bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of flooding stress on the growth and photosynthetic characteristics of napier grass (Pennisetum purpureum Schum. Guimu-1)
Authors: Wen Chenjie , Jiang Yuan , Liu Congzhe , Mo Qiong , Long Yuelin 
Published: August 2024

Abstract

Napier grass (Pennisetum purpureum) holds significant forage value in many tropical and subtropical regions. To investigate the production adaptability of napier grass in flood-prone areas, napier grass seedlings were selected to conduct a flooding stress experiment. By simulating different flooding durations and depths indoors, we studied the changes in the morphology of napier grass leaves, leaf photosynthetic and chlorophyll fluorescence parameters. The results indicated the occurrence of yellow leaves across all three groups. Specifically, under shallow flooding conditions, the tips of the leaves of napier grass turned yellow. In semi-flooding conditions, the leaves of napier grass yellowed, wilted, developed brown spots, and the stem base exhibited reddish-brown swelling. Under fully flooding conditions, the growth of napier grass was significantly impacted, with partial shedding of leaves and stems, and a mortality rate reaching 60% after 12 days. Both shallow and semi-flooding treatments induced the formation of aerial roots at the base of the stem in napier grass. The photosynthetic parameters Pn, Gs, Ci, and Tr of napier grass showed an overall downward trend under flooding stress. After the experiment, the values Fv/Fo, Fv/Fm, NPQ, and Qp of napier grass under flooding conditions were higher than those in the control group at the same time point, while the other two groups were lower than the control. Based on these findings, we conclude that napier grass exhibits good adaptability in shallow flooding environments, and can survive for 12 days under semi-flooding conditions.
Combining viscoelastic damping and nonlinearities to widen the operational speed range of flutter energy harvesting
Authors: Filho Marcelo A. Delgado , Filho André G. Cunha , de Lima Antônio M.G. , Bouhaddi Noureddine , Kacem Najib 
Published: August 2024

Abstract

We propose to use viscoelastic damping with combined hardening and free-play structural nonlinearities to increase the harvesting performance and control the vibration of a pitch and plunge airfoil with piezoelectric transduction. The numerical simulations are performed by direct integration of the equation of motion in the time domain for an unsteady aerodynamic load. Moreover, a fractional derivative model accounts for the viscoelastic material behavior in an efficient way. The effect of each structural nonlinearity is discussed and a good condition for harvesting in terms of cut-in speed and operational speed range is determined. From this condition, it is found that the the viscoelastic damper in pitch can further reduce the cut-in speed in $13$~\%, slightly increase the harvested power and help to reduce the dynamical complexity of the system response. In turn, the viscoelastic damper in plunge can be tuned to control the vibration amplitude at post-critical flow speeds, increasing the operational speed range up to $28$~\% and the power up to two orders of magnitude in some cases. It is shown that the viscoelastic damping conserves a favorable condition for harvesting for temperature variations from $10^{\circ}$C to $35^{\circ}$C.
Is Amnestic Mild Cognitive Impairment a Neuro-Immune Condition?
Authors: Tran-Chi Vinh-Long , Maes Michael , Nantachai Gallayaporn , Hemrungrojn Solaphat , Solmi Marco , Stoyanov Drozdstoy , Stoyanova Kristina , Tunvirachaisakul Chavit 
Published: August 2024

Abstract

Objectives
The pathophysiology of amnestic Mild Cognitive Impairment (aMCI) is largely unknown, although some papers found signs of immune activation. To assess the cytokine network in aMCI after excluding patients with major depression (MDD) and to examine the immune profiles of quantitative aMCI (qMCI) and distress symptoms of old age (DSOA) scores.
Design:
A case-control study.
Setting:
Department of Psychiatry of a University Hospital, Bangkok, Thailand.
Participants
: 61 Thai aMCI participants and 60 healthy old adults (both without MDD).
Measurements
: The Bio-Plex Pro human cytokine 27-plex test kit was used to assay cytokines/chemokines/growth factors in fasting plasma samples.
Results
aMCI is characterized by significant general immunosuppression, and reductions in T helper 1 (Th)1 and T cell growth profiles, the immune-inflammatory responses system, interleukin (IL)1β, IL6, IL7, IL12p70, IL13, GM-CSF, and MCP-1. These 7 cytokines/chemokines exhibit neuroprotective effects at physiologic concentrations. In multivariate analyses, three neurotoxic chemokines, CCL11, CCL5, and CXCL8, emerged as significant predictors of aMCI. Logistic regression showed that aMCI was best predicted by combining IL7, IL1β, MCP-1, years of education (all inversely associated) and CCL5 (positively associated). We found that 38.2% of the variance in the qMCI score was explained by IL7, IL1β, MCP-1, IL13, years of education (inversely associated) and CCL5 (positively associated). The DSOA was not associated with any immune data.
Discussion
A dysbalance between lowered levels of neuroprotective cytokines and chemokines, and relative increases in neurotoxic chemokines are key factors in aMCI. Future MCI research should always control for the confounding effects of affective symptoms.
Power Consumption State Evaluation of Important Power Customers Based on AHP-TOPSIS Algorithm
Authors: Zhang Xixiang , meng qi , Yang Jun 
Published: August 2024

Abstract

In the construction of a new power system, the identification and evaluation of power consumption status of power customers will become an important basis for them to participate in the emerging businesses such as demand response and virtual power plants. In order to ensure the power safety of important power customers, a new evaluation of power consumption status of important power customers based on the AHP(Analytic Hierarchy Process)-TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution) algorithm is proposed by fully mining and applying the power big data. Firstly, a power consumption big data analysis platform based on the Hadoop architecture is built to provide a high-performance platform support for big data analysis. Secondly, nine evaluation indexes are constructed from the three dimensions of voltage, load and synthesis, which objectively and scientifically describes the power consumption status of important power customers. Finally, the AHP-TOPSIS algorithm is used to evaluate and analyze the voltage, load and comprehensive indicators respectively, thus, obtaining the evaluation values of three kinds of indicators. The power consumption status scores of important power customers are determined by the variable weight weighted summation. The rationality and feasibility of the method and algorithm are proved by example analysis and field verification. This method helps to promote the transformation from post fault emergency repair to warning beforehand. It has the multiple effects of ensuring safe power consumption, supporting accurate patrolling and active emergency repair serving.
ENSO phase space dynamics in CMIP models
Authors: Priya Priyamvada , Dommenget Dietmar 
Published: August 2024

Abstract

This study analyses the El Niño-Southern Oscillation (ENSO) phase space as simulated by the Coupled Model Intercomparison Projects 5 and 6 (CMIP5 and CMIP6) models. The ENSO phase space describes the ENSO cycle between the sea surface temperature (SST) anomaly in the eastern equatorial Pacific (T) and the equatorial mean thermocline depth anomaly (h). We find that the characteristics out-of-phase cross-correlation between T and h is shifted to negative values in CMIP models, suggesting that the coupling between T and h is regionally sifted to the east compared to the observed central Pacific. If we consider the CMIP models with an eastward shifted h then the models have better agreements with the observed characteristics. While the models can capture some of the non-linear aspects with high correlations, they do largely underestimate the strength of non-linear ENSO aspects. They underestimate the likelihood of extreme El Niño and discharge states, they cannot capture the enhanced growth rates during the recharge state, the enhanced decay after the discharge state nor the reduced phase transitions after the La Niña phases. Weaker than observed wind-SST feedback and weaker h variability are likely some of the reasons why models cannot fully capture the non-linear ENSO phase space dynamics. Further, we found no indication of significant improvements from the CMIP 5 to 6 ensemble, suggesting that the two ensembles are essentially the same in terms of their ENSO dynamics. There is, however, a large spread within the model ensembles, leading to models with quite different ENSO dynamics.
Atomic Insights into Rare-Earth Element Enrichment and Fractionation in Weathering Crusts
Authors: Zhu Jianxi , ran Lingyu , Wang Gaofeng , Liang Xiaoliang , Ling Bowen , Xu Jie , Wei Jingming , Xi Yunfei , He Hongping 
Published: August 2024

Abstract

Regolith-hosted rare-earth element (REE) deposits are essential for supplying heavy-REEs (HREEs), meeting over 95% of global demand. In weathering crusts, REE enrichment and fractionation form these valuable deposits. While REEs are commonly thought to adsorb onto the Al-basal plane of kaolinite, the atomic-scale adsorption state has not been visualized, and the role of clay minerals in REE fractionation is debated. Using high-resolution atomic force microscopy (HR-AFM), we visualize the atomic-scale adsorption of light-REEs (LREEs) and HREEs on the Si-basal and Al-basal planes of kaolinite in aqueous solutions. Our results show that REEs primarily adsorb on the Si-basal plane, forming a "triangle" pattern, with HREEs preferentially adsorbed over LREEs, unequivocally establishing that clay minerals affect REE fractionation. This adsorption behavior is confirmed on mica, which also exposes Si-basal planes. This study reveals the unexpected significance of the Si-plane in REE adsorption, providing atomic-scale insights into REE enrichment and fractionation mechanisms during the metallogenetic process of regolith-hosted REE deposits. Moreover, the elucidated activity and specificity of the Si-basal plane indicate significant implications for various geochemical processes, such as elemental immobilization, transport, and cycling, due to the abundance of silicates within Earth’s crust.
Dual role of circulating and mucosal Vδ1 T cells in the control of and contribution to persistent HIV-1 infection
Authors: Soriano-Sarabia Natalia , Mann Brendan , Sanz Marta , Clohosey Matthew , Langlands Kayley , Chitrakar Alisha , Moreno Carles , Vitalle Joana , Iannone Marie , Ruiz-Mateos Ezequiel , Deleage Claire , Siegel Marc 
Published: August 2024

Abstract

Curative strategies for human immunodeficiency virus (HIV-1) infection are hindered by incomplete characterization of the latent reservoir and limited enhancement of anti-HIV immune responses. In this study, we identified a novel dual role for peripheral and tissue-resident Vδ1 T cells within the gastrointestinal mucosa of virally suppressed people with HIV. Phenotypic analyses identified an increased frequency of highly differentiated, cytotoxic effector Vδ1 T cells that exerted potent inhibition of HIV-1 replication in vitro coinciding with direct increases in cytolytic function. Conversely, we detected an enrichment of HIV-1 DNA in tissue-resident CD4+Vδ1 T cells in situ. Despite low CD4 expression, we found circulating Vδ1 T cells also contained HIV-1 DNA which was replication-competent. We show that TCR-mediated activation of peripheral Vδ1 T cells induced de novo upregulation of CD4 providing a plausible mechanism for increased permissibility to infection. These findings highlight juxtaposing roles for Vδ1 T cells in HIV-1 persistence including significant contribution to tissue reservoirs.
Detection of Ectopic Beat from Electrocardiography with Deep Convolutional Neural Network
Authors: Shaikh Bilal , Zafar Ana 
Published: August 2024

Abstract

Electrocardiogram (ECG) is a crucial tool for identifying cardiovascular diseases. However, manual evaluation of ECG signals can be tedious and time-consuming, especially when dealing with a large number of cardiac patients. To address this challenge, this study presents a model that categorizes ECG signals into three distinct classes based on their morphological characteristics. Our approach utilizes non-linear features extracted through a convolutional neural network (CNN). The proposed 1D-CNN model architecture comprises three convolutional layers, max pooling layers, and dense layers. This structure automatically extracts distinctive non-linear features from ECG signals and classifies them into five categories: Normal (Normal Beat), Supraventricular ectopic beats and Ventricular ectopic beats. We evaluated our algorithm using the open-source MIT-BIH database and 5-fold cross-validation. The model achieved an accuracy of 97% and an F1 score of 99%.
Exploring the sensitivity of river flow patterns to meteorological drought using multifractal and cross-correlation applications
Authors: Rahmani Farhang , Fattahi Mohammad Hadi 
Published: August 2024

ABSTRACT
Climate change has impacted both the duration and intensity of droughts during the past decades. Rivers are valuable resources that are sensitive to alterations owing to droughts. Thus, due to the river importance and drought pattern changes, the effect of meteorological drought on river flow patterns is critical. In this study, the influence of meteorological drought on the river flow was explored using multifractal detrended fluctuation analysis and the cross-correlation technique. The Hamoon Basin in Iran was selected, and precipitation and river flow data (discharge) from 1966 to 2016 were evaluated. The findings revealed that when the severity and frequency of meteorological droughts augmented, the river flow time-series structure's susceptibility to substantial fluctuations reduced. In addition, the river flow exhibited more correlated behavior. Furthermore, the multifractal behavior of the flow intensified. Furthermore, the highest cross-correlation value was 0.32, which was related to α(0) vs. SPI. This study aimed to investigate the correlation between meteorological conditions and river flow's multifractality, sensitivity to changes, and correlated behavior. It introduces a new approach to understand the relationship between river flow patterns' dynamics and meteorological conditions, which has not been previously explored. The findings are relevant to water resource management, drought and river flow studies, and water engineering.
In silico, In vitro, and In vivo Evaluation of the Anti-alzheimer’s Activity of Berberine
Authors: Theunis Louise T. , Billones Junie B. , Hsiao Chung-Der , Villaflores Oliver B. , Llamasares-Castillo Agnes L. 
Published: August 2024

Background:
Alzheimer’s disease (AD), a progressive neurodegenerative disease for
which there is no effective cure is among the leading causes of death worldwide.

Objectives:
To investigate the potential anti-AD activity of berberine (BBR).

Methods:
In silico assessment included molecular docking and ADMET prediction. BBR’s in vitro
inhibitory activity of the target selected from docking results was assessed via colorimetric inhibitor
screening assay. BBR’s LC50 in adult zebrafish was determined via an Acute Toxicity Study. ZnCl2
concentration for AD induction was determined via toxicity study and T-maze test. Finally, zebrafish
were treated with ZnCl2 alone or simultaneously with either BBR or donepezil and assessed via the
inhibitory avoidance task, followed by ELISA of AD-related biomarker levels in brain tissue.

Results:
The in silico assessment showed BBR’s desirable drug properties and binding affinity on
selected AD-related targets, which was the greatest docking score with AChE. The in vitro IC50 on
AChE was 3.45 μM. The LC50 in adult zebrafish was calculated at 366 ppm. In the T-maze test,
ZnCl2 at 2.5 ppm caused the greatest cognitive impairment accompanied by moderate freezing. In the
inhibitory avoidance test, fish treated with either 100 ppm BBR or 2.5 ppm donepezil had
significantly better performance than ZnCl2-treated fish. ZnCl2-treated zebrafish brain tissue had the
highest Aβ levels and AChE activity of all groups, but these were significantly lower in donepeziland
BBR-treated fish. ZnCl2- and donepezil-treated fish had similar TNF-α levels, whereas BBR
treatment significantly lowered them close to those of untreated fish.

Conclusion:
BBR showed anti-amyloidogenic, anti-AChE, and anti-inflammatory effects, which
support its potential use in AD therapy.
Bilastine-Loaded Transethosome Based Nanogel for the Treatment of Allergic Reactions: An In vitro Characterization
Authors: Mhatre Nandini R. , Rane Bhushan R. , Padave Aditi P. , Jain Ashish S. 
Published: August 2024

Objective:
The goal of the current study was to formulate and evaluate bilastine-loaded
transethosomal nanogel. Bilastine has 60% oral bioavailability, which restricts the rate of absorption and
dissolution and classifies it under BCS class II drugs, which can be overcome by incorporating bilastine in
transethosomal nanogel formulation in the treatment of urticaria.

Methods:
Bilastine-loaded transethosomes were prepared using a thin film hydration method with
different proportions of Tween 80 and ethanol by using a rotary evaporator and incorporated into a
transethosomal gel using Carbopol 934 as a polymer by dispersion method.

Results:
The bilastine-loaded transethosomal formulation was optimized by using the “Box Behnken
design” and evaluated for various parameters. The optimized formulation was found to be stable, as
determined by the zeta potential of −27.0 mV and polydispersity index (PDI) of 0.167, and vesicle
size was found to be 183nm and exhibiting the maximum entrapment efficiency of up to 80.23%.
The drug content of the transethosomal gel was found to be 81.56%. The best results were obtained
with a transethosomal gel prepared with 1% Carbopol 934 (TF7G2). The optimized batch showed
prolonged in-vitro release of bilastine for 8 hrs. Ex vivo skin permeation studies showed 76.23 ±
2.63% permeation in comparison with plain gel.

Conclusion:
Transethosomal nanogel batches were optimized based on drug content, viscosity, uniformity
of drug content, zeta potential, spreadability, pH, drug release, and stability testing, exhibiting
good results. The results of this investigation showed that the transethosomal nanogel loaded
with bilastine might be used to improve bilastine delivery through the skin with greater bioavailability.
Exploring Mothers' Challenges in Living with Children with
Autism Spectrum Disorders: A Content Analysis Study
Authors: Zandi Shirdel , Khazaei Salman , Bashirian Saeed , Mohammadi Reza , Karbin Fatemeh , Jenabi Ensiyeh 
Published: August 2024

Aim:
The present qualitative study was carried out to clarify the experiences of
mothers with respect to living with children with ASD.

Background:
Limited studies have been conducted on the experiences of mothers and their
challenges in living with children with Autism Spectrum Disorder (ASD) in western Iran.

Methods:
A qualitative study utilizing the content analysis approach was designed to elucidate
the experiences of mothers living with children with ASD. Participants were purposefully
selected based on predefined criteria. The study was conducted from March to
September 2023 at the Autism Spectrum Disorder Research Center in Hamedan, Iran.
Sampling continued until data saturation was achieved. Interview transcripts were analyzed
using a content analysis strategy, and main themes and subthemes were identified.
MAXQDA version 10 software was used for data management during analysis.

Results:
Twelve participants were enrolled in this study. Six main themes and 15 subthemes
were identified, representing the challenges of mothers living with children with ASD in
Iran. The study themes included personal development hindrance (constant engagement, reduced
self-attention, limited personal activities), social non-acceptance (social isolation and
others' judgmental behaviors), lack of support infrastructure (limited rehabilitation centers,
limited care centers, and heavy financial burden), caretaking responsibility (constant worry
for the child, stereotypical behaviors, and sleep disturbances in the child), constant attention
requirement (continuous need for care and constant support), and unequal attention within
the family (reduced attention to other children and decreased marital intimacy).

Conclusion:
The results of this research have indicated that mothers of children with ASD
experience high levels of physical and psychological stress and are vulnerable to harm.
Therefore, providing supportive resources to ensure their mental well-being is essential.
Development and Validation of a Computed Tomography–Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study
Authors: Tao Jin , Liu Dan , Hu Fu-Bi , Zhang Xiao , Yin Hongkun , Zhang Huiling , Zhang Kai , Huang Zixing , Yang Kun 
Published: August 2024

Background
As part of the TNM (tumor-node-metastasis) staging system, T staging based on tumor depth is crucial for developing treatment plans. Previous studies have constructed a deep learning model based on computed tomographic (CT) radiomic signatures to predict the number of lymph node metastases and survival in patients with resected gastric cancer (GC). However, few studies have reported the combination of deep learning and radiomics in predicting T staging in GC.

Objective
This study aimed to develop a CT-based model for automatic prediction of the T stage of GC via radiomics and deep learning.

Methods
A total of 771 GC patients from 3 centers were retrospectively enrolled and divided into training, validation, and testing cohorts. Patients with GC were classified into mild (stage T1 and T2), moderate (stage T3), and severe (stage T4) groups. Three predictive models based on the labeled CT images were constructed using the radiomics features (radiomics model), deep features (deep learning model), and a combination of both (hybrid model).

Results
The overall classification accuracy of the radiomics model was 64.3% in the internal testing data set. The deep learning model and hybrid model showed better performance than the radiomics model, with overall classification accuracies of 75.7% (P=.04) and 81.4% (P=.001), respectively. On the subtasks of binary classification of tumor severity, the areas under the curve of the radiomics, deep learning, and hybrid models were 0.875, 0.866, and 0.886 in the internal testing data set and 0.820, 0.818, and 0.972 in the external testing data set, respectively, for differentiating mild (stage T1~T2) from nonmild (stage T3~T4) patients, and were 0.815, 0.892, and 0.894 in the internal testing data set and 0.685, 0.808, and 0.897 in the external testing data set, respectively, for differentiating nonsevere (stage T1~T3) from severe (stage T4) patients.

Conclusions
The hybrid model integrating radiomics features and deep features showed favorable performance in diagnosing the pathological stage of GC.