bluuebunny/crossref_metadata_2025_split huggingface dataset:
Causal Relationships of 38 Risk Factors with Chronic Rhinosinusitis: A Mendelian Randomization Study
Authors: Jia Guobing , Guo Tao , Liu Lei , He Chengshi 
Published: February 2024

Abstract
Background
At present, the identification of risk factors associated with chronic rhinosinusitis (CRS) remains elusive. Our goal was to systematically investigate modifiable risk factors linked to CRS.
Methods
We conducted univariable Mendelian randomization (MR) based on genome-wide association studies (GWAS) to assess the causal relationships between 38 risk factors and CRS. The primary statistical analysis employed the inverse variance weighted (IVW) method, complemented by MR Egger and weighted median methods, in addition to multiple sensitivity analyses. Following this, we performed multivariable MR to consider the potential confounding effects of gastroesophageal reflux disease (GERD) and evaluate direct causal relationships between risk factors and CRS.
Results
Univariable MR results indicated that cigarettes per day, short sleep duration, overall health rating (OHR), hypertension, allergic rhinitis (AR), GERD, bronchial asthma (asthma), atopic dermatitis (AD), and rheumatoid arthritis (RA) were linked to an increased risk of CRS. Conversely, coffee intake, years of schooling, and apolipoprotein A-I were associated with a reduced risk of CRS. No other risk factors showed an association with CRS. When we adjusted for GERD using multivariable MR, the associations of OHR, RA, asthma, AD, and RA with CRS remained statistically significant. However, the previously observed effects of cigarettes per day, coffee intake, short sleep duration, years of schooling, apolipoprotein A-I, and hypertension were no longer apparent.
Conclusions
Our study suggests direct causal relationships between genetically predicted OHR, RA, asthma, AD, and increased risk of CRS. These findings will significantly contribute to advancing the exploration of CRS etiology.
Ancient DNA: a promising tool for understanding the evolutionary history of and human pressure on the highly endangered limpet Patella ferruginea
Authors: Espinosa Free , Haile James , Finlayson Stewart , Giles-Guzman Francisco , Finlayson Geraldine , Finlayson Clive , Fa Darren A. 
Published: February 2024

Abstract

The use of ancient DNA has been proven to be a powerful tool in phylogenetic and phylogeographic studies. Nonetheless, its application in the field of conservation has been limited. Moreover, marine mollusc shells are well preserved in the fossil record, providing invaluable information about the past. However, the extraction of DNA from ancient shells is challenging and has only recently been performed. In this context, Patella ferruginea has been the target of intense research due to its endangered status in the Mediterranean, with several genetic approaches involving the use of fresh soft tissue samples. In the present study, for the first time, DNA from modern and ancient shells was successfully extracted and sequenced for this species, indicating that the COI haplotypes were the same as those already sequenced in previous studies based on fresh soft tissue samples. The fact that DNA retrieved from the Middle Ages had been sequenced for this species prompted further research to compare genetic diversity between ancient and present populations to assess the impact of glacial periods or human harvesting in shaping the genetic diversity of this flagship species.
Epileptic seizure prediction based on Convolutional neural networks and optimization techniques
Authors: shanmugam Dineshkumar , NALLASAMY Viswanathan 
Published: February 2024

Abstract
According to the World Health Organization, fifty million individuals worldwide suffer from epilepsy, a neurological condition. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic activity. The automatic recognition of epilepsy is becoming a crucial concern since manual detection is a tedious and time-consuming process that imposes a tremendous burden on neurologists. To overcome this issue, a seizure prediction technique is proposed based on convolutional neural networks (CNN) for feature extraction and an extreme gradient boosting (XGBoost) algorithm for seizure classification. CHB-MIT dataset is used for training and testing. Moreover , the proposed framework is optimized using a meta-heuristic algorithm called Aquila optimization for efficient and robust performance. The proposed method shows qualitative results achieving 99.02% of accuracy, 98.67% of specificity and 98.12% of sensitivity and the proposed method is compared with state-of-the-art methods.
Bearing fault detection method based on machine learning
Authors: Liu Yong , Xu Xiao-jie , Fu Xiang-jie 
Published: February 2024

Abstract
Traditional fault diagnosis for bearing usually requires a large amount of labeled data for training and deliberate selection of features and the diagnostic results are often too scattered to provide a thorough fault diagnosis procedure. To address these issues, we use data mining techniques to analyze the raw vibration signals of rolling bearings in various fault states under different operating conditions and construct labeled training and test datasets. By introducing the attention mechanism, we utilize convolutional neural networks and bidirectional long and short-term memory prediction models to diagnose deep fault categories and improve prediction accuracy from the perspective of machine learning. In addition, to intuitively describe the operating state of equipment bearings, we construct a visualization interface based on the prediction model. After example analysis, the model constructed in this paper has certain feasibility and effectiveness.
Experimentally-driven mathematical model to understand the effects of matrix deprivation in breast cancer metastasis
Authors: Maiti Sayoni , Rangarajan Annapoorni , Venkatesh K. 
Published: February 2024

Abstract

Normal epithelial cells receive proper signals for growth and survival from their attachment to the underlying extracellular matrix (ECM). They perceive detachment from the ECM as a stress and die. This cell death triggered by matrix detachment is known as anoikis. However, cancer cells are anoikis-resistant. Under normal (adherent) growth conditions, the serine-threonine protein kinase Akt plays a major role in cell proliferation and protein synthesis, maintaining an anabolic state in the cancer cell. In contrast, we showed that the stress due to matrix deprivation is sensed by yet another serine-threonine kinase AMP-activated protein kinase (AMPK) in response to a spike in intracellular calcium. We also showed the existence of an AMPK-Akt double-negative feedback loop in breast cancer cells that regulates their adaptation to matrix deprivation. We illustrated a metabolic switching from an anabolic to a catabolic state upon matrix-deprivation, which aids cancer cell stress-survival. In this study, we utilized these experimental data and developed a mathematical model to capture the pathophysiology of matrix-deprived state in breast cancer cells. To do so, we used the mathematical framework of an insulin-glucagon hormone signaling network that maintains the balance between anabolism and catabolism to maintain metabolic homeostasis. Using this model, we identified several proteins which are perturbed upon matrix deprivation in addition to AMPK and Akt. These include IRS, PI3K, GLUT1, IP3, DAG cAMP, PKA and PDE3. Molecular perturbations revealed that several feedback/crosstalks like PKC to IRS, S6K1 to IRS, cAMP to PKA, AMPK to Akt and DAG to PKC are crucial in maintaining the metabolic switching from an anabolic to catabolic state upon matrix deprivation. Upon removal of these feedback/crosstalks, metabolic switching is curbed. Thus, we have developed a unique mathematical framework to simulate the molecular interplay with metabolic adaptations critical for cancer metastasis.
Pelanggaran-Pelanggaran Teritorialitas di Gerai Mixue Urip Sumoharjo
Authors: Kusumastuti Maria Hesti , Nainggolan Freddy Marihot Rotua , Panjaitan Irwin 
Published: February 2024

Title: Violations of Territoriality at Mixue Urip Sumoharjo Outlet
The food and beverage industry in Indonesia is growing rapidly. One of them is the Mixue outlet, which has mushroomed since 2020. In its development, the strategic location of the Mixue Urip Sumoharjo outlet has made this outlet relatively busy with visitors. This study aims to analyze territorial violations committed by visitors to the Mixue Urip Sumoharjo outlet in response to the narrow dimensions of the Mixue outlet space. The author made systematic observations and analyzed the spatial layout in the first stage. After that, they observed the dine-in area and divided the space into three categories: primary territory, secondary territory, and public territory. The next stage is to analyze and describe territorial violations through layout diagrams in each area. This study found that the dimensions of space influence the user's behavioral response to fulfilling personal space.
Evaluation of satellite rainfall estimates using PERSIANN-CDR and TRMM over three critical cells in Jordan
Authors: Al-Sheriadeh Mohanned , Al-Sharman Anas Riyad 
Published: February 2024

Abstract

Effective management of water resources is heavily dependent on accurate knowledge of rainfall patterns. Satellite rainfall estimates (SREs) have become increasingly popular due to their ability to provide spatial rainfall data. However, the accuracy of SREs is limited by a variety of factors including a lack of observations, inadequate evaluation techniques, and the use of short evaluation durations. To improve our understanding of SREs, this study evaluated the long-term performance of Tropical Rainfall Measuring Mission (TRMM) and PERSIANN-CDR by analyzing their spatiotemporal patterns. Daily, monthly, seasonal, and annual precipitation estimates were evaluated using statistical measures and data from 71 rain gauges across three critical cells in Jordan from 2000 to 2013. The results showed that while both SREs had low accuracy on a daily scale, TRMM 3B43 performed better than PERSIANN-CDR at the monthly level. Additionally, TRMM 3B43 exhibited superior performance during the heavy rainy season, whereas PERSIANN-CDR showed better results during other seasons. In annual studies, TRMM 3B43 was found to be more accurate than PERSIANN-CDR for the north and south cells, while PERSIANN-CDR had a higher correlation coefficient for the middle cell. These findings can contribute to the development of more reliable and accurate SREs, thereby improving water resource management studies.
The impact of government support and farmers’ pro-environmental intention on water-saving irrigation techniques adoption: Evidence using propensity score matching
Authors: Zhang Jingwen , Song Jianfeng 
Published: February 2024

AbstractAgricultural water-saving irrigation (WSI) techniques becomes the main way to build drought resilience in rural areas. The effectiveness of government support in promoting the adoption of water-saving technologies is controversial. There is a research gap concerning farmers with different pro-environmental intentions to the adoption of water-saving behaviors under the influence of government support. This study is based on a survey in Yulin, Shaanxi, China, using 404 sample households. Pro-environmental intentions have been introduced into this paper by using the entropy weight method, and propensity score matching has been used to overcome the confounding between government support and farmers' adoption. Our findings reveal that farmers who received government support exhibit a significantly higher probability of adopting WSI techniques, ranging from 80 to 81.6%, representing a notable increase of 29.0–30.7% compared to those without government support. Furthermore, government support significantly affects farmers with weak pro-environmental intentions by 40.5%, but it does not have a significant impact on farmers with stronger pro-environmental intentions. These insights emphasize the significance of subsidies and advocate for an approach that tailors policies based on varying levels of pro-environmental intentions. This is particularly crucial considering that farmers with weaker intentions may be inclined to adopt the technology but face higher efficiency risks.
Modeling of aquifer geo-hydraulic characteristics with geo-electrical methods at a major coastal aquifer system in Uyo, southern Nigeria
Authors: Udosen Ndifreke I. , Ekanem Aniekan M. , George Nyakno J. 
Published: February 2024

Abstract

Characterization of aquifer geo-hydraulic properties is essential for groundwater management and conservation. This work demonstrates the use of electrical resistivity surveying data in the characterization of important geo-hydraulic properties within a major coastal aquifer in southern Nigeria. Geo-electrical data acquired along profile lines with the use of Schlumberger array configuration were used to determine the aquifer geo-hydraulic parameters such as aquifer resistivity ρb, water resistivity ρw, porosity Φ, formation factor F, hydraulic conductivity K, transmissivity T, permeability Kp, and tortuosity τ. The resistivity inversion models delineated the occurrence of four lithological layers, with resistivity of the geo-layers ranging from 2.4 to 4,393 Ω m. Contour maps were created to show the distribution of the various geo-electrical and geo-hydraulic parameters over the study area. Regression curves showing the relationship between the geo-hydraulic parameters were generated and used to generate empirical relationships between the parameters. The empirical formulas developed for these geo-hydraulic parameters will aid in future work seeking to characterize aquifer zones and model aquifer geo-hydraulic units. In all, electrical resistivity surveying was found to be a noninvasive, cost-effective, and efficient method of characterizing aquifer geo-hydraulic properties via nonpoint geo-electrical data collected over the earth's surface.
Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index
Authors: Rezaiy Reza , Shabri Ani 
Published: February 2024

Abstract

This study introduces ensemble empirical mode decomposition (EEMD) coupled with the autoregressive integrated moving average (ARIMA) model for drought prediction. In the realm of drought forecasting, we assess the EEMD-ARIMA model against the traditional ARIMA approach, using monthly precipitation data from January 1970 to December 2019 in Herat province, Afghanistan. Our evaluation spans various timescales of standardized precipitation index (SPI) 3, SPI 6, SPI 9, and SPI 12. Statistical indicators like root-mean-square error, mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 are employed. To comprehend data features thoroughly, each SPI series initially computed from the original monthly precipitation time series. Subsequently, each SPI undergoes decomposition using EEMD, resulting in intrinsic mode functions (IMFs) and one residual series. The next step involves forecasting each IMF component and residual using the corresponding ARIMA model. To create an ensemble forecast for the initial SPI series, the predicted outcomes of the modeled IMFs and residual series are finally added. Results indicate that EEMD-ARIMA significantly enhances drought forecasting accuracy compared to conventional ARIMA model.
Role of TBK1 Inhibition in Targeted Therapy of Cancer
Authors: Yang Xueqing , Liu Zongliang 
Published: February 2024

Abstract:
TANK-binding kinase 1 (TBK1) is a serine/threonine protein that plays a crucial role in
various biological processes like immunity, autophagy, cell survival, and proliferation. The level
and kinase activity of the TBK1 protein is regulated through post-translational modifications
(PTMs). TBK1 mainly mediates the activation of IRF3/7 and NF-κB signaling pathways while also
participating in the regulation of cellular activities such as autophagy, mitochondrial metabolism,
and cell proliferation. TBK1 regulates immune, metabolic, inflammatory, and tumor occurrence and
development within the body through these cellular activities. TBK1 kinase has emerged as a promising
therapeutic target for tumor immunity. However, its molecular mechanism of action remains
largely unknown. The identification of selective TBK1 small molecule inhibitors can serve as valuable
tools for investigating the biological function of TBK1 protein and also as potential drug candidates
for tumor immunotherapy. The current research progress indicates that some TBK1 inhibitors
(compounds 15,16 and 21) exhibit certain antitumor effects in vitro culture systems. Here, we summarize
the mechanism of action of TBK1 in tumors in recent years and the progress of small molecule
inhibitors of TBK1.
Abdomen Malignant Myxoid Leiomyosarcoma with PDGFRA and TP53
Missense Mutation: A Case Report
Authors: Li Yao-xuan , Yu Jian-ping , Gong Yan-qing , Huang Hai-fu , Wu Xian-lin 
Published: February 2024

Background:
Malignant myxoid leiomyosarcoma (MMLS) is most commonly found in
the uterus but can also occur in other areas, such as the extremities, vulva, chest wall, and abdominal
cavity. This cancer is more prevalent in women and has a poor prognosis with a high rate of recurrence
and a significant percentage of metastasis.

Case Representation:
Herein, we report the case of a 64-year-old female patient who presented with
3-month history of left lower abdominal mass. The patient underwent abdominal malignancy resection
and was subsequently diagnosed with myxoid leiomyosarcoma. The patient experienced a recurrence
and metastasis with significant ascites after the initial surgery and did not respond to treatment
with oral Anrotinib in combination with Tislelizumab immunotherapy. Further genetic testing using
next-generation sequencing (NGS) identified missense mutations in the PDGFRA and TP53 genes in
the patient's plasma, but no mutations in the KIT gene were detected. Immunohistochemical analysis
of the tumor tissue also revealed a negative expression of PD-L1. As a result, we altered her targeted
therapy to Avapritinib, which resulted in significant improvement in her symptoms, including abdominal
distension and pain, a decrease in ascites, and the KPS score increased from 60 points before
treatment to 90 points after treatment SD (stable disease) was achieved for three months after treatment.

Conclusion:
In this case report, we present the instance of a patient with malignant myxoid leiomyosarcoma
with a missense mutation in both the PDGFRA and TP53 genes. We found that targeted
therapy with Avapritinib was effective in achieving a positive outcome in this patient. Our findings
suggest that genetic detection is possible to better understand the biological behavior, genetic characteristics,
and patient's response and tolerance to certain drugs, thus selecting the best treatment plan
for the patient. Avapritinib may be a promising new treatment option for leiomyosarcoma patients
with similar genetic mutations.
Improved Hybrid Approach for Enhancing Protein-Coding Regions Identification in DNA Sequences
Authors: Hassan Emad S. , Dessouky Ahmed M. , Fathi Hesham , Salama Gerges M. , Oshaba Ahmed S. , El-Emary Atef , Abd El‑Samie Fathi E. 
Published: February 2024

Introduction:
Identifying and predicting protein-coding regions within DNA sequences
play a pivotal role in genomic research. This paper introduces an approach for identifying proteincoding
regions in DNA sequences by employing a hybrid methodology that combines digital bandpass
filtering with wavelet transform and various spectral estimation techniques to enhance exon
prediction. Specifically, the Haar and Daubechies wavelet transforms are applied to improve the accuracy
of protein-coding region (exon) prediction, enabling the extraction of intricate details that
may be obscured in the original DNA sequences.

Methods:
This research work showcases the utility of Haar and Daubechies wavelet transforms,
both non-parametric and parametric spectral estimation techniques, and the deployment of a digital
bandpass filter for detecting peaks in exon regions. Additionally, the application of the Electron-Ion
Interaction Potential (EIIP) method for converting symbolic DNA sequences into numerical values
and the utilization of Sum-of-Sinusoids (SoS) mathematical model with optimized parameters further
enrich the toolbox for DNA sequence analysis, ensuring the success of the proposed approach
in modeling DNA sequences, optimally, and accurately identifying genes.

Results:
The outcomes of this approach showcase a substantial enhancement in identification accuracy
for protein-coding regions. In terms of peak location detection, the application of Haar and
Daubechies wavelet transforms enhances the accuracy of peak localization by approximately (0.01,
3-5 dB). When employing non-parametric and parametric spectral estimation techniques, there is an
improvement in peak localization by approximately (0.01, 4 dB) compared to the original signal.
The proposed approach also achieves higher accuracy, when compared with existing ones.

Conclusion:
These findings not only bridge gaps in DNA sequence analysis but also offer a promising
pathway for advancing exonic region prediction and gene identification in genomics research.
The hybrid methodology presented stands as a robust contribution to the evolving landscape of genomic
analysis techniques.
Network Reorganization for Neurophysiological and Behavioral Recovery
Following Stroke
Authors: Qi Yuan , Xu Yujie , Wang Huailu , Wang Qiujia , Li Meijie , Han Bo , Liu Haijie 
Published: February 2024

Abstract:
Stroke continues to be the main cause of motor disability worldwide. While rehabilitation
has been promised to improve recovery after stroke, efficacy in clinical trials has been mixed. We
need to understand the cortical recombination framework to understand how biomarkers for neurophysiological
reorganized neurotechnologies alter network activity. Here, we summarize the principles
of the movement network, including the current evidence of changes in the connections and function
of encephalic regions, recovery from stroke and the therapeutic effects of rehabilitation. Overall,
improvements or therapeutic effects in limb motor control following stroke are correlated with the effects
of interhemispheric competition or compensatory models of the motor supplementary cortex.
This review suggests that future research should focus on cross-regional communication and provide
fundamental insights into further treatment and rehabilitation for post-stroke patients.