bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimation of land subsidence using coupled particle swarm optimization and genetic algorithm: The case of Damghan aquifer
Authors: Ashouri Reza , Emamgholizadeh Samad , Haji Kandy Hooman , Mehdizadeh S. Sadjad , Jamali Saeed 
Published: January 2024

Abstract

Land subsidence, which is mainly caused by over-extraction of groundwater, is one of the most important problems in arid and semi-arid regions. In the present study, seven factors affecting the land subsidence, i.e., types of subsoil, land use, pumping, recharge, thickness of the plain aquifer, distance to the fault, and groundwater depletion were considered as input data for the ALPRIFT framework and intelligence models to map both Subsidence Vulnerability Index (SVI) and prediction of land subsidence, respectively. The hybrid of particle swarm optimization (PSO) and genetic algorithm (GA) (Hybrid PSO-GA) was then used to optimize the weights of the input layers and the estimation of the land subsidence. The capability of the PSO-GA at predictions of land subsidence compared with the typical GA model, and Gene Expression Programming (GEP). The statistical indices R2, RMSE, and MAE were used to assess the accuracy and reliability of the applied models. The results showed that the Hybrid PSO-GA model had R2, RMSE, and MAE equal to 0.91, 1.11 (cm), and 0.94 (cm), respectively. In comparison with the GA, and GEP models, the Hybrid PSO-GA model improved the prediction of land subsidence and reduced RMSE by 24.30 and 16.80%, respectively.
Mitigating Effect of Matricin Against Benzo(a)pyrene-induced Lung
Carcinogenesis in Experimental Mice Model
Authors: Yang Guang , Liu Huining , Xu Siwei , Tian Ziqiang 
Published: January 2024

Background:
Lung cancer is a life-threatening disease that is still prevalent worldwide.
This study aims to evaluate the effects of matricin, a sesquiterpene, on the carcinogenic
agent benzo(a)pyrene [B(a)P]-induced lung cancer in Swiss albino mice.

Methods:
Lung cancer was induced by oral administration of B(a)P at 50 mg/kg b. wt. in model
Swiss-albino mice (group II) as well in experimental group III, and treated with matricin (100
mg/kg b. wt.) in group III. Upon completion of treatment for 18 weeks, the changes in body
weight, tumor formation, enzymatic and non-enzymatic antioxidant levels (GSH, SOD, GPx, GR,
QR, CAT), lipid peroxidation (LPO) level, pro-inflammatory cytokines (TNF-α, IL-6, IL-1β),
immunoglobulin levels (IgG, IgM), apoptosis markers (Bax, Bcl-xL), tumor markers (carcinoembryogenic
antigen (CEA), neuron-specific enolase (NSE)), and histopathological (H&E) alterations
were determined.

Results:
The results indicate that B(a)P caused a significant increase of tumor formation in the
lungs, increased tumor markers and inflammatory cytokines in serum, and depletion of enzymatic/
non-enzymatic antioxidants and immunoglobulins, compared to the untreated control group.
Matricin treatment significantly reversed the changes caused by B(a)P as evidenced by the biochemical
and histopathological assays.

Conclusion:
The changes caused by matricin clearly indicate the cancer-preventive effects of
matricin against B(a)P-induced lung cancer in animal models, which can be attributed to the antioxidant
activity, immunomodulation, and mitigation of the NF-kβ pathway.
Identification and Limit of Detection of Benzene, Chlorobenzene, Benzoic
Acid, Phthalic Acid, and Mellitic Acid in Water Solutions Using Excitation,
Emission, and Single-band Synchronous Fluorescence Spectroscopy
Authors: Sunuwar Suresh , Manzanares Carlos E. 
Published: January 2024

Introduction:
This paper first introduces the use of computer-simulated single-band synchronous
fluorescence (SF) obtained from experimental excitation and emission fluorescence spectra
of a pure compound in solution. The simulation produces a single narrow band with a peak wavelength
that identifies the compound.

Methods:
The method is used to show single peak identification of benzene, chlorobenzene, benzoic
acid, phthalic acid, and mellitic acid in water solutions. Synchronous fluorescence spectroscopy
(SFS) is a variant of fluorescence technique in which excitation and emission scans are simultaneously
acquired and multiplied with a predetermined wavelength difference (Δλ) between the two.
Commercial instruments have this option to get the SFS signals.

Results:
In response to the Δλ selected, the result will be an SFS signal producing a series of peaks
that could be assigned to compounds. Instead of running the same experiment with different
Δλ values to identify the compounds, our simulation program determines a specific Δλ value that
generates a narrow SF band with a distinctive peak wavelength for identification purposes.

Conclusion:
Finally, binary mixtures of chlorobenzene with each compound in water are prepared.
The SFS of the solution is acquired and compared with the SFS bands of the components for identification
purposes. With the commercial lamp fluorimeter employed, the limits of detection are obtained
at the ng/g concentration level with fluorescence emission. Possible limits of detection at lower
concentrations are discussed using a laser source. The presence of these molecules in astrochemical
studies is discussed.
Mechanical Activation as a Stage of Coal Sample Preparation in the
Analysis of Rare Earth Elements Content by Inductively Coupled Plasma
Mass Spectrometry
Authors: Yudina Lidiya I. , Skripkina Tatiana S. , Shatskaya Svetlana S. 
Published: January 2024

Background:
Brown coals are recognized as promising sources of rare earth elements
(REEs). Rare earths are present in both the mineral and organic parts of brown coal.

Objectives:
This study was conducted to investigate the influence of preliminary mechanical activation
in the process of sample preparation prior to analyzing the concentrations of rare earth elements
in brown coal samples of various origins and compositions

Methods:
Four coal samples from different deposits in Russia were selected for the study. Samples
were treated with mechanical activation, without reagents, or mechanochemical activation, with humic
acids added externally as reagents. X-ray phase analysis was carried out with the selected samples.
The quantities of rare-earth elements present in the samples were studied by the method of
high-sensitivity inductively coupled plasma mass spectrometry (ICP-MS).

Results:
It was found that the mechanical activation of coal before dissolution in a mixture of nitric
and hydrofluoric acids leads to an increase in the determined concentration of rare earth elements.
For this study, the expediency of using only nitric acid as an optimal solvent for the elemental analysis
of coal samples was shown. The total concentration of all REE after dissolution of nitric acid and
mechanochemical activation with humic acid reached 2456 g/t in Vanchin coal, 968 g/t in Azeysky
coal, and 24 g/t and 150 g/t in Itatsky and Spetsugli coals, respectively.

Conclusion:
Mechanical activation and mechanochemical treatment can greatly help to facilitate
sample preparation of natural objects, such as coals for elemental analysis, but in some cases, only a
change of solvent is sufficient. When developing technology for concentrating rare earth elements
from coal involving grinding, it is necessary to take into account the fact that mechanical activation
of coal changes its tendency to dissolve, which may affect the results of the analysis and should be
taken into account during experiments.
Increased SLC7A3 Expression Inhibits Tumor Cell Proliferation and Predicts a Favorable Prognosis in Breast Cancer
Authors: He Lifang , Xu Yue , Lin Jiediao , Lin Stanley Li , Cui Yukun 
Published: January 2024

Background:
Arginine plays significant and contrasting roles in breast cancer growth
and survival. However, the factors governing arginine balance remain poorly characterized.

Objective:
We aimed to identify the molecule that governs arginine metabolism in breast cancer
and to elucidate its significance.

Methods:
We analyzed the correlation between the expression of solute carrier family 7 member
3 (SLC7A3), the major arginine transporter, and breast cancer survival in various databases,
including GEPIA, UALCAN, Metascape, String, Oncomine, KM-plotter, CBioPortal and
PrognoScan databases. Additionally, we validated our findings through bioinformatic analyses
and experimental investigations, including colony formation, wound healing, transwell, and
mammosphere formation assays.

Results:
Our analysis revealed a significant reduction in SLC7A3 expression in all breast cancer
subtypes compared to adjacent breast tissues. Kaplan-Meier survival analyses demonstrated that
high SLC7A3 expression was positively associated with decreased nodal metastasis (HR=0.70,
95% CI [0.55, 0.89]), ER positivity (HR=0.79, 95% CI [0.65, 0.95]), and HER2 negativity
(HR=0.69, 95% CI [0.58, 0.82]), and increased recurrence-free survival. Moreover, low
SLC7A3 expression predicted poor prognosis in breast cancer patients for overall survival. Additionally,
the knockdown of SLC7A3 in MCF-7 and MDA-MB-231 cells resulted in increased
cell proliferation and invasion in vitro.

Conclusion:
Our findings indicate a downregulation of SLC7A3 expression in breast cancer
tissues compared to adjacent breast tissues. High SLC7A3 expression could serve as a prognostic
indicator for favorable outcomes in breast cancer patients due to its inhibitory effects on
breast cancer cell proliferation and invasion.
Combination of Ethacrynic Acid and ATRA Triggers Differentiation and/or Apoptosis
of Acute Myeloid Leukemia Cells through ROS
Authors: Li Lu , Xi Hui-Min , Lu Hao , Cai Xun 
Published: January 2024

Background and objective:
All-trans retinoic acid (ATRA), an effective differentiation inducer, has
been applied clinically to treat acute promyelocytic leukemia (APL). Unfortunately, it is not as potent in other
kinds of acute myeloid leukemia (AML). Ethacrynic acid (EA), a classical powerful diuretic, can increase reactive
oxygen species (ROS) contents, which can assist ATRA in inducing differentiation in AML cells. Here, we
investigated the effect of EA combined with ATRA (EA+RA) on some AML cells except APL.

Methods:
Apoptosis and differentiation were determined by morphology, cell viability, Annexin-V assay and
CD11c expression. Western blot analysis and the detection of ROS and mitochondrial transmembrane potentials
(MMP) were used to investigate the mechanisms.

Results:
AML cells exhibited differentiation and/or apoptosis after EA+RA treatment. EA+RA increased the
intracellular ROS contents. EA+RA-induced apoptosis was accompanied by MMP attenuation and caspase-3/7
activation. EA+RA-induced differentiation was along with MEK/ERK and Akt activation and increased expression
of PU.1, CCAAT/enhancer-binding protein β (C/EBPβ) and C/EBPε. N-acetyl-L-cysteine (NAC), an antioxidant,
thoroughly reduced EA+RA-increased ROS, and also inhibited MMP attenuation, the activation of caspase-
3/7, MEK/ERK and Akt pathways, the elevation of PU.1 and C/EBPs, and apoptosis and differentiation. However,
MEK or PI3K specific inhibitors only suppressed EA+RA-triggered differentiation and the elevation of PU.1
and C/EBPs, but not ROS levels.

Conclusion:
EA+RA induced cell apoptosis through ROS dependent MMP attenuation and caspase 3/7 activation
while inducing differentiation by ROS-MEK/ERK-PU.1/C/EBPs and ROS-Akt-PU.1/C/EBPs pathways. In summary,
it may provide innovative ATRA-based combination therapy strategies for AML patients via ROS.
Is Kidney Stone Associated with Thyroid Disease? The United States
National Health and Nutrition Examination Survey 2007-2018
Authors: Xue Wenya , Xue Zihan , Liu Yanbing , Yin Pan , Liu Luyu , Qu Shen’ao , Wu Shaobo , Yang Chuance 
Published: January 2024

Background:
Kidney stones and thyroid disease are two common diseases in the
general population, with multiple common risk factors. The associations between kidney stones
and thyroid disease are unclear.

Aim:
This study aims to assess the association between ‘once had a thyroid disease’ and the
odds of kidney stones.

Methods:
Adult participants from the National Health and Nutrition Examination Survey
(NHANES) 2007-2018 with reliable kidney stone and thyroid disease data were included. Adjusting
for age, gender, race, education level, and marital status, diabetes, hypertension, gout,
angina pectoris, stroke, and asthma, logistic regression was used to examine the relationship
between kidney stones and thyroid illness.

Results:
Using stratified analysis, the association between thyroid illness and kidney stones was
investigated further. Among the participants, 4.9% had kidney stones, and 10.1% had thyroid
disease. Kidney stone was associated with thyroid disease (OR=1.441, (95% CI:1.294-1.604), p
<0.01), which remained significant (OR=1.166, (95% CI:1.041-1.305), p <0.01) after adjustments
with age, gender, race, education level and marital status, diabetes, hypertension, gout,
angina pectoris, stroke, and asthma. Stratified by blood lead, blood cadmium, and blood urea
nitrogen levels in the human body, the odds of kidney stones still increased with once having a
previous thyroid disease.

Conclusions:
In this large nationally representative survey over 10 years, kidney stone was
strongly associated with thyroid disease. In this cross-sectional study, we explored the association
between thyroid disease and kidney stones, which may help clinicians intervene in them
early.
The Expression of Hsa-Mir-1225-5p Limits the Aggressive Biological
Behaviour of Luminal Breast Cancer Cell Lines
Authors: Hernandez Y-Andrés , Gonzalez Janeth , Garcia Reggie , Aristizabal-Pachón Andrés 
Published: January 2024

Introduction:
Numerous genetic and biological processes have been linked to the function of microRNAs (miRNAs), which regulate gene expression by targeting messenger RNA (mRNA). It is commonly acknowledged that miRNAs play a role in the development of disease and the embryology of mammals.

Methods:
To further understand its function in the oncogenic process, the expression of the miRNA profile in cancer has been investigated. Despite being referred to as a noteworthy miRNA in cancer, it is unknown whether hsa-miR-1225-5p plays a part in the in vitro progression of the luminal A and luminal B subtypes of breast cancer. We proposed that a synthetic hsa-miR-1225-5p molecule be expressed in breast cancer cell lines and its activity be evaluated with the aim of studying its function in the development of luminal breast cancer. In terms of the typical cancer progression stages, such as proliferation, survival, migration, and invasion, we investigated the role of hsa-miR-1225-5p in luminal A and B breast cancer cell lines.

Results:
Additionally, using bioinformatics databases, we thoroughly explored the target score-based prediction of miRNA-mRNA interaction. Our study showed that the expression of miR-1225-5p significantly inhibited the in vitro growth of luminal A and B breast cancer cell lines.

Conclusion:
The results were supported by a bioinformatic analysis and a detailed gene network that boosts the activation of signaling pathways required for cancer progression.
Fault Feature Mining and Identification Method of 10kV XLPE Cable Combining SAE and RF
Authors: Liu Taibiao , Zhang Jin , Xie Yibing , Zhang Jingkang 
Published: January 2024

Background:
Feature extraction methods such as statistical features and image features
based on traditional partial discharge spectrograms require the support of expert experience and
prior knowledge, making their engineering applications have significant limitations. Currently,
electrical equipment fault diagnosis is moving towards intelligent and scientific directions, and
how to apply deep learning methods for more accurate, intelligent, and autonomous diagnosis has
become a hot issue in the current field.

background:
Feature extraction methods such as statistical features and image features based on traditional partial discharge spectrograms require the support of expert experience and prior knowledge, making their engineering applications have significant limitations.

Methods:
The construction and optimal selection of high-voltage cable partial discharge feature
space is an important means to improve recognition accuracy. This paper proposes a new method
that can independently and deeply mine partial discharge image features to quickly identify partial
discharge types. Firstly, a self encoder is used to encode and decode partial discharge images, and
a multi-level stack structure is used to achieve the goal of deep feature mining; then, the partial
discharge data features decoded based on a stack self encoder are imported into a random forest
network for type recognition; Finally, based on the random forest algorithm, the redundant feature
space is optimized and compared with other traditional recognition methods.

Results:
The recognition accuracy of the proposed method is about 10% higher than that of traditional
methods.

Conclusion:
The optimized feature parameters can maintain more than 90% recognition accuracy
in both random forest algorithms, common support vector machines, and BP neural networks,
proving the feasibility and effectiveness of the method in this paper.

result:
The recognition accuracy of the proposed method is about 10% higher than that of traditional methods.
Bile Acid Nanoparticles - An Emerging Approach for Site Specific Drug Targeting
Authors: Suvarna Vasanti , Sawant Niserga , Jadhav Pradnya , Desai Namita 
Published: January 2024

:
Bile acids, a group of steroidal acids present in the bile act as biological surfactants and ligands for bile acid transporter proteins for signalling molecules to perform various paracrine and endocrine functions. The enterohepatic circulation of bile acids can be exploited to develop at-tractive drug delivery approaches with improved targetability of facial amphiphiles and enhanced drug bioavailability by improving absorption and metabolic stability. The effectiveness, safety and targetability of nanoparticles conjugated with bile acids and salts have been discussed in the present review. Various modifications of bile acids promoting absorption and oral bioavailability of drugs for treatment of various disease conditions such as cancer, diabetes and psychosis has al-so been discussed. Additionally, neuroprotective effect of bile acids and salts has demonstrated utility in various neurodegenerative disorders. Nanoparticles based on bile acids and salts repre-sent an area of emergent interest due to their unique and modifiable properties for improving ef-fectiveness of drugs.
Manifestation of Psychosis and Impairments of Executive
Functions Emphasize the Interaction of Psychological and
Neurological Dysfunctions in People Who Use Methamphetamine
Authors: Chaidee Nutthika , Kraiwattanapirom Natcharee , Siripornpanich Vorasith , Chetsawang Jirapa , Chetsawang Banthit 
Published: January 2024

Aim:
Cumulative evidence has demonstrated the neurotoxic effect of methamphetamine
(Meth) on the central nervous system. Meth can induce psychotic symptoms and
impairments of cognitive abilities, including executive function (EF).

Method:
In this study, we hypothesized the interaction of the neurotoxic effects of Meth
on psychotic symptoms and EF performances. The Stroop test evaluated the EF performances,
Go/No-Go task, one-back test (OBT), and Wisconsin Card Sorting Test (WCST)
in people who use Meth with psychosis (MWP) and without psychosis (MWOP) compared
with healthy control participants.

Result:
The results showed that MWOP and MWP exhibited EF deficits in attention, working
memory, and initial conceptualization. Moreover, a deficit in inhibition was observed in
MWOP, while poorer processing speed and cognitive flexibility were found in MWP.

Conclusion:
The correlation between psychotic symptoms and poor EF performances was
observed in MWP. These findings underline the interaction of the mechanistic neurotoxic effect
of Meth to induce psychological and neurological dysfunctions in people who use Meth.
Effects of Environment-friendly Polymer Composite Modifiers on the Modulus and Pavement Performance of Asphalt Mixtures
Authors: Wang Yu , Liu Wenyi , Chang Xinyue , Zhang Wengang 
Published: January 2024

Background:
In the current context of low-carbon environment, it is particularly
important to use waste plastics to prepare modifiers that increase the modulus
of the bituminous mixture.

Objectives:
The study aimed to find out the influence of environment-friendly polymer
composite modifier (E-FPCM) on modulus and pavement performance of bituminous
mixture.

Methods:
The influence of the optimum component E-FPCM on the dynamic modulus
(DM) has been explored. The E-FPCM content’s effect on the rheological properties
of bitumen has been analyzed. Also, the influence of E-FPCM on pavement performance
has been analyzed.

Results:
The degree of influence on SR of bituminous mixture has been in the order of
recycled low-density polyethene (R-LDPE) > aromatic oil > lignin fiber. The optimum
composition of E-FPCM has been found to be 10% aromatic oil, 4.8% C9, 62%
R-LDPE, 7.0% lignin fiber, 0.2% antioxidant 1076, 2% silane coupling agent, and
14% mineral powder. By using E-FPCM with the optimum components, the SBS bituminous
mixture or 90# bituminous mixture has been found to meet the standard of a
high-modulus bituminous mixture (HMBM). E-FPCM has been found to reduce the
phase angle (δ) of bitumen and increase the complex shear modulus (G∗) and rutting
factor [G∗/sin(δ)], which may help improve the rutting resistance (RR).

Conclusion:
E-FPCM is beneficial for improving the RR of the bituminous mixture,
and reasonable content of E-FPCM has a great role in improving the water stability
(WS) and low-temperature crack resistance (LTCR) of the bituminous mixture.
Advancements in Data Augmentation and Transfer Learning:
A Comprehensive Survey to Address Data Scarcity Challenges
Authors: Fayaz Salma , Ahmad Shah Syed Zubair , ud din Nusrat Mohi , Gul Naillah , Assad Assif 
Published: January 2024

Abstract:
Deep Learning (DL) models have demonstrated remarkable proficiency in image
classification and recognition tasks, surpassing human capabilities. The observed enhancement
in performance can be attributed to the utilization of extensive datasets. Nevertheless, DL
models have huge data requirements. Widening the learning capability of such models from
limited samples even today remains a challenge, given the intrinsic constraints of small datasets.
The trifecta of challenges, encompassing limited labeled datasets, privacy, poor generalization
performance, and the costliness of annotations, further compounds the difficulty in
achieving robust model performance. Overcoming the challenge of expanding the learning capabilities
of Deep Learning models with limited sample sizes remains a pressing concern even
today. To address this critical issue, our study conducts a meticulous examination of established
methodologies, such as Data Augmentation and Transfer Learning, which offer promising
solutions to data scarcity dilemmas. Data Augmentation, a powerful technique, amplifies
the size of small datasets through a diverse array of strategies. These encompass geometric
transformations, kernel filter manipulations, neural style transfer amalgamation, random erasing,
Generative Adversarial Networks, augmentations in feature space, and adversarial and meta-
learning training paradigms.

:
Furthermore, Transfer Learning emerges as a crucial tool, leveraging pre-trained models to facilitate
knowledge transfer between models or enabling the retraining of models on analogous
datasets. Through our comprehensive investigation, we provide profound insights into how the
synergistic application of these two techniques can significantly enhance the performance of
classification tasks, effectively magnifying scarce datasets. This augmentation in data availability
not only addresses the immediate challenges posed by limited datasets but also unlocks
the full potential of working with Big Data in a new era of possibilities in DL applications.