bluuebunny/crossref_metadata_2025 huggingface dataset:
Heat Shock Protein 70 Genes Are Involved in the Thermal Tolerance of Hippodamia variegata
Authors: Yang Qing , Lu Yanhui
Published: September 2024

Previous studies have shown that the survival and reproduction of Hippodamia variegata are increasingly harmed by progressive increases in temperature (from 32 °C to 35 °C and 38 °C). In this study, transcriptome sequencing analysis was performed on H. variegata, after being exposed to different temperatures (from 32 to 38 °C) for 24 h, using high-throughput sequencing technology. We found the largest number of differentially expressed genes (DEGs) in the 35 °C vs. 32 °C group (1151) followed by the 38 °C vs. 32 °C group (1054) and then the 38 °C vs. 35 °C group (901), indicating that H. variegata expressed the largest number of newly mobilized genes under medium-high temperature (35 °C). Gene functional analysis showed that a large number of DEGs were involved in “Catalytic activity”, “Oxidoreductase activity”, “Metabolic pathways”, and “Longevity regulating pathway-multiple species” gene groups. We randomly selected nine DEGs for validation using qRT-PCR. The results of qRT-PCR were consistent with the transcriptome data, confirming their reliability. Finally, the RNAi results showed that adult survival, longevity, and fecundity were lower in the group in which gene expression of the heat shock proteins (Hsp70-01 and Hsp68) was suppressed than in the control group (injection ds-GFP) at all the experimental temperatures (32, 35, and 38 °C). Our results indicate the important role of the heat shock proteins (Hsp70-01 and Hsp68) in resistance to high-temperature stress in H. variegata and provide a molecular basis for analyzing its thermotolerance mechanism.
De Novo Transcriptome Profiling of Mustard Aphid (Lipaphis erysimi) and Differential Expression of Transcripts Associated with Feeding and Non-Feeding Conditions and Developmental Stages
Authors: Chongtham Rubina , Sharma Manvi , Shukla Rohit Nandan , Joshi Gopal , Kumar Amar , Goel Shailendra , Agarwal Manu , Jagannath Arun
Published: September 2024

Lipaphis erysimi is a specialist aphid of the Indian subcontinent that causes significant yield losses in oilseed Brassicas. Several aphid genes have been used as preferred targets in RNAi-based transgenic plants for aphid resistance. In order to enhance the repertoire of potential target genes for aphid control and to identify the genes associated with aphid feeding and development, we performed a two-way comparative study of differential gene expression profiles between (i) feeding and non-feeding adults and (ii) adult and nymph developmental stages of L. erysimi. De novo RNA-seq of aphids using Illumina technology generated a final transcriptome comprising 52,652 transcripts. Potential transcripts for host selection, detoxification, salivary proteins and effectors, molecular chaperones and developmental genes were identified. Differential gene expression studies identified variations in the expression of 1502 transcripts between feeding and non-feeding adults and 906 transcripts between nymphs and adults. These data were used to identify novel target genes for RNAi-based aphid control and facilitate further studies on the molecular basis of aphid feeding and development.
Left Ventricular Diastolic Dysfunction across Levels of Kidney Function: A Cross-Sectional Study Based on Routine Clinical Practice Data
Authors: Porras Cindy P. , Dal Canto Elisa , van Ommen Anne-Mar L. , Handoko M. Louis , Haitjema Saskia , de Groot Mark C. H. , Bots Michiel L. , Verhaar Marianne C. , Vernooij Robin W. M.
Published: September 2024

Left ventricular diastolic dysfunction (LVDD) commonly coexists with kidney dysfunction. In this study, we investigated the presence of abnormalities in echocardiography parameters indicative of LVDD across stages of kidney function. Methods: We selected patients who visited a university hospital and had a serum creatinine and echocardiography reported in their medical records. Participants were categorized based on their kidney function: normal (estimated glomerular filtration rate [eGFR] ≥ 90 mL/min/1.73 m2), mildly decreased (eGFR: 60–90), moderately decreased (eGFR: 30–60), and severely decreased (eGFR < 30). The relationship between kidney function and echocardiography parameters was examined using logistic and linear regressions. Results: Among 4022 patients (age: 66.5 years [SD: 12.1], 41% women), 26%, 50%, 20%, and 4% had a normal, mildly, moderately, and severely decreased kidney function, respectively. Compared to patients with normal kidney function, patients with mildly decreased kidney function had higher odds for an abnormal E/e′ ratio (OR: 1.51 [95% CI: 1.13, 2.02]). Patients with moderately decreased kidney function presented a higher risk of abnormal E/e′ (OR: 2.90 [95% CI: 2.08, 4.04]), LAVI (OR: 1.62 [95% CI: 1.13, 2.33]), TR velocity (OR: 2.31 [95% CI: 1.49, 3.57]), and LVMI (OR: 1.70 [95% CI: 1.31, 2.20]), while patients with severely decreased kidney function had higher odds for abnormal E/e′ (OR: 2.95 [95% CI: 1.68, 5.17]) and LVMI > 95 g/m2 in women or >115 g/m2 in men (OR: 2.07 [95% CI: 1.27, 3.38]). The linear regression showed a significant inverse association between eGFR and echocardiography parameters, meaning that with worse kidney function, the parameters for LVDD worsened as well. Conclusions: Abnormal echocardiography parameters of LVDD were present even in patients with mildly decreased kidney function. As the kidney function worsened, there was a gradual increase in the risk of abnormal parameters of LVDD.
Predictive Value of Neutrophil–Lymphocyte Ratio and Other Inflammation Indices in Febrile Seizures in Children
Authors: Söğütlü Yakup , Altaş Uğur
Published: September 2024

Objective: There is increasing evidence for the effect of inflammation on the etiology of febrile seizure (FS) patients. We aimed to investigate the role of easily accessible inflammatory markers such as the neutrophil–lymphocyte ratio (NLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), neutrophil–lymphocyte–platelet ratio (NLPR), and pan-immune-inflammation value (PIV) in febrile seizure. Methods: A total of 300 children, including 100 with febrile convulsions (FS), 100 febrile controls (FCs), and 100 healthy controls (HCs), were included in this retrospective study. The FS group was compared with the FC and HC groups in terms of these inflammatory indexes. Results: Between the FS group and the FC group, the neutrophil count was significantly higher in the FS group (p = 0.001) and the lymphocyte count was significantly lower (p < 0.001). The NLR (p < 0.001), SII (p < 0.001), SIRI (p < 0.001), NLPR (p < 0.001), and PIV (p < 0.001) were significantly higher in the FS group than in both the FC and healthy control groups. The optimal cut-off values for predicting FS in febrile conditions were 3.59> for NLR, >870.47 for SII, >1.96 for SIRI, 0.96> for NLPR, and >532.75 for PIV. Conclusions: The inflammatory indices are inexpensive, easily accessible hematological markers that can contribute to the diagnosis of FS.
Numerical Analysis of Leading-Edge Roughness Effects on the Aerodynamic Performance of a Thick Wind Turbine Airfoil
Authors: Zhang Wei , Ma Kuichao , Cai Chang , Sun Xiangyu , Zhang Jun , Zhong Xiaohui , Rong Xiaomin , Li Qing’an
Published: September 2024

The aerodynamic performance of wind turbine airfoils is crucial for the efficiency and reliability of wind energy systems, with leading-edge roughness significantly impacting blade performance. This study conducts numerical simulations on the DU 00-W-401 airfoil to investigate the effects of leading-edge roughness. Results reveal that the rough airfoil exhibits a distinctive “N”-shaped lift coefficient curve. The formation mechanism of this nonlinear lift curve is primarily attributed to the development of the trailing-edge separation vortex and variations in the adverse pressure gradient from the maximum thickness position to the trailing-edge confluence. The impact of different roughness heights is further investigated. It is discovered that when the roughness height is higher than 0.3 mm, the boundary layer can be considered fully turbulent, and the lift curve shows the “N” shape stably. When the roughness height is between 0.07 mm and 0.1 mm, a transitional state can be observed, with several saltation points in the lift curve. The main characteristics of different flow regimes based on different lift curve segments are summarized. This research enhances the understanding of the effects of leading-edge roughness on the aerodynamic performance of a thick wind turbine airfoil, and the simulation method for considering the effect of leading-edge roughness is practical to be applied on large-scale wind turbine blade to estimate the aerodynamic performance under rough leading-edge conditions, thereby supporting advancements in wind turbine technology and promoting the broader adoption of renewable energy.
Experimental Study of Omnidirectional Scattering Characteristics of Complex Scale Targets Based on Coded Signals
Authors: Tang Yongzhuang , Zhou Qidou , Pan Yucun , Lü Xiaojun , Wang Xiaowei
Published: September 2024

To investigate the omnidirectional geometric scattering characteristics of an underwater vehicle and the target detection performance of phase coded (BPSK) signals, acoustic scattering tests were carried out in an anechoic chamber using the Suboff scale model. To mitigate the overlapping interference of the direct wave on the scattering wave in the limited test space, physical suppression with an “anechoic cloak” and direct wave cancellation were proposed. Target echo and reflection wave tests at different offset angles were carried out, and the accuracy of the BPSK signal in acquiring highlight features and the feasibility of anechoic chamber tests were verified through comparison with theoretical range profiles. A series of echo and omnidirectional scattering characteristics were obtained through the experiment and simulation, which verified the effectiveness of the low-frequency submarine model detection (there were still strong scattering waves at the dimensionless frequency ka = 1.88). Comparison tests of CW, LFM, and BPSK signals were carried out, and the measured data proved that the BPSK signal had the advantages of low sidelobe, high resolution, and noise resistance in target detection. The acoustic scattering test method designed in this study and the omnidirectional scattering characteristics obtained can be used as a reference for semi-physical target acoustic scattering simulations and practical multistatic detection.
The Impact of Special Marine Environments Such as the Kuroshio on Hydroacoustic Detection Equipment
Authors: Zhang Xueqin , Yang Kunde , Yu Xiaolin
Published: September 2024

In order to study the impact of acoustic propagation characteristics in the northeastern South China Sea, GEBCO08 global terrain grid data and Argo data were used to numerically simulate the acoustic transmission characteristics of two stations in the northeast South China Sea affected by the Kuroshio. The impact of different marine environments on acoustic transmission characteristics was analyzed. The results show that increasing the deployment depth of a sound source within a certain range will reduce the transmission loss; deploying a sound source near the axis of the surface acoustic channel or the deep-sea acoustic channel will also greatly increase the propagation distance of sound signals; and the presence of topography such as undersea mountains will increase the transmission loss.
Detection of Typical Transient Signals in Water by XGBoost Classifier Based on Shape Statistical Features: Application to the Call of Southern Right Whale
Authors: Zhou Zemin , Qu Yanrui , Zhu Boqing , Zhang Bingbing
Published: September 2024

Whale sound is a typical transient signal. The escalating demands of ecological research and marine conservation necessitate advanced technologies for the automatic detection and classification of underwater acoustic signals. Traditional energy detection methods, which focus primarily on amplitude, often perform poorly in the non-Gaussian noise conditions typical of oceanic environments. This study introduces a classified-before-detect approach that overcomes the limitations of amplitude-focused techniques. We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. The integration of these features significantly enhances model performance, promoting the broader application of marine acoustic remote sensing technologies. This research contributes to the advancement of marine bioacoustic monitoring, offering a reliable, rapid, and training-efficient method suitable for practical deployment.
Exploring the Biocontrol Potential of Phanerochaete chrysosporium against Wheat Crown Rot
Authors: Liu Lei , Jin Yaqiong , Lian Huijuan , Yin Qianxi , Wang Hailei
Published: September 2024

The worldwide occurrence of wheat crown rot, predominantly caused by the pathogen Fusarium pseudograminearum, has a serious impact on wheat production. Numerous microorganisms have been employed as biocontrol agents, exhibiting effectiveness in addressing a wide array of plant pathogens through various pathways. The mycelium of the white-rot fungus Phanerochaete chrysosporium effectively inhibits the growth of F. pseudograminearum by tightly attaching to it and forming specialized penetrating structures. This process leads to the release of intracellular inclusions and the eventual disintegration of pathogen cells. Furthermore, volatile organic compounds and fermentation products produced by P. chrysosporium exhibit antifungal properties. A comprehensive understanding of the mechanisms and modalities of action will facilitate the advancement and implementation of this biocontrol fungus. In order to gain a deeper understanding of the mycoparasitic behavior of P. chrysosporium, transcriptome analyses were conducted to examine the interactions between P. chrysosporium and F. pseudograminearum at 36, 48, and 84 h. During mycoparasitism, the up-regulation of differentially expressed genes (DEGs) encoding fungal cell-wall-degrading enzymes (CWDEs), iron ion binding, and mycotoxins were mainly observed. Moreover, pot experiments revealed that P. chrysosporium not only promoted the growth and quality of wheat but also hindered the colonization of F. pseudograminearum in wheat seedlings. This led to a delay in the development of stem base rot, a reduction in disease severity and incidence, and the activation of the plant’s self-defense mechanisms. Our study provides important insights into the biocontrol mechanisms employed by P. chrysosporium against wheat crown rot caused by F. pseudograminearum.
Ocular Ultrasound in the Diagnosis of Optic Neuropathies: A Review of the Literature
Authors: Coppola Alessia , Abbinante Giulia , De Pascale Ilaria , Gagliardi Vincenzo , Salerno Giulio , Pellegrino Alfonso , Vitiello Livio
Published: September 2024

Background: Optic neuropathies represent one of the most frequent causes of vision loss, and they can manifest alone or in conjunction with neurological or systemic symptoms and signs. In recent years, the diagnostic techniques used to detect optic neuropathies have significantly improved, facilitating diagnosis and improving treatment. Among these, ocular ultrasound has assumed a fundamental role, although with conflicting results in the published scientific literature. For this reason, the aim of this review is to analyze the role of ocular ultrasound in the precise and targeted diagnosis of optic neuropathies to better understand the presumed potential of this precious diagnostic tool in the management of these ocular and neurological disorders. Methods: We carried out a search on PubMed and Scopus utilizing terms related to optic neuropathies and ocular ultrasound, including only relevant English full-length research articles, case reports, or case series. Results: Most of the papers published in the scientific literature use only the B-scan ultrasound technique without considering the more precise and objective standardized A-scan technique that allows for performing more accurate diagnostic tests, such as the “30-degree test” and the “optic nerve exercise test”. Conclusions: Future clinical trials and research on optic neuropathies should also consider the use of the standardized A-scan technique in order to compare clinical findings not only with B-scan ultrasonography but also with other noninvasive procedures that could be helpful in reaching the correct diagnosis.
Endochondral Ossification for Spinal Fusion: A Novel Perspective from Biological Mechanisms to Clinical Applications
Authors: Ge Rile , Liu Chenjun , Zhao Yuhong , Wang Kaifeng , Wang Xiluan
Published: September 2024

Degenerative scoliosis (DS), encompassing conditions like spondylolisthesis and spinal stenosis, is a common type of spinal deformity. Lumbar interbody fusion (LIF) stands as a conventional surgical intervention for this ailment, aiming at decompression, restoration of intervertebral height, and stabilization of motion segments. Despite its widespread use, the precise mechanism underlying spinal fusion remains elusive. In this review, our focus lies on endochondral ossification for spinal fusion, a process involving vertebral development and bone healing. Endochondral ossification is the key step for the successful vertebral fusion. Endochondral ossification can persist in hypoxic conditions and promote the parallel development of angiogenesis and osteogenesis, which corresponds to the fusion process of new bone formation in the hypoxic region between the vertebrae. The ideal material for interbody fusion cages should have the following characteristics: (1) Good biocompatibility; (2) Stable chemical properties; (3) Biomechanical properties similar to bone tissue; (4) Promotion of bone fusion; (5) Favorable for imaging observation; (6) Biodegradability. Utilizing cartilage-derived bone-like constructs holds promise in promoting bony fusion post-operation, thus warranting exploration in the context of spinal fusion procedures.
Analyzing the Selective Stock Price Index Using Fractionally Integrated and Heteroskedastic Models
Authors: Contreras-Reyes Javier E. , Zavala Joaquín E. , Idrovo-Aguirre Byron J.
Published: September 2024

Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile’s stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China’s economic slowdown in 2012 and the subprime crisis in 2008.
COVID-19 and Uncertainty Effects on Tunisian Stock Market Volatility: Insights from GJR-GARCH, Wavelet Coherence, and ARDL
Authors: Trabelsi Emna
Published: September 2024

This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility when using both the conditional variance and the realized volatility. Interestingly, aggregate indices related to government interventions exhibit substantial impacts on the realized volatility, indicating a relative resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more subtle outcomes in terms of the correlations of both measures of volatility to the same metrics. Our econometric implications bear on the application of such a technique, as well as on the use of the realized volatility as an accurate measure of the “true” value of volatility. Nevertheless, the measures and actions undertaken by the authorities do not exclude fear and insecurity from investors due to another virus or any other crisis. The positive and long-term impact on the volatility of US equity market uncertainty, VIX, economic policy uncertainty (EPU), and the infectious disease EMV tracker (IDEMV) is obvious through the autoregressive distributed lag model (ARDL). A potential vulnerability of the Tunisian stock market to future shocks is not excluded. Government and stock market authorities should grapple with economic and financial fallout and always instill investor confidence. Importantly, our results put mechanisms such as overreaction to public news and (in)efficient use of information under test. Questioning the accuracy of announcements is then recommended.
Spatio-Temporal Evolution and Multi-Scenario Modeling Based on Terrestrial Carbon Stocks in Xinjiang
Authors: Liu Xiaohuang , Xue Zijing , Liu Jiufen , Zhao Xiaofeng , Fu Yujia , Wang Ran , Luo Xinping , Xing Liyuan , Wang Chao , Zhao Honghui
Published: September 2024

The increase in atmospheric CO2 leads to global warming and ecological environment deterioration. Carbon storage modeling and assessment can promote the sustainable development of the ecological environment. This paper took Xinjiang as the study area, analyzed the spatial and temporal evolution of land use in four periods from 1990 to 2020, explored the spatial relationship of carbon stocks using the InVEST model, and coupled the GMOP model with the PLUS model to carry out multiple scenarios for the future simulation of land use in the study area. We found (1) Over time, the types with an increasing area were mainly impervious and cropland, and the types with a decreasing area were grassland, snow/ice, and barren; spatially, the types were predominantly barren and grassland, with the conversion of grassland to cropland being more evident in the south of Northern Xinjiang and north of Southern Xinjiang. (2) The evolutionary pattern of terrestrial carbon stocks is increasing and then decreasing in time, and the carbon sink areas are concentrated in the Tarim River Basin and the vicinity of the Ili River; spatially, there are differences in the aggregation between the northern, southern, and eastern borders. By analyzing the transfer in and out of various categories in Xinjiang over the past 30 years, it was obtained that the transfer out of grassland reduced the carbon stock by 5757.84 × 104 t, and the transfer out of Barren increased the carbon stock by 8586.12 × 104 t. (3) The land use layout of the sustainable development scenario is optimal under the conditions of satisfying economic and ecological development. The reduction in terrestrial carbon stocks under the 2020–2030 sustainable development scenario is 209.79 × 104 t, which is smaller than the reduction of 830.79 × 104 t in 2010–2020. Land optimization resulted in a lower loss of carbon stocks and a more rational land-use layout. Future planning in Xinjiang should be based on sustainable development scenarios, integrating land resources, and achieving sustainable economic and ecological development.
Community-Based Participatory Research on Urban Environmental Conflicts: Sand Quarries in Northern Bogotá
Authors: Guzmán Adriana Hernández , Hernández Guzmán Diego , Milanés Celene B. , Ramírez Omar , Herrera Tapias Belinha , Montero Ofelia Pérez , Gutiérrez Reyes Atenas , Zielinski Seweryn
Published: September 2024

The paper is about community-based participatory research on environmental conflicts about sand quarries in northern Bogotá. Community-based participatory research is applied to understand the environmental conflicts of mining activities. The approach was suitable for improving the relevance and acceptance of research in the local community. It invited community members to participate and uphold their views about the social conflicts and environmental impacts arising from mining activities. The findings highlight the need for participatory environmental governance, integrating local communities and power relations analysis in environmental decision-making.
Significant Differences in Microbial Soil Properties, Stoichiometry and Tree Growth Occurred within 15 Years after Afforestation on Different Parent Material
Authors: Babur Emre
Published: September 2024

The mineralogical composition of the parent material, together with plant species and soil microorganisms, constitutes the foundational components of an ecosystem’s energy cycle. Afforestation in arid-semi arid regions plays a crucial role in preventing erosion and enhancing soil quality, offering significant economic and ecological benefits. This study evaluated the effects of afforestation and different parent materials on the physicochemical and microbiological properties of soils, including microbial basal respiration (MR), as well as how these changes in soil properties after 15 years influence plant growth. For this purpose, various soil physicochemical parameters, MR, soil microbial biomass carbon (Cmic), stoichiometry (microbial quotient = Cmic/Corg = qMic and metabolic quotient = MR/Cmic = qCO2), and tree growth metrics such as height and diameter were measured. The results indicated that when the physicochemical and microbiological properties of soils from different bedrock types, along with the average values of tree growth parameters, were analyzed, afforestation areas with limestone bedrock performed better than those with andesite bedrock. Notably, sensitive microbial properties, such as Cmic, MR, and qMic, were positively influenced by afforestation. The highest values of Cmic (323 μg C g−1) and MR (1.3 CO2–C g−1 h−1) were recorded in soils derived from limestone. In contrast, the highest qCO2 was observed in the control plots of soils with andesite parent material (7.14). Considering all the measured soil properties, the samples can be ranked in the following order: limestone sample (LS) > andesite sample (AS) > limestone control (LC) > andesite control (AC). Similarly, considering measured plant growth parameters were ranked as LS > AS. As a result, the higher plant growth capacity and carbon retention of limestone soil indicate that it has high microbial biomass and microbial activity. This study emphasizes the importance of selecting suitable parent material and understanding soil properties to optimize future afforestation efforts on bare lands.
Synchronization of Bidirectionally Coupled Fractional-Order Chaotic Systems with Unknown Time-Varying Parameter Disturbance in Different Dimensions
Authors: Zhang Chunli , Gao Yangjie , Yao Junliang , Qian Fucai
Published: September 2024

In this article, the synchronization of bidirectionally coupled fractional-order chaotic systems with unknown time-varying parameter disturbance in different dimensions is investigated. The scale matrices are designed to address the problem of the synchronization for fractional-order chaotic systems across two different dimensions. Congelation of variables is used to deal with the unknown time-varying parameter disturbance. Based on Lyapunov’s stability theorem, the synchronization controllers in different dimensions are obtained. At the same time, adaptive laws of the unknown disturbance can be designed. Benefiting from the proposed methods, we verify all the synchronization errors can converge to zero as time approaches infinity, regardless of whether in n-D or m-D synchronization, simultaneously ensuring that both control and estimation signals are bounded. Finally, simulation studies based on fractional-order financial systems are carried out to validate the effectiveness of the proposed synchronization method.
Prediction of Wind Turbine Gearbox Oil Temperature Based on Stochastic Differential Equation Modeling
Authors: Su Hongsheng , Ding Zonghao , Wang Xingsheng
Published: September 2024

Aiming at the problem of high failure rate and inconvenient maintenance of wind turbine gearboxes, this paper establishes a stochastic differential equation model that can be used to fit the change of gearbox oil temperature and adopts an iterative computational method and Markov-based modified optimization to fit the prediction sequence in order to realize the accurate prediction of gearbox oil temperature. The model divides the oil temperature change of the gearbox into two parts, internal aging and external random perturbation, adopts the approximation theorem to establish the internal aging model, and uses Brownian motion to simulate the external random perturbation. The model parameters were calculated by the Newton–Raphson iterative method based on the gearbox oil temperature monitoring data. Iterative calculations and Markov-based corrections were performed on the model prediction data. The gearbox oil temperature variations were simulated in MATLAB, and the fitting and testing errors were calculated before and after the iterations. By comparing the fitting and testing errors with the ordinary differential equations and the stochastic differential equations before iteration, the iterated model can better reflect the gear oil temperature trend and predict the oil temperature at a specific time. The accuracy of the iterated model in terms of fitting and prediction is important for the development of preventive maintenance.
Accelerating the Speed of Convergence for High-Order Methods to Solve Equations
Authors: Behl Ramandeep , Argyros Ioannis K. , Alharbi Sattam
Published: September 2024

This article introduces a multistep method for developing sequences that solve Banach space-valued equations. It provides error estimates, a radius of convergence, and uniqueness results. Our approach improves the applicability of the recommended method and addresses challenges in applied science. The theoretical advancements are supported by comprehensive computational results, demonstrating the practical applicability and robustness of the earlier method. We ensure more reliable and precise solutions to Banach space-valued equations by providing computable error estimates and a clear radius of convergence for the considered method. We conclude that our work significantly improves the practical utility of multistep methods, offering a rigorous and computable approach to solving complex equations in Banach spaces, with strong theoretical and computational results.
Some Fractional Integral and Derivative Formulas Revisited
Authors: González-Santander Juan Luis , Mainardi Francesco
Published: September 2024

In the most common literature about fractional calculus, we find that Dtαaft=It−αaft is assumed implicitly in the tables of fractional integrals and derivatives. However, this is not straightforward from the definitions of Itαaft and Dtαaft. In this sense, we prove that Dt0ft=It−α0ft is true for ft=tν−1logt, and ft=eλt, despite the fact that these derivations are highly non-trivial. Moreover, the corresponding formulas for Dtα−∞t−δ and Itα−∞t−δ found in the literature are incorrect; thus, we derive the correct ones, proving in turn that Dtα−∞t−δ=It−α−∞t−δ holds true.
Robust Liu Estimator Used to Combat Some Challenges in Partially Linear Regression Model by Improving LTS Algorithm Using Semidefinite Programming
Authors: Altukhaes Waleed B. , Roozbeh Mahdi , Mohamed Nur A.
Published: September 2024

Outliers are a common problem in applied statistics, together with multicollinearity. In this paper, robust Liu estimators are introduced into a partially linear model to combat the presence of multicollinearity and outlier challenges when the error terms are not independent and some linear constraints are assumed to hold in the parameter space. The Liu estimator is used to address the multicollinearity, while robust methods are used to handle the outlier problem. In the literature on the Liu methodology, obtaining the best value for the biased parameter plays an important role in model prediction and is still an unsolved problem. In this regard, some robust estimators of the biased parameter are proposed based on the least trimmed squares (LTS) technique and its extensions using a semidefinite programming approach. Based on a set of observations with a sample size of n, and the integer trimming parameter h ≤ n, the LTS estimator computes the hyperplane that minimizes the sum of the lowest h squared residuals. Even though the LTS estimator is statistically more effective than the widely used least median squares (LMS) estimate, it is less complicated computationally than LMS. It is shown that the proposed robust extended Liu estimators perform better than classical estimators. As part of our proposal, using Monte Carlo simulation schemes and a real data example, the performance of robust Liu estimators is compared with that of classical ones in restricted partially linear models.
Few-Shot Learning Sensitive Recognition Method Based on Prototypical Network
Authors: Yuan Guoquan , Zhao Xinjian , Li Liu , Zhang Song , Wei Shanming
Published: September 2024

Traditional machine learning-based entity extraction methods rely heavily on feature engineering by experts, and the generalization ability of the model is poor. Prototype networks, on the other hand, can effectively use a small amount of labeled data to train models while using category prototypes to enhance the generalization ability of the models. Therefore, this paper proposes a prototype network-based named entity recognition (NER) method, namely the FSPN-NER model, to solve the problem of difficult recognition of sensitive data in data-sparse text. The model utilizes the positional coding model (PCM) to pre-train the data and perform feature extraction, then computes the prototype vectors to achieve entity matching, and finally introduces a boundary detection module to enhance the performance of the prototype network in the named entity recognition task. The model in this paper is compared with LSTM, BiLSTM, CRF, Transformer and their combination models, and the experimental results on the test dataset show that the model outperforms the comparative models with an accuracy of 84.8%, a recall of 85.8% and an F1 value of 0.853.
An Alternative Analysis of Computational Learning within Behavioral Neuropharmacology in an Experimental Anxiety Model Investigation
Authors: Vargas-Moreno Isidro , Acosta-Mesa Héctor Gabriel , Rodríguez-Landa Juan Francisco , Avendaño-Garrido Martha Lorena , Fernández-Demeneghi Rafael , Herrera-Meza Socorro
Published: September 2024

Behavioral neuropharmacology, a branch of neuroscience, uses behavioral analysis to demonstrate treatment effects on animal models, which is fundamental for pre-clinical evaluation. Typically, this determination is univariate, neglecting the relevant associations for understanding treatment effects in animals and humans. This study implements regression trees and Bayesian networks from a multivariate perspective by using variables obtained from behavioral tests to predict the time spent in the open arms of the elevated arm maze, a key variable to assess anxiety. Three doses of allopregnanolone were analyzed and compared to a vehicle group and a diazepam-positive control. Regression trees identified cut-off points between the anxiolytic and anxiogenic effects, with the anxiety index standing out as a robust predictor, combined with the percentage of open-arm entries and the number of entries. Bayesian networks facilitated the visualization and understanding of the interactions between multiple behavioral and biological variables, demonstrating that treatment with allopregnanolone (2 mg) emulates the effects of diazepam, validating the multivariate approach. The results highlight the relevance of integrating advanced methods, such as Bayesian networks, into preclinical research to enrich the interpretation of complex behavioral data in animal models, which can hardly be observed with univariate statistics.
Navigating the Proteomic Landscape of Menopause: A Review
Authors: Katamesh Basant E. , Futela Pragyat , Vincent Ann , Thilagar Bright , Whipple Mary , Hassan Abdul Rhman , Abuelazm Mohamed , Nanda Sanjeev , Anstine Christopher , Singla Abhinav
Published: September 2024

Background and Objectives: Proteomics encompasses the exploration of protein composition, regulation, function, and pathways. Its influence spans diverse clinical fields and holds promise in addressing various women’s health conditions, including cancers, osteoporosis, and cardiovascular disorders. However, no comprehensive summary of proteomics and menopausal health exists. Our objective was to summarize proteomic profiles associated with diseases and disorders in peri- and postmenopausal women. Materials and Methods: We conducted a comprehensive search of databases including PubMed, Google Scholar, the Cochrane database, Elsevier, and ScienceDirect until 2022. A total of 253 studies were identified, and 41 studies met the inclusion criteria to identify data of interest. These included the study design, disease, and proteomics/proteins of significance, as described by the authors. Results: The 41 studies covered diverse areas, including bone disorders (10 studies), cardiovascular diseases (5 studies), oncological malignancies (10 studies), and various conditions, such as obesity, nonalcoholic liver disease, the effects of hormone replacement therapy, and neurological diseases (16 studies). The results of our study indicate that proteomic profiles correlate with heart disease in peri- and postmenopausal women, with distinct sex differences. Furthermore, proteomic profiles significantly differ between women with and without osteoporosis. Additionally, patients with breast, ovarian, and endometrial cancer exhibit notable variations in proteomic profiles compared to those without these conditions. Conclusions: Proteomics has the potential to enhance risk assessment and disease monitoring in peri- and postmenopausal women. By analyzing unique protein profiles, clinicians can identify individuals with heightened susceptibility to specific diseases or those already affected by established conditions. This review suggests that there is sufficient preliminary data related to proteomics in peri- and postmenopausal women for early identification of cardiovascular disease, osteoporosis, and cancers, disease monitoring, and tailoring individualized therapies. Rigorous validation studies involving large populations are essential before drawing definitive conclusions regarding the clinical applicability of proteomic findings.
Simulation of Localized Stress Impact on Solidification Pattern during Plasma Cladding of WC Particles in Nickel-Based Alloys by Phase-Field Method
Authors: Wei Dongsheng , Chen Ming , Zhang Chunlin , Ai Xingang , Xie Zhiwen
Published: September 2024

As materials science continues to advance, the correlation between microstructure and macroscopic properties has garnered growing interest for optimizing and predicting material performance under various operating conditions. The phase-field method has emerged as a crucial tool for investigating the interplay between microstructural characteristics and internal material properties. In this study, we propose a phase-field approach to couple two-phase growth with stress–strain elastic energy at the mesoscale, enabling the simulation of local stress effects on the solidified structure during the plasma cladding of WC particles and nickel-based alloys. This model offers a more precise prediction of microstructural evolution influenced by stress. Initially, the phase field of WC-Ni binary alloys was modeled, followed by simulations of actual local stress conditions and their impacts on WC particles and nickel-based alloys with ProCAST and finite element analysis software. The results indicate that increased stress reduces grain boundary migration, decelerates WC particle dissolution and diffusion, and diminishes the formation of reaction layers and Ostwald ripening. Furthermore, experimental validation corroborated that the model’s predictions were consistent with the observed microstructural evolution of WC particles and nickel-based alloy composites.
Neural Network Approach for Modelling and Compensation of Local Surface-Tilting-Dependent Topography Measurement Errors in Coherence Scanning Interferometry
Authors: Gao Sai , Li Zhi , Brand Uwe
Published: September 2024

The topography measurement accuracy of coherence scanning interferometry (CSI) suffers from the local characteristic of micro-structured surfaces, such as local surface slopes. A cylindrical reference artefact made of single-mode fiber with high roundness and low roughness has been proposed in this manuscript to traceably investigate the surface tilting induced measurement deviations using coherence scanning interferometry with high NA objectives. A feed-forward neural network (FF-NN) is designed and trained to model and thereafter compensate the systematic measurement deviations due to local surface tilting. Experimental results have verified that the FF-NN approach can well enhance the accuracy of the CSI for radius measurement of cylindrical samples up to 0.3%. Further development of the FF-NN for modelling of the measurement errors in CSI due to the optical properties of surfaces including areal roughness is outlined.
Editorial for the Topic on Advanced Laser Fabrication Technologies for Cross-Field Applications
Authors: Ma Zhuo-Chen , Liu Xue-Qing , Han Bing
Published: September 2024

The field of laser fabrication technologies has seen remarkable advancements in recent years [...]
The Vaginal Microbiome: Patient- versus Physician-Collected Microbial Swab: A Pilot Study
Authors: Gao Xu Shan , Groot Thomas , Schoenmakers Sam , Louwers Yvonne , Budding Andries , Laven Joop
Published: September 2024

The composition of the vaginal microbiota prior to an IVF/IVF-ICSI treatment can predict the chance of achieving a pregnancy. To improve clinical applicability and be more patient-friendly, the self-collection of vaginal samples would be preferable. However, the reliability of patient-collected samples compared to physician-collected samples remains unclear. This study compares microbiome outcomes from patient-collected versus physician-collected vaginal samples. This is a prospective pilot study consisting of two cohorts: Cohort I involved patient self-sampling of the vagina, followed by a physician-collected vaginal swab, while Cohort II involved the reversed order of collection. The interspace profiling (IS-Pro) technique was used to analyze the microbiota composition in all samples. From May 2021 to March 2022, a total of 444 samples were collected from n = 222 patients (aged 21–44 years), with Cohort I (n = 109) and Cohort II (n = 113). The vaginal microbiome composition of both cohorts was highly similar, regardless of the sampling order, with a mean cosine similarity of 0.93 (95% CI 0.91, 0.95) in Cohort I and 0.94 (95% CI 0.92, 0.96) in Cohort II. Furthermore, ANOVA analysis revealed no significant differences in bacterial species abundance between physician- and patient-collected samples, nor between first and second sample collections. The self-collection of vaginal samples can be considered comparable to physician-collected samples and indicates a more patient-friendly and convenient collection of the vaginal microbiome in an outpatient clinical setting.
Oxidative Stress in the Murine Model of Extraparenchymal Neurocysticercosis
Authors: Generoso Diego , Martins Tatiane de Camargo , Camacho Camila Renata Corrêa , Segredo Manuella Pacífico de Freitas , Batah Sabrina Setembre , Fabro Alexandre Todorovic , Sciutto Edda , Fleury Agnès , Hamamoto Filho Pedro Tadao , Zanini Marco Antônio
Published: September 2024

Oxidative stress is associated with several infectious diseases, as well as the severity of inflammatory reactions. The control of inflammation during parasite destruction is a target of neurocysticercosis treatment, as inflammation is strongly related to symptom severity. In this study, we investigated the presence of malondialdehyde and protein carbonyl, two by-products of reactive oxygen species (ROS), in an experimental model of extraparenchymal neurocysticercosis. Twenty male and twenty female rats were inoculated with 50 cysts of Taenia crassiceps in the subarachnoid space of the cisterna magna. Ten animals (five males and five females) were used as controls. Three months after inoculation, their brains were harvested for oxidative stress and histological assessments. Infected animals had higher scores for inflammatory cell infiltrates, malondialdehyde, and protein carbonyl. These results encourage future efforts to monitor oxidative stress status in neurocysticercosis, particularly in the context of controlling inflammation.
Lactic Acid Bacteria–Gut-Microbiota-Mediated Intervention towards Inflammatory Bowel Disease
Authors: Li Diantong , Liu Zhenjiang , Fan Xueni , Zhao Tingting , Wen Dongxu , Huang Xiaodan , Li Bin
Published: September 2024

Inflammatory bowel disease (IBD), encompassing ulcerative colitis (UC) and Crohn’s disease (CD), arises from intricate interactions involving genetics, environment, and pharmaceuticals with an ambiguous pathogenic mechanism. Recently, there has been an increasing utilization of lactic acid bacteria (LAB) in managing IBD, attributed to their ability to enhance intestinal barrier function, mitigate inflammatory responses, and modulate gut microbiota. This review initiates by elucidating the pathogenesis of IBD and its determinants, followed by an exploration of the mechanisms underlying LAB therapy in UC and CD. Special attention is directed towards their influence on intestinal barrier function and homeostasis regulated by gut microbiota. Furthermore, the review investigates the complex interplay among pivotal gut microbiota, metabolites, and pathways associated with inflammation. Moreover, it underscores the limitations of LAB in treating IBD, particularly in light of their varying roles in UC and CD. This comprehensive analysis endeavors to offer insights for the optimized application of LAB in IBD therapy.