Many studies have been conducted to develop a low-profile antenna that can substitute a whip antenna but most of them are for relatively high frequency (GHz) applications. In this paper, a planar low-profile antenna operating in the VHF band is proposed. The height of the antenna is only 9 cm by utilizing a quarter-mode substrate-integrated waveguide (QMSIW) structure with shorting pins at the edges. The latter promotes omnidirectional radiations despite its short height. The antenna design was optimized by 3D full-wave simulation software. Subsequently, an antenna prototype was fabricated and tested. The results demonstrated that the antenna resonated at 86 MHz with a maximum gain of 1.7 dB along the azimuth direction.
Deep Learning for In-Situ Layer Quality Monitoring during Laser-Based Directed Energy Deposition (LB-DED) Additive Manufacturing Process
Authors: Hespeler Steven , Dehghan-Niri Ehsan , Juhasz Michael , Luo Kevin , Halliday Harold S.
Published: September 2022

Defects are a leading issue for the rejection of parts manufactured through the Directed Energy Deposition (DED) Additive Manufacturing (AM) process. In an attempt to illuminate and advance in situ quality monitoring and control of workpieces, we present an innovative data-driven method that synchronously collects sensing data and AM process parameters with a low sampling rate during the DED process. The proposed data-driven technique determines the important influences that individual printing parameters and sensing features have on prediction at the inter-layer qualification to perform feature selection. Three Machine Learning (ML) algorithms including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) are used. During post-production, a threshold is applied to detect low-density occurrences such as porosity sizes and quantities from CT scans that render individual layers acceptable or unacceptable. This information is fed to the ML models for training. Training/testing are completed offline on samples deemed “high-quality” and “low-quality”, utilizing only features recorded from the build process. CNN results show that the classification of acceptable/unacceptable layers can reach between 90% accuracy while training/testing on a “high-quality” sample and dip to 65% accuracy when trained/tested on “low-quality”/“high-quality” (respectively), indicating over-fitting but showing CNN as a promising inter-layer classifier.
Fault Diagnosis Method of Intelligent Substation Protection System Based on Gradient Boosting Decision Tree
Authors: Ding Wei , Chen Qing , Dong Yuzhan , Shao Ning
Published: September 2022

In order to improve the efficiency of the devices’ fault diagnosis of the protection systems of intelligent substation, a fault diagnosis method based on a gradient boosting decision tree (GBDT) was proposed. Using the integrated alarm information, the device self-checking information, the link information of generic object-oriented substation event (GOOSE) and sampled value (SV) and the sampling value information generated during the fault of the protection system, the fault feature information set is constructed. According to different fault characteristics, the protection system faults are classified into simple faults and complex faults to improve the diagnosis efficiency. Using GBDT training rules, a fault diagnosis model of protection system based on GBDT is established and fault diagnosis steps are given. This study takes a 110 kV intelligent substation in southern China as an example, to verify the effectiveness and accuracy of the proposed fault diagnosis method, and compared it with the existing methods in terms of the accuracy. The diagnostic accuracy in the case of false alarms and the case of multiple faults are verified. The results show that the method can meet the practical engineering application.
An Insight into Neuropeptides Inhibitors in the Biology of Colorectal Cancer: Opportunity and Translational Perspectives
Authors: Srivastava Ankit , Rikhari Deeksha , Pradhan Biswajita , Bharadwaj Kaushik Kumar , Gaballo Antonio , Quarta Alessandra , Jena Mrutyunjay , Srivastava Sameer , Ragusa Andrea
Published: September 2022

Neuropeptides are mainly secreted from the human central and peripheral nervous systems. Neuropeptides bind to its cognate rhodopsin-like G-protein coupled receptor (GPCR) and perform various physiological functions. Conventional cancer treatments in clinical practice still present many drawbacks due to the lack of selectivity toward the target cell, drug-resistance, and side-effects, thus pushing for the development of new therapeutic agents and therapies. Recent research suggests that neuropeptides influence cancer cell proliferation, invasion, metastasis, and angiogenesis and, therefore, they could be exploited as a target for novel anticancer therapies. Very recently, targeted approaches that inhibit neuropeptides and their associated receptors are being developed in cancer treatment. This review focuses on various neuropeptides and their potential utility as drug targets by different inhibitors as a recently identified approach to cancer prevention, with particular emphasis on colorectal cancer.
Special Issue on Social Network Analysis
Authors: Faralli Stefano , Velardi Paola
Published: September 2022

Social network analysis (SNA) is a research area of computer science with the purpose to represent people and their social interactions as graphs, and then, analyze these graphs using network and graph theory [...]
Technologies and Applications of Communications in Road Transport
Authors: Jiménez Felipe
Published: September 2022

The use of communications for the transmission of information in the field of transport allows the introduction of services that increase safety and efficiency, both at the vehicle and mobility levels [...]
An End-to-End Mutually Interactive Emotion–Cause Pair Extractor via Soft Sharing
Authors: Wang Beilun , Ma Tianyi , Lu Zhengxuan , Xu Haoqing
Published: September 2022

Emotion–cause pair extraction (ECPE), i.e., extracting pairs of emotions and corresponding causes from text, has recently attracted a lot of research interest. However, current ECPE models face two problems: (1) The common two-stage pipeline causes the error to be accumulated. (2) Ignoring the mutual connection between the extraction and pairing of emotion and cause limits the performance. In this paper, we propose a novel end-to-end mutually interactive emotion–cause pair extractor (Emiece) that is able to effectively extract emotion–cause pairs from all potential clause pairs. Specifically, we design two soft-shared clause-level encoders in an end-to-end deep model to measure the weighted probability of being a potential emotion–cause pair. Experiments on standard ECPE datasets show that Emiece achieves drastic improvements over the original two-step ECPE model and other end-to-end models in the extraction of major emotional cause pairs. The effectiveness of soft sharing and the applicability of the Emiece framework are further demonstrated by ablation experiments.
Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
Authors: Ribas Dayana , Miguel Antonio , Ortega Alfonso , Lleida Eduardo
Published: September 2022

This paper proposes a Deep Learning (DL) based Wiener filter estimator for speech enhancement in the framework of the classical spectral-domain speech estimator algorithm. According to the characteristics of the intermediate steps of the speech enhancement algorithm, i.e., the SNR estimation and the gain function, there is determined the best usage of the network at learning a robust instance of the Wiener filter estimator. Experiments show that the use of data-driven learning of the SNR estimator provides robustness to the statistical-based speech estimator algorithm and achieves performance on the state-of-the-art. Several objective quality metrics show the performance of the speech enhancement and beyond them, there are examples of noisy vs. enhanced speech available for listening to demonstrate in practice the skills of the method in simulated and real audio.
Performance Evaluation of an Imaging Radiation Portal Monitor System
Authors: Vasiljević Jana , Cederwall Bo
Published: September 2022

An organic scintillator-based radiation portal monitor (RPM) prototype system with imaging capabilities has been developed based on the neutron–gamma emission tomography technique. The technique enables rapid detection and precise location of small amounts of special nuclear materials, such as plutonium, using time and energy correlations between fast neutrons and gamma rays from spontaneous fission with low false-alarm rates. These capabilities, in addition to state-of-the-art detection of various gamma-emitting sources, enables the novel imaging RPM concept to efficiently address global security threats from terrorism and the proliferation of nuclear weapons. The detector approach is simple and versatile and can easily be adapted for different applications in nuclear security, public safety, nuclear emergency response, and radiological surveying. In this work, basic performance parameters of the imaging RPM prototype system developed at KTH have been evaluated.
5G Based on MNOs for Critical Railway Signalling Services: Future Railway Mobile Communication System
Authors: González-Plaza Ana , Gutiérrez Cantarero Rafael , Arancibia Banda Rafael B. , Briso Rodríguez César
Published: September 2022

This paper describes the prototype and tests carried out to demonstrate the feasibility of implementing 4G/5G systems based on mobile network operators (MNOs) to transmit signalling critical data for railway systems as part of a possible solution for the Future Railway Mobile Communication System (FRMCS). This communication system design is performed from a protocol-stack perspective, introducing the KPIs for the physical layer (RSRP, RSRQ and SINR), network layer (latency and jitter) and application layer (signalling communication timeout). The analysis focuses on the characterisation of signalling critical railway data to assess the data traffic behaviour in a telecommunications laboratory. Finally, the study validates the approach in a realistic railway environment.
Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields
Authors: Monsalve Nora C. , López-Quílez Antonio
Published: September 2022

In this paper, we propose a structured additive regression (STAR) model for modeling the occurrence of a disease in fields or nurseries. The methodological approach involves a Gaussian field (GF) affected by a spatial process represented by an approximation to a Gaussian Markov random field (GMRF). This modeling allows the building of maps with prediction probabilities regarding the presence of a disease in plants using Bayesian kriging. The advantage of this modeling is its computational benefit when compared with known spatial hierarchical models and with the Bayesian inference based on Markov chain Monte Carlo (MCMC) methods. Inference through the use of the integrated nested Laplace approximation (INLA) with the stochastic partial differential equation (SPDE) approach facilitates the handling of large datasets in excellent computation times. Our approach allows the evaluation of different sampling strategies, from which we obtain inferences and prediction maps with similar behaviour to those obtained when we consider all subjects in the study population. The analysis of the different sampling strategies allows us to recognize the relevance of spatial components in the studied phenomenon. We demonstrate how Bayesian kriging can incorporate sources of uncertainty associated with the prediction parameters, which leads to more realistic and accurate estimation of the uncertainty. We illustrate the methodology with samplings of Citrus macrophylla affected by the tristeza virus (CTV) grown in a nursery.
Analysis of the Development Trend of Sports Research in China and Taiwan Using Natural Language Processing
Authors: Ho Tu-Kuang , Shih Wei-Yuan , Kao Wen-Yang , Hsu Chin-Hsien , Wu Cheng-Ying
Published: September 2022

Background: A digital text abstract presents the essential information of an article, and we can find the trend and value of the research by analyzing it rigorously and digging up knowledge. Therefore, this study focuses on the abstracts of index journals in China and Taiwan from July 2010 to June 2020 (a total of 3283 abstracts). Methods: Through the concepts of text mining and natural language processing (NLP), it constructs processes such as text retrieval, text segmentation and word cloud analysis, TF-IDF weight analysis, co-word analysis, network analysis, and trend analysis, and analyses a large amount of text data. Results: The results show that the scope of research in China covers the fields of social sports and sports science, and research in Taiwan covers both natural and social sciences. The network diagram highlights the richness of sports-related research fields in the two regions, but research on sports philosophy is relatively rare. Conclusions: It is suggested that all disciplines/departments should re-allocate the same resources, so as to show a balanced development trend and help expand a new chapter in the sports academic field.
Improved Secure Deep Neural Network Inference Offloading with Privacy-Preserving Scalar Product Evaluation for Edge Computing
Authors: Li Jiarui , Zhang Zhuosheng , Yu Shucheng , Yuan Jiawei
Published: September 2022

Enabling deep learning inferences on resource-constrained devices is important for intelligent Internet of Things. Edge computing makes this feasible by outsourcing resource-consuming operations from IoT devices to edge devices. In such scenarios, sensitive data shall be protected while transmitted to the edge. To address this issue, one major challenge is to efficiently execute inference tasks without hampering the real-time operation of IoT applications. Existing techniques based on complex cryptographic primitives or differential privacy are limited to either efficiency or model accuracy. This paper addresses this challenge with a lightweight interactive protocol by utilizing low-latency IoT-to-edge communication links for computational efficiency. We achieve this with a new privacy-preserving scalar product evaluation technique that caters to the unique requirements of deep learning inference. As compared to the state-of-the-art, our solution offers improved trade-offs among privacy, efficiency, and utility. Experimental results on a Raspberry Pi 4 (Model B) show that our construction can achieve over 14× acceleration versus local execution for AlexNet inference over ImageNet. The proposed privacy-preserving scalar-product-evaluation technique can also be used as a general primitive in other applications.
ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model
Authors: Li Zhiheng , Gu Tongcheng , Li Bing , Xu Wubin , He Xin , Hui Xiangyu
Published: September 2022

Thus far, few studies have been conducted on fine-grained classification tasks for the latest convolutional neural network ConvNeXt, and no effective optimization method has been made available. To achieve more accurate fine-grained classification, this paper proposes two attention embedding methods based on ConvNeXt network and designs a new bilinear CBAM; simultaneously, a multiscale, multi-perspective and all-around attention framework is proposed, which is then applied in ConvNeXt. Experimental verification shows that the accuracy rate of the improved ConvNeXt for fine-grained image classification reaches 87.8%, 91.2%, and 93.2% on fine-grained classification datasets CUB-200-2011, Stanford Cars, and FGVC Aircraft, respectively, showing increases of 2.7%, 0.3% and 0.4%, respectively, compared to those of the original network without optimization, and increases of 3.7%, 8.0% and 2.0%, respectively, compared to those of the traditional BCNN. In addition, ablation experiments are set up to verify the effectiveness of the proposed attention framework.
Modeling of Water Losses in Hydraulic Tunnels under Pressure Based on Stepwise Regression Method
Authors: Radovanović Slobodan , Milivojević Milovan , Stojanović Boban , Obradović Srđan , Divac Dejan , Milivojević Nikola
Published: September 2022

In this paper, we presented a methodology for efficient and accurate modeling of water losses in hydraulic tunnels under inside internal water pressure, based on multiple linear regression (MLR). The methodology encompasses all steps needed to obtain an adequate mathematical relation between total water losses and relevant measurements in the tunnel, such as reservoir water level, piezometric levels, concrete and water temperatures, size of cracks, etc. Once the data are preprocessed and input variables were chosen, correlation analysis and PCA (principal component analysis) reduction were performed in order to obtain the pool of regression functions. Through an iterative process, according to stepwise regression principles, the most adequate MLR model in terms of accuracy and complexity was chosen. The methodology presented has been validated in modeling water losses in the hydraulic tunnel under the pressure of PSHPP “Bajina Bašta” in the Republic of Serbia. The obtained results have shown significantly better accuracy compared to the results published by other authors, proving that the developed model can be used as a powerful tool in future analyses of tunnel losses and remediation planning.
Whey Protein Isolate-Based Edible Coatings Incorporated with Jojoba Oil as a Novel Approach for Improving the Quality of Fresh-Cut Root Parsley during Refrigerated Storage
Authors: Galus Sabina , Mikus Magdalena , Ciurzyńska Agnieszka , Janowicz Monika
Published: September 2022

In this paper, the effect of whey-protein-isolate-based edible coatings with the addition of jojoba oil at concentrations of 1 and 2% on the qualitative characteristics of fresh-cut root parsley was evaluated. Changes in hardness, color parameters, and the contents of polyphenols and flavonoids over 28 days of refrigerated storage, as well as changes in structure, were examined. It was observed that fresh-cut parsley, uncoated and coated with a protein solution, was characterized by a decrease in hardness, from 59.32 and 59.88 to 50.98 and 48.33 N, respectively, while parsley coated with whey protein isolate with the addition of jojoba oil showed an increase in hardness during storage, from 56.28 to 66.23 N and from 52.17 to 60.49 for 1% and 2% of jojoba-oil-containing formulations, respectively. The L*, a*, and b* color parameters and hue angle mostly remained at similar levels, which indicate the maintenance of the desired color, but changes in the values were observed during storage. Parameter L* was between 79.56 and 85.33 in the control samples and between 72.54 and 84.19 in the coated samples. It was shown that the use of whey protein coatings and storage time had a positive impact on the contents of polyphenols and flavonoids in the fresh-cut parsley. The highest changes in polyphenols, from 3.13 to 9.82 mg GAE/g d.m., were observed for the samples coated with the formulation containing 1% of jojoba oil. The highest increase in flavonoid content, from 23.65 to 40.60 mg QE/g d.m., was observed for the samples coated with the film-forming solution with 2% of jojoba oil. Scanning electron microscopy showed a smaller number of pores in the vegetable tissue as a result of the coatings, and this was the most noticeable for the samples without the jojoba oil. Storage deteriorated the quality of the fresh-cut parsley surface, and the drying effect was visible. The use of protein coatings incorporated with jojoba oil modifies the quality characteristics of fresh-cut parsley, and this can find application in reducing the waste of minimally processed vegetables during storage.
A Machine Learning Approach to Support Treatment Identification for Chiari I Malformation
Authors: Mesin Luca , Ponzio Francesco , Carlino Christian Francesco , Lenge Matteo , Noris Alice , Leo Maria Carmela , Sica Michela , McGreevy Kathleen , Fabrik Erica Leila Ahngar , Giordano Flavio
Published: September 2022

Chiari I malformation is characterized by the herniation of cerebellar tonsils below the foramen magnum. It is often accompanied by syringomyelia and neurosurgical management is still controversial. In fact, it is frequent that some symptomatic patients initially undergo bony decompression of the posterior fossa and need in a short time more invasive surgery with higher morbility (e.g., decompression of posterior fossa with dural plastic, with or without tonsillar coarctation) because of unsatisfactory results at MRI controls. This study proposes a machine learning approach (based on SVM classifier), applied to different morphometric indices estimated from sagittal MRI and some information on the patient (i.e., age and symptoms at diagnosis), to recognize patients with higher risk of syringomyelia and clinical deterioration. Our database includes 58 pediatric patients who underwent surgery treatment. A negative outcome at 1 year from the intervention was observed in 38% of them (accuracy of 62%). Our algorithm allows us to increase the accuracy to about 71%, showing it to be a valid support to neurosurgeons in refining the clinical picture.
Rheology of Variable Viscosity-Based Mixed Convective Inclined Magnetized Cross Nanofluid with Varying Thermal Conductivity
Authors: Darvesh Adil , Sajid Tanveer , Jamshed Wasim , Ayub Assad , Shah Syed , Eid Mohamed , Hussain Syed , Akram Mohammad , Hafeez Muhammad , Krawczuk Marek
Published: September 2022

Cross nanofluid possesses an extraordinary quality among the various fluidic models to explore the key characteristics of flowing fluid during very low and very high shear rates and its viscosity models depend upon shear rate. The current study establishes the numerical treatment regarding variable viscosity-based mixed convective inclined magnetized Cross nanofluid with varying thermal conductivities over the moving permeable surface. Along with variable thermal conductivities, we considered thermal radiation, thermophoresis, and the Brownian motion effect. An inclined magnetic field was launched for velocity scrutiny and the heat transfer fact was numerically seen by mixed convective conditions. Similarity variables were actioned on generated PDEs of the physical model and conversion was performed into ODEs. Numerical results showed that the frictional force and Nusselt quantity considerably influence the skinning heat transfer processes over the geometry of a moving permeable surface. Furthermore, less velocity was noticed for the greater suction parameter and the Brownian motion parameter corresponds to lower mass transport.
Quantitative Study of the Maceral Groups of Laminae Based on Support Vector Machine
Authors: Wu Yuanzhe , Fan Yunpeng , Liu Yan , Li Kewen , Zeng Tingxiang , Ma Yong , Tian Yongjing , Xu Yaohui , Wen Zhigang , Xie Xiaomin , Teng Juan
Published: September 2022

Identifying organic matter in laminae is fundamental to petroleum geology; however, many factors restrict manual quantification. Therefore, computer recognition is an appropriate method for accurately identifying microscopic components. In this study, we used support vector machine (SVM) to classify the preprocessed photomicrographs into seven categories: pyrite, amorphous organic matter, mineral matter, alginite, sporinite, vitrinite, and inertinite. Then, we performed a statistical analysis of the classification results and highlighted spatial aggregation of some categories using the kernel density estimation method. The results showed that the SVM can satisfactorily identify the macerals and minerals of the laminae, and its overall accuracy, kappa, precision, recall, and F1 are 82.86%, 0.80, 85.15%, 82.86%, and 82.75%, respectively. Statistical analyses revealed that pyrite was abundantly distributed in bright laminae; vitrinite and sporinite were abundantly distributed in dark laminae; and alginite and inertinite were equally distributed. Finally, the kernel density maps showed that all classification results, except inertinite, were characterized by aggregated distributions: pyrite with the distribution of multi-core centers, alginite, and sporinite with dotted distribution, and vitrinite with stripe distribution, respectively. This study may provide a new method to quantify the organic matter in laminae.
Vertical Distribution of Atmospheric Ice Nucleating Particles in Winter over Northwest China Based on Aircraft Observations
Authors: Wu Jiaxin , Yin Yan , Chen Kui , He Chuan , Jiang Hui , Zheng Bohua , Li Bin , Li Yuanyuan , Lv Yiying
Published: September 2022

The concentration of ice nucleating particles (INPs) in the cloud layer affects cloud processes more importantly than at the ground level. To make up for deficiencies in the observation of the vertical distribution of INPs over different background regions of China, airborne sampling of INPs was carried out at the altitudes of 2000–5500 m over Turpan, Xinjiang, northwest China on 29 December 2019, and the samples were analyzed in a static vacuum water vapor diffusion chamber. The sources and relationships of the INPs with meteorological conditions and the variation of the concentrations of aerosol particles of different sizes were explored. The results indicate that the concentration of INPs varies from 0.25 to 15.7 L−1 when the nucleation temperature changes from −17 to −26 °C and the relative humidity with respect to water (RHw) varies from 95 to 105%. The existence of an inversion layer near the planetary boundary layer (PBL) inhibits the vertical transport of aerosols, thus affecting the vertical distribution of INP concentration. INPs in the free atmosphere mainly originate from fine-mode aerosol particles transported from long distances by westerly winds and do not change significantly with height. The air mass in the PBL is short-range transported, and the INP concentration reaches 15.7 L−1 at T =−26 °C and RHw = 105%, which is obviously higher than that above the PBL. The analyses provide evidence that the meteorological conditions played an important part in regulating the vertical distribution of INPs.
Shake-Off Process in Non-Sequential Single-Photon Double Ionization of Closed-Shell Atomic Targets
Authors: Kheifets Anatoli S.
Published: September 2022

Amusia and Kheifets in 1984 introduced a Green’s function formalism to describe the effect of many-electron correlation on the ionization spectra of atoms. Here, we exploit this formalism to model the shake-off (SO) process, leading to the non-sequential single-photon two-electron ionization (double photoionization—DPI) of closed-shell atomic targets. We separate the SO process from another knock-out (KO) mechanism of DPI and show the SO prevalence away from the DPI threshold. We use this kinematic regime to validate our model by making a comparison with more elaborate techniques, such as convergent and time-dependent close coupling. We also use our model to evaluate the attosecond time delay associated with the SO process. Typically, the SO is very fast, taking only a few attoseconds to complete. However, it can take much longer in the DPI of strongly correlated systems, such as the H− ion as well as the subvalent shells of the Ar and Xe atoms and Cl− ion.
Fermentation of the Cucurbita ficifolia Fruit Juice: Its Antioxidant Activity and Effects on the Glycemia
Authors: Barrón-Álvarez Nayeli , Prado-Barragán Lilia Arely , Fortis-Barrera María de los Ángeles , Alarcon-Aguilar Francisco Javier
Published: September 2022

Cucurbita ficifolia is an edible plant whose fruits have hypoglycemic, anti-inflammatory, and antioxidant activities. Fermentation might improve these properties. This research aims to perform and characterize its fermentation in native and induced conditions with Lactobacillus plantarum (Lp) and evaluate its antioxidant activity and effect on glycemia. Fresh juice from mature fruits was characterized. One portion of this juice was spontaneously left to ferment (native fermentation), and the other was inoculated with Lp (controlled fermentation). Fermentation was monitored each 8 h by 56 h to measure microbial growth, pH, acidity, sugars, soluble protein, polyphenols and flavonoids, antioxidant activity, and effects on glycemia. In native fermentation, the growth of total microorganisms increased up to 32 h, decreasing at the end of the process. In Lp fermentation, total microorganisms increased until 16 h to stay constant at the end, with a predominance of Lp. The pH and the sugars decreased in the two fermentations, while polyphenol and flavonoid increased. In spontaneous fermentation, these changes were lesser. Both fermentations, like fresh juice, preserve functional properties (antioxidant, alpha-glucosidase inhibition, and hypoglycemia). The fermentation of this juice with Lp may develop functional beverages, which is significant due to its consumption as an edible fruit with medicinal properties.
Identification of New Drug Target in Staphylococcus lugdunensis by Subtractive Genomics Analysis and Their Inhibitors through Molecular Docking and Molecular Dynamic Simulation Studies
Authors: Alhamhoom Yahya , Hani Umme , Bennani Fatima Ezzahra , Rahman Noor , Rashid Md Abdur , Abbas Muhammad Naseer , Rastrelli Luca
Published: September 2022

Staphylococcus lugdunensis is a coagulase-negative, Gram-positive, and human pathogenic bacteria. S. lugdunensis is the causative agent of diseases, such as native and prosthetic valve endocarditis, meningitis, septic arthritis, skin abscesses, brain abscess, breast abscesses, spondylodiscitis, post-surgical wound infections, bacteremia, and peritonitis. S. lugdunensis displays resistance to beta-lactam antibiotics due to the production of beta-lactamases. This study aimed to identify potential novel essential, human non-homologous, and non-gut flora drug targets in the S. lugdunensis strain N920143, and to evaluate the potential inhibitors of drug targets. The method was concerned with a homology search between the host and the pathogen proteome. Various tools, including the DEG (database of essential genes) for the essentiality of proteins, the KEGG for pathways analysis, CELLO V.2.5 for cellular localization prediction, and the drug bank database for predicting the druggability potential of proteins, were used. Furthermore, a similarity search with gut flora proteins was performed. A DNA-binding response-regulator protein was identified as a novel drug target against the N920143 strain of S. lugdunensis. The three-dimensional structure of the drug target was modelled and validated with the help of online tools. Furthermore, ten thousand drug-like compounds were retrieved from the ZINC15 database. The molecular docking approach for the DNA-binding response-regulator protein identified ZINC000020192004 and ZINC000020530348 as the most favorable compounds to interact with the active site residues of the drug target. These two compounds were subjected to an MD simulation study. Our analysis revealed that the identified compounds revealed more stable behavior when bound to the drug target DNA-binding response-regulator protein than the apostate.
Synthetic Extracellular Matrices for 3D Culture of Schwann Cells, Hepatocytes, and HUVECs
Authors: Ma Chiyuan , Liu Kaizheng , Li Qin , Xiong Yue , Xu Cuixiang , Zhang Wenya , Ruan Changshun , Li Xin , Lei Xiaohua
Published: September 2022

Synthetic hydrogels from polyisocyanides (PIC) are a type of novel thermoreversible biomaterials, which can covalently bind biomolecules such as adhesion peptides to provide a suitable extracellular matrix (ECM)-like microenvironment for different cells. Although we have demonstrated that PIC is suitable for three-dimensional (3D) culture of several cell types, it is unknown whether this hydrogel sustains the proliferation and passaging of cells originating from different germ layers. In the present study, we propose a 3D culture system for three representative cell sources: Schwann cells (ectoderm), hepatocytes (endoderm), and endothelial cells (mesoderm). Both Schwann cells and hepatocytes proliferated into multicellular spheroids and maintained their properties, regardless of the amount of cell-adhesive RGD motifs in long-term culture. Notably, Schwann cells grew into larger spheroids in RGD-free PIC than in PIC-RGD, while HL-7702 showed the opposite behavior. Endothelial cells (human umbilical vein endothelial cells, HUVECs) spread and formed an endothelial cell (EC) network only in PIC-RGD. Moreover, in a hepatocyte/HUVEC co-culture system, the characteristics of both cells were well kept for a long period in PIC-RGD. In all, our work highlights a simple ECM mimic that supports the growth and phenotype maintenance of cells from all germ layers in the long term. Our findings might contribute to research on biological development, organoid engineering, and in vitro drug screening.
Fatty Acids: Essential Nutrients and Important Biomarkers
Authors: Gladyshev Michail I.
Published: September 2022

Fatty acids (FA) are well-known, important components of human nutrition [...]
Residues in the 1st Transmembrane-Spanning Helix Are Important for GABAAρ Receptor Function
Authors: Crowther Kate M. , Mesoy Susanne M. , Lummis Sarah C. R.
Published: September 2022

GABAAρ receptors are a subfamily of the GABAA receptor family of pentameric ligand-gated ion channels (pLGICs). Each subunit has a common structure, including a transmembrane domain of four α-helices (M1–M4). The aim of this study was to identify important M1 residues in the GABAAρ receptor (GABAAρR), using mutagenesis and functional assays combined with bioinformatic approaches. Alanine substitution of 12 of the 23 M1 residues yielded receptors with altered functional parameters, indicating these residues contribute to GABAAρR function. Further mutations reveal the properties that are important for function in critical residues, and, using a GABAAρR homology model, we suggest amino acid interactions that could be important. Phylogenetic analysis comparing GABAAR and other pLGICs subunits reveals most M1 residue properties linked to GABAAρR function are ancestrally ancient, but some are more recent acquisitions. Multiple sequence alignment of M1 residues across GABAAR subunits reveal three residues are well conserved except in GABAAR α subunits. Substitution of ρ1 subunit residues to their α1 subunit equivalents showed one alters functional parameters. Overall, the data provide a comprehensive picture of M1 residues that contribute to GABAAρR function, and illustrate how they might do so.
SARS-CoV-2 Invasion and Pathological Links to Prion Disease
Authors: Lukiw Walter J. , Jaber Vivian R. , Pogue Aileen I. , Zhao Yuhai
Published: September 2022

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the COVID-19 disease, is a highly infectious and transmissible viral pathogen that continues to impact human health globally. Nearly ~600 million people have been infected with SARS-CoV-2, and about half exhibit some degree of continuing health complication, generically referred to as long COVID. Lingering and often serious neurological problems for patients in the post-COVID-19 recovery period include brain fog, behavioral changes, confusion, delirium, deficits in intellect, cognition and memory issues, loss of balance and coordination, problems with vision, visual processing and hallucinations, encephalopathy, encephalitis, neurovascular or cerebrovascular insufficiency, and/or impaired consciousness. Depending upon the patient’s age at the onset of COVID-19 and other factors, up to ~35% of all elderly COVID-19 patients develop a mild-to-severe encephalopathy due to complications arising from a SARS-CoV-2-induced cytokine storm and a surge in cytokine-mediated pro-inflammatory and immune signaling. In fact, this cytokine storm syndrome: (i) appears to predispose aged COVID-19 patients to the development of other neurological complications, especially those who have experienced a more serious grade of COVID-19 infection; (ii) lies along highly interactive and pathological pathways involving SARS-CoV-2 infection that promotes the parallel development and/or intensification of progressive and often lethal neurological conditions, and (iii) is strongly associated with the symptomology, onset, and development of human prion disease (PrD) and other insidious and incurable neurological syndromes. This commentary paper will evaluate some recent peer-reviewed studies in this intriguing area of human SARS-CoV-2-associated neuropathology and will assess how chronic, viral-mediated changes to the brain and CNS contribute to cognitive decline in PrD and other progressive, age-related neurodegenerative disorders.
Advances in the Application of Electrospun Drug-Loaded Nanofibers in the Treatment of Oral Ulcers
Authors: Zhou Yangqi , Wang Menglong , Yan Chao , Liu Hui , Yu Deng-Guang
Published: September 2022

Oral ulcers affect oral and systemic health and have high prevalence in the population. There are significant individual differences in the etiology and extent of the disease among patients. In the treatment of oral ulcers, nanofiber films can control the drug-release rate and enable long-term local administration. Compared to other drug-delivery methods, nanofiber films avoid the disadvantages of frequent administration and certain side effects. Electrospinning is a simple and effective method for preparing nanofiber films. Currently, electrospinning technology has made significant breakthroughs in energy-saving and large-scale production. This paper summarizes the polymers that enable oral mucosal adhesion and the active pharmaceutical ingredients used for oral ulcers. Moreover, the therapeutic effects of currently available electrospun nanofiber films on oral ulcers in animal experiments and clinical trials are investigated. In addition, solvent casting and cross-linking methods can be used in conjunction with electrospinning techniques. Based on the literature, more administration systems with different polymers and loading components can be inspired. These administration systems are expected to have synergistic effects and achieve better therapeutic effects. This not only provides new possibilities for drug-loaded nanofibers but also brings new hope for the treatment of oral ulcers.
Proinflammatory and Cancer-Promoting Pathobiont Fusobacterium nucleatum Directly Targets Colorectal Cancer Stem Cells
Authors: Cavallucci Virve , Palucci Ivana , Fidaleo Marco , Mercuri Antonella , Masi Letizia , Emoli Valeria , Bianchetti Giada , Fiori Micol Eleonora , Bachrach Gilad , Scaldaferri Franco , Maulucci Giuseppe , Delogu Giovanni , Pani Giovambattista
Published: September 2022

Intestinal bacterial communities participate in gut homeostasis and are recognized as crucial in bowel inflammation and colorectal cancer (CRC). Fusobacterium nucleatum (Fn), a pathobiont of the oral microflora, has recently emerged as a CRC-associated microbe linked to disease progression, metastasis, and a poor clinical outcome; however, the primary cellular and/or microenvironmental targets of this agent remain elusive. We report here that Fn directly targets putative colorectal cancer stem cells (CR-CSCs), a tumor cell subset endowed with cancer re-initiating capacity after surgery and chemotherapy. A patient-derived CSC line, highly enriched (70%) for the stem marker CD133, was expanded as tumor spheroids, dissociated, and exposed in vitro to varying amounts (range 100–500 MOI) of Fn. We found that Fn stably adheres to CSCs, likely by multiple interactions involving the tumor-associated Gal-GalNac disaccharide and the Fn-docking protein CEA-family cell adhesion molecule 1 (CEACAM-1), robustly expressed on CSCs. Importantly, Fn elicited innate immune responses in CSCs and triggered a growth factor-like, protein tyrosine phosphorylation cascade largely dependent on CEACAM-1 and culminating in the activation of p42/44 MAP kinase. Thus, the direct stimulation of CSCs by Fn may contribute to microbiota-driven colorectal carcinogenesis and represent a target for innovative therapies.