bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of perioperative indices and postoperative complications between two surgical treatments for benign tumours in the infratemporal parapharyngeal space: Mandibular ascending branch longitudinal osteotomy versus paramedian osteotomy
Authors: Hao ZHU , Xiaowen Zhao , Mengjie Zheng , Li Zhang 
Published: March 2024

Abstract

Background: This was a retrospective analysis of perioperative indicators and postoperative complications of ascending branch longitudinal osteotomy versus paramedian osteotomy for treating patients with benign tumours. The aim was to provide a reference for the surgical treatment of benign tumours in the infratemporal parapharyngeal space.
Methods: Clinical medical records and imaging data for 58 patients with benign tumours in the infratemporal parapharyngeal region admitted to the Oral and Maxillofacial Surgery Department of the General Hospital of Northern Theater Command were collected from November 2010 to June 2023. 28 patients underwent mandibular median osteotomy (Group A) and 30 patients underwent mandibular ascending branch longitudinal osteotomy (Group B) for the treatment of benign tumours. Comparative analysis of intraoperative bleeding, operation time, and postoperative complications between the two groups was conducted using the t test and chi-square test.
Results: The intraoperative blood loss and operation time in Group B were significantly lower than those in Group A (p <0.05), and the incidence of postoperative complications such as limited mouth opening, gap infection, and nerve injury was significantly lower than that in Group A (p <0.05). Patients were followed up from June to December. Postoperatively, temporary mouth opening was limited, the reduction in masticatory function gradually returned to the normal level within approximately 1-3 months, and facial nerve damage returned to normal within approximately 3-6 months after surgery.
Conclusion: 1. When the tumour is confined to the infratemporal parapharyngeal space, ascending longitudinal osteotomy has the advantages of less intraoperative bleeding, less surgical time and fewer postoperative complications. 2. Paramedian osteotomy is more suitable when a tumour in the infratemporal parapharyngeal region invades the pterygopalatine fossa upwards, grows beyond the trailing edge of the maxilla backwards, or invades the skull base downwards.
Sustained mTOR inhibition with sirolimus improves respiratory outcomes in LAM patients with COVID-19
Authors: Zhang Miao-yan , Yang Luning , Cheng Chongsheng , Zhang Tengyue , Zhang Shuhuai , Hu Danjing , Wang Hanghang , Zhang Xiaoxin , Yang Yanli , Tian Xinlun , Xu Kai-Feng 
Published: March 2024

Abstract

Background
Lymphangioleiomyomatosis (LAM) is a rare lung disease that predominantly affects women and can lead to severe respiratory complications. The impact of COVID-19 on LAM patients, particularly regarding the use of mammalian target of rapamycin (mTOR) inhibitors, remains poorly understood. This study investigates the clinical outcomes of LAM patients with COVID-19 and evaluates the role of sustained mTOR inhibition in respiratory outcomes.
Results
Our cohort included 186 LAM patients with COVID-19. Prior to infection, 72.6% were on mTOR inhibitors, with 29.6% discontinuing therapy due to infection. The hospitalization rate was 1.1%, with no reported need for invasive ventilation or fatalities. Patients with FEV1 less than 70% predicted had a higher risk of dyspnea exacerbation and supplemental oxygen requirement. Continuation of mTOR inhibitor therapy was associated with a lower risk of SpO2 decline, especially among patients with impaired lung function. Vaccination status did not significantly affect the prognosis.
Conclusions
LAM patients with COVID-19 showed a low rate of severe illness and mortality, with impaired lung function correlating with worse respiratory outcomes. Continued mTOR inhibitor therapy during COVID-19 infection may improve respiratory outcomes, suggesting the importance of maintaining treatment during viral pandemics.
Alpha-synuclein-induced nigrostriatal degeneration and pramipexole treatment disrupt frontostriatal plasticity
Authors: Benoit-Marand Marianne , Chevalier Sarah , Decourt Mélina , Francheteau Maureen , Balbous-Gautier Anaïs , Fernagut Pierre-Olivier 
Published: March 2024

Abstract

Parkinson’s disease is characterized by the degeneration of substantia nigra pars compacta (SNc) dopaminergic neurons, leading to motor and cognitive symptoms. Numerous cellular and molecular adaptations following neurodegeneration or dopamine replacement therapy (DRT) have been described in motor networks but little is known regarding associative basal ganglia loops. This study investigated the contributions of nigrostriatal degeneration and pramipexole (PPX) on neuronal activity in the orbitofrontal cortex (OFC), frontostriatal plasticity and markers of synaptic plasticity. Bilateral nigrostriatal degeneration was induced by viral-mediated expression of human mutated alpha-synuclein in the SNc. Juxtacellular recordings were performed in anesthetized rats to evaluate neuronal activity in the OFC. Recordings in the dorsomedial striatum (DMS) were performed and spike probability in response to OFC stimulation was measured before and after a high frequency stimulation (HFS). Post-mortem analysis included stereological assessment of nigral neurodegeneration, BDNF and TrkB protein levels. Nigrostriatal neurodegeneration led to altered firing patterns of OFC neurons that were restored by PPX. HFS of the OFC led to an increased spike probability in the DMS, while dopaminergic loss had an opposite effect. PPX led to a decreased spike probability following HFS in control rats and failed to counteract the effect of dopaminergic neurodegeneration. These alterations were associated with decreased levels of BDNF and TrkB in the DMS. This study demonstrates that nigral dopaminergic loss and PPX both contribute to alter fronstostriatal transmission, precluding adequate information processing in associative basal ganglia loops as a gateway for the development of non-motor symptoms or non-motor side-effects of DRT.
The role of the information content of the Moroccan treasury securities real yield curve in forecasting the future economic situation in Morocco
Authors: ALAOUI Mohammed Hassan , SOUSSI Outmane Noufail 
Published: March 2024

Abstract
This study aims to investigate the role of the slope of the real interest rate curve of Treasury bills in predicting the future economic situation in Morocco. Economic theory suggests that the slope of rates can be explained by three factors: the effects of monetary policy, expectations of monetary policy, and the intertemporal smoothing of consumption. Our theoretical model focuses on the latter explanation, suggesting that households aim to maintain a constant level of consumption, regardless of fluctuations in interest rates and the economic situation. To examine this relationship, we constructed a regression model that includes several lagged financial and/or monetary explanatory variables, such as real interest rates. This analysis utilizes quarterly data from 2007 to 2020. Our results underscore the importance of the information embedded in the structure of real interest rates, particularly the spread of real interest rates, for analyzing and forecasting future economic growth in Morocco.
Feedback-Driven Insight Generation and Recommendation for Health Self-Management
Authors: Susaiyah Allmin , Härmä Aki , Reiter Ehud , Balloccu Simone , Petković Milan 
Published: March 2024

Abstract
Purpose: This study aims to investigate the impact of personalized health insights generated from wearable device data on users' health behaviors. The primary objective is to assess whether user feedback-driven algorithms enhance the relevance and effectiveness of health insights, ultimately influencing positive changes in users' daily activities.
Methods: A two-month field study was conducted with 25 healthy volunteers using Mi Band 6 wearable devices. Participants were divided into test and control groups, and the test group received personalized insights recommended by a neural network-based algorithm fine-tuned by user feedback. The data collected included various health parameters such as calories burned, step count, heart rate, heart minutes, active minutes, sleep duration, sleep time and sleep segments. Insights were provided through a Telegram chatbot and user feedback was collected through a rating system.
Results: The study revealed that the test group, which considered user feedback for insight recommendations, showed a significant improvement in daily activity compared to the control group. The relevance of the insights over time, as evidenced by feedback regression trends, showed a notable increase in the test group. Additional analyzes explored the relationship between insight delivery timing, user feedback, and delays, providing insights into user engagement patterns.
Conclusion: This research highlights the effectiveness of personalized health insights generated from wearable data in positively influencing user health behaviors. Incorporating feedback from users into recommendation algorithms greatly enhances the relevance and effectiveness of insights, encouraging behavioral improvements. The results emphasize the significance of timing when providing insights and propose potential areas for future investigation, such as utilizing Graph Neural Networks to improve recommendation systems. In general, personalized insights from wearables have the potential to empower individuals to manage their health and well-being effectively.
Utility of Radial Endobronchial Ultrasound with a Guide Sheath for Peripheral Pulmonary Lesions in Patients with Pulmonary Emphysema
Authors: Zhou Lefei , Wang Feng , Wang Yan , Wang Zhen , Xu Lili , Tong Zhaohui 
Published: March 2024

Abstract
Background
Radial endobronchial ultrasound with a guide sheath for transbronchial biopsy (EBUS-GS-TBB) can be considered for diagnosing peripheral pulmonary lesions (PPLs) with fewer complications in patients with emphysema. However, the utility and safety of bronchoscopy for PPLs in the proximity of emphysema-area lesions remain unclear. The aim of this study was to assess the efficacy and complications of the initial diagnostic procedure of bronchoscopy with EBUS-GS-TBB according to the proximity of PPLs to emphysema areas, along with factors affecting the successful diagnostic yield for PPLs, and to identify the feasibility of molecular and genetic testing using EBUS-GS-TBB-obtained tumor samples.
Methods
The medical records of 278 consecutive patients with PPLs who underwent EBUS-GS-TBB without X-ray fluoroscopy guidance were screened. We compared PPLs with emphysema in such lesions. PPLs with emphysema were divided into two groups: PPLs located in non-emphysema areas and those inside or near emphysema areas.
Results
This study included 84 patients with emphysema (non-emphysema area group = 46; inside or near emphysema area group = 38). The diagnostic yield was significantly higher for PPLs located in non-emphysema areas than for PPLs inside or near emphysema areas (82.6% vs. 52.6%, p = 0.013). Multivariate analysis revealed that PPLs located in non-emphysema areas (odds ratio = 5.614) and EBUS images within lesions were significant factors affecting diagnostic yield. Further, the utility of EBUS-GS-TBB for PPLs based on the proximity of PPLs to emphysema-area lesions in patients with emphysema is safety.
Conclusions
In patients with emphysema, the positional relation of PPLs to emphysema lesions and EBUS images within lesions were important factors affecting successful diagnosis using EBUS-GS-TBB.
Feistel Cipher based Enhanced Image Encryption Algorithm with Dynamic Key Generation
Authors: Mehta Deevya , Savla Jenil , Iyer Sridhar , Patil Nilesh 
Published: March 2024

Abstract
Various block cipher algorithms are developed keeping feistel cipher as base. These algorithms comprise of substitution boxes, permutation boxes and transposition technique. They follow symmetric key encryption method. This paper also proposes a symmetric key block cipher technique which can be used for encrypting both text and images before transmitting these over internet. The algorithm has 16 rounds of operations with a unique combination of s-boxes, p-boxes and transposition method. The key generation is dynamic similar to AES and key size can be extended as per requirement. Three different images of different qualities were tested and a 49.997% average diffusion in original image was observed along with original image being decrypted as it was. The security in terms of brute force attack, differential and linear cryptanalysis was analysed and a comparison with AES was also performed. At the end it was concluded that the algorithm mentioned is an effective an secure method of encrypting images to transmit over the communication channels.
Detection of Vehicles and Analysis of Traffic Volume by Real-Time Video-graphic Technique using Python Algorithm
Authors: Ramadan Osman Kantunsung , K R. Srinivasa , Reddy Avuthu Narender 
Published: March 2024

Abstract

Today, one of the most pressing challenges in every region of the world is transportation. Every nation approaches its transportation issues differently by its demands and within its capacity. Volume is crucial for transportation system planning, design, and operation. The procedure involves identifying the predominant volume of traffic on roadways at a certain place during a specified time period. It is quantified by the quantity of cars per minute, hour, and day. Translating the flow of different vehicle classes into a single unit called the passenger car unit is crucial for representing traffic flow on a route over a certain time period. The volume of traffic is dynamic and changes throughout the day. The amount of daily traffic fluctuates on different days of the week, different months, and seasons of the year. Intelligent transport system (ITS) traffic management is a vital component. Intelligent Transportation Systems (ITS) is a system that incorporates sophisticated communications, information, and electronic technologies into transportation infrastructure and vehicles. ITS is designed to reduce traffic congestion, enhance safety, and decrease transit times and fuel use. The advancement of Intelligent Transportation Systems (ITS) has led to a rising need for real-time traffic data. In the past, manual estimates were the most prevalent method. Several observers employ technology, such as mechanical and electronic counting boards, to count the number of passing cars, their sorts, and even the number of people. Currently, there are various standard surveillance technologies to capture real-time traffic data, such as image processing and inductive loop detection. Using these approaches, traffic statistics, vehicle speeds, and classifications may be collected. However, they have certain drawbacks, such as their restricted scope and high cost of installation and maintenance. The study, therefore, presented diminutive research on using an algorithm for detecting vehicles and analyzing traffic volume using real-time video graphic techniques to combat some of these challenges. This research was conducted using the image detection model YOLO. The algorithm counts traffic volume and was compared with the manual count traffic volume to check the reliability of the proposed model. In this research, we outlined a five-tasked methodology viz, recording of traffic video (data collection), developing an algorithm for detection and counting of vehicles, testing of the algorithm with recorded video, exporting data for analysis, and comparing algorithm count with manual count traffic volume to check the trustworthiness of the model. The research relived a high accuracy level of detected vehicles as well as, yield minimal percentage errors when subjected to mean absolute percentage error computations. Also, the results of the research fell into the accepted zone of the GEH (Goffrey E. Havers) statistics.
Extreme Phenotype Sampling and Next Generation Sequencing to Identify Genetic Variants Associated with Tacrolimus in African American Kidney Transplant Recipients
Authors: Mohamed Moataz , Guo Bin , Wu Baolin , Schladt David , Muthusamy Amutha , Guan Weihua , Abrahante Juan , Onyeaghala Guillaume , Saqr Abdelrahman , Pankratz Nathan , Agarwal Gaurav , Mannon Roslyn , Matas Arthur , Oetting William , Remmel Rory , Israni Ajay , Jacobson Pamala , Dorr Casey 
Published: March 2024

Abstract

African American (AA) kidney transplant recipients (KTRs) have poor outcomes, which may in-part be due to tacrolimus (TAC) sub-optimal immunosuppression. We previously determined the common genetic regulators of TAC pharmacokinetics in AAs which were CYP3A5 *3, *6, and *7. To identify low-frequency variants that impact TAC pharmacokinetics, we used extreme phenotype sampling and compared individuals with extreme high (n=58) and low (n=60) TAC troughs (N=515 AA KTRs). Targeted next generation sequencing was conducted in these two groups. Median TAC troughs in the high group were 7.7 ng/ml compared with 6.3 ng/ml in the low group, despite lower daily doses of 5 versus 12mg, respectively. Of 34,542 identified variants across 99 genes, 1,406 variants were suggestively associated with TAC troughs in univariate models (p-value <0.05), however none were significant after multiple testing correction. We suggest future studies investigate additional sources of TAC pharmacokinetic variability such as drug-drug-gene interactions and pharmacomicrobiome.
Quantum-Resilient Cryptographic Primitives: An Innovative Modular Hash Learning Algorithm to Enhanced Security in the Quantum Era
Authors: SINGH MANRAJ , Singh Sunil K. , Kumar Sudhakar , Preet Mehak , Arya Varsha , Gupta Brij B 
Published: March 2024

Abstract
In the era of rapid advancements in quantum computing, classical cryptographic primitives are facing unprecedented security challenges. Shor's algorithm, a powerful quantum tool, poses a significant threat to existing hash functions, raising concerns about data integrity and confidentiality. In response to these emerging threats, the paper introduces a novel Quantum-Resistant Hash Algorithm (QRHA) named the 'Modular Hash Learning Algorithm (MHLA).' MHLA has been meticulously designed with a strong focus on fundamental principles of quantum resistance and incorporates advanced mathematical and algorithmic techniques to enhance its security. This paper presents a comprehensive examination of MHLA's resistance to both classical and quantum attacks, with a particular emphasis on Shor's algorithm. Our research demonstrates that MHLA offers improved security in the context of the quantum era, ensuring data confidentiality and integrity. Moreover, MHLA boasts an efficient algorithmic complexity with a time complexity of $O(m \cdot n \cdot k)$ for the matrix-vector product. This research contributes to the ongoing efforts to fortify cryptographic systems against the evolving landscape of quantum computing.
Thermal Stress Accommodation in Sodium/Inconel 718 Heat Pipes
Authors: Hu Longfei , LI Bo , Luo Xiaoguang , Chen Siyuan , Yu Jijun , Ai Bangcheng , Zhang Mohan 
Published: March 2024

Abstract
In this study, sodium/Inconel 718 heat pipes with and without topological capillary were designed and fabricated in China Academy of Aerospace Aerodynamics. Then their startup properties were investigated systematically. It was found that startup temperature of sodium/Inconel 718 heat pipes was 420oC. During startup tests, all the heat pipes startup successfully. Topological capillary did not affect startup properties of sodium/Inconel 718 heat pipes. After tests, all the heat pipes still kept their integrity. Thermal deformation was not observed. A further thermal stress simulation indicated that topological capillary could accommodate thermal stresses in sodium/Inconel 718 heat pipes. With topological capillary addition, thermal stresses in the heat pipes decreased by about 21%. Moreover, the bended sodium/Inconel 718 heat pipe could startup successfully in an anti-gravity condition, being instructive for development of flexible and anti-gravity high temperature heat pipes.
Power of Ensemble Techniques for Brain Age Prediction Using Machine Learning Models
Authors: Ravi V , Baghel Himanshu Singh , Chinamsetty Nainika , Prathap Varun , Reka S. Sofana 
Published: March 2024

Abstract

Aging has a profound impact on brain structure and function, resulting in cognitive decline and an increased susceptibility to neurodegenerative diseases. The brain age gap is defined as the difference between an individual's estimated brain age and their actual age, which is considered a potential marker of overall brain health and may indicate structural abnormalities. Considering this work, machine learning models are used to estimate the brain age based on brain imaging data from four prominent repositories namely IXI dataset, Calgary Campinas, Sparse Linear Method, and Sign Agnostic Learning with Derivatives respectively This work is done based on five regression models such as XGBoost (Extreme Gradient Boosting), Support Vector Regression (SVR), Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and K-Nearest Neighbors (KNN) regression, and also considering ensemble models such as Stacking, Bagging, and Boosting. The dataset contains T1-weighted Magnetic resonance imaging (MRI) images from over 1800 patients, and approximately 143 features were extracted using the reliable tool FreeSurfer (6.0). In this study, a major analysis was performed using grid search and cross-validation to train the models and optimize the hyperparameters to prevent overfitting. The results exhibit that a stacked ensemble model of SVR and XGBoost outperformed the other models, with a mean absolute error (MAE) of 4.65 and R2 value of 0.92 on the training dataset while 6.62 and 0.85 on the test set respectively. The validation results indicated that regression models and ensemble techniques for brain age prediction provide a powerful combination of interpretability, accuracy, and robustness.
Application and mechanism of a novel flocculation reagent for mechanical dewatering: A case study on slurry separation in a large-diameter slurry shield
Authors: Zhang Jian , Ren Guoping , Feng Tugen , Liang Yu , Yin Yihao 
Published: March 2024

Abstract

A novel C-type flocculation reagent was developed to address the high water content of sludge and the environmental pollution caused by wastewater discharge during the mechanical dewatering of the Zhanjiang Bay undersea tunnel. Microscopic parameters, such as granularity and zeta potential, were analyzed to elucidate the mechanism of flocculation and dewatering in C-type reagents. The applicability of the C-type reagent was further validated in a field test and compared to that of the conventional reagent. The results showed that the C-type reagent reduced the water content of the sludge by approximately 20% and decreased the supernatant turbidity by approximately 79%. The C-type reagent demonstrated enhanced bridging and charge neutralization activities, thereby facilitating the aggregation of fine particles (<10 μm) into larger particles (>50 μm), ultimately enhancing the efficiency of flocculation. The C-type reagent can also facilitate the formation of a calcium drainage channel skeleton with enhanced water permeability and reduce slurry viscosity to improve the efficiency of mechanical dewatering. These effects enhanced the pore size distribution of the slurry, resulting in a range of 346 to 8656 nm, which was approximately 15% to 41% greater than the overall pore size of the original slurry. The on-site utilization of 0.5% C-type reagent, compared to the original reagent, can enhance the solid content of the sludge by 35.7%, resulting in a 20% reduction in cost. Moreover, it meets the required sewage indicators, such as pH, and thus qualifies for direct discharge. The findings of this study offer valuable insights for the selection of environmentally friendly and efficient reagents in the context of the combined flocculation-mechanical dewatering method.