bluuebunny/crossref_metadata_2025_split huggingface dataset:
GATA1 insufficiencies in dysmegakaryopoiesis of myelodysplastic syndromes
Authors: Xiao Zhijian , Li Fuhui , Zhang Yudi , Li Chengwen , Sun Qi , Liu Jinqin , Xu Zefeng , Li Bing , Qu Shiqiang , Pan Lijuan , GAO QINGYAN , Jiao Meng , Qin Tiejun 
Published: June 2024

Abstract

GATA1 is one of critical transcription factors for megakaryopoiesis and platelet production. Our study aimed to explore the correlations between GATA1 expression and dysmegakaryopoiesis in myelodysplastic syndromes (MDS). Data of blood cell counts, cytogenetics and TP53 mutation status from 90 MDS patients at diagnosis were collected. Firstly, we assessed GATA1 expression level of megakaryocytes by performing immunohistochemical staining on paraffin-embedded bone marrow biopsy sections from these patients. According to GATA1 expression level of megakaryocytes and positive megakaryocyte percentage, we assigned each patient a GATA1 score. Compared with TP53-wildtype patients, GATA1 scores significantly decreased in TP53-mutated patients (P < 0.001). Patients with abnormal karyotypes showed decreased GATA1 scores than those with normal karyotypes (P = 0.024). GATA1 expression levels were significantly downregulated in dysplastic megakaryocytes, especially micromegakaryocytes, compared with normal megakaryocytes (P < 0.001). Furthermore, we explored the correlation between GATA1 expression levels and cytogenetic abnormalities of the same megakaryocyte using the morphology antibody chromosome (MAC) technique on fresh bone marrow smears. We found that GATA1-negative megakaryocytes had higher frequencies of cytogenetic abnormalities. Our results indicated that decreased GATA1 expression level of megakaryocytes was significantly associated with TP53 mutations, abnormal karyotypes and dysmegakaryopoiesis in MDS, suggesting that downregulation of GATA1 expression levels of megakaryocytes plays a critical role in the pathogenesis of MDS.
Recyclable Lead-Free Metal-Organic Framework/Perovskite Catalyst for Efficient Click Reaction
Authors: Kahkhaie Leila Rezaie , Oveisi Ali Reza , Sanchooli Esmael , Daliran Saba , Khajeh Mostafa 
Published: June 2024

Abstract

This study reports the synthesis and characterization of a novel CsCu2I3@UiO-66(Ce)-NH2 hybrid material through the state-of-the-art in-situ growth of the lead-free and non-toxic CsCu2I3 perovskite within the porous UiO-66(Ce)-NH2. The composite exhibits a high surface area with the CsCu2I3 nanostructures uniformly dispersed within the UiO-66(Ce)-NH2 framework without pore blockage. The host-guest CsCu2I3@UiO-66(Ce)-NH2 was considered as an effective and stable catalyst for the one-pot three-component copper(I)-catalyzed intermolecular alkyne-azide cycloaddition (CuAAC) or click reaction. Under optimized conditions, utilizing water at room temperature, the nominal catalyst exhibited superior activity, outperforming its individual components. Remarkably, the CsCu2I3@UiO-66(Ce)-NH2 catalyst demonstrated good recyclability and reusability over several catalytic runs. Mechanistic studies unveiled a synergistic cooperation between the CsCu2I3 and MOF, leading to the enhanced catalytic performance and improved stability of the perovskite. The developed multifunctional porous solid offers potential applications in catalysis and related fields, paving the way for innovative and sustainable organic synthesis and beyond.
Bilateral asymmetry of lower limb muscle synergies during walking
Authors: Sharifi Ali , Esmaeili Hamed , Ziaei Mostafa , Bashiri Behzad 
Published: June 2024

Abstract

Walking is a complex motor skill involving significant interaction within the CNS and various biomechanical elements. Asymmetry is one of the most apparent characteristics of walking, which is controlled by muscle synergies. The asymmetry in muscle synergies during walking is not well understood. This study aimed to investigate limb asymmetry in synergistic control of the selected lower limb muscles during walking. The study included sixteen healthy male participants, and bilateral lower limb muscle activities were recorded during walking. Muscle synergies and relative muscle weights were extracted using non-negative matrix factorization. The results showed that for both dominant and non-dominant legs five synergies are sufficient to reconstruct the original signal. Activation patterns in the first, fourth, and fifth synergies showed high similarity, with differences in relative muscle weights observed in the first and third synergies. However, in the second synergy, the similarity of activation patterns between the legs was small with variations in specific lower limb muscles. These findings suggest that synergy structure is independent of leg dominance, but the similarities of activation patterns range from small to high between dominant and non-dominant legs.
New solitons and breather-like solutions to a (2+1)-dimensional coupled variable-coefficient Schrödinger equation in optical fibers
Authors: Wang Xingye , Gao Ben 
Published: June 2024

Abstract

The nonlinear Schrödinger equation is an important theoretical model for ultra-long and ultra-fast transportation. Therefore, the situation of its normal propagation and collision has significant implications for practical applications. However, current researchs mostly focus on solutions of one dimensional or two dimensional Schrödinger equations with constant coefficients. Although these solutions possess good properties, they are often too unilateral. In this paper, our research mainly focuses on solutions of a (2+1)-dimensional coupled variable-coefficient Schrödinger equation. Through a explicit transformation, the solutions of this equation in the form of one-soliton, two-soliton, three-soliton, and bright-dark-soliton-breather-like waves can be acquired by utilizing the developed Hirota bilinear method. Furthermore, we will explicate that the collisions among two-soliton, bright-dark-two-breather-like are elastic with the help of the asymptotic analysis method. At last, we will exhibit their collisions via illustrations.
Numerical Study on Post-Combustion Chamber Impact on Hybrid Rocket Performance
Authors: Dabanović Andrija , Martin Joel , May Stefan , Wartemann Viola 
Published: June 2024

Abstract

In chemical propulsion, propellant masses are generally the largest part of the overall weight. Hence, combustion efficiency is a key aspect of the system’s performance and small improvements within the combustion process can result in significant changes in project costs or mission longevity. To analyze detailed chemical and physical processes, direct measurements are cost-intensive and limited to lab–scale devices. Numerical investigations have therefore become a widely used tool to simulate the inner flow and combustion of propulsion systems. In hybrid rocket engines, post–combustion chambers are used to improve the mixing and combustion process. Nevertheless, these components imply additional structural mass and engine length. This study analyzes the influence of different post-combustion chamber lengths on the combustion efficiency. The simulations were performed in two–dimensional, axisymmetric domains with fuel mass flow and detailed combustion modeling. The efficiency is shown to increase linearly with increasing post–chamber length. However, it is significantly influenced by turbulator components. With these results, the qualitative influence of post– combustion chambers is analyzed, enabling optimization of engine design while ensuring sufficient combustion efficiency.
Comprehensive enhancements for machine-learning based cloud resource orchestration algorithms
Authors: Pintye István , Kovács József , Lovas Róbert 
Published: June 2024

Abstract

With the increasing utilisation of machine-learning algorithms, autoscaling methods have the potential to provide more sophisticated control mechanisms for the cloud application operators over virtualized resources in terms of provisioning. This paper introduces machine learning-based autoscaling approach introducing several improvements including metric selection, proactivity, various enhancements of the neural network among others. Our enhanced machine learning models enables the autoscaler algorithm to react more quickly to sudden load changes, increase proactivity while using fewer resources and reducing Quality of Service (QoS) violations for cloud-based services. The comprehensive measurements indicate that QoS violations may reduce by up to 80%, while the level of resource utilization either remains constant or decreases slightly (by 3-4%) in certain applications. In cases where there was no reduction in QoS violations, the utilization of resources saw a significant decline, falling between 20-50%. The proposed changes have been analyzed and tested under various conditions, representing 3 distinct and common use cases in cloud environments. This developed process has been implemented on the science cloud of the Hungarian Research Network supporting the operation of the infrastucture that hosts over 300 scientific projects.
Challenges in Developing Learners’ English-Speaking Skills at the Tertiary Level in the EFL Context: Teachers’ and Learners’ Perceptions
Authors: Islam Md. Syful , Roy Sukanto 
Published: June 2024

Abstract

The paper aims to explore the challenges teachers and learners face in developing learners’ English-speaking skills at the tertiary level in the EFL context. The study has been conducted by following a mixed-method approach. A survey has been conducted on 44 tertiary-level students to collect quantitative data and three in-person interviews have been conducted with three tertiary-level educators to collect qualitative data. The findings of the study reveal that learners’ anxiety, lack of confidence, practice, motivation, and supportive environment, fear of criticism and negative judgment, and lack of language lab are some of the vital challenges learners and teachers encounter in developing their speaking skills. In addition, the practice of only reading and writing skills-oriented exams at the secondary and higher secondary levels and giving less importance to speaking skills are also vital challenges in developing learners’ speaking skills. To develop learners' speaking skills, teachers should provide proper guidance and a supportive environment, and learners should practice speaking skills as much as possible.
A Multilingual Neural Machine Translation Model for Low Resource North Eastern Languages
Authors: Sarkar Sunita , Das Sneha , Nath Basab , Mukhopadhyay Somnath 
Published: June 2024

Abstract

Multilingual neural machine translation (MNMT) has emerged as a powerful approach to facilitate cross-lingual communications. However, developing MNMT systems for low resource languages in linguistically diverse regions remains a significant challenge due to scarcity of parallel data. This study introduces an innovative MNMT model designed for the linguistically diverse North-East Indian region, leveraging the transformer architecture. The proposed system employs shared encoders and language- specific decoders for English to Indic language translation while using language-specific decoders for and a shared decoder for the reverse direction. To address data scarcity, we incorporate back-translation techniques to augment the parallel corpus. Experiments on six North-East Indian languages (Assamese, Bodo, Khasi, Manipuri, Mizo and Nepali) demonstrated good results across different evaluation metrics like BLEU, ROUGE,METEOR and TER.
Long-term analysis of changes in cooling degree-days in West Africa under global warming
Authors: Dicko Kagou , Umaru Emmanuel Tanko , Sanogo Souleymane , Okhimamhe Appollonia Aimiosino , Löwner Ralf 
Published: June 2024

Abstract

Monitoring energy consumption in response to rising temperatures has become extremely important in all regions of the globe. In West Africa, where the climate is predominantly tropical, the energy required for cooling is a major challenge. Among the various methods for evaluating energy requirements, the degree-day method is best known for its ability to estimate the heating, ventilation, and air-conditioning (HVAC) needs of buildings. This research used three decades of weather station data to assess the cooling degree-days (CDD) in two major West African cities, Kano and Bamako, across a range of base temperatures from 22°C to 30°C. The results indicate an increase in cooling degree days for Kano, while Bamako experienced a decrease in these parameters over the same period. Nonetheless, Bamako required a relatively higher cooling demand for all base temperatures. Furthermore, the study showed that the years 1998 and 2015 had the most significant impact on Kano and Bamako, with CDD values ranging from 2220°C-day to 218°C-day for Kano and from 2425°C-day to 276°C-day for Bamako. The study also found that a lower base temperature leads to higher energy consumption, while a higher base temperature leads to lower energy consumption. This information provides a useful reference for governments and policy-makers to achieve energy efficiency and reduce greenhouse gas emissions.
ExCS: Accelerating Code Search with Code Expansion
Authors: Huang Siwei , Cai Bo , Yu Yaoxiang , Luo Jian 
Published: June 2024

Abstract

Efficiently searching and reusing code from expansive codebases is pivotal for enhancing developers' productivity. In recent times, the emergence of deep learning-driven neural ranking models, characterized by their vast dimensions and intricate interaction mechanisms, has been noteworthy. Yet, these models, in real-world scenarios, pose computational challenges due to their high dimensionality. Moreover, models rooted in interaction necessitate querying every piece of code within a voluminous corpus. While these methodologies offer superior accuracy, their online retrieval process is considerably more time-consuming compared to traditional Information Retrieval (IR) techniques. Addressing this, we introduce "ExCS", an innovative code search tool designed to expedite the code search process without compromising on accuracy. ExCS innovatively employs code expansion in its offline phase, leveraging predictions on potential queries for specific codes, thereby enriching the code's semantic depth. During online retrieval, ExCS prioritizes IR-based methods to pinpoint a concise set of persuasive candidates. Our evaluations, conducted on the Java dataset from CodeSearchNet, reveal that ExCS achieves a remarkable 90% reduction in retrieval duration while maintaining an impressive 99% retrieval accuracy.
The Concussion, Exercise, and Brain Networks (ConExNet) study: A cohort study aimed at understanding the effects of sub-maximal aerobic exercise on resting state functional brain activity in pediatric concussion.
Authors: Sharma Bhanu , Koelink Eric , DeMatteo Carol , Noseworthy Michael D. , Timmons Brian W. 
Published: June 2024

Abstract

Background Recent scientific evidence has challenged the traditional “rest-is-best” approach for concussion management. It is now thought that “exercise-is-medicine” for concussion, owing to dozens of studies which demonstrate that sub-maximal, graded aerobic exercise can reduce symptom burden and time to symptom resolution. However, the primaryneuropathologyof concussion is altered functional brain activity. To date, no studies have examined the effects of sub-maximal aerobic exercise on resting state functional brain activity in pediatric concussion. In addition, although exercise is now more widely prescribed following concussion, its cardiopulmonary response is not yet well understood in this population. Our study has two main goals. The first is to understand whether there are exercise-induced resting state functional brain activity differences in children with concussion vs. healthy controls. The second is to profile the physiological response to exercise and understand whether it differs between groups. Methods We will perform a single-center, controlled, prospective cohort study of pediatric concussion at a large, urban children’s hospital and academic center. Children with sport-related concussion (aged 12–17 years) will be recruited within 4-weeks of injury by our clinical study team members. Key inclusion criteria include: medical clearance to exercise, no prior concussion or neurological history, and no implants that would preclude MRI. Age- and sex-matched healthy controls will be required to meet the same inclusion criteria and will be recruited through the community. The study will be performed over two visits separated by 24–48 hours. Visit 1 involves exercise testing (following the current clinical standard for concussion) and breath-by-breath gas collection using a metabolic cart. Visit 2 involves two functional MRI (fMRI) scans interspersed by 10-minutes of treadmill walking at an intensity calibrated to Visit 1 findings. To address sub-objectives, all participants will be asked to self-report symptoms daily and wear a waist-worn tri-axial accelerometer for 28-days after Visit 2. Discussion Our study will advance the growing exercise-concussion field by allowing us to understand whether exercise impacts outcomes beyond symptoms in pediatric concussion. We will also be able to profile the cardiopulmonary response to exercise, which may allow for further understanding (and eventual optimization) of the role of exercise in concussion management. Trial registration: Not applicable.
The causal relationship between extensive perivascular space burden and ischemic stroke and its subtypes and transient ischemic attack: A Mendelian randomization study
Authors: Chu Xuehong , Shen Yingjie , Wang Yaolou , Dong Xiao , Liu Yuanyuan , Feng Yan , Li Chuanhui , Zhao Wenbo , Ji Xunming , Jiang Miaowen , Li Ming , Wu Chuanjie 
Published: June 2024

Abstract

Background
Clinical studies suggest a strong link between extensive perivascular space (EPVS) and ischemic stroke (IS), including its subtypes, and transient ischemic attack (TIA), but it's uncertain if the relationship is genetically causal.
Methods
We utilize summary data from large-scale Genome-wide Association Studies (GWAS) to investigate the association between EPVS in different locations and IS, its subtypes, and TIA through Mendelian randomization (MR) analysis. Various MR methods are employed to assess the causal relationship between EPVS and IS, its subtypes, and TIA. We apply multivariable MR to mitigate potential confounding factors and conduct sensitivity analyses to enhance result robustness. Subsequently, meta-analysis is utilized to integrate causal relationships between EPVS in different locations and IS from various sources. Additionally, reverse MR is employed to observe the impact of various IS types on EPVS. Finally, linkage disequilibrium score regression is conducted to assess genetic correlations between exposures and outcomes.
Results
EPVS burden in both the white matter (OR, 1.12; 95% CI, 1.01–1.25; P = 0.04) and the basal ganglia (OR, 1.57; 95% CI, 1.30–1.89; P < 0.01) are significant risk factors for IS. EPVS burden in the basal ganglia is also a risk for IS (small-vessel) (OR, 4.56; 95% CI, 2.57–8.27; P = 5.95E-07). Additionally, there appears to be a potential increase in extensive basal ganglia perivascular space burden following IS and TIA.
Conclusion
Extensive white matter perivascular space burden and extensive basal ganglia perivascular space burden may serve as important indicators for predicting IS.
Relationship between obesity and iron metabolism: Insights from NHANES and Mendelian randomization studies
Authors: Zhao Jingjing , Zhong Hua , Zhao Jinjin , Wang Guoqiang , Wang Zhaohui , Zhang Xiuwen , Lu Guangjian , Wang Hui 
Published: June 2024

Abstract

Background
Obesity is associated with various health and nutritional issues, including impaired iron metabolism. However, the causal relationship is debatable, and the connection between obesity and iron metabolism remains inconclusive. This study aimed to investigate the relationship between obesity and iron metabolism using an observational cohort study and Mendelian randomization (MR).
Methods
A total of 4,981 individuals were included in the cohort study after screening participants from the National Health and Nutrition Examination Survey (NHANES) cycles of 2003–2006 and 2017–2018. A multivariable linear regression model was used to analyze the association between body mass index (BMI) and iron metabolism indicators (serum iron [SI], serum ferritin [SF], transferrin saturation [TSAT], total iron binding capacity [TIBC], and soluble transferrin receptor [sTfR]). Then, a two-sample MR analysis was conducted to verify causality.
Results
The results showed that SI and TSAT were lower, while SF and sTfR were higher in the obesity group compared to normal-weight individuals. After adjusting for confounding factors in the multivariable linear regression models, BMI was found to be significantly negatively correlated with SI (β = -0.15, 95% CI: -0.17 to -0.12, P < 0.001) and TSAT (β = -0.23, 95% CI: -0.28 to -0.19, P < 0.001), and positively associated with SF (β = 0.57, 95% CI: -0.15 to 1.29, P = 0.120) and sTfR (β = 0.02, 95% CI: 0.02 to 0.03, P < 0.001). However, the difference between BMI and TIBC was not statistically significant (β = 0.02, 95% CI: -0.02 to 0.06, P = 0.328). The MR findings suggested that genetically predisposed BMI was linked to reduced levels of SI (β = -0.073, 95% CI: -0.140 to 0.004, P = 0.036) and TSAT (β = -0.11, 95% CI: -0.18 to -0.04, P = 0.001), and increased levels of SF (β = 0.14, 95% CI: 0.069 to 0.21, P = 0.035), but no causality between BMI and sTfR. Genetically determined iron levels did not show any association with BMI.
Conclusion
Although altered iron status may not increase the risk of obesity, a correlation and causal relationship between obesity and iron metabolism was observed.
Elevating the Inference Performance of LLMs with Reverse Inference Federation
Authors: Li Qinian , Gu Yuetian 
Published: June 2024

Abstract

Natural language processing has seen impressive progress, driven by increasingly sophisticated models capable of performing complex linguistic tasks. The introduction of reverse inference federation represents a novel and significant advancement in optimizing the inference performance of these models, offering a scalable solution that distributes computational workloads across multiple nodes. Detailed modifications to the GPT-Neo architecture, coupled with innovative task allocation and synchronization algorithms, have led to substantial improvements in inference speed, accuracy, and resource utilization. Extensive experimentation and rigorous statistical analysis have validated the effectiveness of this approach, demonstrating its potential to enhance the efficiency and scalability of large language models. By leveraging distributed computing techniques, reverse inference federation addresses the computational challenges associated with real-time inference, providing a robust framework that ensures optimal resource utilization and reduced latency. The findings highlight the transformative impact of distributing inference tasks, setting a new benchmark for performance optimization in natural language processing applications.