bluuebunny/crossref_metadata_2025_split huggingface dataset:
Endothelial calcium dynamics elicited by ATP release from red blood cells
Authors: Nayak Ananta Kumar , Das Sovan Lal , Misbah Chaouqi 
Published: June 2024

AbstractRed blood cells (RBCs) exhibit an interesting response to hydrodynamic flow, releasing adenosine triphosphate (ATP). Subsequently, these liberated ATP molecules initiate a crucial interaction with endothelial cells (ECs), thereby setting off a cascade involving the release of calcium ions (Ca$$^{2+}$$

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). Ca$$^{2+}$$

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exerts control over a plethora of cellular functions, and acts as a mediator for dilation and contraction of blood vessel walls. This study focuses on the relationship between RBC dynamics and Ca$$^{2+}$$

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dynamics, based on numerical simulations under Poiseuille flow within a linear two-dimensional channel. It is found that the concentration of ATP depends upon a variety of factors, including RBC density, channel width, and the vigor of the flow. The results of our investigation reveals several features. Firstly, the peak amplitude of Ca$$^{2+}$$

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per EC escalates in direct proportion to the augmentation of RBC concentration. Secondly, increasing the flow strength induces a reduction in the time taken to reach the peak of Ca$$^{2+}$$

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concentration, under the condition of a constant channel width. Additionally, when flow strength remains constant, an increase in channel width corresponds to an elevation in calcium peak amplitude, coupled with a decrease in peak time. This implies that Ca$$^{2+}$$

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signals should transition from relatively unconstrained channels to more confined pathways within real vascular networks. This notion gains support from our examination of calcium propagation in a linear channel. In this scenario, the localized Ca$$^{2+}$$

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release initiates a propagating wave that gradually encompasses the entire channel. Notably, our computed propagation speed agrees with observations.
REST-dependent downregulation of von Hippel-Lindau tumor suppressor promotes autophagy in SHH-medulloblastoma
Authors: Singh Ashutosh , Cheng Donghang , Swaminathan Jyothishmathi , Yang Yanwen , Zheng Yan , Gordon Nancy , Gopalakrishnan Vidya 
Published: June 2024

AbstractThe RE1 silencing transcription factor (REST) is a driver of sonic hedgehog (SHH) medulloblastoma genesis. Our previous studies showed that REST enhances cell proliferation, metastasis and vascular growth and blocks neuronal differentiation to drive progression of SHH medulloblastoma tumors. Here, we demonstrate that REST promotes autophagy, a pathway that is found to be significantly enriched in human medulloblastoma tumors relative to normal cerebella. In SHH medulloblastoma tumor xenografts, REST elevation is strongly correlated with increased expression of the hypoxia-inducible factor 1-alpha (HIF1α)—a positive regulator of autophagy, and with reduced expression of the von Hippel-Lindau (VHL) tumor suppressor protein – a component of an E3 ligase complex that ubiquitinates HIF1α. Human SHH-medulloblastoma tumors with higher REST expression exhibit nuclear localization of HIF1α, in contrast to its cytoplasmic localization in low-REST tumors. In vitro, REST knockdown promotes an increase in VHL levels and a decrease in cytoplasmic HIF1α protein levels, and autophagy flux. In contrast, REST elevation causes a decline in VHL levels, as well as its interaction with HIF1α, resulting in a reduction in HIF1α ubiquitination and an increase in autophagy flux. These data suggest that REST elevation promotes autophagy in SHH medulloblastoma cells by modulating HIF1α ubiquitination and stability in a VHL-dependent manner. Thus, our study is one of the first to connect VHL to REST-dependent control of autophagy in a subset of medulloblastomas.
Visualization of incrementally learned projection trajectories for longitudinal data
Authors: Malepathirana Tamasha , Senanayake Damith , Gautam Vini , Engel Martin , Balez Rachelle , Lovelace Michael D. , Sundaram Gayathri , Heng Benjamin , Chow Sharron , Marquis Christopher , Guillemin Gilles J. , Brew Bruce , Jagadish Chennupati , Ooi Lezanne , Halgamuge Saman 
Published: June 2024

AbstractLongitudinal studies that continuously generate data enable the capture of temporal variations in experimentally observed parameters, facilitating the interpretation of results in a time-aware manner. We propose IL-VIS (incrementally learned visualizer), a new machine learning pipeline that incrementally learns and visualizes a progression trajectory representing the longitudinal changes in longitudinal studies. At each sampling time point in an experiment, IL-VIS generates a snapshot of the longitudinal process on the data observed thus far, a new feature that is beyond the reach of classical static models. We first verify the utility and correctness of IL-VIS using simulated data, for which the true progression trajectories are known. We find that it accurately captures and visualizes the trends and (dis)similarities between high-dimensional progression trajectories. We then apply IL-VIS to longitudinal multi-electrode array data from brain cortical organoids when exposed to different levels of quinolinic acid, a metabolite contributing to many neuroinflammatory diseases including Alzheimer’s disease, and its blocking antibody. We uncover valuable insights into the organoids’ electrophysiological maturation and response patterns over time under these conditions.
A deep image classification model based on prior feature knowledge embedding and application in medical diagnosis
Authors: Xu Chen , Wu Jiangxing , Zhang Fan , Freer Jonathan , Zhang Zhongqun , Cheng Yihua 
Published: June 2024

AbstractAiming at the problem of image classification with insignificant morphological structural features, strong target correlation, and low signal-to-noise ratio, combined with prior feature knowledge embedding, a deep learning method based on ResNet and Radial Basis Probabilistic Neural Network (RBPNN) is proposed model. Taking ResNet50 as a visual modeling network, it uses feature pyramid and self-attention mechanism to extract appearance and semantic features of images at multiple scales, and associate and enhance local and global features. Taking into account the diversity of category features, channel cosine similarity attention and dynamic C-means clustering algorithms are used to select representative sample features in different category of sample subsets to implicitly express prior category feature knowledge, and use them as the kernel centers of radial basis probability neurons (RBPN) to realize the embedding of diverse prior feature knowledge. In the RBPNN pattern aggregation layer, the outputs of RBPN are selectively summed according to the category of the kernel center, that is, the subcategory features are combined into category features, and finally the image classification is implemented based on Softmax. The functional module of the proposed method is designed specifically for image characteristics, which can highlight the significance of local and structural features of the image, form a non-convex decision-making area, and reduce the requirements for the completeness of the sample set. Applying the proposed method to medical image classification, experiments were conducted based on the brain tumor MRI image classification public dataset and the actual cardiac ultrasound image dataset, and the accuracy rate reached 85.82% and 83.92% respectively. Compared with the three mainstream image classification models, the performance indicators of this method have been significantly improved.
Investigating the impact of alumina nanoparticles in coconut oil distillate biodiesel to lessen emissions in direct injection diesel engine
Authors: Rajesh K. , Bibin Chidambaranathan , Soundararajan Gopinath , Ashok Kumar R. , Arunkumar S. , Devarajan Yuvarajan , Kaliappan Nandagopal 
Published: June 2024

AbstractPetroleum fuels are commonly used for automobiles. However, the continuous depletion and exhaust gas emission causes serious problems. So, there is a need for an alternative eco-friendly fuel. Biodiesel is a type of fuel manufactured through a process called transesterification, which involves converting vegetable oils into a usable form. The process parameters of the transesterification process were optimized using the Taguchi method to achieve maximum biodiesel yield. However, the main problem of biodiesel is its high cost which could be reduced by using low-cost feedstock. To address this challenge, biodiesel (BCFAD) is derived from coconut fatty acid distillate (CFAD), a by-product obtained from refining coconut oil. This work uses BCFAD and BCFAD with Alumina nanoparticles as fuels. Alumina nanoparticles in the mass fraction of 25 ppm, 50 ppm, and 100 ppm are dispersed in BCFAD. The investigation results reveal an increase of 6.5% in brake thermal efficiency for BCFAD with 100 ppm nanoparticles when compared to BCFAD. There is a reduction of 29.29% of hydrocarbon and 34% of Carbon monoxide emissions with BCFAD100 in comparison with diesel. However, there is a marginal increase in NOx emission with the increase in nanoparticles. The heat release rate and cylinder pressure of BCFAD100 are comparable to diesel fuel. It was concluded that the utilization of BCFAD with a nanoparticle dispersion of 100 ppm is suitable for direct use as fuel in diesel engines.
Motor assessment of X-linked dystonia parkinsonism via machine-learning-based analysis of wearable sensor data
Authors: Parisi Federico , Corniani Giulia , Bonato Paolo , Balkwill David , Acuna Patrick , Go Criscely , Sharma Nutan , Stephen Christopher D. 
Published: June 2024

AbstractX-linked dystonia parkinsonism (XDP) is a neurogenetic combined movement disorder involving both parkinsonism and dystonia. Complex, overlapping phenotypes result in difficulties in clinical rating scale assessment. We performed wearable sensor-based analyses in XDP participants to quantitatively characterize disease phenomenology as a potential clinical trial endpoint. Wearable sensor data was collected from 10 symptomatic XDP patients and 3 healthy controls during a standardized examination. Disease severity was assessed with the Unified Parkinson’s Disease Rating Scale Part 3 (MDS-UPDRS) and Burke-Fahn-Marsden dystonia scale (BFM). We collected sensor data during the performance of specific MDS-UPDRS/BFM upper- and lower-limb motor tasks, and derived data features suitable to estimate clinical scores using machine learning (ML). XDP patients were at varying stages of disease and clinical severity. ML-based algorithms estimated MDS-UPDRS scores (parkinsonism) and dystonia-specific data features with a high degree of accuracy. Gait spatio-temporal parameters had high discriminatory power in differentiating XDP patients with different MDS-UPDRS scores from controls, XDP freezing of gait, and dystonic/non-dystonic gait. These analyses suggest the feasibility of using wearable sensor data for deriving reliable clinical score estimates associated with both parkinsonian and dystonic features in a complex, combined movement disorder and the utility of motion sensors in quantifying clinical examination.
Characterization of resistance and virulence factors in livestock-associated methicillin-resistant Staphylococcus aureus
Authors: Beshiru Abeni , Igbinosa Isoken H. , Akinnibosun Olajide , Ogofure Abraham G. , Dunkwu-Okafor Afamefuna , Uwhuba Kate E. , Igbinosa Etinosa O. 
Published: June 2024

AbstractThe study investigated the economic concerns associated with livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) in livestock (cow), examining its connection to severe infections, antimicrobial resistance (AMR), and virulence factors. The research, conducted in Edo State, Nigeria, analyzed 400 samples (200 rectal and 200 nasal swabs) collected between March 2018 and February 2019. MRSA prevalence was identified using conventional culture-based methods and polymerase chain reaction (PCR) techniques, revealing 63.5% (n = 254) for Staphylococcus aureus and 55% (n = 220) for MRSA. Of the 76 mecA-positive MRSA isolates, 64.5% (n = 49) exhibited multidrug resistance (MDR) while the remaining were sensitive to specific antimicrobials. Key virulence genes, such as PVL (81.6%; n = 62) and tsst-1 (44.7%; n = 34), were prevalent, along with AMR genes like mecC, tetM, ermA, ermC, vanA, and vanC. Staphylococcal chromosomal cassette mec (SCCmec) typing identified different types, notably II, IVa, and IVb. Biofilm formation, a crucial virulence factor varied in strength, is associated with icaA and icaB genes (p < 0.01). The findings highlighted substantial AMR and biofilm-forming capacity within LA-MRSA isolates, emphasizing the importance of ongoing surveillance for informed treatment strategies, AMR policies, and control measures against MDR staphylococcal infections.
Enhancement of ionospheric heating effect by chemical release
Authors: Zhao Hai-Sheng , Feng Jie , Xu Zheng-Wen , Liu Ya-Xin , Xue Kun , Wu Jian , Wang Cheng , Xie Shou-Zhi , Peng Huai-Yun 
Published: June 2024

AbstractThe ionosphere can be artificially modified by employing ground-based high-power high-frequency electromagnetic waves to irradiate the ionosphere. This modification is achieved through the nonlinear interaction between the electromagnetic waves and the ionospheric plasma, leading to changes in the physical properties and structure of the ionosphere. The degree of artificial modification of the ionosphere is closely related to the heating energy density of high-frequency pump waves. Due to the high density of neutral constituents in the lower ionosphere and the high frequency of electron-neutral collisions, the energy of heating pump waves will be absorbed and attenuated during the penetration of the low ionosphere, seriously affecting the heating effect. This paper proposes a method to reduce the absorption of ionospheric heating pump waves by releasing electron attachment chemicals into low ionosphere to form a large-scale electron density hole. A model for mitigating pump waves absorption based on SF6 release is established, and the absorption at different frequencies is quantitatively calculated. The propagation characteristics of high-frequency signals in ionospheric holes are studied using a three-dimensional ray tracing method, and the results demonstrate that the chemical release method not only reduces the absorption attenuation of heating pump waves but also forms spherical electron density holes, which exhibit a focusing effect on the heating beam and enhance the heating effect. The results are of great significance for understanding the nonlinear interaction between electromagnetic wave and ionospheric plasma and improving the ionospheric heating efficiency.
Associations between pain intensity, psychosocial factors, and pain-related disability in 4285 patients with chronic pain
Authors: Landmark Live , Sunde Hans Fredrik , Fors Egil A. , Kennair Leif Edward Ottesen , Sayadian Annahita , Backelin Caroline , Reme Silje Endresen 
Published: June 2024

AbstractPain, a widespread challenge affecting daily life, is closely linked with psychological and social factors. While pain clearly influences daily function in those affected, the complete extent of its impact is not fully understood. Given the close connection between pain and psychosocial factors, a deeper exploration of these aspects is needed. In this study, we aim to examine the associations between psychosocial factors, pain intensity, and pain-related disability among patients with chronic pain. We used data on 4285 patients from the Oslo University Hospital Pain Registry, and investigated pain-related disability, pain intensity, pain catastrophizing, psychological distress, perceived injustice, insomnia, fatigue, and self-efficacy. We found significant associations between all psychosocial variables and pain-related disability, even after adjusting for demographic factors. In the multiple regression model, sleep problems and pain intensity were identified as primary contributors, alongside psychological distress, and fatigue. Combined, these factors accounted for 26.5% of the variability in pain-related disability, with insomnia and pain intensity exhibiting the strongest associations. While the direction of causation remains unclear, our findings emphasize the potential of interventions aimed at targeting psychosocial factors. Considering the strong link between psychosocial factors and pain-related disability, interventions targeting these factors—particularly insomnia—could reduce disability and enhance quality of life in those who suffer.
Global soil respiration estimation based on ecological big data and machine learning model
Authors: Liu Jiangnan , Hu Junguo , Liu Haoqi , Han Kanglai 
Published: June 2024

AbstractSoil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R2 = 0.78216, root mean squared error (RMSE) = 285.8964 gCm−2y−1, and mean absolute error (MAE) = 180.4186 gCm−2y−1, which was more suitable for the estimation of large-scale soil respiration. In terms of the importance of environmental factors, unlike previous studies, we found that the influence of geographical location was greater than that of MAP. Another new finding was that enhanced vegetation index 2 (EVI2) had a higher contribution to soil respiration estimates than the enhanced vegetation index (EVI) and normalized vegetation index (NDVI). Our results confirm the potential of utilizing ecological big data for spatially large-scale Rs estimations. Ecological big data and machine learning algorithms can be considered to improve the spatial distribution patterns and driver analysis of Rs.
Real-life effectiveness of sacubitril/valsartan in older Belgians with heart failure, reduced ejection fraction and most severe symptoms
Authors: Maury Eléonore , Belmans Ann , Bogaerts Kris , Vancayzeele Stefaan , Jansen Mieke 
Published: June 2024

AbstractWe assessed the real-world effectiveness of sacubitril/valsartan in patients with chronic heart failure (HF) and reduced ejection fraction (HFrEF) with an emphasis on those with older age (≥ 75 years) or with New York Heart Association (NYHA) class IV, for whom greater uncertainty existed regarding clinical outcomes. We conducted a retrospective cohort study based on patient-level linkage of electronic healthcare datasets. Data from all adults with HFrEF in Belgium receiving a prescription for sacubitril/valsartan between 01-November-2016 and 31-December-2018 were collected, with a follow-up of > 6 years. The total study population comprised 5446 patients, older than the PARADIGM-HF trial participants, and with higher NYHA class (all P < 0.0001). NYHA class improved following sacubitril/valsartan initiation (P < 0.0001 baseline vs. reassessment). Most concomitant medications were reduced. Remarkably, the risk of hospitalization for a cardiovascular reason and for HF was reduced by > 26% in the overall cohort, and in subgroups of patients ≥ 75 years, with NYHA class III/IV (all P < 0.0001) or with NYHA class IV (P < 0.05), vs. baseline. All-cause mortality did not increase in real-world patients with NYHA class III/IV. The results support the long-term beneficial effects of sacubitril/valsartan in older patients and in those experiencing the most severe symptoms.
Simultaneous screening of overexpressed genes in breast cancer for oncogenic drivers and tumor dependencies
Authors: Mofunanya Adaobi , Cameron Eleanor R. , Braun Christian J. , Celeste Frank , Zhao Xiaoyu , Hemann Michael T. , Scott Kenneth L. , Li Jinyu , Powers Scott 
Published: June 2024

AbstractThere are hundreds of genes typically overexpressed in breast cancer cells and it's often assumed that their overexpression contributes to cancer progression. However, the precise proportion of these overexpressed genes contributing to tumorigenicity remains unclear. To address this gap, we undertook a comprehensive screening of a diverse set of seventy-two genes overexpressed in breast cancer. This systematic screening evaluated their potential for inducing malignant transformation and, concurrently, assessed their impact on breast cancer cell proliferation and viability. Select genes including ALDH3B1, CEACAM5, IL8, PYGO2, and WWTR1, exhibited pronounced activity in promoting tumor formation and establishing gene dependencies critical for tumorigenicity. Subsequent investigations revealed that CEACAM5 overexpression triggered the activation of signaling pathways involving β-catenin, Cdk4, and mTOR. Additionally, it conferred a growth advantage independent of exogenous insulin in defined medium and facilitated spheroid expansion by inducing multiple layers of epithelial cells while preserving a hollow lumen. Furthermore, the silencing of CEACAM5 expression synergized with tamoxifen-induced growth inhibition in breast cancer cells. These findings underscore the potential of screening overexpressed genes for both oncogenic drivers and tumor dependencies to expand the repertoire of therapeutic targets for breast cancer treatment.
Associations between circadian alignment and cognitive functioning in a nationally representative sample of older adults
Authors: Leahy Sophie , Xiao Qian , Yeung Chris Ho Ching , Figueiro Mariana G. 
Published: June 2024

AbstractProper alignment of activity-rest and light–dark patterns allows for healthy bodily functions to occur at optimal times of the day. Disruptions to this alignment may cause poor sleep as well as physical, mental, and cognitive problems. The purpose of this cross-sectional study was to determine if poorer circadian alignment was associated with decreased cognitive functioning among older (> 60 years) participants in the National Health and Nutrition Examination Survey. We utilized actigraphy-based rest-activity and dark-light measurements to calculate phasor magnitude (strength of circadian alignment coupling) and phasor angle (phase difference between activity-rest and light–dark cycles). Multiple linear regression models were used to determine associations of phasor magnitude and angle with performance in various cognitive tests, including Digit Symbol Substitution Test score (DSSS), CERAD Savings Percentage (CSP), and Animal Fluency Test (AFT) score. The results showed that a lower phasor magnitude (which indicates decreased strength of alignment coupling between rest-activity and dark–light cycles) was significantly associated with decreased DSSS (indicating slower processing speed and poorer working memory) when controlling for many important sociodemographic factors. However, this association became non-significant when accounting for sleep duration and total physical activity. Phasor angle did not have a significant association with any of the cognitive scores. Overall, we provided evidence indicating that circadian alignment may be a predictor of cognitive performance. Future studies should investigate whether improving circadian alignment may improve cognitive function and prevent cognitive decline.