bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal method for reliable lateral spread response monitoring during microvascular decompression surgery for hemifacial spasm
Authors: Cho Kyung Rae , Lee Hyun-Seok , Kim Minsoo , Park Sang-Ku , Park Kwan 
Published: December 2023

AbstractIn this study, we propose an optimal method for monitoring the key electrophysiological sign, the Lateral Spread Response (LSR), during microvascular decompression (MVD) surgery for hemifacial spasm (HFS). Current monitoring methods and interpretations of LSR remain unclear, leading to potential misinterpretations and undesirable outcomes." We prospectively collected data from patients undergoing MVD for HFS, including basic demographics, clinical characteristics, and surgical outcomes. Stimulation intensity was escalated by 1 mA increments to identify the optimal range for effective LSR. We designated the threshold at which we can observe LSR as THR1 and THR2 for when LSR disappears, with high-intensity stimulation (30 mA) designated as THR30. Subsequently, we compared abnormal muscle responses (AMR) between the optimal range (between THR1 and THR2) and THR30. Additionally, we conducted an analysis to identify and assess factors associated with artifacts and their potential impact on clinical outcomes. As stimulation intensity increases, the onset latency to detect AMR was shortened. The first finding of the study was high intensity stimulation caused artifact that mimic the wave of LSR. Those artifacts were observed even after decompression thus interfere interpretation of disappearance of LSR. Analyzing the factors related to the artifact, we found the AMR detected at onset latency below 9.6 ms would be the lateral spreading artifact (LSA) rather than true LSR. To avoid false positive LSR from LSA, we should stepwise increase stimulation intensity and not to surpass the intensity that cause LSR onset latency below 10 ms.
Lipid species profiling of bronchoalveolar lavage fluid cells of horses housed on two different bedding materials
Authors: Mönki Jenni , Holopainen Minna , Ruhanen Hanna , Karikoski Ninja , Käkelä Reijo , Mykkänen Anna 
Published: December 2023

AbstractThe lipidome of equine BALF cells has not been described. The objectives of this prospective repeated-measures study were to explore the BALF cells’ lipidome in horses and to identify lipids associated with progression or resolution of airway inflammation. BALF cells from 22 horses exposed to two bedding materials (Peat 1—Wood shavings [WS]—Peat 2) were studied by liquid chromatography-tandem mass spectrometry (LC–MS/MS). The effects of bedding on lipid class and species compositions were tested with rmANOVA. Correlations between lipids and cell counts were examined. The BALF cells’ lipidome showed bedding-related differences for molar percentage (mol%) of 60 species. Whole phosphatidylcholine (PC) class and its species PC 32:0 (main molecular species 16:0_16:0) had higher mol% after Peat 2 compared with WS. Phosphatidylinositol 38:4 (main molecular species 18:0_20:4) was higher after WS compared with both peat periods. BALF cell count correlated positively with mol% of the lipid classes phosphatidylserine, sphingomyelin, ceramide, hexosylceramide, and triacylglycerol but negatively with PC. BALF cell count correlated positively with phosphatidylinositol 38:4 mol%. In conclusion, equine BALF cells’ lipid profiles explored with MS-based lipidomics indicated subclinical inflammatory changes after WS. Inflammatory reactions in the cellular lipid species composition were detected although cytological responses indicating inflammation were weak.
The effect of uneven surfaces on inter-joint coordination during walking in children with cerebral palsy
Authors: Dussault-Picard C. , Cherni Y. , Ferron A. , Robert M. T. , Dixon P. C. 
Published: December 2023

AbstractClinical gait analysis on uneven surfaces contributes to the ecological assessment of gait deviations of children with spastic cerebral palsy (CP). Walking on uneven surfaces requires specific motor strategies, which can be assessed by lower-limb kinematic and inter-joint coordination analyses. This study aimed to assess and compare kinematics and inter-joint coordination between children with CP and their typically developing (TD) peers when walking on even and two levels of uneven surfaces (medium and high). A total of 17 children with CP and 17 TD children (11.5 ± 3.5 and 10.4 ± 4.5 years old, respectively) were asked to complete 6–8 gait trials on a 4-m walkway of each surface (n = 3) in randomized blocks while fit with retro-reflective markers on their lower-limbs. Children with CP showed proximal gait adaptations (i.e., hip and knee) on uneven surfaces. Compared with the TD group, the CP group showed decreased hip extension during late stance (49–63%, d = 0.549, p < 0.001), and a more in-phase knee-hip coordination strategy during swing phase (75–84% of gait cycle, d = 1.035, p = 0.029 and 92–100%, d = 1.091, p = 0.030) when walking on an uneven (high), compared to even surface. This study provides a better understanding of kinematic strategies employed by children with spastic CP when facing typical daily life gait challenges. Further studies are needed to evaluate the benefits of integrating uneven surfaces in rehabilitation care.
Vectorial characterization of surface wave via one-dimensional photonic-atomic structure
Authors: Salmanpour M. Asadolah , Mosleh M. , Hamidi S. M. 
Published: December 2023

AbstractQuantitative assessment of polarization properties of waves opens up the way for effective exploitation of them in many amazing applications. Tamm surface waves (TSW) that propagate on the interface of periodic dielectric media are proposed for many applications in numerous reports. The polarization state of TSW is not simply intuitive and would not be extracted from reflection spectra. Here considering orientation sensitive nature of the interaction between polarized electromagnetic wave and atom, we try to quantitatively characterize the polarization state of TSWs, excited on the surface of the 1D photonic crystal. To do this we performed direct contact between TSW and rubidium atomic gas by fabrication of a one-dimensional photonic crystal-atomic vapor cell and applied a moderate external magnetic field to create geometrical meaning and a sense of directionality to dark lines in reflection intensity. Our experimental results indicate that transition lines in the reflection spectrum of our hybrid system modify dependent on the orientation of the applied magnetic field and the transverse spin of TSW. We have used these changes to redefine the geometry of Voigt and Faraday for evanescent waves, especially Tamm surface waves.In the end, we performed simple mathematical operations on absorption spectra and extract the ratio of longitudinal and transverse electric field components of the polarization vector of TSW equal to$$\frac{2}{5}$$25.
Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia
Authors: Krali Olga , Marincevic-Zuniga Yanara , Arvidsson Gustav , Enblad Anna Pia , Lundmark Anders , Sayyab Shumaila , Zachariadis Vasilios , Heinäniemi Merja , Suhonen Janne , Oksa Laura , Vepsäläinen Kaisa , Öfverholm Ingegerd , Barbany Gisela , Nordgren Ann , Lilljebjörn Henrik , Fioretos Thoas , Madsen Hans O. , Marquart Hanne Vibeke , Flaegstad Trond , Forestier Erik , Jónsson Ólafur G. , Kanerva Jukka , Lohi Olli , Norén-Nyström Ulrika , Schmiegelow Kjeld , Harila Arja , Heyman Mats , Lönnerholm Gudmar , Syvänen Ann-Christine , Nordlund Jessica 
Published: December 2023

AbstractGenomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification of ALL in retrospective cohorts as well as for up-front diagnostics. ALLIUM uses DNA methylation and gene expression data from 1131 Nordic ALL patients to predict 17 ALL subtypes with high accuracy. ALLIUM was used to revise and verify the molecular subtype of 281 B-cell precursor ALL (BCP-ALL) cases with previously undefined molecular phenotype, resulting in a single revised subtype for 81.5% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides a comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries.
Single-organoid analysis reveals clinically relevant treatment-resistant and invasive subclones in pancreatic cancer
Authors: Le Compte Maxim , De La Hoz Edgar Cardenas , Peeters Sofía , Fortes Felicia Rodrigues , Hermans Christophe , Domen Andreas , Smits Evelien , Lardon Filip , Vandamme Timon , Lin Abraham , Vanlanduit Steve , Roeyen Geert , Van Laere Steven , Prenen Hans , Peeters Marc , Deben Christophe 
Published: December 2023

AbstractPancreatic ductal adenocarcinoma (PDAC) is one of the most lethal diseases, characterized by a treatment-resistant and invasive nature. In line with these inherent aggressive characteristics, only a subset of patients shows a clinical response to the standard of care therapies, thereby highlighting the need for a more personalized treatment approach. In this study, we comprehensively unraveled the intra-patient response heterogeneity and intrinsic aggressive nature of PDAC on bulk and single-organoid resolution. We leveraged a fully characterized PDAC organoid panel (N = 8) and matched our artificial intelligence-driven, live-cell organoid image analysis with retrospective clinical patient response. In line with the clinical outcomes, we identified patient-specific sensitivities to the standard of care therapies (gemcitabine-paclitaxel and FOLFIRINOX) using a growth rate-based and normalized drug response metric. Moreover, the single-organoid analysis was able to detect resistant as well as invasive PDAC organoid clones, which was orchestrates on a patient, therapy, drug, concentration and time-specific level. Furthermore, our in vitro organoid analysis indicated a correlation with the matched patient progression-free survival (PFS) compared to the current, conventional drug response readouts. This work not only provides valuable insights on the response complexity in PDAC, but it also highlights the potential applications (extendable to other tumor types) and clinical translatability of our approach in drug discovery and the emerging era of personalized medicine.
A breast cancer classification and immune landscape analysis based on cancer stem-cell-related risk panel
Authors: Hu Haihong , Zou Mingxiang , Hu Hongjuan , Hu Zecheng , Jiang Lingxiang , Escobar David , Zhu Hongxia , Zhan Wendi , Yan Ting , Zhang Taolan 
Published: December 2023

AbstractThis study sought to identify molecular subtypes of breast cancer (BC) and develop a breast cancer stem cells (BCSCs)-related gene risk score for predicting prognosis and assessing the potential for immunotherapy. Unsupervised clustering based on prognostic BCSC genes was used to determine BC molecular subtypes. Core genes of BC subtypes identified by non-negative matrix factorization algorithm (NMF) were screened using weighted gene co-expression network analysis (WGCNA). A risk model based on prognostic BCSC genes was constructed using machine learning as well as LASSO regression and multivariate Cox regression. The tumor microenvironment and immune infiltration were analyzed using ESTIMATE and CIBERSORT, respectively. A CD79A+CD24-PANCK+-BCSC subpopulation was identified and its spatial relationship with microenvironmental immune response state was evaluated by multiplexed quantitative immunofluorescence (QIF) and TissueFAXS Cytometry. We identified two distinct molecular subtypes, with Cluster 1 displaying better prognosis and enhanced immune response. The constructed risk model involving ten BCSC genes could effectively stratify patients into subgroups with different survival, immune cell abundance, and response to immunotherapy. In subsequent QIF validation involving 267 patients, we demonstrated the existence of CD79A+CD24-PANCK+-BCSC in BC tissues and revealed that this BCSC subtype located close to exhausted CD8+FOXP3+ T cells. Furthermore, both the densities of CD79A+CD24-PANCK+-BCSCs and CD8+FOXP3+T cells were positively correlated with poor survival. These findings highlight the importance of BCSCs in prognosis and reshaping the immune microenvironment, which may provide an option to improve outcomes for patients.
Post-crotonylation oxidation by a monooxygenase promotes acetyl-CoA synthetase degradation in Streptomyces roseosporus
Authors: Ma Bing-Bing , Sun Chen-Fan , Zhou Jing-Yi , Gu Shuai-Lei , Dai Xin-Yi , Chen Yan-Zhen , Zhao Qing-Wei , Mao Xu-Ming 
Published: December 2023

AbstractProtein post-translational modifications (PTMs) with various acyl groups play central roles in Streptomyces. But whether these acyl groups can be further modified, and the influences of these potential modifications on bacterial physiology have not been addressed. Here in Streptomyces roseosporus with rich crotonylation, a luciferase monooxygenase LimB is identified to elaborately regulate the crotonylation level, morphological development and antibiotic production by oxidation on the crotonyl groups of an acetyl-CoA synthetase Acs. This chemical modification on crotonylation leads to Acs degradation via the protease ClpP1/2 pathway and lowered intracellular crotonyl-CoA pool. Thus, we show that acyl groups after PTMs can be further modified, herein named post-PTM modification (PPM), and LimB is a PTM modifier to control the substrate protein turnover for cell development of Streptomyces. These findings expand our understanding of the complexity of chemical modifications on proteins for physiological regulation, and also suggest that PPM would be widespread.
A multi-sensor approach for increased measurements of floods and their societal impacts from space
Authors: Munasinghe Dinuke , Frasson Renato Prata de Moraes , David Cédric H. , Bonnema Matthew , Schumann Guy , Brakenridge G. Robert 
Published: December 2023

AbstractMerging observations from multiple satellites is necessary to ensure that extreme hydrological events are consistently observed. Here, we evaluate the potential improvements to flood detectability afforded by combining data collected globally by Landsat, Sentinel-2, and Sentinel-1. The enhanced temporal sampling increased the number of floods with at least 1 useful image (≤20% clouds) from 7% for single sensors to up to 66% for a potential multi-sensor product. As dramatic as the increased coverage is, the socioeconomic impacts are even more tangible. In the pre-Sentinel era, only 22% of the total population displaced by flood events benefitted from having high-resolution images, whereas a potential multi-sensor product would serve 75% of the displaced population. Additionally, the merged dataset could observe up to 100% of floods caused by challenging drivers, e.g., tropical cyclones, tidal surges, including those rarely seen by single sensors, and thereby enable insights into governing mechanisms of these events.
Robotic right ventricle is a biohybrid platform that simulates right ventricular function in (patho)physiological conditions and intervention
Authors: Singh Manisha , Bonnemain Jean , Ozturk Caglar , Ayers Brian , Saeed Mossab Y. , Quevedo-Moreno Diego , Rowlett Meagan , Park Clara , Fan Yiling , Nguyen Christopher T. , Roche Ellen T. 
Published: December 2023

AbstractThe increasing recognition of the right ventricle (RV) necessitates the development of RV-focused interventions, devices and testbeds. In this study, we developed a soft robotic model of the right heart that accurately mimics RV biomechanics and hemodynamics, including free wall, septal and valve motion. This model uses a biohybrid approach, combining a chemically treated endocardial scaffold with a soft robotic synthetic myocardium. When connected to a circulatory flow loop, the robotic right ventricle (RRV) replicates real-time hemodynamic changes in healthy and pathological conditions, including volume overload, RV systolic failure and pressure overload. The RRV also mimics clinical markers of RV dysfunction and is validated using an in vivo porcine model. Additionally, the RRV recreates chordae tension, simulating papillary muscle motion, and shows the potential for tricuspid valve repair and replacement in vitro. This work aims to provide a platform for developing tools for research and treatment for RV pathophysiology.
Nano scale instance-based learning using non-specific hybridization of DNA sequences
Authors: Su Yanqing , Lin Wanmin , Chu Ling , Zan Xiangzhen , Xu Peng , Zhang Fengyue , Liu Bo , Liu Wenbin 
Published: December 2023

AbstractDNA, or deoxyribonucleic acid, is a powerful molecule that plays a fundamental role in storing and processing genetic information of all living organisms. In recent years, scientists have harnessed hybridization powers between DNA molecules to perform various computing tasks in DNA computing and DNA storage. Unlike specific hybridization, non-specific hybridization provides a natural way to measure similarity between the objects represented by different DNA sequences. We utilize such property to build an instance-based learning model which recognizes an object by its similarity with other samples. The handwriting digit images in MNIST dataset are encoded by DNA sequences using a deep learning encoder. And the reverse complement sequence of a query image is used to hybridize with the training instance sequences. Simulation results by NUPACK show that this classification model by DNA could achieve 95% accuracy on average. Wet-lab experiments also validate the predicted yield is consistent with the hybridization strength. Our work proves that it is feasible to build an effective instance-based classification model for practical application.
Cutoff wavenumber analysis of arbitrarily shaped waveguides using regularized method of fundamental solutions with excitation sources
Authors: Yan Tingting , He Di , Xiong Xia , Hu Shengbo , Hu Bo 
Published: December 2023

AbstractThe method of fundamental solutions with excitation source (MFS‐ES) is a reliable method for determining the eigenvalues of a two‐dimensional hollow waveguide. However, the accuracy in MFS‐ES is extremely dependent on the choice of the auxiliary boundary. In this work, to avoid the problem, a regularized MFS‐ES (RMFS‐ES) is proposed. First, to overcome the shortcomings of MFS, a regularized method of fundamental Solutions (RMFS) is proposed by borrowing the ideas of singular boundary method (SBM) and single layer regularized meshless method (SRMM). Second, the expressions for the fundamental solutions at the singularities are given by utilizing the subtraction and adding‐back technique (SAB). Third, the excitation source method is introduced to overcome the problem that the RMFS may also have spurious frequencies. Finally, to improve the accuracy of RMFS‐ES in solving polygonal waveguide eigenvalue, a wedge scheme (WS) is given. Numerical results show that the meshless RMFS‐ES is a promising numerical method for waveguide eigenvalue problems.
Real‐time surgical tool detection with multi‐scale positional encoding and contrastive learning
Authors: Loza Gerardo , Valdastri Pietro , Ali Sharib 
Published: December 2023

AbstractReal‐time detection of surgical tools in laparoscopic data plays a vital role in understanding surgical procedures, evaluating the performance of trainees, facilitating learning, and ultimately supporting the autonomy of robotic systems. Existing detection methods for surgical data need to improve processing speed and high prediction accuracy. Most methods rely on anchors or region proposals, limiting their adaptability to variations in tool appearance and leading to sub‐optimal detection results. Moreover, using non‐anchor‐based detectors to alleviate this problem has been partially explored without remarkable results. An anchor‐free architecture based on a transformer that allows real‐time tool detection is introduced. The proposal is to utilize multi‐scale features within the feature extraction layer and at the transformer‐based detection architecture through positional encoding that can refine and capture context‐aware and structural information of different‐sized tools. Furthermore, a supervised contrastive loss is introduced to optimize representations of object embeddings, resulting in improved feed‐forward network performances for classifying localized bounding boxes. The strategy demonstrates superiority to state‐of‐the‐art (SOTA) methods. Compared to the most accurate existing SOTA (DSSS) method, the approach has an improvement of nearly 4% on mAP and a reduction in the inference time by 113%. It also showed a 7% higher mAP than the baseline model.
Dual‐granularity feature fusion in visible‐infrared person re‐identification
Authors: Cai Shuang , Yang Shanmin , Hu Jing , Wu Xi 
Published: December 2023

AbstractVisible‐infrared person re‐identification (VI‐ReID) aims to recognize images of the same person captured in different modalities. Existing methods mainly focus on learning single‐granularity representations, which have limited discriminability and weak robustness. This paper proposes a novel dual‐granularity feature fusion network for VI‐ReID. Specifically, a dual‐branch module that extracts global and local features and then fuses them to enhance the representative ability is adopted. Furthermore, an identity‐aware modal discrepancy loss that promotes modality alignment by reducing the gap between features from visible and infrared modalities is proposed. Finally, considering the influence of non‐discriminative information in the modal‐shared features of RGB‐IR, a greyscale conversion is introduced to extract modality‐irrelevant discriminative features better. Extensive experiments on the SYSU‐MM01 and RegDB datasets demonstrate the effectiveness of the framework and superiority over state‐of‐the‐art methods.
Extracting long‐term spatiotemporal characteristics of traffic flow using attention‐based convolutional transformer
Authors: Sattarzadeh Ali Reza , Pathirana Pubudu N. , Kutadinata Ronny , Huynh Van Thanh 
Published: December 2023

AbstractPredicting traffic flow is vital for optimizing transportation efficiency, reducing fuel consumption, and minimizing commute times. While artificial intelligence tools have been effective in addressing this, there have been some difficulties in processing spatial and temporal data. Current transformer‐based methods, although cutting‐edge for traffic prediction, encounter challenges with handling long sequences and capturing temporal relations effectively. Addressing these, the research introduces a model combining multi‐scale attention modules within transformer layers. This model employs spatio‐temporal transformer blocks, enriched with multi‐scale convolutional attention mechanisms, allowing for a deeper understanding of temporal and spatial traffic patterns. This unique attention mechanism enhances data feature interpretation, leading to heightened prediction precision. The tests on extensive traffic datasets showcase the model's prowess in capturing both local and global traffic features, resulting in superior traffic status predictions. In summary, the innovative model offers an efficacious approach to long‐sequence traffic data learning and temporal relationship extraction, setting a new benchmark in traffic flow prediction accuracy.