bluuebunny/crossref_metadata_2025 huggingface dataset:
Linear Mixed Model for Genotype Selection of Sorghum Yield
Authors: Tesfa Mulugeta , Zewotir Temesgen , Derese Solomon Assefa , Belay Denekew Bitew , Shimelis Hussein
Published: February 2023

Data analysis using the General linear model assumes the factors to be fixed effects, and the BLUE method, which is based on their mean performance, is appropriate to select the best performing genotypes. The linear mixed model incorporates fixed and random effects that are very important to compare a genotype’s performance through BLUP. The purpose of this study was to identify the best performing genotypes that provided a high grain yield using a mixed model, compare the mean performance of genotypes on grain yield using BLUP and BLUE, and determine the impact of drought on sorghum production in Ethiopia. The experiment used water availability as a treatment, and each replication within the treatment levels used a lattice square design for data collection. The design consisted of 14 × 14 square experimental units (plots) comprising 196 genotypes, where each row of the square was represented as a block receiving 14 genotypes. The phenotypic characteristics were measured for the study. The statistical methods used for the study were ANOVA and the linear mixed model to identify the best performing genotypes of sorghum. The study found that sorghum production was influenced by drought, which restricted sorghum growth due to a shortage of water. The implementation of irrigation increased the grain yield from 2.48 to 3.17 t/ha, indicating that the difference in grain yield between treatments (with and without irrigation) was 0.69 t/ha. The study compared the general linear model and linear mixed model, and the investigation revealed that the mixed model was more accurate than the general linear model. The linear mixed model selected the best performing genotypes in grain yield with better accuracy. It is recommended to use the linear mixed model to select the best performing genotypes in grain yield.
WFT-Fati-Dec: Enhanced Fatigue Detection AI System Based on Wavelet Denoising and Fourier Transform
Authors: Sedik Ahmed , Marey Mohamed , Mostafa Hala
Published: February 2023

As the number of road accidents increases, it is critical to avoid making driving mistakes. Driver fatigue detection is a concern that has prompted researchers to develop numerous algorithms to address this issue. The challenge is to identify the sleepy drivers with accurate and speedy alerts. Several datasets were used to develop fatigue detection algorithms such as electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and electromyogram (EMG) recordings of the driver’s activities e.g., DROZY dataset. This study proposes a fatigue detection system based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) with machine learning and deep learning classifiers. The FFT and DWT are used for feature extraction and noise removal tasks. In addition, the classification task is carried out on the combined EEG, EOG, ECG, and EMG signals using machine learning and deep learning algorithms including 1D Convolutional Neural Networks (1D CNNs), Concatenated CNNs (C-CNNs), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), k-Nearest Neighbor (KNN), Quadrature Data Analysis (QDA), Multi-layer Perceptron (MLP), and Logistic Regression (LR). The proposed methods are validated on two scenarios, multi-class and binary-class classification. The simulation results reveal that the proposed models achieved a high performance for fatigue detection from medical signals, with a detection accuracy of 90% and 96% for multiclass and binary-class scenarios, respectively. The works in the literature achieved a maximum accuracy of 95%. Therefore, the proposed methods outperform similar efforts in terms of detection accuracy.
Impact of Solidified Municipal Sludge as Temporary Covering Soil on the Stability of Landfill Slope
Authors: Sun Tao , Fan Xihui , Xu Haoqing , Zhang Nan , Luo Hanxuan , Lv Yiyan , Wang Liyan
Published: February 2023

Solidified municipal sludge is widely used as temporary covering soil in landfill. Due to the complex leachate of landfill, solidified municipal sludge has the problems of stagnant water and strength degradation. In order to investigate the influence of solidified municipal sludge on the stability of landfills, a landfill slope model with SEEP/W and SLPOE/W was established, using the actual infiltration as the boundary condition. Based on the changes in permeability and strength of the intermediate overburden layer, the migration law of leachate in a landfill under the condition of an intermediate overburden layer was analyzed. The relationship between landfill slope safety, climatic conditions and deterioration of the intermediate overburden layer was further explored. The results show that the permeability of the intermediate overburden layer affects the distribution of leachate and the height of stagnant water. During the rainstorm period, the safety factor of the landfill slope decreased rapidly from 1.4 to 1.0 or even lower. With the decrease in the shear strength of the intermediate overburden, the safety factor of the landfill slope was reduced to less than 1.0. Therefore, maintaining the permeability and strength of solidified municipal sludge at a certain level is required to ensure the safety and stability of landfill slopes.
Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
Authors: Zhang Zheng , Chen Haibo , Huang Xiaoli
Published: February 2023

In the context of global climate change, air quality prediction work has a substantial impact on humans’ daily lives. The current extensive usage of machine learning models for air quality forecasting has resulted in significant improvements to the sector. The long short-term memory network is a deep learning prediction model, which adds a forgetting layer to a recurrent neural network and has several applications in air quality prediction. The experimental data presented in this research include air pollution data (SO2, NO2, PM10, PM2.5, O3, and CO) and meteorological data (temperature, barometric pressure, humidity, and wind speed). Initially, using air pollution data to calculate the air pollution index (AQI) and the wavelet transform with the adaptive Stein risk estimation threshold is utilized to enhance the quality of meteorological data. Using detrended cross-correlation analysis (DCCA), the mutual association between pollution elements and meteorological elements is then quantified. On short, medium, and long scales, the prediction model’s accuracy increases by 1%, 1.6%, 2%, and 5% for window sizes (h) of 24, 48, 168, and 5000, and the efficiency increases by 5.72%, 8.64%, 8.29%, and 3.42%, respectively. The model developed in this paper has a substantial improvement effect, and its application to the forecast of air quality is of immense practical significance.
Deep Learning Applications for Dyslexia Prediction
Authors: Alqahtani Norah Dhafer , Alzahrani Bander , Ramzan Muhammad Sher
Published: February 2023

Dyslexia is a neurological problem that leads to obstacles and difficulties in the learning process, especially in reading. Generally, people with dyslexia suffer from weak reading, writing, spelling, and fluency abilities. However, these difficulties are not related to their intelligence. An early diagnosis of this disorder will help dyslexic children improve their abilities using appropriate tools and specialized software. Machine learning and deep learning methods have been implemented to recognize dyslexia with various datasets related to dyslexia acquired from medical and educational organizations. This review paper analyzed the prediction performance of deep learning models for dyslexia and summarizes the challenges researchers face when they use deep learning models for classification and diagnosis. Using the PRISMA protocol, 19 articles were reviewed and analyzed, with a focus on data acquisition, preprocessing, feature extraction, and the prediction model performance. The purpose of this review was to aid researchers in building a predictive model for dyslexia based on available dyslexia-related datasets. The paper demonstrated some challenges that researchers encounter in this field and must overcome.
Tool Wear Prediction in Glass Fiber Reinforced Polymer Small-Hole Drilling Based on an Improved Circle Chaotic Mapping Grey Wolf Algorithm for BP Neural Network
Authors: Hu Shanshan , Liu Hui , Feng Yufei , Cui Chang , Ma Yujie , Zhang Genge , Huang Xuechuang
Published: February 2023

Glass fiber reinforced polymer (GFRP) is a typical difficult-to-process material. Its drilling quality is directly affected by the processing technology and tool life; burrs, tearing, delamination and other defects will reduce the service life of GFRP structural parts. Through drilling damage and tool wear experiments of GFRP, the thrust force, vibration amplitude, the number of processed holes, feed rate and cutting speed were found to be the main factors in drilling damage and tool wear. Using those main factors as the input layer, a tool wear and delamination factors prediction model was established based on an improved circle chaotic mapping (CCM) Grey Wolf algorithm for a back propagation (BP) neural network. Compared with the original BP neural network, the maximum prediction error of the improved BP neural network model was reduced by 71.2% and the root mean square (RMS) prediction error was reduced by 63.82%. The maximum prediction error of the delamination factor at the entrance was less than 3%, and the maximum prediction error of the delamination factor at the exit was less than 1%. The prediction results showed that the BP neural network model optimized by an improved circle chaotic mapping Grey Wolf algorithm can better predict the GFRP drilling quality and tool wear, and had higher accuracy, optimization efficiency and better robustness than the ordinary BP neural network.
Kirchhoff’s Analogy between the Kapitza Pendulum Stability and Buckling of a Wavy Beam under Tensile Loading
Authors: Ramachandran Rahul , Nosonovsky Michael
Published: February 2023

The Kirchhoff analogy between the oscillation of a pendulum (in the time domain) and the static bending of an elastic beam (in the spatial domain) is applied to the stability analysis of an inverted pendulum on a vibrating foundation (the Kapitza pendulum). The inverted pendulum is stabilized if the frequency and amplitude of the vibrating foundation exceed certain critical values. The system is analogous to static bending a wavy (patterned) beam subjected to a tensile load with appropriate boundary conditions. We analyze the buckling stability of such a wavy beam, which is governed by the Mathieu equation. Micro/nanopatterned structures and surfaces have various applications including the control of adhesion, friction, wettability, and surface-pattern-induced phase control.
Eva Hesse: Emergent Self-Portrait
Authors: McQuistion Lauren A.
Published: February 2023

The artist Eva Hesse (1936–1970) and her work, ranging from traditional painting and drawing to highly inventive bas-relief and sculptural form, are frequently interpreted through the lenses of biography, psychology, and gender, contributing to a prevailing narrative of a troubled and tragic artist figure. This dominant understanding of Hesse’s oeuvre has largely emerged from the interpretation of the artist’s own words, in the form of diary entries and interviews, and the published interpretations of these texts by scholars, peers, and critics, who frequently dwell on the narrative of Hesse’s short and challenging life. However, a closer look at the documentation of the artist’s own process of making, one that combined a near-daily writing practice, close annotations of choices made and executed in her work, and her emphasis on material experimentation, reveals an alternative reading of her writing and work. This paper will first explore the origins of the existing scholarship dedicated to Hesse’s writing that has contributed to the gendered and tragic mythos surrounding the artist and her work. This paper will then provide a re-reading of Hesse’s practice through the example of the work Repetition Nineteen, demonstrating her textual and material process as a deeply entangled set of relations between artist, process, and material contributing to a still-emerging portrait of the artist and her contributions.
Interlaboratory Study on Brake Particle Emissions Part II: Particle Number Emissions
Authors: Mathissen Marcel , Grigoratos Theodoros , Gramstat Sebastian , Mamakos Athanasios , Vedula RaviTeja , Agudelo Carlos , Grochowicz Jaroslaw , Giechaskiel Barouch
Published: February 2023

The Particle Measurement Programme (PMP) informal working group co-ordinated a global interlaboratory study (ILS) on brake wear particle emissions with the participation of 16 laboratories in 2021. Two articles present the results of the ILS: (I) particulate matter mass (PM) and (II) particle number (PN) emissions. The test matrix covered different brake systems, including ECE and NAO pad materials with grey cast iron discs and a drum brake. Regarding PN, the study measured the total particle number from approximately 10 nm to 2.5 µm (TPN). Some testing facilities measured solid particle number emissions (SPN) in parallel. The mean TPN concentrations ranged from 9.1 × 108 #/km/brake to 1.1 × 1010 #/km/brake. TPN and SPN emission levels were comparable, except for one lab that measured very high volatile particle emissions for one brake system. The minimum and maximum SPN emissions for a given brake differed by a factor of 2.5 ± 0.5, comparable to data from exhaust SPN ILS measurements. This article provides an overview of lessons learned and subsequent measures incorporated in an upcoming global technical regulation to reduce measurement variability when sampling and measuring brake particle emissions for light-duty vehicles up to 3.5 t.
Manifestation of the Early 20th Century Warming in the East-European Plain: Atmospheric Circulation Anomalies and Its Connection to the North Atlantic SST and Sea Ice Variability
Authors: Popova Valeria , Aldonina Tatiana , Bokuchava Daria
Published: February 2023

A study of the climatic characteristics and annual runoff of the Volga and Severnaya Dvina rivers demonstrates that, on the East European Plain (EEP), Early Twentieth Century Warming (ETCW) manifested in a multiyear drought between 1934 and 1940; this drought has no analogues in this region in terms of intensity and duration according to Palmer’s classification, and caused extreme hydrological events. The circulation conditions during this event were characterized by an extensive anticyclone over Eastern Europe, combined with a cyclonic anomaly in the circumpolar region. An analysis of the spatial features of sea surface temperature (SST) anomalies indicate that the surface air temperature (SAT) anomalies in July on the EEP during ETCW were related not only to the North Atlantic (NA) warming and positive AMO phase, but also to a certain spatial pattern of SST anomalies characteristic of the 1920–1950 period. The difference between the SST anomalies of the opposite sign in the different NA zones, used as the indicator of the obtained spatial pattern, shows the quite close relations between the July SAT anomalies on the EEP and the atmospheric circulation patterns responsible for them. The positive phase of the Atlantic Multidecadal Oscillation (AMO) and the expansion of the subtropical high-pressure belt to the north and to the east can be considered as global-scale drivers of this phenomenon. The AMO also impacts the sea ice cover in the Barents–Kara Sea region, which, in turn, could have led to specific atmospheric circulation patterns and contributed to droughts on the EEP in the 1930s.
Spatio-Temporal Coupling Analysis of Differences in Regional Grain–Economy–Population and Water Resources
Authors: Xia Tingting , Wang Yang , Zhang Shuai
Published: February 2023

In the context of climate change and the intensification of population activities, differences in regional water resources are the main drivers leading to these resources’ unbalanced development. This problem can be addressed by exploring spatial differences and spatio-temporal patterns. Based on the spatio-temporal trends of grain production, social economy, population, and water resources in the Tarim River Basin from 2005 to 2020, this paper uses the barycenter model coupling situation model to analyze the evolutionary trend of the barycenter, spatial overlap and spatio-temporal coupling degree for each index. The results show the following: (1) The spatio-temporal distribution of grain production was larger in the east than in the west. Grain production increased by 18.10% across the basin, and the migration rate of the grain center of gravity (CG) was 16.61 km/a. (2) The spatio-temporal distribution of the economy was greater in the south than in the north, with a cumulative migration of 323.10 km to the southwest. During the study period, the population remained concentrated in the western portion of the basin, increasing 25.18% compared to the population prior to 2005. The spatial variation range of total water resources was small, showing a trend of slow increase from west to east. (3) The overlap between GDP and population was enhanced, with the coupling showing a slow rising trend. The overlap of water resources and grain space also increased and the consistency index was high. From 2011 to 2020, the average consistency index was 0.594 and the coupling degree of the two factors was enhanced. By combining grain production, economy, population, and water resources with the center of gravity model, this paper reveals the spatial differences of relevant factors in the Tarim River Basin, providing an important reference value for regional socio-economic development and the rational allocation of water resources.
On a Coupled Differential System Involving (k,ψ)-Hilfer Derivative and (k,ψ)-Riemann–Liouville Integral Operators
Authors: Samadi Ayub , Ntouyas Sotiris K. , Ahmad Bashir , Tariboon Jessada
Published: February 2023

We investigate a nonlinear, nonlocal, and fully coupled boundary value problem containing mixed (k,ψ^)-Hilfer fractional derivative and (k,ψ^)-Riemann–Liouville fractional integral operators. Existence and uniqueness results for the given problem are proved with the aid of standard fixed point theorems. Examples illustrating the main results are presented. The paper concludes with some interesting findings.
Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach
Authors: Inui Atsuyuki , Nishimoto Hanako , Mifune Yutaka , Yoshikawa Tomoya , Shinohara Issei , Furukawa Takahiro , Kato Tatsuo , Tanaka Shuya , Kusunose Masaya , Kuroda Ryosuke
Published: February 2023

The diagnosis of osteoporosis is made by measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA). Machine learning, one of the artificial intelligence methods, was used to predict low BMD without using DXA in elderly women. Medical records from 2541 females who visited the osteoporosis clinic were used in this study. As hyperparameters for machine learning, patient age, body mass index (BMI), and blood test data were used. As machine learning models, logistic regression, decision tree, random forest, gradient boosting trees, and lightGBM were used. Each model was trained to classify and predict low-BMD patients. The model performance was compared using a confusion matrix. The accuracy of each trained model was 0.772 in logistic regression, 0.739 in the decision tree, 0.775 in the random forest, 0.800 in gradient boosting, and 0.834 in lightGBM. The area under the curve (AUC) was 0.595 in the decision tree, 0.673 in logistic regression, 0.699 in the random forest, 0.840 in gradient boosting, and 0.961, which was the highest, in the lightGBM model. Important features were BMI, age, and the number of platelets. Shapley additive explanation scores in the lightGBM model showed that BMI, age, and ALT were ranked as important features. Among several machine learning models, the lightGBM model showed the best performance in the present research.
Ultrasound Histotripsy on a Viable Perfused Whole Porcine Liver: Histological Aspects, Including 3D Reconstruction of the Histotripsy Site
Authors: Froghi Saied , Hall Andrew , Hanafi Bin Jalal Arif , Andrade Matheus Oliveira de , Mohammad Hadi Layla , Rashidi Hassan , Gélat Pierre , Saffari Nader , Davidson Brian , Quaglia Alberto
Published: February 2023

Non-invasive therapeutic-focused ultrasound (US) can be used for the mechanical dissociation of tissue and is described as histotripsy. We have performed US histotripsy in viable perfused ex vivo porcine livers as a step in the development of a novel approach to hepatocyte cell transplantation. The histotripsy nidus was created with a 2 MHz single-element focused US transducer, producing 50 pulses of 10 ms duration, with peak positive and negative pressure values of P+ = 77.7 MPa and P− = –13.7 MPaat focus, respectively, and a duty cycle of 1%. Here, we present the histological analysis, including 3D reconstruction of histotripsy sites. Five whole porcine livers were retrieved fresh from the abattoir using human transplant retrieval and cold static preservation techniques and were then perfused using an organ preservation circuit. Whilst under perfusion, histotripsy was performed to randomly selected sites on the live. Fifteen lesional sites were formalin-fixed and paraffin-embedded. Sections were stained with Haematoxylin and Eosin and picro-Sirius red, and they were also stained for reticulin. Additionally, two lesion sites were used for 3D reconstruction. The core of the typical lesion consisted of eosinophilic material associated with reticulin loss, collagen damage including loss of birefringence to fibrous septa, and perilesional portal tracts, including large portal vein branches, but intact peri-lesional hepatic plates. The 3D reconstruction of two histotripsy sites was successful and confirmed the feasibility of this approach to investigate the effects of histotripsy on tissue in detail.
Chemistry and Pharmacology of Bergenin or Its Derivatives: A Promising Molecule
Authors: Salimo Zeca M. , Yakubu Michael N. , da Silva Emanuelle L. , de Almeida Anne C. G. , Chaves Yury O. , Costa Emmanoel V. , da Silva Felipe M. A. , Tavares Josean F. , Monteiro Wuelton M. , de Melo Gisely C. , Koolen Hector H. F.
Published: February 2023

Bergenin is a glycosidic derivative of trihydroxybenzoic acid that was discovered in 1880 by Garreau and Machelart from the rhizomes of the medicinal plant Bergenia crassifolia (currently: Saxifraga crassifolia—Saxifragaceae), though was later isolated from several other plant sources. Since its first report, it has aroused interest because it has several pharmacological activities, mainly antioxidant and anti-inflammatory. In addition to this, bergenin has shown potential antimalarial, antileishmanial, trypanocidal, antiviral, antibacterial, antifungal, antinociceptive, antiarthritic, antiulcerogenic, antidiabetic/antiobesity, antiarrhythmic, anticancer, hepatoprotective, neuroprotective and cardioprotective activities. Thus, this review aimed to describe the sources of isolation of bergenin and its in vitro and in vivo biological and pharmacological activities. Bergenin is distributed in many plant species (at least 112 species belonging to 34 families). Both its derivatives (natural and semisynthetic) and extracts with phytochemical proof of its highest concentration are well studied, and none of the studies showed cytotoxicity for healthy cells.
Molecular Targets in Campylobacter Infections
Authors: Heimesaat Markus M. , Backert Steffen , Alter Thomas , Bereswill Stefan
Published: February 2023

Human campylobacteriosis results from foodborne infections with Campylobacter bacteria such as Campylobacter jejuni and Campylobacter coli, and represents a leading cause of bacterial gastroenteritis worldwide. After consumption of contaminated poultry meat, constituting the major source of pathogenic transfer to humans, infected patients develop abdominal pain and diarrhea. Post-infectious disorders following acute enteritis may occur and affect the nervous system, the joints or the intestines. Immunocompromising comorbidities in infected patients favor bacteremia, leading to vascular inflammation and septicemia. Prevention of human infection is achieved by hygiene measures focusing on the reduction of pathogenic food contamination. Molecular targets for the treatment and prevention of campylobacteriosis include bacterial pathogenicity and virulence factors involved in motility, adhesion, invasion, oxygen detoxification, acid resistance and biofilm formation. This repertoire of intervention measures has recently been completed by drugs dampening the pro-inflammatory immune responses induced by the Campylobacter endotoxin lipo-oligosaccharide. Novel pharmaceutical strategies will combine anti-pathogenic and anti-inflammatory effects to reduce the risk of both anti-microbial resistance and post-infectious sequelae of acute enteritis. Novel strategies and actual trends in the combat of Campylobacter infections are presented in this review, alongside molecular targets applied for prevention and treatment strategies.
KI04 an Aminoglycosides-Derived Molecule Acts as an Inhibitor of Human Connexin46 Hemichannels Expressed in HeLa Cells
Authors: Chang Cheng-Wei T. , Poudyal Naveena , Verdugo Daniel A. , Peña Francisca , Stehberg Jimmy , Retamal Mauricio A.
Published: February 2023

Background: Connexins (Cxs) are proteins that help cells to communicate with the extracellular media and with the cytoplasm of neighboring cells. Despite their importance in several human physiological and pathological conditions, their pharmacology is very poor. In the last decade, some molecules derived from aminoglycosides have been developed as inhibitors of Cxs hemichannels. However, these studies have been performed in E. coli, which is a very simple model. Therefore, our main goal is to test whether these molecules have similar effects in mammalian cells. Methods: We transfected HeLa cells with the human Cx46tGFP and characterized the effect of a kanamycin-derived molecule (KI04) on Cx46 hemichannel activity by time-lapse recordings, changes in phosphorylation by Western blot, localization by epifluorescence, and possible binding sites by molecular dynamics (MD). Results: We observed that kanamycin and KI04 were the most potent inhibitors of Cx46 hemichannels among several aminoglycosides, presenting an IC50 close to 10 μM. The inhibitory effect was not associated with changes in Cx46 electrophoretic mobility or its intracellular localization. Interestingly, 5 mM DTT did not reverse KI04 inhibition, but the KI04 effect completely disappeared after washing out KI04 from the recording media. MD analysis revealed two putative binding sites of KI04 in the Cx46 hemichannel. Results: These results demonstrate that KI04 could be used as a Cx46 inhibitor and could help to develop future selective Cx46 inhibitors.
Upregulation of Apolipoprotein L6 Improves Tumor Immunotherapy by Inducing Immunogenic Cell Death
Authors: Liu Kecheng , Chen Yutong , Li Bixiang , Li Yaning , Liang Xinyue , Lin Hai , Luo Lisi , Chen Tianliang , Dai Yalan , Pang Wenzheng , Zeng Linjuan
Published: February 2023

In the past few years, immune checkpoint blockade (ICB) therapy has emerged as a breakthrough treatment for cancers and has demonstrated inspiring effects in tumor patients with Epstein-Barr virus (EBV) infection. To allow more patients to benefit from immunotherapy, exploring novel biomarkers based on EBV-related tumors and immunotherapy cohorts was pursued in the present study. The essential biomarkers that may enhance antitumor immunity across EBV-related tumors were identified using the large-scale transcriptomic profiles of EBV-associated tumors and tumor immunotherapy cohorts. The clinical significance of vital genes was evaluated in multiple tumor immunotherapy cohorts. Moreover, the potential function of essential genes in immunotherapy was explored via bioinformatic analyses and verified by qRT-PCR, Western blot analysis, CCK8 assay and flow cytometry. Apolipoprotein L6 (APOL6) was considered the essential biomarker for enhancing antitumor immunity across EBV-positive tumors. The upregulation of APOL6 was correlated with increased response rates and prolonged survival in multiple tumor immunotherapy cohorts. Bioinformatic analyses suggested that APOL6 may enhance tumor immunotherapy by inducing immunogenic cell death. Pancreatic cancer cells transfected with APOL6 overexpression plasmid underwent apoptosis, necroptosis, and pyroptosis with immunogenic features. The biomarker upregulated in EBV-related tumors could further elucidate the drivers of immunotherapy response. The upregulation of APOL6 could improve immunotherapy by triggering immunogenic cell death, thus offering a new target to optimize cancer immunotherapy.
A Mitochondrial Perspective on Noncommunicable Diseases
Authors: Zheng Yifan , Zhang Jing , Zhu Xiaohong , Wei Yuanjuan , Zhao Wuli , Si Shuyi , Li Yan
Published: February 2023

Mitochondria are the center of energy metabolism in eukaryotic cells and play a central role in the metabolism of living organisms. Mitochondrial diseases characterized by defects in oxidative phosphorylation are the most common congenital diseases. Meanwhile, mitochondrial dysfunction caused by secondary factors such as non-inherited genetic mutations can affect normal physiological functions of human cells, induce apoptosis, and lead to the development of various diseases. This paper reviewed several major factors and mechanisms that contribute to mitochondrial dysfunction and discussed the development of diseases closely related to mitochondrial dysfunction and drug treatment strategies discovered in recent years.
Efficacy and Tolerability of Perampanel in Brain Tumor-Related Epilepsy: A Systematic Review
Authors: Rossi Jessica , Cavallieri Francesco , Bassi Maria Chiara , Biagini Giuseppe , Rizzi Romana , Russo Marco , Bondavalli Massimo , Iaccarino Corrado , Pavesi Giacomo , Cozzi Salvatore , Giaccherini Lucia , Najafi Masoumeh , Pisanello Anna , Valzania Franco
Published: February 2023

(1) Background: Epilepsy is a frequent comorbidity in patients with brain tumors, in whom seizures are often drug-resistant. Current evidence suggests that excess of glutamatergic activity in the tumor microenvironment may favor epileptogenesis, but also tumor growth and invasiveness. The selective non-competitive α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor antagonist perampanel (PER) was demonstrated to be efficacious and well-tolerated in patients with focal seizures. Moreover, preclinical in vitro studies suggested a potential anti-tumor activity of this drug. In this systematic review, the clinical evidence on the efficacy and tolerability of PER in brain tumor-related epilepsy (BTRE) is summarized. (2) Methods: Five databases and two clinical trial registries were searched from inception to December 2022. (3) Results: Seven studies and six clinical trials were included. Sample size ranged from 8 to 36 patients, who received add-on PER (mean dosage from 4 to 7 mg/day) for BTRE. After a 6–12 month follow-up, the responder rate (% of patients achieving seizure freedom or reduction ≥ 50% of seizure frequency) ranged from 75% to 95%, with a seizure freedom rate of up to 94%. Regarding tolerability, 11–52% of patients experienced non-severe adverse effects (most frequent: dizziness, vertigo, anxiety, irritability). The retention rate ranged from 56% to 83%. However, only up to 12.5% of patients discontinued the drug because of the adverse events. (4) Conclusions: PER seems to be efficacious, safe, and well-tolerated in patients with BTRE. Further randomized studies should be conducted in more homogeneous and larger populations, also evaluating the effect of PER on tumor progression, overall survival, and progression-free survival.
Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models
Authors: Hossain Amran , Islam Mohammad Tariqul , Rahman Tawsifur , Chowdhury Muhammad E. H. , Tahir Anas , Kiranyaz Serkan , Mat Kamarulzaman , Beng Gan Kok , Soliman Mohamed S.
Published: February 2023

Automated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and monitoring of the progression of brain disease. The manual detection, classification, and segmentation of tumors are extremely time-consuming but crucial tasks due to the tumor’s pattern. In this paper, we propose a new lightweight segmentation model called MicrowaveSegNet (MSegNet), which segments the brain tumor, and a new classifier called the BrainImageNet (BINet) model to classify the RMW images. Initially, three hundred (300) RMW brain image samples were obtained from our sensors-based microwave brain imaging (SMBI) system to create an original dataset. Then, image preprocessing and augmentation techniques were applied to make 6000 training images per fold for a 5-fold cross-validation. Later, the MSegNet and BINet were compared to state-of-the-art segmentation and classification models to verify their performance. The MSegNet has achieved an Intersection-over-Union (IoU) and Dice score of 86.92% and 93.10%, respectively, for tumor segmentation. The BINet has achieved an accuracy, precision, recall, F1-score, and specificity of 89.33%, 88.74%, 88.67%, 88.61%, and 94.33%, respectively, for three-class classification using raw RMW images, whereas it achieved 98.33%, 98.35%, 98.33%, 98.33%, and 99.17%, respectively, for segmented RMW images. Therefore, the proposed cascaded model can be used in the SMBI system.
Advancing Luciferase-Based Antibody Immunoassays to Next-Generation Mix and Read Testing
Authors: Burbelo Peter D. , Ji Youngmi , Iadarola Michael J.
Published: February 2023

Antibody measurements play a central role in the diagnosis of many autoimmune and infectious diseases. One antibody detection technology, Luciferase Immunoprecipitation Systems (LIPS), utilizes genetically encoded recombinant luciferase antigen fusion proteins in an immunoglobulin capture format to generate robust antibody measurement with high diagnostic sensitivity and specificity. The LIPS technology has been highly useful in detecting antibodies for research diagnostics and the discovery of new autoantigens. The methodology of the assay requires immunoglobulin binding reagents such as protein A/G beads and washing steps to process the immune complex before antibody levels are measured by light production with a luminometer. Recently, simplified mix and read immunoassays based on split components of the nanoluciferase enzyme in a complementation format have been developed for antibody measurements without requiring immunoglobulin-capturing beads or washing steps. The mix and read immunoassays utilize two or three nanoluciferase fragments which when reconstituted via antigen-specific antibody binding generate a functional enzyme. At present, these split luciferase tests have been developed mainly for detecting SARS-CoV-2 antibodies. Here, we describe the traditional LIPS technology and compare it to the new split luciferase methodologies focusing on their technical features, strengths, limitations, and future opportunities for diagnostic research, and clinical applications.
Recent Prospects of Carbonaceous Nanomaterials-Based Laccase Biosensor for Electrochemical Detection of Phenolic Compounds
Authors: Verma Sakshi , Thakur Deeksha , Pandey Chandra Mouli , Kumar Devendra
Published: February 2023

Phenolic compounds (PhCs) are ubiquitously distributed phytochemicals found in many plants, body fluids, food items, medicines, pesticides, dyes, etc. Many PhCs are priority pollutants that are highly toxic, teratogenic, and carcinogenic. Some of these are present in body fluids and affect metabolism, while others possess numerous bioactive properties such as retaining antioxidant and antimicrobial activity in plants and food products. Therefore, there is an urgency for developing an effective, rapid, sensitive, and reliable tool for the analysis of these PhCs to address their environmental and health concern. In this context, carbonaceous nanomaterials have emerged as a promising material for the fabrication of electrochemical biosensors as they provide remarkable characteristics such as lightweight, high surface: volume, excellent conductivity, extraordinary tensile strength, and biocompatibility. This review outlines the current status of the applications of carbonaceous nanomaterials (CNTs, graphene, etc.) based enzymatic electrochemical biosensors for the detection of PhCs. Efforts have also been made to discuss the mechanism of action of the laccase enzyme for the detection of PhCs. The limitations, advanced emerging carbon-based material, current state of artificial intelligence in PhCs detection, and future scopes have also been summarized.
Is REM Density a Measure of Arousal during Sleep?
Authors: Barbato Giuseppe
Published: February 2023

Rapid eye movements (REMs), an expression of REM sleep phasic activity, occur against a stable background of cortical desynchronization and the absence of axial tone. The significance of REMs during the sleep period was initially attributed to the mental content of dreams, linking the REMs to the dream scenario. Although fascinating, the so-called “scanning hypothesis” has not been supported by consistent evidence, and thus an alternative hypothesis is necessary to understand REMs significance during sleep. Some data suggest that the frequency of REMs during the REM sleep period, known as REM density, might be related to sleep depth or arousal during sleep. REM density increases across the night concomitantly with the progressive reduction in sleep pressure, and consistently it is higher at the circadian time when arousal appears to be higher, and it is decreased in those conditions, such as after sleep deprivation, which produce increased sleep pressure. REM density is also increased in major affective disorders, and it has been suggested either as a risk factor to develop the illness or as a predictive index of response to drug treatment. Disfunction of the neurotransmitter systems involved in arousal mechanisms and wake/sleep control might underlie the altered REM density described in depression. Understanding of the REM density mechanisms could help to untangle functional significance and regulation of REM sleep. Following the seminal idea of Aserinsky that REM density is an index of sleep satiety, it may also provide a sensitive measure of sleep homeostasis in addition to, or even as an alternative to, the consolidated analysis of slow wave activity. REM density can also be utilized to explore those mechanisms which end sleep, and considered a physiological marker which indicate during sleep the “time to wake”.
Selective Impairments in Fine Neural Tuning for Print in Chinese Children with Developmental Dyslexia
Authors: Xue Licheng , Zhao Jing , Weng Xuchu
Published: February 2023

Neural tuning for print refers to differential neural responses (e.g., the N1 component of event-related potentials) to different orthographic forms and other visual stimuli. While impaired neural tuning for print has been well established in dyslexic children who read alphabetic scripts, it remains unclear whether such effects exist in dyslexic children who read Chinese, which dramatically differs in visual and linguistic characteristics from alphabetic words. To fill this gap, we examined two levels of the neural tuning for print: coarse tuning (i.e., false character vs. stroke combination), and fine tuning (i.e., sub-lexical tuning: pseudo character vs. false character; and lexical tuning: real character vs. pseudo character). Using the event-related potential technique, we examined 14 typically developing children and 16 dyslexic children who were screened from 216 nine-year-old children in the third grade. For typically developing children, we observed both coarse and sub-lexical tuning. Critically, for dyslexic children, we found stronger N1 for false character than for stroke combination, suggesting intact coarse tuning, but a reduced N1 difference between false character and pseudo character, suggesting impaired sub-lexical tuning. These results clearly show selective impairments in fine neural tuning at the sub-lexical level in Chinese dyslexic children. Our findings may be associated with unique features of Chinese characters.
Numerical Cognition after Brain Injury: Is There a Relationship between Subitizing and Arithmetical Abilities?
Authors: Gosling Esther , Demeyere Nele , Dowker Ann
Published: February 2023

Subitizing is the ability to enumerate small quantities efficiently and automatically. Counting is a strategy adopted for larger numerosities resulting in a near linear increase in response time with each increase in quantity. Some developmental studies suggest that being able to subitize efficiently may be a predictor of later arithmetical ability. Being able to enumerate small quantities efficiently may be necessary for at least some aspects of arithmetical skill and understanding to develop. According to this view, arithmetic ability ultimately depends upon subitizing. If this were the case, when acquired brain injury results in impaired performance on subitizing tasks, mathematical performance may also be impaired. The following study tested eleven healthy control participants and nine chronic patients with acquired brain injury on tasks focused on visual enumeration, addition and multiplication to explore a potential relationship between subitizing ability and calculation performance. No overall correlations were found between subitizing and addition or multiplication speed. However, a very clear subitizing impairment was found in two patients who then demonstrated very different levels of preserved addition skills. The dissociations found and the large inter-individual variability supports a more componential view of arithmetical ability.
Early Strength Evolution of Cement Grouts Adopted in Reinforced Concrete Subjected to Na2SO4 Corrosion
Authors: Xu Yinshan , Zhou Jianqiang , Jin Sujing
Published: February 2023

Due to the harsh application environment, sodium sulfate corrosion poses a considerable threat to the performance of reinforced concrete. Considering high fluidization and early strength cement grouts (HECG) are widely adopted in the repair engineering of reinforced concrete, HECG are also subjected to the action of corrosive substances. Even though numerous research results disclosed the effect of sodium sulfate corrosion on the performance and the degradation mechanisms of conventional cement grouts, the degradation mechanisms of HECG under sodium sulfate corrosion are still to be studied because HECG have the exceptional characteristics of high fluidization and early strength that conventional cement grouts do not have. Hence, the compressive and flexural strengths of HECG after corrosion with different sodium sulfate solution concentrations and corrosion times were tested in this study. Moreover, the evolution of the microstructure of HECG in the process of corrosion was analyzed with a Scanning Electron Microscope (SEM) test. The changes in the hydration products were investigated through the X-ray diffraction (XRD) test and the Differential Scanning Calorimeter (DSC) test. Consequently, the degradation mechanisms of HECG in the case of different sodium sulfate solution concentrations and corrosion times were revealed. The results indicate that the corrosion of sodium sulfate and hydration exists simultaneously when the corrosion time is less than 7 days. When the corrosion time is 14 days, the hydration of HECG was complete. The main source of the early strength of HECG is that the C-S-H gel crosses and clings to form a solid and compact whole in three-dimensional space. The effect of Na2SO4 on the early strength formation of HECG is that the corrosion of Na2SO4 can postpone the conversion of CH crystal into C-S-H gel and damage the generated CH crystal. This study on the degradation mechanisms of HECG exposed to sodium sulfate corrosion can provide new ideas for sulfate resistance and durability design of HECG.
Spatial Characterization Analysis of Residential Material Stock and its Driving Factors: A Case Study of Xi’an
Authors: Shen Lina , Yang Qi , Yan Haoyue
Published: February 2023

Since the beginning of the 21st century, driven by industrialization and its corresponding economic development, China has been experiencing a period of rapid urbanization. The continued expansion of residential space contributes to material stocks of residential buildings, accounting for a large proportion of the total material stocks. Based on a 4D-GIS model, we studied spatiotemporal distribution characteristics and driving factors of residential building material stock in the central urban area of Xi’an from 1992 to 2021. The study innovatively combined this with the spatial development rule, development speed, and expansion direction of cities to analyze the relationship between stock growth and urban development. We found that residential development in central Xi’an is still undergoing a relatively rapid developmental stage. The spatial growth of residential building stock has a distinct agglomeration pattern, showing the characteristics of multi-center agglomeration, and the hot spots of stock growth are concentrated on and expanding to the edges of central cities. The growth of residential building stock has a distinct direction, primarily in the northeast–southwest direction, consistent with the pattern of urban expansion. We also found that social, economic, and transport-related factors are the main drivers of growth of residential building material stock. This study can help policymakers, urban planners, and environmental planners consider the rational development and utilization of land resources and building materials, and it lays a research foundation for the recycling of construction waste in the future.
Transient Absorption Spectrum Analysis for Photothermal Catalysis Perovskite Materials
Authors: Tian Jindan , Liu Lili , Nian Hongqiang , Guo Qiangsheng , Sha Na , Zhao Zhe
Published: February 2023

To gain insight into photocatalytic behavior, transient absorption spectroscopy (TAS) was used to study LaCoxMn1−xO3, LaMnxNi1−xO3 and LaNixCo1−xO3 (x = 0, 0.2, 0.4, 0.6, 0.8 and 1.0) on a microsecond time scale. The results show that the electron lifetime is key to determining the photocatalytic reduction of CO2. This is the first time that the photogenerated electron lifetime in perovskite has been proposed to express the performance of the photocatalytic reduction of CO2 with H2O into CH4. In all cases, the decay curve can be well explained by two consecutive first-order kinetics, indicating that the electron exists within two major populations: one with a short lifetime and the other one with a long lifetime. The long-lived electrons are the rate-limiting species for the photocatalytic reaction and are related to the activity of the photocatalytic reduction of CO2 with H2O to produce CH4. For different photocatalysts, we find that the longer the electron decay lifetime is, the stronger the electron de-trapping ability is, and the electrons perform more activity. In this paper, TAS can not only detect the micro-dynamics process of carriers, but it is also demonstrated to be an easy and effective method for screening the most active catalyst in various catalysts for the photocatalytic reduction of CO2 with H2O accurately and quickly.