bluuebunny/crossref_metadata_2025 huggingface dataset:
The Prebiotic Effects of an Extract with Antioxidant Properties from Morus alba L. Contribute to Ameliorate High-Fat Diet-Induced Obesity in Mice
Authors: Rodríguez-Sojo María Jesús , Ruiz-Malagón Antonio Jesús , Hidalgo-García Laura , Molina-Tijeras Jose Alberto , Diez-Echave Patricia , López-Escanez Laura , Rosati Lucrezia , González-Lozano Elena , Cenis-Cifuentes Laura , García-García Jorge , García Federico , Robles-Vera Iñaki , Romero Miguel , Duarte Juan , Cenis José Luis , Lozano-Pérez Antonio Abel , Gálvez Julio , Rodríguez-Cabezas María Elena , Rodríguez-Nogales Alba
Published: April 2023

Obesity is a global health issue, in which modifications in gut microbiota composition have a key role. Different therapeutic strategies are being developed in combination with diet and exercise, including the use of plant extracts, such as those obtained from Morus alba L. leaves. Recent studies have revealed their anti-inflammatory and antioxidant properties. The aim of the present work was to evaluate whether the beneficial effects of M. alba L. leaf extract in high-fat diet-induced obesity in mice is correlated with its impact on gut microbiota. The extract reduced body weight gain and attenuated lipid accumulation, as well as increased glucose sensitivity. These effects were associated with an amelioration of the obesity-associated inflammatory status, most probably due to the described antioxidant properties of the extract. Moreover, M. alba L. leaf extract mitigated gut dysbiosis, which was evidenced by the restoration of the Firmicutes/Bacteroidota ratio and the decrease in plasma lipopolysaccharide (LPS) levels. Specifically, the extract administration reduced Alistipes and increased Faecalibaculum abundance, these effects being correlated with the beneficial effects exerted by the extract on the obesity-associated inflammation. In conclusion, anti-obesogenic effects of M. alba L. leaf extract may be mediated through the amelioration of gut dysbiosis.
Synergic Effects of Nano Additives on Mechanical Performance and Microstructure of Lightweight Cement Mortar
Authors: Du Yiying , Korjakins Aleksandrs
Published: April 2023

Owing to their convenient manufacture, transportation, low energy consumption, and environmental impacts, lightweight cement composites have been applied as building and construction materials. However, its decreased density is associated with a reduction in mechanical strength. In most existing investigations, attempts have been made to improve mechanical behaviours via supplementary cementitious or fibre materials, whereas limited studies have been implemented on the effects of nano additives, especially their synergic influence. In this study, industrial waste fly ash cenosphere (FAC) has been utilized as lightweight aggregate by 73.3% cement weight to fabricate sustainable lightweight cement mortar (LWCM). Carbon nanotubes (CNTs) at a dosage of 0.05%, 0.15%, and 0.45% and nano silica (NS) with the content of 0.2%, 0.6%, and 1.0% by cement weight have been applied as modifying additives. Experiments were carried out to test flexural strength, compressive strength, and water absorption. SEM, TG, and XRD analyses were conducted to evaluate microstructure and hydration characteristics. Based on the outcomes, the inclusion of CNTs and NS can effectively increase flexural and compressive strength and reduce absorbed water weight. The analysis of SEM, TG, and XRD reveals that the binary usage of CNTs and NS can improve pore structure and facilitate hydration reaction.
Sorption Behaviour of Ibuprofen Using Activated Carbon Derived from Rose Geranium (Pelargonium graveolens L.) Leaves
Authors: Thabede Patience Mapule , Mtunzi Fanyana , Nyamukamba Pardon
Published: April 2023

This research assessed the adsorption of a pharmaceutical compound, ibuprofen, using rose geranium (Pelargonium graveolens L.) leaves to prepare low-cost activated carbon through orthophosphoric acid (H3PO4) activation. The activated carbon from rose geranium leaves (AC-RGL) was characterized by TGA, SEM and FTIR. The results were compared with those from natural rose geranium leaves (Raw-RGL). The influence of chemical parameters for the uptake of ibuprofen on both adsorbents was evaluated through adsorption experiments. The results were subjected to adsorption models, kinetics models and thermodynamic studies to determine the distribution of ibuprofen in the solid and liquid phases. The results for both Raw-RGL and AC-RGL best fitted the Freundlich model, and the kinetic studies were shown to be pseudo-first order. The thermodynamic evaluation suggested exothermic and spontaneous process sorption for ibuprofen on both adsorbents. The maximum sorption capacities for AC-RGL and Raw-RGL were 113.76 and 74.12 mg/g, respectively. This work confirms that low-cost rose geranium leaves can be used as a potential adsorbent for the sorption of ibuprofen in solution.
Deep Learning-Assisted Transmit Antenna Classifiers for Fully Generalized Spatial Modulation: Online Efficiency Replaces Offline Complexity
Authors: Jadhav Hindavi Kishor , Kumaravelu Vinoth Babu
Published: April 2023

In this work, deep learning (DL)-based transmit antenna selection (TAS) strategies are employed to enhance the average bit error rate (ABER) and energy efficiency (EE) performance of a spectrally efficient fully generalized spatial modulation (FGSM) scheme. The Euclidean distance-based antenna selection (EDAS), a frequently employed TAS technique, has a high search complexity but offers optimal ABER performance. To address TAS with minimal complexity, we present DL-based approaches that reframe the traditional TAS problem as a classification learning problem. To reduce the energy consumption and latency of the system, we presented three DL architectures in this study, namely a feed-forward neural network (FNN), a recurrent neural network (RNN), and a 1D convolutional neural network (CNN). The proposed system can efficiently process and make predictions based on the new data with minimal latency, as DL-based modeling is a one-time procedure. In addition, the performance of the proposed DL strategies is compared to two other popular machine learning methods: support vector machine (SVM) and K-nearest neighbor (KNN). While comparing DL architectures with SVM on the same dataset, it is seen that the proposed FNN architecture offers a ~3.15% accuracy boost. The proposed FNN architecture achieves an improved signal-to-noise ratio (SNR) gain of ~2.2 dB over FGSM without TAS (FGSM-WTAS). All proposed DL techniques outperform FGSM-WTAS.
Response of Guobu Slope Displacement to Rainfall and Reservoir Water Level with Time-Series InSAR and Wavelet Analysis
Authors: Pang Lei , Li Conghua , Liu Dayuan , Zhang Fengli , Chen Bing
Published: April 2023

Reservoir bank landslides are a frequent phenomenon, and the stability of these landslides is affected by two essential factors: rainfall and reservoir level changes. Studying the response patterns of reservoir bank landslide movements to these variables is crucial in preventing their occurrence and mitigating their effects. To this end, this study employed 103 European Space Agency (ESA) Copernicus Sentinel-1 images and the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) technique to obtain a time series of the Guobu slope deformation from September 2015 to December 2019. The Guobu slope showed significant toppling damage. The satellite line of sight (LOS) detected a maximum subsidence rate of −447 mm/y (the negative sign indicates movement away from the satellite, i.e., subsidence) in the upper section of the slope. Subsequently, three wavelet tools were used to quantitatively analyze the effect of rainfall and reservoir water level on the deformation of the Guobu slope. The results demonstrate a positive correlation between rainfall and the deformation of the Guobu slope. Moreover, the deformation lags behind the rainfall by approximately 70 days. In contrast, the reservoir water level and the deformation of the Guobu slope exhibit an inverse relationship. The deformation of the leading edge of the slope body lags behind the reservoir level by approximately 19 days, while the middle and upper sections of the slope body, which have the most significant rate of variability, lag by about 80 days. Among these factors, rainfall plays a dominant role in the deformation of the Guobu slope, while reservoir levels play a synergistic role. The findings of this study highlight the importance of monitoring and understanding the impact of changes in rainfall and reservoir water levels on the stability of reservoir bank landslides. This understanding is crucial in preventing the occurrence of such landslides and minimizing their impact. The use of remote sensing techniques, together with wavelet analysis, enables the accurate and timely monitoring of the deformation of the Guobu slope, providing valuable insights for disaster warnings and disaster prevention and reduction efforts.
Research on Air-Conditioning Cooling Load Correction and Its Application Based on Clustering and LSTM Algorithm
Authors: Li Honglian , Shang Li , Li Chengwang , Lei Jiaxiang
Published: April 2023

Climate change and urban heat island effects affect the energy consumption of buildings in urban heat islands. In order to meet the requirements of engineering applications for detailed daily design parameters for air conditioning, the 15-year summer meteorological data for Beijing and Shanghai and the corresponding average heat island intensity data were analyzed. Using the CRITIC objective weighting method and K-means clustering analysis, the hourly change coefficient, β, of dry bulb temperature was calculated, and the LSTM algorithm was used to predict the changing trends in β. Finally, the air conditioning load model for a hospital was established using DeST (version DeST3.0 1.0.2107.14 20220712) software, and the air conditioning cooling load in summer was calculated and predicted. The results show that, compared with the original design days, regional differences in the new design days are more obvious, the maximum temperature and time have changed, and the design days parameters are more consistent with the local meteorological conditions. Design day temperatures in Shanghai are expected to continue rising for some time to come, while those in Beijing are expected to gradually return to previous levels. Among hospital buildings, the cooling load of outpatient buildings in Beijing and Shanghai will decrease by 0.69% and increase by 12.61% and by 12.12% and 15.51%, respectively, under the influence of the heat island effect. It is predicted to decrease by 1.35% and increase by 29.75%, respectively, in future. The cooling load of inpatient buildings in Beijing and Shanghai increased by 0.27% and 6.71%, respectively, and increased by 7.13% and 8.09%, respectively, under the influence of the heat island effect, and is predicted to decrease by 0.93% and increase by 16.07%, respectively, in future.
Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
Authors: Yang Guang , Guan Kainan , Zou Li , Sun Yibo , Yang Xinhua
Published: April 2023

The cold metal transfer (CMT) process is widely used in thin plate welding because of its characteristics of low heat input and stable arc. In actual production, a larger weld gap, misalignment, or other problems due to assembly error lead to serious welding defects, such as burn-through and a lack of fusion. The arc sound contains a wealth of information related to the quality of the weld. This work analyzes the mechanism of CMT arc sound generation, as well as the correlation between the time–frequency spectrum of the arc sound signal and welding quality. This paper studies the extraction of the multi-channel time–frequency spectrum of an arc sound and inputs it to a custom convolutional neural network for the CMT welding defect identification of thin aluminum alloy plates. The experimental result shows that the average accuracy of the proposed model is 91.49% in the defect identification of a CMT arc-welded aluminum alloy sheet, which is higher than that of the single-channel time–frequency convolutional neural network and other traditional classification models.
The Difficulty of Measuring the Roughness of Glossy Surfaces Using the Triangulation Principle
Authors: Ružbarský Juraj
Published: April 2023

In the experiment, the roughness was measured on a machined surface with high gloss, which was also the main requirement for the test samples. For this reason, the samples made of stainless steel A304 and aluminum alloy AW 2017 were created by a progressive laser using material-cutting technology. This article explains a contact-free measurement method that uses the triangulation principle, which constitutes the basis on which the device used in the experiment, i.e., the laser profilometry, works. The surface roughness of the cut surfaces was examined on the manufactured samples through the selected roughness parameters of Ra and Rz. These parameters are commonly used in industry to quantify the roughness of a surface. The values measured in a contact-free manner were then compared with the reference values measured in a contact manner. Data from individual experimental measurements were graphed as dependencies based on which problem areas of measuring the roughness of glossy material surfaces with laser profilometry were described. Laser profilometry is a non-contact method for measuring the roughness of surfaces, and given the presented results of the experimental measurements and selected roughness parameters of the cut surface using a laser, we do not recommend using it for materials that have a glossy surface.
Design of Manufacturing Systems Based on Digital Shadow and Robust Engineering
Authors: Mourtzis Dimitris , Balkamos Nikos
Published: April 2023

In the era of digital transformation, industry is facing multiple challenges due to the need for implementation of the Industry 4.0 standards, as well as the volatility of customer demands. The latter has created the need for the design and operation of more complex manufacturing systems and networks. A case study derived from Process Industries (PIs) is adopted in this research work in order to design a framework for flexible design of production lines, automation of quality control points, and improvement of the performance of the manufacturing system. Therefore, a Digital Shadow of a production line is developed to collect, analyze and identify potential issues (bottlenecks). An edge computing system for reliable and low-latency communications is also implemented. The digital model is validated using statistical Design Of Experiments (DOE) and ANalysis Of VAriance (ANOVA). For the assessment of what-if scenarios, the Digital Shadow model will be used in order to evaluate and find the desired solution. Ultimately, the goal of this research work is to improve the design and performance of the industry’s production section, as well as to increase the production rate and the product mix.
Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
Authors: Javeed Ashir , Saleem Muhammad Asim , Dallora Ana Luiza , Ali Liaqat , Berglund Johan Sanmartin , Anderberg Peter
Published: April 2023

Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a χ2 statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model (χ2_RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model χ2_RF achieved the highest accuracy of 94.59%. The proposed model χ2_RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model χ2_RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module (χ2).
Anisotropy Corrected FMC/TFM Based Phased Array Ultrasonic Imaging in an Austenitic Buttering Layer
Authors: Ponseenivasan S. , Kumar Anish , Rajkumar K. V.
Published: April 2023

For the narrow gap dissimilar weld between a ferritic steel and a nickel base superalloy, a nickel base alloy buttering layer is deposited on the ferritic steel side as an intermediate layer. The bonding between the buttering layer and the ferritic steel is required to be inspected from the buttering layer side. The buttering layer exhibits very high elastic anisotropy due to elongated columnar grains with preferred orientations. In this paper, the effect of elastic anisotropy on the phased array ultrasonic imaging of defects in the buttering layer is demonstrated for data acquired in full matrix capture (FMC) mode and reconstructed with the total focusing method (TFM). The anisotropy in the buttering layer leads to distorted flaw images, which limits the lateral resolution and defect detection sensitivity. Angle-dependent ultrasonic velocity measured in through-transmission FMC mode has been used for processing the FMC data to obtain high-resolution TFM images with improved sensitivity. The velocity values used are in line with the grain orientations observed by electron-backscatter diffraction (EBSD) studies. Further, an alternate approach is also proposed to obtain a TFM image with improved resolution using a suitable isotropic velocity. The approach can be implemented in any commercial phased array ultrasonic system having the FMC-TFM feature.
The Modification of Titanium Surface by Decomposition of Tannic Acid Coating
Authors: Kaczmarek-Szczepańska Beata , Zasada Lidia , Michalska-Sionkowska Marta , Vishnu Jithin , Manivasagam Geetha
Published: April 2023

Titanium is one of the most widely used metals in implantology owing to its reduced modulus, improved corrosion resistance and good biocompatibility. In spite of its excellent biocompatibility, it does not exhibit inherent antibacterial and antioxidant activity. Tannic acid is a naturally occurring polyphenol compound which exhibits excellent antibacterial, antioxidant and antimutagenic activity. The development of tannic acid-based coatings on the titanium surface holds great potential to reduce the risks associated with implant applications, thereby increasing the longevity of implants. In the present study, tannic acid was deposited on the titanium surface and the surface displayed a slightly improved hydrophilic character with an increase in surface energy. The release kinetics of tannic acid from titanium surface was analyzed and it showed an initial burst effect followed by a gradual decrease over time. Hemolysis tests revealed the erythrocyte compatibility of the developed surfaces. The improved hydrophilicity observed the release kinetics of tannic acid and reduced hemolysis rates revealed the potential of this facile technique for implant surface engineering applications.
Multi-Class Transfer Learning and Domain Selection for Cross-Subject EEG Classification
Authors: Maswanganyi Rito Clifford , Tu Chungling , Owolawi Pius Adewale , Du Shengzhi
Published: April 2023

Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-session and cross-subject variability with more accurate intention prediction. In this case, TL utilizes knowledge (signal features) in the source domain(s) to improve the classification in the target domain. However, current existing transfer learning approaches on EEG-based BCI are mostly limited to two-class cross-subject classification problems, while multi-class problems are only implemented with a focus on within-subject classification due to the complexity of multi-class cross-subject classification problems. In this paper, we first extended the transfer learning approaches to a multi-class cross-subject scenario, then investigated the reason for transfer learning performance being poor in multi-class cross-subject classification. Secondly, we address the challenge of significant sessional and subject-to-subject variations originating from both known and unknown factors. It is discovered that such variations have a massive influence on the classification because of the negative transfer (NT) across domains. Based on this discovery, we propose a multi-class transfer learning approach based on multi-source manifold feature transfer learning (MMFT) framework and an enhanced version to minimize the effects of NT. The proposed multi-class transfer learning approach extends the existing MMFT to multi-class cases. Then enhanced multi-class MMFT firstly searches for domains with high transferability and selects only the best combination among source domains (SD), then utilize the best-selected combination of domains for transfer learning. Experimental results illustrate that the proposed multi-class MMFT can be employed in the cross-subject classification of both three-class and four-class problems. Experimental results also demonstrated that the enhanced multi-class MMFT could effectively minimize the effect of negative transfer and significantly increase the prediction rates across individual target domains (TD). The highest classification accuracy (CA) of 98% is obtained by the enhanced multi-class MMFT.
Simulation and Estimation of the Inter-Source Category and/or Inter-Pollutant Emission Offset Ratios for a Heavy Industry City
Authors: Chen Tu-Fu , Chen Bo-Yan , Chang Ken-Hui
Published: April 2023

Kao-Ping Air Basin (KPAB) is a heavy industrial area, and also the first and only air quality total quantity control district in Taiwan. Pollutant emission offsetting is an important tool to reduce pollution source emissions and improve air quality in the total quantity control district. In this study, an air quality model was employed to evaluate the sensitivity of SOX, NOX, and primary PM2.5 emissions from point, mobile, and fugitive sources on PM2.5 concentrations in KPAB. The findings show that the emission offset ratios of mobile PM2.5-to-point PM2.5 and fugitive PM2.5-to-point PM2.5 were both greater than one in urban areas (1.3 and 2.0, respectively) and both less than one in non-urban areas. The offset ratios of point SOX-to-point PM2.5 and point NOX-to-point PM2.5 were significantly greater than one, especially those in urban areas (20 and 60, respectively) were higher than those in non-urban areas by more than 2–4 times. No matter whether in urban or non-urban areas, the offset ratio of mobile NOX-to-point NOX was close to one, and the offset ratios of point NOX-to-point PM2.5 and mobile NOX-to-point PM2.5 were similar. The above findings were closely related to the proximity of point sources to densely populated urban areas in KPAB.
Default Prediction with Industry-Specific Default Heterogeneity Indicators Based on the Forward Intensity Model
Authors: Ni Zhengfang , Jiang Minghui , Zhan Wentao
Published: April 2023

When predicting the defaults of a large number of samples in a region, this will be affected by industry default heterogeneity. To build a credit risk model that is more suitable for Chinese-listed firms, which have highly industry-specific default heterogeneity, we extend the forward intensity model to predict the defaults of Chinese-listed firms with information about the default heterogeneity of industries. Compared with the original model, we combine the Bayes approach with the forward intensity model to generate time-varying industry-specific default heterogeneity indicators. Our model can capture co-movements of different industries that cannot be observed based on the original forward intensity model so that the model can flexibly adjust the firm’s PD according to the industry. In addition, we also consider the impact of default heterogeneity in other industries by studying the influence of the level and trends of other industries’ default heterogeneity on a firm’s credit risk. Finally, we compute PDs for 4476 firms from January 2001 to December 2019 for 36 prediction horizons. The extended model improves the prediction accuracy ratios both for the in-sample and out-of-sample firm’s PDs for all 36 horizons. Almost all the accuracy ratios of the prediction horizons’ PDs are increased by more than 6%. In addition, our model also reduces the gap between the aggregated PDs and the realized number of defaults. Our industry-specific default heterogeneity indicator is helpful to improve the model’s performance, especially for predicting defaults in a large portfolio, which is of significance for credit risk management in China and other regions.
Multi-Objective Optimization of the Robustness of Complex Networks Based on the Mixture of Weighted Surrogates
Authors: Nie Junfeng , Yu Zhuoran , Li Junli
Published: April 2023

Network robustness is of paramount importance. Although great progress has been achieved in robustness optimization using single measures, such networks may still be vulnerable to many attack scenarios. Consequently, multi-objective network robustness optimization has recently garnered greater attention. A complex network structure plays an important role in both node-based and link-based attacks. In this paper, since multi-objective robustness optimization comes with a high computational cost, a surrogate model is adopted instead of network controllability robustness in the optimization process, and the Dempster–Shafer theory is used for selecting and mixing the surrogate models. The method has been validated on four types of synthetic networks, and the results show that the two selected surrogate models can effectively assist the multi-objective evolutionary algorithm in finding network structures with improved controllability robustness. The adaptive updating of surrogate models during the optimization process leads to better results than the selection of two surrogate models, albeit at the cost of longer processing times. Furthermore, the method demonstrated in this paper achieved better performance than existing methods, resulting in a marked increase in computational efficiency.
A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges
Authors: Teixeira Felipe Lage , Costa Miguel Rocha e , Abreu José Pio , Cabral Manuel , Soares Salviano Pinto , Teixeira João Paulo
Published: April 2023

Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC’s), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.
Colostrum Quality Assessment in Dairy Goats: Use of an On-Farm Optical Refractometer
Authors: Pérez-Marín Carlos C. , Cano David , Arrebola Francisco A. , Petrusha Valerii H. , Skliarov Pavlo M. , Entrenas José A. , Pérez-Marín Dolores C.
Published: April 2023

Failure of passive immunity transfer is one of the main causes of increased susceptibility to infectious agents in newborn kids. To ensure successful transfer of passive immunity, kids need to be fed high-quality colostrum, containing an adequate concentration of IgG. This work evaluated the quality of colostrum obtained in the first 3 days postpartum from Malagueña dairy goats. The IgG concentration in colostrum was measured using an ELISA as a reference method, and it was estimated by optical refractometer. Colostrum composition in terms of fat and protein was also determined. The mean concentration of IgG was 36.6 ± 2.3 mg/mL, 22.4 ± 1.5 mg/mL and 8.4 ± 1.0 mg/mL on days 1, 2 and 3 after parturition, respectively. Brix values obtained using the optical refractometer were 23.2%, 18.6% and 14.1% for days 1, 2 and 3, respectively. In this population, 89% of goats produced high-quality colostrum with IgG concentrations of >20 mg/mL on the day of parturition, but this percentage declined dramatically over the following 2 days. The quality of the fresh colostrum estimated with the optical refractometer was positively correlated with those obtained using ELISA (r = 0.607, p = 0.001). This study highlights the importance of feeding first-day colostrum to newborn kids and demonstrates that the optical Brix refractometer is suitable for the on-farm estimation of IgG content in colostrum.
Whole-Transcriptome Analysis of Repeated Low-Level Sarin-Exposed Rat Hippocampus and Identification of Cerna Networks to Investigate the Mechanism of Sarin-Induced Cognitive Impairment
Authors: Shi Jingjing , Liu Dongxin , Jin Qian , Chen Xuejun , Zhang Ruihua , Shi Tong , Zhu Siqing , Zhang Yi , Zong Xingxing , Wang Chen , Li Liqin
Published: April 2023

Sarin is a potent organophosphorus nerve agent that causes cognitive dysfunction, but its underlying molecular mechanisms are poorly understood. In this study, a rat model of repeated low-level sarin exposure was established using the subcutaneous injection of 0.4 × LD50 for 21 consecutive days. Sarin-exposed rats showed persistent learning and memory impairment and reduced hippocampal dendritic spine density. A whole-transcriptome analysis was applied to study the mechanism of sarin-induced cognitive impairment, and a total of 1035 differentially expressed mRNA (DEmRNA), including 44 DEmiRNA, 305 DElncRNA, and 412 DEcircRNA, were found in the hippocampus of sarin-treated rats. According to Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and Protein–Protein Interaction (PPI) analysis, these DERNAs were mainly involved in neuronal synaptic plasticity and were related to the pathogenesis of neurodegenerative diseases. The circRNA/lncRNA–miRNA–mRNA ceRNA network was constructed, in which Circ_Fmn1, miR-741-3p, miR-764-3p, miR-871-3p, KIF1A, PTPN11, SYN1, and MT-CO3 formed one circuit, and Circ_Cacna1c, miR-10b-5p, miR-18a-5p, CACNA1C, PRKCD, and RASGRP1 constituted another circuit. The balance between the two circuits was crucial for maintaining synaptic plasticity and may be the regulatory mechanism by which sarin causes cognitive impairment. Our study reveals the ceRNA regulation mechanism of sarin exposure for the first time and provides new insights into the molecular mechanisms of other organophosphorus toxicants.
Both Nuclear and Membrane Estrogen Receptor Alpha Impact the Expression of Estrogen Receptors and Plasticity Markers in the Mouse Hypothalamus and Hippocampus
Authors: Mazid Sanoara , Waters Elizabeth M. , Lopez-Lee Chloe , Poultan Kamakura Renata , Rubin Batsheva R. , Levin Ellis R. , McEwen Bruce S. , Milner Teresa A.
Published: April 2023

Estrogens via estrogen receptor alpha (ERα) genomic and nongenomic signaling can influence plasticity processes in numerous brain regions. Using mice that express nuclear only ERα (NOER) or membrane only ERα (MOER), this study examined the effect of receptor compartmentalization on the paraventricular nucleus of the hypothalamus (PVN) and the hippocampus. The absence of nuclear and membrane ERα expression impacted females but not males in these two brain areas. In the PVN, quantitative immunohistochemistry showed that the absence of nuclear ERα increased nuclear ERβ. Moreover, in the hippocampus CA1, immuno-electron microscopy revealed that the absence of either nuclear or membrane ERα decreased extranuclear ERα and pTrkB in synapses. In contrast, in the dentate gyrus, the absence of nuclear ERα increased pTrkB in synapses, whereas the absence of membrane ERα decreased pTrkB in axons. However, the absence of membrane only ERα decreased the sprouting of mossy fibers in CA3 as reflected by changes in zinc transporter immunolabeling. Altogether these findings support the idea that both membrane and nuclear ERα contribute overlapping and unique actions of estrogen that are tissue- and cellular-specific.
Dose-Dependent Effects in Plasma Oncotherapy: Critical In Vivo Immune Responses Missed by In Vitro Studies
Authors: He Yuanyuan , Gong Fanwu , Jin Tao , Liu Qi , Fang Haopeng , Chen Yan , Wang Guomin , Chu Paul K. , Wu Zhengwei , Ostrikov Kostya (Ken)
Published: April 2023

Cold atmospheric plasma (CAP) generates abundant reactive oxygen and nitrogen species (ROS and RNS, respectively) which can induce apoptosis, necrosis, and other biological responses in tumor cells. However, the frequently observed different biological responses to in vitro and in vivo CAP treatments remain poorly understood. Here, we reveal and explain plasma-generated ROS/RNS doses and immune system-related responses in a focused case study of the interactions of CAP with colon cancer cells in vitro and with the corresponding tumor in vivo. Plasma controls the biological activities of MC38 murine colon cancer cells and the involved tumor-infiltrating lymphocytes (TILs). In vitro CAP treatment causes necrosis and apoptosis in MC38 cells, which is dependent on the generated doses of intracellular and extracellular ROS/RNS. However, in vivo CAP treatment for 14 days decreases the proportion and number of tumor-infiltrating CD8+T cells while increasing PD-L1 and PD-1 expression in the tumors and the TILs, which promotes tumor growth in the studied C57BL/6 mice. Furthermore, the ROS/RNS levels in the tumor interstitial fluid of the CAP-treated mice are significantly lower than those in the MC38 cell culture supernatant. The results indicate that low doses of ROS/RNS derived from in vivo CAP treatment may activate the PD-1/PD-L1 signaling pathway in the tumor microenvironment and lead to the undesired tumor immune escape. Collectively, these results suggest the crucial role of the effect of doses of plasma-generated ROS and RNS, which are generally different in in vitro and in vivo treatments, and also suggest that appropriate dose adjustments are required upon translation to real-world plasma oncotherapy.
Downregulation of Bmal1 Expression in Celiac Ganglia Protects against Hepatic Ischemia-Reperfusion Injury
Authors: Feng Jiarui , Zhang Lilong , Xue Enfu , Qiu Zhendong , Hu Ning , Wang Kunpeng , Su Yingru , Wang Weixing
Published: April 2023

Hepatic ischemia-reperfusion injury (HIRI) significantly contributes to liver dysfunction following liver transplantation and hepatectomy. However, the role of the celiac ganglion (CG) in HIRI remains unclear. Adeno-associated virus was used to silence Bmal1 expression in the CG of twelve beagles that were randomly assigned to the Bmal1 knockdown group (KO-Bmal1) and the control group. After four weeks, a canine HIRI model was established, and CG, liver tissue, and serum samples were collected for analysis. The virus significantly downregulated Bmal1 expression in the CG. Immunofluorescence staining confirmed a lower proportion of c-fos+ and NGF+ neurons in TH+ cells in the KO-Bmal1 group than in the control group. The KO-Bmal1 group exhibited lower Suzuki scores and serum ALT and AST levels than the control group. Bmal1 knockdown significantly reduced liver fat reserve, hepatocyte apoptosis, and liver fibrosis, and it increased liver glycogen accumulation. We also observed that Bmal1 downregulation inhibited the hepatic neurotransmitter norepinephrine, neuropeptide Y levels, and sympathetic nerve activity in HIRI. Finally, we confirmed that decreased Bmal1 expression in CG reduces TNF-α, IL-1β, and MDA levels and increases GSH levels in the liver. The downregulation of Bmal1 expression in CG suppresses neural activity and improves hepatocyte injury in the beagle model after HIRI.
Phenotypic Classification of Eye Colour and Developmental Validation of the Irisplex System on Population Living in Malakand Division, Pakistan
Authors: Rahat Murad Ali , Akbar Fazal , Rasool Akhtar , Ilyas Muhammad , Rakha Allah , Shams Sulaiman , Jelani Musharraf , Bibi Fehmida , Shirah Bader H. , Abdulkareem Angham Abdulrhman , Naseer Muhammad Imran , Israr Muhammad
Published: April 2023

The core objective of forensic DNA typing is developing DNA profiles from biological evidence for personal identification. The present study was designed to check the validation of the IrisPlex system and the Prevalence of eye colour in the Pakhtoon population residing within the Malakand Division. Methods: Eye colour digital photographs and buccal swab samples of 893 individuals of different age groups were collected. Multiplexed SNaPshot single base extension chemistry was used, and the genotypic results were analysed. Snapshot data were used for eye colour prediction through the IrisPlex and FROG-kb tool. Results: The results of the present study found brown eye colour to be the most prevalent eye colour in comparison to intermediate and blue coloured. Overall, individuals with brown-coloured eyes possess CT (46.84%) and TT (53.16%) genotypes. Blue eye-coloured individuals are solely of the CC genotype, while individuals of intermediate eye colour carry CT (45.15%) and CC (53.85%) genotypes in rs12913832 SNP in the HERC2 gene. It was also revealed that brown-coloured eyes individuals were dominant among all age groups followed by intermediate and blue. Statistical analysis between particular variables and eye colour showed a significant p-value (<0.05) for rs16891982 SNP in SLC45A2 gene, rs12913832 SNP in HERC2 gene, rs1393350 SNP in SLC45A2, districts and gender. The rest of the SNPs were non-significant with eye colour, respectively. The rs12896399 SNP and SNP rs1800407 were found significant with rs16891982 SNP. The result also demonstrated that the study group differs from the world population based on eye colour. The two eye colour prediction results were compared, and it was discovered that IrisPlex and FROG-Kb had similar higher prediction ratios for Brown and Blue eye colour. Conclusions: The results of the current study revealed brown eye colour to be the most prevalent amongst members of the local population of Pakhtoon ethnicity in the Malakand Division of northern Pakistan. A set of contemporary human DNA samples with known phenotypes are used in this research to evaluate the custom panel’s prediction accuracy. With the aid of this forensic test, DNA typing can be supplemented with details about the appearance of the person from whom the sample was taken in cases involving missing persons, ancient human remains, and trace samples. This study may be helpful for future population genetics and forensics studies.
Reactive Oxygen Species Regulation of Chemoresistance and Metastatic Capacity of Melanoma: Role of the Cancer Stem Cell Marker CD271
Authors: Beretti Francesca , Gatti Martina , Zavatti Manuela , Bassoli Sara , Pellacani Giovanni , Maraldi Tullia
Published: April 2023

BRAF mutations are present in 30–50% of cases of cutaneous melanoma, and treatment with selective BRAF and MEK inhibitors has been introduced. However, the development of resistance to these drugs often occurs. Chemo-resistant melanoma cells show increased expression of CD271, a stem cell marker that features increased migration. Concordantly, resistance to the selective inhibitor of oncogenic BRAFV600E/K, vemurafenib, is mediated by the increased expression of CD271. It has recently been shown that the BRAF pathway leads to an overexpression of the NADPH oxidase Nox4, which produces reactive oxygen species (ROS). Here, we examined in vitro how Nox-derived ROS in BRAF-mutated melanoma cells regulates their drug sensitivity and metastatic potential. We demonstrated that DPI, a Nox inhibitor, reduced the resistance of a melanoma cell line (SK-MEL-28) and a primary culture derived from a BRAFV600E-mutated biopsy to vemurafenib. DPI treatment affected the expression of CD271 and the ERK and Akt signaling pathways, leading to a drop in epithelial–mesenchymal transition (EMT), which undoubtedly promotes an invasive phenotype in melanoma. More importantly, the scratch test demonstrated the efficacy of the Nox inhibitor (DPI) in blocking migration, supporting its use to counteract drug resistance and thus cell invasion and metastasis in BRAF-mutated melanoma.
Neurological Disease Modeling Using Pluripotent and Multipotent Stem Cells: A Key Step towards Understanding and Treating Mucopolysaccharidoses
Authors: Carvalho Sofia , Santos Juliana Inês , Moreira Luciana , Gonçalves Mariana , David Hugo , Matos Liliana , Encarnação Marisa , Alves Sandra , Coutinho Maria Francisca
Published: April 2023

Despite extensive research, the links between the accumulation of glycosaminoglycans (GAGs) and the clinical features seen in patients suffering from various forms of mucopolysaccharidoses (MPSs) have yet to be further elucidated. This is particularly true for the neuropathology of these disorders; the neurological symptoms are currently incurable, even in the cases where a disease-specific therapeutic approach does exist. One of the best ways to get insights on the molecular mechanisms driving that pathogenesis is the analysis of patient-derived cells. Yet, not every patient-derived cell recapitulates relevant disease features. For the neuronopathic forms of MPSs, for example, this is particularly evident because of the obvious inability to access live neurons. This scenario changed significantly with the advent of induced pluripotent stem cell (iPSC) technologies. From then on, a series of differentiation protocols to generate neurons from iPSC was developed and extensively used for disease modeling. Currently, human iPSC and iPSC-derived cell models have been generated for several MPSs and numerous lessons were learnt from their analysis. Here we review most of those studies, not only listing the currently available MPS iPSC lines and their derived models, but also summarizing how they were generated and the major information different groups have gathered from their analyses. Finally, and taking into account that iPSC generation is a laborious/expensive protocol that holds significant limitations, we also hypothesize on a tempting alternative to establish MPS patient-derived neuronal cells in a much more expedite way, by taking advantage of the existence of a population of multipotent stem cells in human dental pulp to establish mixed neuronal and glial cultures.
Enhanced Integrity of White Matter Microstructure in Mind–Body Practitioners: A Whole-Brain Diffusion Tensor Imaging Study
Authors: Xie Yingrong , Cai Kelong , Dai Jingang , Wei Gaoxia
Published: April 2023

Tai Chi Chuan (TCC) is an increasingly popular multimodal mind–body practice with potential cognitive benefits, yet the neurobiological mechanisms underlying these effects, particularly in relation to brain white matter (WM) microstructure, remain largely unknown. In this study, we used diffusion tensor imaging (DTI) and the attention network test (ANT) to compare 22 TCC practitioners and 18 healthy controls. We found extensive differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) between the two groups. Specifically, TCC practitioners had significantly different diffusion metrics in the corticospinal tract (CST), fornix (FX)/stria terminalis (ST), and cerebral peduncle (CP). We also observed a significant correlation between increased FA values in the right CP and ANT performance in TCC practitioners. Our findings suggest that optimized regional WM microstructure may contribute to the complex information processing associated with TCC practice, providing insights for preventing cognitive decline and treating neurological disorders with cognitive impairment in clinical rehabilitation.
Prolonged Intrinsic Neural Timescales Dissociate from Phase Coherence in Schizophrenia
Authors: Lechner Stephan , Northoff Georg
Published: April 2023

Input processing in the brain is mediated by phase synchronization and intrinsic neural timescales, both of which have been implicated in schizophrenia. Their relationship remains unclear, though. Recruiting a schizophrenia EEG sample from the B-SNIP consortium dataset (n = 134, 70 schizophrenia patients, 64 controls), we investigate phase synchronization, as measured by intertrial phase coherence (ITPC), and intrinsic neural timescales, as measured by the autocorrelation window (ACW) during both the rest and oddball-task states. The main goal of our paper was to investigate whether reported shifts from shorter to longer timescales are related to decreased ITPC. Our findings show (i) decreases in both theta and alpha ITPC in response to both standard and deviant tones; and (iii) a negative correlation of ITPC and ACW in healthy subjects while such correlation is no longer present in SCZ participants. Together, we demonstrate evidence of abnormally long intrinsic neural timescales (ACW) in resting-state EEG of schizophrenia as well as their dissociation from phase synchronization (ITPC). Our data suggest that, during input processing, the resting state’s abnormally long intrinsic neural timescales tilt the balance of temporal segregation and integration towards the latter. That results in temporal imprecision with decreased phase synchronization in response to inputs. Our findings provide further evidence for a basic temporal disturbance in schizophrenia on the different timescales (longer ACW and shorter ITPC), which, in the future, might be able to explain common symptoms related to the temporal experience in schizophrenia, for example temporal fragmentation.