bluuebunny/crossref_metadata_2025_split huggingface dataset:
Music therapy with adult burn patients in the intensive care unit: short-term analysis of electrophysiological signals during music-assisted relaxation
Authors: Cordoba-Silva Jose , Maya Rafael , Valderrama Mario , Giraldo Luis Felipe , Betancourt-Zapata William , Salgado-Vasco Andrés , Marín-Sánchez Juliana , Gómez-Ortega Viviana , Ettenberger Mark 
Published: October 2024

AbstractBurn patients often face elevated pain, anxiety, and depression levels. Music therapy adds to integrative care in burn patients, but research including electrophysiological measures is limited. This study reports electrophysiological signals analysis during Music-Assisted Relaxation (MAR) with burn patients in the Intensive Care Unit (ICU). This study is a sub-analysis of an ongoing trial of music therapy with burn patients in the ICU. Electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) were recorded during MAR with nine burn patients. Additionally, background pain levels (VAS) and anxiety and depression levels (HADS) were assessed. EEG oscillation power showed statistically significant changes in the delta (p < 0.05), theta (p = 0.01), beta (p < 0.05), and alpha (p = 0.05) bands during music therapy. Heart rate variability tachograms high-frequencies increased (p = 0.014), and low-frequencies decreased (p = 0.046). Facial EMG mean frequency decreased (p = 0.01). VAS and HADS scores decreased − 0.76 (p = 0.4) and − 3.375 points (p = 0.37) respectively. Our results indicate parasympathetic system activity, attention shifts, reduced muscle tone, and a relaxed state of mind during MAR. This hints at potential mechanisms of music therapy but needs to be confirmed in larger studies. Electrophysiological changes during music therapy highlight its clinical relevance as a complementary treatment for ICU burn patients.Trial registration: Clinicaltrials.gov (NCT04571255). Registered September 24th, 2020. https//classic.clinicaltrials.gov/ct2/show/NCT04571255.
Upper airway resistance during use of a laryngeal mask airway is flow-dependent and dominated by the laryngeal resistance
Authors: Hell Johannes , Schmutz Axel , Schumann Stefan 
Published: October 2024

AbstractDuring use of a laryngeal mask airway, resistance of the device and larynx contribute to the upper airway resistance. Detailed understanding of this combined resistance is crucial to support spontaneously breathing patients appropriately or to take the right measures during respiratory problems. However, their resistive behavior and which of these components determine the upper airway resistance predominantly, has not been characterized systematically. Pressure-flow-relationships of different sizes of a laryngeal mask airway, of a laryngeal model with vocal cord angles between 10° and 60° and of the combination of a laryngeal mask airway size 4 and the laryngeal model were measured. Results were fitted to the expanded Rohrer’s equation and resistances were calculated. The laryngeal mask airway and the laryngeal model showed a nonlinear flow-dependent resistive behavior. Decreasing size of the laryngeal mask airway, decreasing vocal cord angles, and increasing flow rates resulted in increased resistances (all p < 0.001). Resistance of the laryngeal mask and the laryngeal model added up to the combined resistance in a summative way, where the vocal cord angle determines 59–98% of the combined resistance in adults. The upper airway resistance during the use of a laryngeal mask airway is a summative resistance with a flow-dependent, nonlinear behavior. Upper airway resistance in adults is primarily determined by the vocal cord angle during use of a laryngeal mask.
Reverse engineering in medical application: literature review, proof of concept and future perspectives
Authors: Wakjira Yosef , Kurukkal Navaneethan S. , Lemu Hirpa G. 
Published: October 2024

AbstractReverse engineering, a process of extracting information or knowledge from existing objects or systems, has gained significant attention in various fields, including medicine. This article presents a comprehensive literature review and a proof of concept on the application of reverse engineering in the medical field. The review particularly focuses on the reverse engineering process, available technologies, and their specific relevance to the medical domain. Various imaging techniques, such as computed tomography and magnetic resonance imaging, are discussed in respect of their integration with reverse engineering methodologies. Furthermore, the article explores the wide range of medical applications facilitated by reverse engineering, including prosthetics, implants, tissue engineering, and surgical planning. The potential of reverse engineering to enhance personalized medicine and patient-specific treatments is highlighted. A detailed proof of concept focusing on femur reconstruction is a significant component of the article. The proof of concept showcases the practical implementation of reverse engineering techniques to assist in designing and manufacturing precise custom-made implants and bone reconstruction. It emphasizes the integration of patient-specific anatomical data obtained through imaging technologies and the subsequent utilization of reverse engineering processes for anatomical reconstruction (solid modeling). Overall, this article provides an extensive overview of reverse engineering in medical applications, incorporating a literature review and a case study. The findings highlight reverse engineering’s potential to advance medical practices, improve patient outcomes, and foster personalized treatments. The review emphasizes the reverse engineering process, available technologies, and their specific relevance to the medical field, as well as their potential and effectiveness in advancing medical practices.
Deep hematologic response to RD treatment in patients with multiple myeloma is associated with overexpression of IL-17R in CD138+ plasma cells
Authors: Kulig Piotr , Łuczkowska Karolina , Machaliński Bogusław , Baumert Bartłomiej 
Published: October 2024

AbstractLenalidomide (LEN) is widely used immunomodulatory drug (IMiD). Nonetheless, despite its efficacy, over time patients become resistant to LEN and relapse. Due to high clinical relevance, drug resistance in MM is being thoroughly investigated. However, less is known about predictors of good response to LEN-based treatment. The aim of this study was to identify molecular pathways associated with good and long response to LEN. The study included newly diagnosed MM patients (NDMM) and MM patients treated with first-line LEN and dexamethasone (RD) who achieved and least very good partial remission (VGPR). RNA was isolated from MM cells and new-generation sequencing was performed. Obtained results were validated with qRT-PCR. A global increase in gene expression was found in the RD group compared to NDMM, suggesting the involvement of epigenetic mechanisms. Moreover, upregulation of genes controlling the interaction within MM niche was detected. Next, genes controlling immune response were upregulated. In particular, the gene encoding the IL-17 receptor was overexpressed in the RD group which is a novel finding. This should be emphasized because IL-17-related signaling can potentially be targeted, providing the rationale for future research. Establishing the molecular background associated with long-lasting and profound response to LEN may improve LEN-based chemotherapy regimens and facilitate the development of adjuvant therapies to enhance its anti-MM activity.
Quantitative phase imaging verification in large field-of-view lensless holographic microscopy via two-photon 3D printing
Authors: Wdowiak Emilia , Rogalski Mikołaj , Arcab Piotr , Zdańkowski Piotr , Józwik Michał , Trusiak Maciej 
Published: October 2024

AbstractLarge field-of-view (FOV) microscopic imaging (over 100 mm2) with high lateral resolution (1–2 μm) plays a pivotal role in biomedicine and biophotonics, especially within the label-free regime. Lensless digital holographic microscopy (LDHM) is promising in this context but ensuring accurate quantitative phase imaging (QPI) in large FOV LDHM is challenging. While phantoms, 3D printed by two-photon polymerization (TPP), have facilitated testing small FOV lens-based QPI systems, an equivalent evaluation for lensless techniques remains elusive, compounded by issues such as twin-image and beam distortions, particularly towards the detector’s edges. Here, we propose an application of TPP over large area to examine phase consistency in LDHM. Our research involves fabricating widefield phase test targets with galvo and piezo scanning, scrutinizing them under single-shot twin-image corrupted conditions and multi-frame iterative twin-image minimization scenarios. By measuring the structures near the detector’s edges, we verified LDHM phase imaging errors across the entire FOV, with less than 12% phase value difference between areas. Our findings indicate that TPP, followed by LDHM and Linnik interferometry cross-verification, requires new design considerations for precise large-area photonic manufacturing. This research paves the way for quantitative benchmarking of large FOV lensless phase imaging, enhancing understanding and further development of LDHM technique.
High-precision and lightweight small-target detection algorithm for low-cost edge intelligence
Authors: Xiao Linsong , Li Wenzao , Yao Sai , Liu Hantao , Ren Dehao 
Published: October 2024

AbstractThe proliferation of edge devices driven by advancements in Internet of Things (IoT) technology has intensified the challenge of achieving high-precision small target detection, as it demands extensive computational resources. This amplifies the conflict between the need for precise detection and the requirement for cost-efficiency across numerous edge devices. To solve this problem, this paper introduces an enhanced target detection algorithm, MSGD-YOLO, built upon YOLOv8. The Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) module is enhanced through the integration of the Ghost module and dynamic convolution, resulting in a more lightweight architecture while enhancing feature generation. Additionally, Spatial Pyramid Pooling with Enhanced Local Attention Network (SPPELAN) replaces Spatial Pyramid Pooling Fast (SPPF) to expand the receptive field, optimizing multi-level feature aggregation for improved performance. Furthermore, a novel Multi-Scale Ghost Convolution (MSGConv) and Multi-Scale Generalized Feature Pyramid Network (MSGPFN) are introduced to enhance feature fusion and integrate multi-scale information. Finally, four optimized dynamic convolutional detection heads are employed to capture target features more accurately and improve small target detection precision. Evaluation on the VisDrone2019 dataset shows that compared with YOLOv8-n, MSGD-YOLO improves mAP@50 and mAP@50–95 by 14.1% and 11.2%, respectively. In addition, the model not only achieves a 16.1% reduction in parameters but also attains a processing speed of 24.6 Frames Per Second (FPS) on embedded devices, thereby fulfilling real-time detection requirements.
Glial cells undergo rapid changes following acute chemogenetic manipulation of cortical layer 5 projection neurons
Authors: Vadisiute Auguste , Meijer Elise , Therpurakal Rajeevan Narayanan , Mueller Marissa , Szabó Florina , Messore Fernando , Jursenas Alfonsas , Bredemeyer Oliver , Krone Lukas B. , Mann Ed , Vyazovskiy Vladyslav , Hoerder-Suabedissen Anna , Molnár Zoltán 
Published: October 2024

AbstractBidirectional communication between neurons and glial cells is crucial to establishing and maintaining normal brain function. Some of these interactions are activity-dependent, yet it remains largely unexplored how acute changes in neuronal activity affect glial-to-neuron and neuron-to-glial dynamics. Here, we use excitatory and inhibitory designer receptors exclusively activated by designer drugs (DREADD) to study the effects of acute chemogenetic manipulations of a subpopulation of layer 5 cortical projection and dentate gyrus neurons in adult (Rbp4Cre) mouse brains. We show that acute chemogenetic neuronal activation reduces synaptic density, and increases microglia and astrocyte reactivity, but does not affect parvalbumin (PV+) neurons, only perineuronal nets (PNN). Conversely, acute silencing increases synaptic density and decreases glial reactivity. We show fast glial response upon clozapine-N-oxide (CNO) administration in cortical and subcortical regions. Together, our work provides evidence of fast, activity-dependent, bidirectional interactions between neurons and glial cells.
An l-isosorbide-based reactive chiral dopant with high helical twisting power for cholesteric liquid crystal polymers reflecting left-handed circularly polarized light
Authors: Dinc Ramazan Umut , Lub Johan , Kragt Augustinus J. J. , Schenning Albert P. H. J. 
Published: October 2024

We report an l-isosorbide-based reactive chiral dopant with high helical twisting power for cholesteric liquid crystal polymers reflecting left-handed circularly polarized light.
Pre‐trained SAM as data augmentation for image segmentation
Authors: Wu Junjun , Rao Yunbo , Zeng Shaoning , Zhang Bob 
Published: October 2024

AbstractData augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset. Initially, data augmentation mainly involved some simple transformations of images. Later, in order to increase the diversity and complexity of data, more advanced methods appeared and evolved to sophisticated generative models. However, these methods required a mass of computation of training or searching. In this paper, a novel training‐free method that utilises the Pre‐Trained Segment Anything Model (SAM) model as a data augmentation tool (PTSAM‐DA) is proposed to generate the augmented annotations for images. Without the need for training, it obtains prompt boxes from the original annotations and then feeds the boxes to the pre‐trained SAM to generate diverse and improved annotations. In this way, annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model. Multiple comparative experiments on three datasets are conducted, including an in‐house dataset, ADE20K and COCO2017. On this in‐house dataset, namely Agricultural Plot Segmentation Dataset, maximum improvements of 3.77% and 8.92% are gained in two mainstream metrics, mIoU and mAcc, respectively. Consequently, large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.
Low‐light image enhancement via lightweight custom non‐linear transform network
Authors: Li Yang 
Published: October 2024

AbstractConvolutional neural network (CNN)‐based models have shown significant progress in low light image enhancement. However, many existing models possess a large number of parameters, making them unsuitable for deployment on terminal devices. Moreover, adjustments to brightness, contrast, and colour in images are often non‐linear, and convolution is not the best at capturing complex non‐linear relationships in image data. To address these issues, a model based on an end‐to‐end custom non‐linear transform network (CNTNet) is proposed. CNTNet combines a custom non‐linear transform layer with CNN layers to achieve image contrast and detail enhancement. The CNT layer in this model introduces transformation parameters at multiple scales to manipulate input images within various ranges. CNTNet progressively processes images by stacking multiple non‐linear transform layers and convolutional layers while integrating residual connections to capture and leverage subtle image features. The final output is generated through convolutional layers to obtain enhanced images. Experimental results of CNTNet demonstrate that, while maintaining a comparable level of image quality evaluation metrics to mainstream models, it significantly reduces the parameter count to only 2K.
Improving electric vehicle charging forecasting: A hybrid deep learning approach for probabilistic predictions
Authors: Jahromi Ali Jamali , Masoudi Mohammad Reza , Mohammadi Mohammad , Afrasiabi Shahabodin 
Published: October 2024

AbstractElectric vehicles (EVs) have gained significant attention recently. Despite their advantages, challenges in the power grid, such as providing necessary information for optimal operation, persist. High‐precision forecasting techniques are essential to address the nonlinear and complex behavior of EV charging. A hybrid structure based on deep learning, called LSTLNet, has been proposed. LSTLNet combines convolutional neural networks (CNN), gated recurrent neural networks (GRU), attention mechanisms (AM), and automatic regression (AR) models. This combination improves the deterministic forecasting model and addresses the weaknesses of CNN and GRU. Deterministic prediction, which determines only one point of consumption charge, is prone to error. Therefore, probabilistic forecasting, represented as a probability distribution function (PDF) containing comprehensive statistical information, is preferred. A smooth band limit maximum likelihood (SBLM) estimator is used to indirectly predict the PDF from the data. Comparative results with conventional shallow and deep methods for similar time series forecasting demonstrate the superiority of the proposed method for both deterministic and probabilistic forecasting.
Graphene nanoribbon FET technology‐based OTA for optimizing fast and energy‐efficient electronics for IoT application: Next‐generation circuit design
Authors: Hashmi Faraz , Nizamuddin M , Farshori Mohammad Aleem , Amin Syed Umar , Khan Zafar Iqbal 
Published: October 2024

AbstractThe Internet of Things (IoT) and portable electronic devices are pivotal in enhancing living standards, with battery efficiency and compact design being critical for these devices. Analogue sensors, integral to advanced artificial intelligence, often necessitate complex real‐time processing and actuation. This study examines the performance of one‐dimensional armchair graphene nanoribbons in graphene nanoribbon field‐effect transistors (GNRFETs). It compares graphene nanoribbon‐based triple cascode operational transconductance amplifiers (GNRFET‐TCOTAs) with conventional CMOS‐based TCOTA. The results reveal substantial enhancements in the GNRFET‐based TCOTAs: the pure GNR‐TCOTA variant shows a remarkable 33.8% increase in DC gain and significant improvements in transconductance, slew rate, and gain‐bandwidth, with enhancements of 8.48, 5.85, and 8.56 times, respectively. Furthermore, the pure GNRFET TCOTA exhibits higher speed, lower energy‐delay product, and settling time compared to Si‐CMOS‐based TCOTA. The study also investigates the impact of critical design parameters on circuit performance. Overall, the research highlights the potential of GNRFETs to optimize TCOTA circuits, offering a path towards more efficient and compact electronic devices, thereby advancing the state of nanoelectronics and supporting the growth of high‐performance IoT systems.
Mourir : travail du trépas ou euthanasie ?
Authors: Josserand Serge Alain 
Published: October 2024

La loi sur la fin de vie légalisera une exception au commandement du surmoi collectif « tu ne tueras point », fondateur de toute société. Inspirée par une opinion compassionnelle, cette exception appelle le contradictoire, qui procède ici de la connaissance. C’est par le « signal de mort à venir » que le sujet entrera dans la fin de vie, en appelant au « travail du trépas », qui lui permettra de s’effacer psychiquement avant le retrait de la vie, à la faveur de la relation exclusive à un autre. Cette relation peut être soit entravée par les états dissociés de fin de vie, qui font surgir un sentiment d’abandon, et une demande d’euthanasie ; soit récusée par une position philosophique autour du sentiment de dignité.
Restrictive immigration policies and MNE innovation
Authors: Nayak Deepak , Moreira Solon , Mudambi Ram 
Published: October 2024

Abstract
High-skill migrant workers significantly contribute to advanced economies by directly generating knowledge-intensive assets and serving as bridges to knowledge in their origin countries. However, rising populist sentiment has led to increased immigration restrictions in wealthy economies like the U.S. and the European Union. This study examines the impact of the 2004 H-1B visa cap reduction on U.S.-based multinational enterprises (MNEs). We use a sample of 371,856 patents assigned to 707 U.S.-based MNEs. We find that post-shock, MNEs increased the geographic dispersion of their global R&D workforce, rather than replacing foreigners with local American workers. Despite this, the firms experienced a decline in innovation performance, likely due to elevated coordination challenges. Interestingly, sectors relying more on codified knowledge demonstrated increased R&D team dispersion with less impact on innovation. This suggests that the geographic proximity of innovation teams is crucial for tacit knowledge-intensive sectors. These findings highlight the complex consequences of immigration restrictions and suggest boundary conditions on the effectiveness of work-from-anywhere models in knowledge-intensive industries.
Calibration of MAJIS (Moons and Jupiter Imaging Spectrometer): V. Validation with mineral samples and reference materials
Authors: Rodriguez S. , Vincendon M. , Haffoud P. , Langevin Y. , Poulet F. , Quirico E. , Pilorget C. , Filacchione G. , Carter J. , Brunetto R. , Lecomte B. , Guiot P. , Dumesnil C. , Piccioni G. 
Published: October 2024

MAJIS (Moons and Jupiter Imaging Spectrometer) is the imaging spectrometer onboard ESA’s JUICE (JUpiter and ICy Moons Explorer) spacecraft that operates in the visible and near/mid-infrared between 0.5 and 5.54 μm. Before the launch of JUICE in April 2023, MAJIS underwent a comprehensive on-ground calibration campaign in between August and September 2021 in the IAS (Institut d’Astrophysique Spatiale, Université Paris-Saclay) calibration facilities. Among all the operations, calibration sequences using a set of natural mineral samples and synthetic reference materials were acquired in order to characterize MAJIS performances under conditions assumed to be close to certain future observation configurations. Here, we analyze these calibration measurements using comparison with laboratory reference spectra to quantify MAJIS spectral and spatial performances while observing these solid surfaces. We first assess the MAJIS absolute spectral calibration of the visible and near-infrared channel covering half of the wavelength range. We then quantify spectral performances in terms of global spectral slopes, band detection, band shape, and depth retrievals, over most of the spectral range using six mineral samples. We conclude that for most configurations, the MAJIS instrument demonstrates excellent spectral performances compliant with the requirements. MAJIS can, however, be affected by stray light contributions, notably for wavelengths lower than about 1.2 μm, and some performances of the instrument may then be significantly impacted depending on viewing conditions. In particular, we have identified cases of spectral contrast reduction up to 40%, absolute spectral shifts up to 2–3 nm, and spectral smile variability by +/1 nm. Finally, we used the MAJIS internal scanning mirror to test its ability to construct hyperspectral images of a few samples: we present the first band depth maps derived with MAJIS while observing a serpentine/carbonate sample, as well as an evaluation of MAJIS spatial point spread function. Overall, the analysis of MAJIS behavior while observing samples confirms most MAJIS expected performance requirements, while revealing subtle spectral perturbations that may be related to stray light and viewing conditions. These differences will be further investigated in-flight during the cruise, with a solar reflected target such as the Moon, as well as Jupiter before the JUICE orbital insertion.