bluuebunny/crossref_metadata_2025_split huggingface dataset:
Endosomal sorting results in a selective separation of the protein corona from nanoparticles
Authors: Han Shen , da Costa Marques Richard , Simon Johanna , Kaltbeitzel Anke , Koynov Kaloian , Landfester Katharina , Mailänder Volker , Lieberwirth Ingo 
Published: January 2023

AbstractThe formation of the protein corona is a well-known effect when nanoparticles (NP) are exposed to biological environments. The protein corona is the most important factor, which determines the rate and route of endocytosis, and decisively impacts cellular processes and even the release of the active pharmaceutical ingredient from the nanoparticles. While many studies concentrate on the effect of the protein corona formation extracellularly or the uptake consequences, little is known about the fate of the protein corona inside of cells. Here, we reconstruct for the first time the separation of the protein corona from the NPs by the cell and their further fate. Ultimately, the NPs and protein corona are separated from each other and end up in morphologically different cellular compartments. The cell directs the NPs towards recycling endosomes, whereas the protein corona gathers in multivesicular bodies. From this, we conclude that the NPs are prepared for subsequent exocytosis, while the protein corona remains in the cell and is finally metabolized there.
Electrolyte engineering via ether solvent fluorination for developing stable non-aqueous lithium metal batteries
Authors: Zhao Yan , Zhou Tianhong , Mensi Mounir , Choi Jang Wook , Coskun Ali 
Published: January 2023

AbstractFluorination of ether solvents is an effective strategy to improve the electrochemical stability of non-aqueous electrolyte solutions in lithium metal batteries. However, excessive fluorination detrimentally impacts the ionic conductivity of the electrolyte, thus limiting the battery performance. Here, to maximize the electrolyte ionic conductivity and electrochemical stability, we introduce the targeted trifluoromethylation of 1,2-dimethoxyethane to produce 1,1,1-trifluoro-2,3-dimethoxypropane (TFDMP). TFDMP is used as a solvent to prepare a 2 M non-aqueous electrolyte solution comprising bis(fluorosulfonyl)imide salt. This electrolyte solution shows an ionic conductivity of 7.4 mS cm–1 at 25 °C, an oxidation stability up to 4.8 V and an efficient suppression of Al corrosion. When tested in a coin cell configuration at 25 °C using a 20 μm Li metal negative electrode, a high mass loading LiNi0.8Co0.1Mn0.1O2-based positive electrode (20 mg cm–2) with a negative/positive (N/P) capacity ratio of 1, discharge capacity retentions (calculated excluding the initial formation cycles) of 81% after 200 cycles at 0.1 A g–1 and 88% after 142 cycles at 0.2 A g–1 are achieved.
Stem cell–nanomedicine system as a theranostic bio-gadolinium agent for targeted neutron capture cancer therapy
Authors: Lai Yen-Ho , Su Chia-Yu , Cheng Hung-Wei , Chu Chao-Yi , Jeng Long-Bin , Chiang Chih-Sheng , Shyu Woei-Cherng , Chen San-Yuan 
Published: January 2023

AbstractThe potential clinical application of gadolinium-neutron capture therapy (Gd-NCT) for glioblastoma multiforme (GBM) treatment has been compromised by the fast clearance and nonspecific biodistribution of gadolinium-based agents. We have developed a stem cell–nanoparticle system (SNS) to actively target GBM for advanced Gd-NCT by magnetizing umbilical cord mesenchymal stem cells (UMSCs) using gadodiamide-concealed magnetic nanoparticles (Gd-FPFNP). Nanoformulated gadodiamide shielded by a dense surface composed of fucoidan and polyvinyl alcohol demonstrates enhanced cellular association and biocompatibility in UMSCs. The SNS preserves the ability of UMSCs to actively penetrate the blood brain barrier and home to GBM and, when magnetically navigates by an external magnetic field, an 8-fold increase in tumor-to-blood ratio is achieved compared with clinical data. In an orthotopic GBM-bearing rat model, using a single dose of irradiation and an ultra-low gadolinium dose (200 μg kg−1), SNS significantly attenuates GBM progression without inducing safety issues, prolonging median survival 2.5-fold compared to free gadodiamide. The SNS is a cell-based delivery system that integrates the strengths of cell therapy and nanotechnology, which provides an alternative strategy for the treatment of brain diseases.
Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST
Authors: Liu Wei , Liao Xu , Luo Ziye , Yang Yi , Lau Mai Chan , Jiao Yuling , Shi Xingjie , Zhai Weiwei , Ji Hongkai , Yeong Joe , Liu Jin 
Published: January 2023

AbstractSpatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.
DNA methylation analysis explores the molecular basis of plasma cell-free DNA fragmentation
Authors: An Yunyun , Zhao Xin , Zhang Ziteng , Xia Zhaohua , Yang Mengqi , Ma Li , Zhao Yu , Xu Gang , Du Shunda , Wu Xiang’an , Zhang Shuowen , Hong Xin , Jin Xin , Sun Kun 
Published: January 2023

AbstractPlasma cell-free DNA (cfDNA) are small molecules generated through a non-random fragmentation procedure. Despite commendable translational values in cancer liquid biopsy, however, the biology of cfDNA, especially the principles of cfDNA fragmentation, remains largely elusive. Through orientation-aware analyses of cfDNA fragmentation patterns against the nucleosome structure and integration with multidimensional functional genomics data, here we report a DNA methylation – nuclease preference – cutting end – size distribution axis, demonstrating the role of DNA methylation as a functional molecular regulator of cfDNA fragmentation. Hence, low-level DNA methylation could increase nucleosome accessibility and alter the cutting activities of nucleases during DNA fragmentation, which further leads to variation in cutting sites and size distribution of cfDNA. We further develop a cfDNA ending preference-based metric for cancer diagnosis, whose performance has been validated by multiple pan-cancer datasets. Our work sheds light on the molecular basis of cfDNA fragmentation towards broader applications in cancer liquid biopsy.
Effect of surface termination on the balance between friction and failure of Ti3C2Tx MXenes
Authors: Yang Quanpeng , Eder Stefan J. , Martini Ashlie , Grützmacher Philipp G. 
Published: January 2023

AbstractReactive molecular dynamics simulations of Ti3C2Tx with three different surface terminations were used to understand friction and failure of MXenes during sliding at normal pressures from 2–20 GPa and temperatures from 300–1100 K. The O-terminated MXene had the smallest shear stress at low pressures and temperatures, but failed at more severe conditions due to interlayer bonding and the formation of Ti–O–Ti bridges between MXene layers. Failure was not observed for the OH-terminated MXene or a heterostructure combining O- and OH-terminations. For these, at less severe operating conditions, shear stress was smaller for the OH-terminated MXene, while the opposite was observed at higher temperatures and pressures. These trends were explained in terms of adhesion and the complex effect of hydrogen atoms that can either facilitate or hinder sliding, depending on the termination and conditions. Results show that friction and failure are affected by and potentially tunable via MXene surface termination.
Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach
Authors: Seo Jae Won , Park Suyoung , Kim Young Jae , Hwang Jung Han , Yu Sung Hyun , Kim Jeong Ho , Kim Kwang Gi 
Published: January 2023

AbstractEarly diagnosis of deep venous thrombosis is essential for reducing complications, such as recurrent pulmonary embolism and venous thromboembolism. There are numerous studies on enhancing efficiency of computer-aided diagnosis, but clinical diagnostic approaches have never been considered. In this study, we evaluated the performance of an artificial intelligence (AI) algorithm in the detection of iliofemoral deep venous thrombosis on computed tomography angiography of the lower extremities to investigate the effectiveness of using the clinical approach during the feature extraction process of the AI algorithm. To investigate the effectiveness of the proposed method, we created synthesized images to consider practical diagnostic procedures and applied them to the convolutional neural network-based RetinaNet model. We compared and analyzed the performances based on the model’s backbone and data. The performance of the model was as follows: ResNet50: sensitivity = 0.843 (± 0.037), false positives per image = 0.608 (± 0.139); ResNet152 backbone: sensitivity = 0.839 (± 0.031), false positives per image = 0.503 (± 0.079). The results demonstrated the effectiveness of the suggested method in using computed tomography angiography of the lower extremities, and improving the reporting efficiency of the critical iliofemoral deep venous thrombosis cases.
Micro-structure and morphology of tailings sand under different oxidation and acidification degree
Authors: Wang Gang , Liu Xiqi , Song Leibo , Ma Xiaoming , Chen Wenzhao , Qiao Jiaxing 
Published: January 2023

AbstractThe tailings pond is a dangerous source of man-made debris flow with high potential energy. The oxidative acidification of tailings may cause the instability of the pond and induce serious safety accidents. The influence of oxidation and acidification degree on macro mechanical properties of tailings is discussed from the aspects of mineral composition and microstructure. The results show that as the degree of oxidation and acidification of tailings sand increases, the overall structural performance and load-bearing capacity decrease, and its cohesion (c) and internal friction angle (φ) show a decreasing trend. In fact, the engineering properties of tailings with different oxidation and acidification degrees are dominated by the physicochemical composition and structural characteristics. On the one hand, as the degree of oxidation increases, acidic substance will neutralize with CaCO3 and CaMg(CO3)2, resulting in the loss of cemented substance and the decrease of cementation force between tailing sand particles as well as the gradual destruction of the integrity of tailing sand. On the other hand, the increase of oxidation and acidification degree of tailing sand leads to a gradual reduction of outline (2D) fractal dimension and gray surface (3D) fractal dimension of surface laminated structure as well as the obvious reduction of laminated structure and its roughness of tailings sand.
Artificial intelligence-based locoregional markers of brain peritumoral microenvironment
Authors: Riahi Samani Zahra , Parker Drew , Akbari Hamed , Wolf Ronald L. , Brem Steven , Bakas Spyridon , Verma Ragini 
Published: January 2023

AbstractIn malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients’ survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status (t test, Wilcoxon rank sum test, linear regression; p < 0.01). Clustering of patients using the proposed markers reveals distinct survival groups (logrank; p < 10−5, Cox hazard ratio = 1.82; p < 0.005). Our findings provide a panel of markers as surrogates of infiltration that might capture novel insight about underlying biology of peritumoral microstructural heterogeneity, providing potential biomarkers of prognosis pertaining to survival and molecular stratification, with applicability in clinical decision making.
Characteristics of immune clusters and cell abundance in patients with different subtypes of nonparoxysmal atrial fibrillation
Authors: Ying Hangying , Guo Wenpu , Yu Pengcheng , Qiu Hangyuan , Jiang Ruhong , Jiang Chenyang 
Published: January 2023

AbstractAtrial fibrillation (AF) is the most common sustained arrhythmia in clinical practice. Inflammation plays an important role in the initiation and perpetuation of AF. The present study was conducted to characterize immune clusters in nonparoxysmal AF and to distinguish immune subtypes of nonparoxysmal AF. Immune-related algorithms (CIBERSORT, ESTIMATE, and ssGSEA) were used to evaluate the immune cluster characterization and cell abundance, and multivariable logistics analysis was performed to determine the most relevant immune cells. We identified differentially expressed genes (DEGs) and used consensus clustering analysis to identify nonparoxysmal AF subtypes. Weighted gene coexpression network analysis (WGCNA) was used for finding highly correlated gene sets and attach to external sample traits. And it was conducted twice to identify the immune- and subtype- related modules. Finally, Metascape was used to compare the biological functions of the two nonparoxysmal AF subtypes we obtained. CytoHubba was used to identify the hub genes of these two subtypes. Based on the results of bioinformatics analysis, regulatory T cells, resting NK cells, active mast cells and neutrophils were considered to be closely related to nonparoxysmal AF. The brown module was identified as the most relevant module to the above immune cells by WGCNA. We identified two major nonparoxysmal AF subtypes by consensus clustering analysis and their enriched biological functions by Metascape. The hub genes are TYROBP, PTPRC, ITGB2, SPI1, PLEK, and CSF1R in permanent AF and JAM3, S100P, ARPC5, TRIM34, and GREB1L in persistent AF. This study revealed two major nonparoxysmal AF subtypes and eleven hub genes, which provide potential therapeutic targets for anti-inflammatory treatments of nonparoxysmal AF.
In-silico study of asymmetric remodeling of tumors in response to external biochemical stimuli
Authors: Amereh Meitham , Akbari Mohsen , Nadler Ben 
Published: January 2023

AbstractAmong different hallmarks of cancer, understanding biomechanics of tumor growth and remodeling benefits the most from the theoretical framework of continuum mechanics. Tumor remodeling initiates when cancer cells seek new homeostasis in response to the microenvironmental stimuli. Cells within a growing tumor are capable to remodel their inter- and intra-connections and become more mobile to achieve a new homeostasis. This mobility enables the tumor to undergo large deformation. In this work, we studied the remodeling of homogeneous tumors, at their early stage of growth, in the context of continuum mechanics. We developed an evolution law for the remodeling-associated deformation which correlates the remodeling to a characteristic tensor of external stimuli. The asymmetric remodeling and the induced mechanical stresses were analyzed for different types of biochemical distributions. To experimentally investigate the model, we studied the remodeling of human glioblastoma (hGB) tumoroids in response to the gradient of nutrients. Using a tumoroid-on-a-chip platform, the degree of remodeling was estimated for the ellipsoidal tumoroids over time. It was observed that higher gradient of nutrients induces higher degree of ellipticity suggesting that the gradient of nutrient is a characteristic property of nutrient distribution that derives the remodeling. We also showed that remodeling gives rise to heterogeneity in cell distribution forming circumferentially aligned cells within the tumors. Compared to the existing studies on tumor growth, our work provides a biomechanical module that relates the remodeling to biochemical stimuli, and allows for large deformation. It also includes experimental component, a necessary but challenging step, that connects the theory and reality to evaluate the practicability of the model.
Longitudinal individual predictions from irregular repeated measurements data
Authors: Eekhout Iris , van Buuren Stef , Visser Bram , Bink Marco C. A. M. , Huisman Abe 
Published: January 2023

AbstractIntensive longitudinal data can be used to explore important associations and patterns between various types of inputs and outcomes. Nonlinear relations and irregular measurement occasions can pose problems to develop an accurate model for these kinds of data. This paper focuses on the development, fitting and evaluation of a prediction model with irregular intensive longitudinal data. A three-step process for developing a prediction tool for (daily) monitoring and prediction is outlined and illustrated for intensive weight measurements in piglets. Step 1 addresses a nonlinear relation in the data by developing and applying a normalizing transformation. Step 2 addresses the intermittent nature of the time points by aligning the measurement times to a common time grid with a broken-stick model. Step 3 addresses the prediction problem by selecting and evaluating inputs and covariates in the model to obtain accurate predictions. The final model predicts future outcomes accurately, while allowing for nonlinearities between input and output and for different measurement histories of individuals. The methodology described can be used to develop a tool to deal with intensive irregular longitudinal data that uses the available information in an optimal way. The resulting tool demonstrated to perform well for piglet weight prediction and can be adapted to many different applications.
Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems
Authors: Sahoo Karam Kumar , Ghosh Raghunath , Mallik Saurav , Roy Arup , Singh Pawan Kumar , Zhao Zhongming 
Published: January 2023

AbstractThe Human Activity Recognition (HAR) problem leverages pattern recognition to classify physical human activities as they are captured by several sensor modalities. Remote monitoring of an individual’s activities has gained importance due to the reduction in travel and physical activities during the pandemic. Research on HAR enables one person to either remotely monitor or recognize another person’s activity via the ubiquitous mobile device or by using sensor-based Internet of Things (IoT). Our proposed work focuses on the accurate classification of daily human activities from both accelerometer and gyroscope sensor data after converting into spectrogram images. The feature extraction process follows by leveraging the pre-trained weights of two popular and efficient transfer learning convolutional neural network models. Finally, a wrapper-based feature selection method has been employed for selecting the optimal feature subset that both reduces the training time and improves the final classification performance. The proposed HAR model has been tested on the three benchmark datasets namely, HARTH, KU-HAR and HuGaDB and has achieved 88.89%, 97.97% and 93.82% respectively on these datasets. It is to be noted that the proposed HAR model achieves an improvement of about 21%, 20% and 6% in the overall classification accuracies while utilizing only 52%, 45% and 60% of the original feature set for HuGaDB, KU-HAR and HARTH datasets respectively. This proves the effectiveness of our proposed wrapper-based feature selection HAR methodology.
Distinct components of cardiovascular health are linked with age-related differences in cognitive abilities
Authors: King Deborah L. O. , Henson Richard N. , Kievit Rogier , Wolpe Noham , Brayne Carol , Tyler Lorraine K. , Rowe James B. ,   Cam-CAN, Bullmore Edward T. , Calder Andrew C. , Cusack Rhodri , Dalgleish Tim , Duncan John , Matthews Fiona E. , Marslen-Wilson William D. , Shafto Meredith A. , Campbell Karen , Cheung Teresa , Davis Simon , Geerligs Linda , McCarrey Anna , Mustafa Abdur , Price Darren , Samu David , Taylor Jason R. , Treder Matthias , van Belle Janna , Williams Nitin , Mitchell Daniel , Fisher Simon , Eising Else , Knights Ethan , Bates Lauren , Emery Tina , Erzinçlioglu Sharon , Gadie Andrew , Gerbase Sofia , Georgieva Stanimira , Hanley Claire , Parkin Beth , Troy David , Auer Tibor , Correia Marta , Gao Lu , Green Emma , Henriques Rafael , Allen Jodie , Amery Gillian , Amuntse Liana , Barcroft Anne , Castle Amanda , Dias Cheryl , Dowrick Jonathan , Fair Melissa , Fisher Hayley , Goulding Anna , Grewale Adarsh , Hale Geoff , Hilton Andrew , Johnson Frances , Johnston Patricia , Kavanagh-Williamson Thea , Kwasniewska Magdalena , McMinn Alison , Norman Kim , Penrose Jessica , Roby Fiona , Rowland Diane , Sargeant John , Squire Maggie , Stevens Beth , Stoddart Aldabra , Stone Cheryl , Thompson Tracy , Yazlik Ozlem , Barnes Dan , Dixon Marie , Hillman Jaya , Mitchell Joanne , Villis Laura , Tsvetanov Kamen A. 
Published: January 2023

AbstractCardiovascular ageing contributes to cognitive impairment. However, the unique and synergistic contributions of multiple cardiovascular factors to cognitive function remain unclear because they are often condensed into a single composite score or examined in isolation. We hypothesized that vascular risk factors, electrocardiographic features and blood pressure indices reveal multiple latent vascular factors, with independent contributions to cognition. In a population-based deep-phenotyping study (n = 708, age 18–88), path analysis revealed three latent vascular factors dissociating the autonomic nervous system response from two components of blood pressure. These three factors made unique and additive contributions to the variability in crystallized and fluid intelligence. The discrepancy in fluid relative to crystallized intelligence, indicative of cognitive decline, was associated with a latent vascular factor predominantly expressing pulse pressure. This suggests that higher pulse pressure is associated with cognitive decline from expected performance. The effect was stronger in older adults. Controlling pulse pressure may help to preserve cognition, particularly in older adults. Our findings highlight the need to better understand the multifactorial nature of vascular aging.