bluuebunny/crossref_metadata_2025_split huggingface dataset:
LRRK2 is involved in neutrophil chemotaxis and is required for the GTP-binding activity of mitofusin 2 upon fMLP stimulation
Authors: Mazaki Yuichi , Handa Haruka , Fumoto Yoshizuki , Horinouchi Takahiro , Onodera Yasuhito 
Published: August 2023

Abstract
Background
Neutrophils heavily depend on glycolysis for energy under normal conditions. In contrast, neutrophils require energy supplied by mitochondrial oxidative phosphorylation (OXPHOS) during chemotaxis. However, the mechanism by which the energy supply changes from glycolysis to OXPHOS remains unknown. Leucine-rich repeat kinase 2 (LRRK2) is partially present in the outer mitochondrial membrane fraction. Lrrk2-deficient cells show mitochondrial fragmentation and reduced OXPHOS activity. We have previously reported that mitofusin (MFN) 2 is involved in chemotaxis and OXPHOS activation upon chemoattractant N-formyl-Met-Leu-Phe (fMLP) stimulation in differentiated HL-60 (dHL-60) cells. It has been previously reported that LRRK2 binds to MFN2 and partially colocalizes with MFN2 at the mitochondrial membranes. This study investigated whether LRRK2 is involved in chemotaxis and MFN2 activation in neutrophils and dHL-60 cells.
Methods
Lrrk2 knockout neutrophils and LRRK2 knockdown dHL-60 cells were used to examine the possible involvement of LRRK2 in chemotaxis. The relationship between LRRK2 and MFN2 was investigated by measuring the GTP-binding activity of MFN2 in LRRK2 knockdown dHL-60 cells. The effects of LRRK2 kinase activity on chemotaxis were examined using LRRK2 kinase inhibitor MLi-2.
Results
fMLP-induced chemotactic activity was reduced in Lrrk2 knockout neutrophils and LRRK2 knockdown dHL-60 cells. LRRK2 knockdown dHL-60 cells showed reduce OXPHOS activity and suppressed mitochondrial morphological change, similar to MFN2 knockdown dHL-60 cells. The amount of LRRK2 in the mitochondrial fraction and the GTP-binding activity of MFN2 increased upon fMLP stimulation. In LRRK2 knockdown dHL-60 cells, fMLP stimulated MFN2 GTP-binding activity was suppressed. Furthermore, the kinase activity of LRRK2 was reduced upon fMLP stimulation, and LRRK2 kinase inhibition by MLi-2 increased the migration to fMLP.
Conclusions
LRRK2 is involved in neutrophil chemotaxis and the GTP-binding activity of MFN2 upon fMLP stimulation. Furthermore, the kinase activity of LRRK2 plays an important role in chemotaxis.
U-Net Based Vortex Detection in Bose-Einstein Condensates with Automatic Correction for Manually Mislabeled Data
Authors: Ye Jing , Huang Yue , Liu Keyan 
Published: August 2023

Abstract
Quantum vortices in Bose-Einstein condensates (BECs) are essential phenomena in condensed matter physics, and precisely locating their positions, especially the vortex core, is a prerequisite for studying their properties. With the rise of machine learning, there is a potential to expedite the localization process and provide accurate predictions. However, traditional machine learning requires a considerable amount of manual data annotation, leading to uncontrollable accuracy. In this paper, we utilize the U-Net method to detect vortex positions accurately at the pixel level and propose an Automatic Correction Labeling (ACL) approach to optimize the acquisition of data sets for vortex localization in BECs. This approach addresses inaccuracies in the labeled vortex positions and improves the accuracy of vortex localization, especially the vortex core positions, while enhancing the tolerance for human labeling. The main process involves Rough Labeling -- Machine Learning -- Probability Region Search -- Data Relabeling -- Machine Learning again. The objective of ACL is to obtain more accurate labeled data for model retraining. Through vortex localization experiments conducted in a two-dimensional Bose-Einstein condensate, our results demonstrate the following: 1. Even under conditions of biased and missing manual annotations, U-Net can still accurately locate vortex positions; 2. Vortices exhibit certain regularities, and training U-Net with a small number of samples yields excellent predictive results; 3. The machine learning vortex locator based on the iterative ACL method effectively corrects errors in manually annotated data, significantly improving the model's performance metrics, thus enhancing the precision and metrics of vortex localization. This substantial advancement in the application of machine learning in vortex localization provides an effective approach for vortex dynamics localization. Furthermore, this method of obtaining more accurate approximate human labels through machine learning offers new insights for machine learning in other types of image recognition problems.
Effects of Different Feature Representations on Global Ionospheric TEC Model Based on Spatiotemporal Convolutions
Authors: Gao Xin , Yao Yibin , Wang Yang 
Published: August 2023

AbstractThe Total Electron Content (TEC) is a commonly used parameter for characterizing the morphology of the ionosphere. In this paper, we propose a novel feature representation of the time, called the Day and Night Map (DNM), for improving the global ionospheric TEC model. The specific effects of different feature variables (e.g., TEC, DNM, solar and geomagnetic indices) on forecasting global TEC maps are also analyzed. We apply factorized 3D convolutions, a new form of spatiotemporal convolutions, to construct the global TEC model. The global ionospheric TEC maps with a 2-hour temporal resolution are provided by the Center for Orbit Determination in Europe (CODE). The data sets that are resampled by a data segmentation strategy are used to train and evaluate the model. Results show that the DNM has a significant improvement in forecasting global TEC maps compared to the model with a single TEC feature, especially in the middle and low latitudes where large-scale ionospheric TEC anomalies occur frequently. Solar and geomagnetic indices facilitate the prediction capability of the global TEC model, but more indices are not always better. We demonstrate that matching appropriate feature variables with the structural characteristic of the deep learning algorithm is significantly important.
The relationship between chronic anemia caused by hematologic disease and cognitive impairment
Authors: Xu Liaoyang , Zhou Hang , Zhou Xinyu , Wei Jifeng 
Published: August 2023

Abstract
Patients with hematology disease ( such as aplastic anemia, primary myelofibrosis, and myelodysplastic syndrome) always in the condition of moderate and severe anemia for a long time. However, this chronic anemia condition impact on cognitive function was not well studied. We aim to explore the relationship between chronic anemia and cognitive function. We conducted a cross-sectional study. Collecting patients’ clinical dates and demographic characteristics from blood routine examination and self report. Objective cognition function was assessed by Chinese versions of Montreal Cognitive Assessment ( MoCA), total score of cognitive function and subscores of cognitive domains were calculated for each. Associations with chronic anemia and cognitive function were estimated using logistic regression. A total of 214 people including 70 chronic anemia and 144 non-anemia. Chronic anemia was independent factor for overall cognitive impairment, visual space and execution, attention, abstract and delayed recall (P < 0.05). The longer time of chronic anemia, the more possibility to have cognitive decline (P < 0.05). 36.5 months is a cutoff line for cognitive impairment among patients with chronic anemia. Chronic anemia can cause cognitive impairment; the longer time of chronic anemia, the easier to have cognitive decline.
Nonlinear modelling and dynamics of spatial multi-link rigid-flexible manipulator with moving platform
Authors: Kumar Pravesh , Pratiher Barun 
Published: August 2023

Abstract
The demand of developing lighter manipulators especially in various long-reach applications has been immensely escalated and in most of these applications, the presence of inherent structural flexibilities is more inevitable that causes vibration. As a result, this undesirable residual vibration reduces the working efficiency and positioning accuracy. For the first time, the present work formulates a nonlinear dynamical model of a spatial multi-link manipulator mounted on a mobile platform incorporating rigid and flexible links, and payload to study the end-point residual vibration characteristics. The dynamic modelling takes into account of coupled geometric and inertial nonlinearities due to impinging motion quality between joints, actuators, and elastic link deflections. The manipulator consists of rigid and two three-dimensional flexible links driven by prismatic joint and revolute joints, respectively. The flexible links assume the Euler-Bernoulli beam elements and time-dependent in-plane motion is provided to the rigid link. Sets of nonlinear governing equations of motion have been developed analytically using Hamilton’s variational principle. An independent generalized coordinates system is then used to obtain a nonlinear reduced form by discretizing the spatio-temporal equations of motion and study the trajectory dynamics of the robotic manipulator. The residual vibration characteristics at the payload end have been graphically investigated by imparting generalized sinusoidal and bang-bang torque profiles at respective joints. Nonlinear structural flexibility and material’s class can play key factors that influence the residual end-point vibration. It is proven that Bang-Bang torque profile widens the settling period in residual vibration due to its complex transition characteristics as compared to sinusoidal motion profile for a specific torque duty cycle. The numerical simulations demonstrate that the end-point residual vibrations and joint deflections are significantly influenced by the changes in physical and geometric variables which may lead to positioning errors and control of spatial flexible manipulators.
Effect of the Emotional Antisaccade Tasks on Salivary Trace Elements and Resting State EEG in Patients with Obsessive-Compulsive Disorder
Authors: Portnova Galina V. , Khayrullina Guzal , Mikheev Ivan V. , Byvsheva Sophia M. , Proskurnina Elena V. , Martynova Olga V. 
Published: August 2023

Abstract
The study of salivary microelements and their neurophysiological and behavioral correlates in patients with obsessive-compulsive disorder (OCD) is a pressing issue in modern psychiatry, which, however, lacks adequate research at this time, especially in terms of emotional or cognitive effort. In this study, we tested the dynamics of behavioral parameters, electroencephalogram (EEG) parameters, and salivary iron, copper, manganese, magnesium, and zinc in 30 healthy volunteers and 30 OCD patients using an emotional antisaccade task with an overlap design. We recorded both 64-channel EEG and eye-movement data and investigated the task-solving effectiveness and linear and non-linear features of the EEG resting states before and following the experimental paradigm. Our research revealed consistently increased manganese concentrations in OCD patients compared to healthy volunteers associated with higher amplitude variability of the EEG and symptom severity. The dynamics of salivary microelements and resting state EEG associated with task solving differed between groups. In healthy volunteers, there was a decrease in salivary iron with an increase in high-frequency PSD. In patients with OCD, there was a decrease in salivary copper with increased Hjorth mobility of the EEG.
Risk Assessment of Industrial Microbes Using a Terrestrial Mesocosm Platform
Authors: Arnolds Kathleen , Higgins Riley C. , Crandall Jennifer , Li Gabriella , Linger Jeffrey G. , Guarnieri Michael T. 
Published: August 2023

Abstract
Industrial microbes and bio-derived products have emerged as an integral component of the bioeconomy, with an array of agricultural, bioenergy, and biomedical applications. However, the rapid development of microbial biotechnology raises concerns related to environmental escape of laboratory microbes, detection and tracking thereof, and resultant impact upon native ecosystems. Indeed, though wild-type and genetically modified microbes are actively deployed in industrial bioprocesses, an understanding of microbial interactivity and impact upon the environment is severely lacking. In particular, the persistence and sustained ecosystem impact of industrial microbes following laboratory release or unintentional laboratory escape remains largely unexplored. Herein, we investigate the applicability of soil-sorghum mesocosms for the ecological risk assessment of the industrial microbe, Saccharomyces cerevisiae. We developed and applied a suite of diagnostic and bioinformatic analyses, including digital droplet PCR, microscopy, and phylogenomic analyses to assess the impacts of a terrestrial ecosystem perturbation event over a 30-day time course. The platform enables reproducible, high sensitivity tracking of S. cerevisiae in a complex soil microbiome and analysis of the impact upon abiotic soil characteristics and soil microbiome population dynamics and diversity. The resultant data indicate that even though S. cerevisiae is relatively short-lived in the mesocosm, a single perturbation event can have sustained impact upon soil composition and underlying microbial populations, underscoring the necessity for more comprehensive risk assessment and development of mitigation and biocontainment strategies in industrial bioprocesses.
Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube
Authors: Kim Hyunbum , Koh Daeyeon , Jung Yohan , Han Hyunjun , Kim Jongbaeg , Joo Younghoon 
Published: August 2023

Abstract
To prevent immediate mortality in patients with a tracheostomy tube, it is essential to ensure timely suctioning or replacement of the tube. Breathing sounds at the entrance of tracheostomy tubes were recorded with a microphone and analyzed using a spectrogram to detect airway problems. The sounds were classified into three categories based on the waveform of the spectrogram according to the obstacle status: normal breathing sounds (NS), vibrant breathing sounds (VS) caused by movable obstacles, and sharp breathing sounds (SS) caused by fixed obstacles. A total of 3,950 breathing sounds from 23 patients were analyzed. Despite neither the patients nor the medical staff recognizing any airway problems, the number and percentage of NS, VS, and SS were 1,449 (36.7%), 1,313 (33.2%), and 1,188 (30.1%), respectively. Artificial intelligence (AI) was utilized to automatically classify breathing sounds. MobileNet and Inception_v3 exhibited the highest sensitivity and specificity scores of 0.9441 and 0.9414, respectively. When classifying into three categories, ResNet_50 showed the highest accuracy of 0.9027, and AlexNet showed the highest accuracy of 0.9660 in abnormal sounds. Classifying breathing sounds into three categories is very useful to decide whether to suction or change the tracheostomy tubes, and AI can accomplish this with high accuracy.
A Systematic Review of Seizure Onset Pattern in SEEG and a Proposal for Morphological Classification
Authors: Yindeedej Vich , Uda Takehiro , Tanoue Yuta , Kojima Yuichiro , Kawashima Toshiyuki , Koh Saya , Uda Hiroshi , Nishiyama Taro , Takagawa Masanari , Shuto Futoshi , Goto Takeo 
Published: August 2023

Abstract
Background: Seizure onset pattern (SOP) represents an alteration of electroencephalography (EEG) morphology at the beginning of seizure activity in epilepsy. With stereotactic electroencephalography (SEEG), a method for intracranial EEG evaluation, many morphological SOP classifications have been reported without established consensus. These inconsistent classifications with ambiguous terminology present difficulties to communication among epileptologists.
Methods: We systematically reviewed SOP in SEEG by searching the PubMed database. Reported morphological classifications and the ambiguous terminology used were collected. After thoroughly reviewing all reports, we reconsidered the definitions of these terms and explored a more consistent and simpler morphological SOP classification.
Results: Of the 458 studies initially found,8 studies were finally included after screening and excluding irrelevant studies. We reconsidered EEG onset, period for determining type of SOP, core electrode and other terms in SEEG. We proposed a morphological SOP classification comprising four major types with three special subtypes.
Conclusions: A systematic review of SOP in SEEG was performed. Our classification may be suitable for describing SOP morphology.
Derivation and Comparative Analysis of End-To-End Power Models for 5G RAN Architectures
Authors: Doorgakant Bhuvaneshwar , Fowdur Tulsi Pawan 
Published: August 2023

Abstract
A considerable rise in the conventional trajectory of energy consumption is expected to be introduced by 5G which already consumes four times more energy than 4G. Consequently, energy efficiency is a major concern in 5G in contrast to earlier generations due to the further densification of cells. This paper proposes end-to-end power models for three different 5G RAN deployment architectures, namely the 5G distributed RAN, the 5G centralised RAN with dedicated hardware and the 5G Cloud Centralised-RAN. The end-to-end modelling of the power consumption of a complete 5G system is obtained by combining the power models of individual components such as the Base Station, the Core Network, front-haul, mid-haul and backhaul links, as applicable for the different architectures. The variation of the end-to-end power consumption of the 5G networks against key dependent parameters is analysed. The apportionment of power consumption at the different segments of the 5G networks is also calculated and discussed. It was found that the Decentralised 5G scenario had the highest power consumption of the three models analysed. On the other hand, the Centralised 5G and the 5G Cloud C-RAN consumed 12% and 20% less power respectively as compared to the Decentralised solution. Moreover, calculations demonstrated that the power consumption attributed to Base Stations varied between 74–78% of the Global 5G network power consumption. Thus, this study provides valuable insights for understanding the power consumption of various 5G architectures thereby laying the foundation for their sustainable deployment.
Molecular Property Diagnostic Suite Compound Library (MPDS-CL): A Structure based Classification of the Chemical Space
Authors: John Lijo , Nagamani Selvaraman , Mahanta Hridoy Jyoti , Vaikundamani S. , Kumar Nandan , Kumar Asheesh , Jamir Esther , Priyadarsinee Lipsa , Sastry G. Narahari 
Published: August 2023

Abstract
Molecular Property Diagnostic Suite-Compound Library (MPDS-CL), is an open-source galaxy-based cheminformatics web-portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, which are obtained from 42 publicly available databases were curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions. These are further subjected to 56-bit fingerprint-based classification algorithm which led to a formation of 56 structurally well-defined classes. The classes thus obtained were further divided into clusters based on their molecular weight. Thus, the entire set of molecules was put in 56 different classes and 625 clusters. This led to the assignment of a unique ID, named as MPDS-Aadhar card, for each of these 149 169 443 molecules. Aadhar card is akin to the unique number given to citizens in India (similar to the SSN in US, NINO in UK). MPDS-CL unique features are: a) several search options, such as exact structure search, substructure search, property-based search, fingerprint-based search, using SMILES, InChIKey and key-in; b) automatic generation of information for the processing for MPDS and other galaxy tools; c) providing the class and cluster of a molecule which makes it easier and fast to search for similar molecules and d) information related to the presence of the molecules in multiple databases. The MPDS-CL can be accessed at http://mpds.neist.res.in:8086/.
Targeted combination of Bevacizumab demonstrates superior efficacy for stage IV Adenocarcinoma of the lung with EGFR mutation: A Real World Study
Authors: Yu Yongkui , Xu Lei , Wei Xiufeng , Xie Hou-nai , Chen Yongfeng , Li Haomiao , Liu Qi , Sun Haibo , Ma Haibo , Xing Wenqun 
Published: August 2023

Abstract
Background
Research on bevacizumab combined with EGFR-TKIs is limited to erlotinib, and the NCCN guidelines only recommend erlotinib combined with bevacizumab as a nonfirst-line option. Consequently, To address the real-world efficacy of various treatment regimens, we conducted this study.
Methods
A total of 11,893 patients were screened. The patients were divided into three distinct groups: single targeted group(T), targeted combined chemotherapy group(TC) and targeted combined bevacizumab group(TB), and. The targeted therapy plans encompassed the utilization of first-, second-, and third-generation targeted drugs, namely, Iresa, Ireko, Echtenib, Afatinib, and Osimertinib. The chemotherapy regimen consisted of pemetrexed in combination with platinum, administered in a 21-day cycle for a total of 4–6 cycles. Subsequently, the patients continued receiving targeted drugs until disease progression. Statistical analysis was performed using the R programming language. The survival analysis compared patients treated with the three groups and who received first-generation combined targeted bevacizumab with single T. Subgroup analysis was performed for each factor.
Results
The study included a total of 822 patients, comprising 308 males (36.2%) and 542 females (63.8%), with ages ranging from 26 to 88 years (mean age of 58.9 ± 11.0 years). A total of 591 cases were group T, 152 cases were TC, and 107 cases were TB. The five-year survival of TB (78.5%, 95% CI: 0.625–0.985) was found to be significantly higher than TC (63.1%, 95% CI: 0.54.7-0.728) and T (44.1%, 95% CI: 0.387–0.503). Subgroup analysis revealed that among patients with stage IV adenocarcinoma of the lung with EGFR-sensitive mutations, women (p = 0.05), ≤ 65 years old (p = 0.05), no history of hypertension (p = 0.04), no history of diabetes (p = 0.04), no history of smoking (p = 0.05), smoking index ≤ 200 years (p = 0.04), and no brain metastasis (p = 0.05) who treated with TB experienced a survival advantage. The combination of bevacizumab with first-generation targeted drugs demonstrated superior efficacy compared to first-generation and third-generation targeted drugs (HR = 1.63, 95% CI: 1.47–1.81, p < 0.0001).
Conclusion
In patients diagnosed with stage IV adenocarcinoma of the lung with EGFR-sensitive mutations, TB demonstrates superior efficacy compared to TC and T. Furthermore, the efficacy of first-generation targeting combined with bevacizumab surpasses that of single targeting.