bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of risk for bronchiolitis obliterans syndrome after allogeneic hematopoietic cell transplantation with myeloablative conditioning regimens
Authors: Duque-Afonso Jesús , Rassner Paraschiva , Walther Kristin , Ihorst Gabriele , Wehr Claudia , Marks Reinhard , Wäsch Ralph , Bertz Hartmut , Köhler Thomas , Frye Björn Christian , Stolz Daiana , Zeiser Robert , Finke Jürgen , Maas-Bauer Kristina 
Published: September 2024

AbstractBronchiolitis obliterans syndrome (BOS), as chronic manifestation of graft-versus-host disease (GVHD), is a debilitating complication leading to lung function deterioration in patients after allogeneic hematopoietic cell transplantation (allo-HCT). In the present study, we evaluated BOS development risk in patients after receiving myeloablative conditioning (MAC) regimens. We performed a retrospective analysis of patients undergoing allo-HCT, who received MAC with busulfan/cyclophosphamid (BuCy, n = 175) busulfan/fludarabin (FluBu4, n = 29) or thiotepa/busulfan/fludarabine (TBF MAC, n = 37). The prevalence of lung disease prior allo-HCT, smoking status, GvHD prophylaxis, HCT-CI score, EBMT risk score and GvHD incidence varied across the groups. The cumulative incidence of BOS using the NIH diagnosis consensus criteria at 2 years after allo-HCT was 8% in FluBu4, 23% in BuCy and 19% in TBF MAC (p = 0.07). In the multivariate analysis, we identified associated factors for time to BOS such as FEV1<median (99% of predicted) (HR = 2.39, p = 0.004), CMV patient serology positivity (HR = 2.11, p = 0.014), TLC < 80% of predicted (HR = 0.12, p = 0.02) and GvHD prophylaxis with in vivo T-cell depletion (HR = 0.29, p = 0.001) as predictors of BOS. In summary, we identified risk factors for BOS development in patients receiving MAC conditioning. These findings might serve to identify patients at risk, who might benefit from closely monitoring or early therapeutic interventions.
Spatial multiplexed immunofluorescence analysis reveals coordinated cellular networks associated with overall survival in metastatic osteosarcoma
Authors: Lacinski Ryan A. , Dziadowicz Sebastian A. , Melemai Vincent K. , Fitzpatrick Brody , Pisquiy John J. , Heim Tanya , Lohse Ines , Schoedel Karen E. , Llosa Nicolas J. , Weiss Kurt R. , Lindsey Brock A. 
Published: September 2024

AbstractPatients diagnosed with advanced osteosarcoma, often in the form of lung metastases, have abysmal five-year overall survival rates. The complexity of the osteosarcoma immune tumor microenvironment has been implicated in clinical trial failures of various immunotherapies. The purpose of this exploratory study was to spatially characterize the immune tumor microenvironment of metastatic osteosarcoma lung specimens. Knowledge of the coordinating cellular networks within these tissues could then lead to improved outcomes when utilizing immunotherapy for treatment of this disease. Importantly, various cell types, interactions, and cellular neighborhoods were associated with five-year survival status. Of note, increases in cellular interactions between T lymphocytes, positive for programmed cell death protein 1, and myeloid-derived suppressor cells were observed in the 5-year deceased cohort. Additionally, cellular neighborhood analysis identified an Immune-Cold Parenchyma cellular neighborhood, also associated with worse 5-year survival. Finally, the Osteosarcoma Spatial Score, which approximates effector immune activity in the immune tumor microenvironment through the spatial proximity of immune and tumor cells, was increased within 5-year survivors, suggesting improved effector signaling in this patient cohort. Ultimately, these data represent a robust spatial multiplexed immunofluorescence analysis of the metastatic osteosarcoma immune tumor microenvironment. Various communication networks, and their association with survival, were described. In the future, identification of these networks may suggest the use of specific, combinatory immunotherapeutic strategies for improved anti-tumor immune responses and outcomes in osteosarcoma.
Tracking neurons across days with high-density probes
Authors: van Beest Enny H. , Bimbard Célian , Fabre Julie M. J. , Dodgson Sam W. , Takács Flóra , Coen Philip , Lebedeva Anna , Harris Kenneth D. , Carandini Matteo 
Published: September 2024

AbstractNeural activity spans multiple time scales, from milliseconds to months. Its evolution can be recorded with chronic high-density arrays such as Neuropixels probes, which can measure each spike at tens of sites and record hundreds of neurons. These probes produce vast amounts of data that require different approaches for tracking neurons across recordings. Here, to meet this need, we developed UnitMatch, a pipeline that operates after spike sorting, based only on each unit’s average spike waveform. We tested UnitMatch in Neuropixels recordings from the mouse brain, where it tracked neurons across weeks. Across the brain, neurons had distinctive inter-spike interval distributions. Their correlations with other neurons remained stable over weeks. In the visual cortex, the neurons’ selectivity for visual stimuli remained similarly stable. In the striatum, however, neuronal responses changed across days during learning of a task. UnitMatch is thus a promising tool to reveal both invariance and plasticity in neural activity across days.
Generative language models on nucleotide sequences of human genes
Authors: İhtiyar Musa Nuri , Özgür Arzucan 
Published: September 2024

AbstractLanguage models, especially transformer-based ones, have achieved colossal success in natural language processing. To be precise, studies like BERT for natural language understanding and works like GPT-3 for natural language generation are very important. If we consider DNA sequences as a text written with an alphabet of four letters representing the nucleotides, they are similar in structure to natural languages. This similarity has led to the development of discriminative language models such as DNABERT in the field of DNA-related bioinformatics. To our knowledge, however, the generative side of the coin is still largely unexplored. Therefore, we have focused on the development of an autoregressive generative language model such as GPT-3 for DNA sequences. Since working with whole DNA sequences is challenging without extensive computational resources, we decided to conduct our study on a smaller scale and focus on nucleotide sequences of human genes, i.e. unique parts of DNA with specific functions, rather than the whole DNA. This decision has not significantly changed the structure of the problem, as both DNA and genes can be considered as 1D sequences consisting of four different nucleotides without losing much information and without oversimplification. First of all, we systematically studied an almost entirely unexplored problem and observed that recurrent neural networks (RNNs) perform best, while simple techniques such as N-grams are also promising. Another beneficial point was learning how to work with generative models on languages we do not understand, unlike natural languages. The importance of using real-world tasks beyond classical metrics such as perplexity was noted. In addition, we examined whether the data-hungry nature of these models can be altered by selecting a language with minimal vocabulary size, four due to four different types of nucleotides. The reason for reviewing this was that choosing such a language might make the problem easier. However, in this study, we found that this did not change the amount of data required very much.
Deep learning domain adaptation to understand physico-chemical processes from fluorescence spectroscopy small datasets and application to the oxidation of olive oil
Authors: Michelucci Umberto , Venturini Francesca 
Published: September 2024

AbstractFluorescence spectroscopy is a fundamental tool in life sciences and chemistry, with applications in environmental monitoring, food quality control, and biomedical diagnostics. However, analysis of spectroscopic data with deep learning, in particular of fluorescence excitation-emission matrices (EEMs), presents significant challenges due to the typically small and sparse datasets available. Furthermore, the analysis of EEMs is difficult due to their high dimensionality and overlapping spectral features. This study proposes a new approach that exploits domain adaptation with pretrained vision models, along with a novel interpretability algorithm to address these challenges. Thanks to specialised feature engineering of the neural networks described in this work, we are now able to provide deeper insights into the physico-chemical processes underlying the data. The proposed approach is demonstrated through the analysis of the oxidation process in extra virgin olive oil (EVOO), showing its effectiveness in predicting quality indicators and identifying the spectral bands and thus the molecules involved in the process. This work describes a significantly innovative approach to deep learning for spectroscopy, transforming it from a black box into a tool for understanding complex biological and chemical processes.
Attentional templates for target features versus locations
Authors: Jimenez Mikel , Wang Ziyi , Grubert Anna 
Published: September 2024

AbstractVisual search is guided by visual working memory representations (i.e., attentional templates) that are activated prior to search and contain target-defining features (e.g., color). In the present study, we tested whether attentional templates can also contain spatial target properties (knowing where to look for) and whether attentional selection guided by such feature-specific templates is equally efficient than selection that is based on feature-specific templates (knowing what to look for). In every trial, search displays were either preceded by semantic color or location cues, indicating the upcoming target color or location, respectively. Qualitative differences between feature- and location-based template guidance were substantiated in terms of selection efficiency in low-load (one target color/location) versus high-load trials (two target colors/locations). Behavioral and electrophysiological (N2pc) measures of target selection speed and accuracy were combined for converging evidence. In line with previous studies, we found that color search was highly efficient, even under high-low conditions, when multiple attentional templates were activated to guide attentional selection in a spatially global fashion. Importantly, results in the location task almost perfectly mirrored the findings of the color task, suggesting that multiple templates for different target locations were activated concurrently when two possible target locations were task relevant. Our findings align with accounts that assume a common neuronal network during preparation for location and color search, but regard spatial and feature-based selection mechanisms as independent.
Use of a structure-borne sound-based in-process sensor system to identify Weld seam irregularities during electron beam welding
Authors: Wolf Christian , Sommer Niklas , Böhm Stefan 
Published: September 2024

AbstractIn this publication, an in-process quality assurance method for electron beam welding based on a structure-borne sound emission test for the detection of weld irregularities arising in the process is presented. For this purpose, different sheet materials, i.e., AISI 304, AZ31 and AlMg3, were welded in a butt-joint and the resulting process noises were recorded by means of two acoustic emission sensors specifically designed for structure-borne sound. During the welding experiments, typical irregularities, e.g. incidence points, pore lines and cracks, were deliberately induced. Subsequently, the recorded acoustic signals were examined with regard to defect-specific abnormalities. Various methods in the time and frequency domain as well as pre-trained machine learning models were used to analyze the acoustic emission data. The results show that the investigated irregularities can be identified and distinguished from other process emissions, eventually enabling a robust means of identification for weld seam irregularities and, thus, opening pathways towards cost-effective in-process quality control.
Dual carbon economy modeling in advanced economies
Authors: Xu Sheng , Gu Weiyu 
Published: September 2024

AbstractUtilizing the cross-sectional fully modified OLS (FMOLS) method, this paper tackles the pressing need to address climate change disasters, particularly in the context of recent COVID-19 restrictions. By developing an econometric model specifically for advanced economies, the study aims to enhance understanding and reduce carbon emissions. Examining ten advanced economies from 1995 to 2019, it identifies a significant gap in the literature concerning the integration of demographic, social, economic, and political (DSEP) factors in dual carbon economy models. The results show that a 1% improvement in good governance is associated with a 0.23% reduction in carbon footprint, due to stricter environmental regulations. In contrast, a 1% increase in economic growth results in a 0.17% rise in carbon footprint, driven by industrial activities. Similarly, a 1% rise in electricity consumption leads to a 0.32% increase in carbon footprint, reflecting dependence on fossil fuels. Additionally, a 1% increase in population size corresponds to a 0.21% rise in carbon footprint, linked to higher urban energy consumption. Finally, a 1% increase in the poverty ratio results in a 0.17% rise in carbon footprint, underscoring the influence of socio-economic factors on sustainability. The paper advocates for a comprehensive approach to fostering a dual carbon economy, emphasizing green economic growth, smart, sustainable cities, social sustainability literacy, and sustainable governance.
Estimating the effects of x-ray radiations on lymphocytes by minimally invasive holographic imaging flow cytometry
Authors: Pirone Daniele , La Verde Giuseppe , Behal Jaromir , Arrichiello Cecilia , Muto Paolo , Kurelac Ivana , Bagnale Laura , Sirico Daniele Gaetano , Medugno Mario , Del Giudice Danila , Memmolo Pasquale , Bianco Vittorio , Miccio Lisa , Maffettone Pier Luca , Ferraro Pietro , Pugliese Mariagabriella 
Published: September 2024

Abstract
Nowadays, radiotherapy (RT) is a consolidated treatment for the cancer care. In fact, ionizing radiations are employed in over 50% of cancer patients. However, some side effects are correlated with RT, such as the radiation-induced lymphopenia (RIL). RIL is due to circulating lymphocytes (LCs) that pass through the irradiation field. LCs are the most radiosensitive blood cells, therefore RT can affect the count and activity of LCs. LCs are an important component of the human immune system, thus RIL has been linked with worse outcomes in multiple solid tumors and poor survival. As the occurrence rate of RIL is ∼40%–70% of patients treated with RT, an effective tool for predicting and minimizing RIL is highly demanded. Here we propose the proof-of-concept of a minimally invasive approach to monitor alterations induced by the radiation exposure inside LCs. To this aim, we combine label-free Holographic Imaging Flow Cytometry and Machine Learning to study Jurkat cells as model of T-LCs irradiated with two x-ray doses (i.e. 2 and 10 Gy of 6 MeV photons). The proposed method allows correlating the morphological features extracted by the quantitative phase-contrast maps of irradiated LCs flowing in a microfluidic chip to their radiation response. Furthermore, we train several machine learning models at different time points after RT for assessing the best strategy to reveal its effect on irradiated LCs. The attained results pave the way to future and deeper investigations for the development of a label-free, minimally invasive, and high-throughput tool for predicting and minimizing the RIL side effects.
The role of proton battery technologies in future global energy storage
Authors: Rezaei Niya Seyed Mohammad , Heidari Shahin , Andrews John 
Published: September 2024

Abstract
The very large global demand for energy storage as inherently-variable renewable-energy sources meet an increasing proportion of total electricity demand will be difficult to meet solely with existing technologies. Hence additional storage technologies that are safe and based on abundant primary resources are likely to come into play to facilitate the transition to zero net emissions at the global level. One such promising technology is the ‘proton battery’, which in its most general form is a rechargeable battery based on proton transfer and reversible electrochemical hydrogen storage. In the present review, a general definition of a proton battery is first proposed, since the term has been used broadly and somewhat inconsistently to date. The literature over the past thirty years on this technology is then critically reviewed, covering both proton batteries that meet the definition proposed in this paper as well as those that are merely self-identified. To the extent possible through published information, the performances of this range of cells are compared in terms of key parameters such as electrical energy stored per unit mass, cyclability, self-discharge and scale reached. The proton battery design developed by our group at RMIT is described in more detail, both theoretically and in terms of experimentally-measured performance, as an exemplar of a system that has already demonstrated a competitive storage capacity at a significant scale. In conclusion, potential future applications for proton batteries, and some directions for the research and development necessary to enable this potential to be realised, are proposed.
Fabrication and characterization of bioresorbable, electroactive and highly regular nanomodulated cell interfaces
Authors: Lunghi Alice , Velluto Federica , Lisa Luana Di , Genitoni Matteo , Biscarini Fabio , Focarete Maria Letizia , Gualandi Chiara , Bianchi Michele 
Published: September 2024

Abstract
Biomaterial-based implantable scaffolds capable of promoting physical and functional reconnection of injured spinal cord and nerves represent the latest frontier in neural tissue engineering. Here, we report the fabrication and characterization of self-standing, biocompatible and bioresorbable substrates endowed with both controlled nanotopography and electroactivity, intended for the design of transient implantable scaffolds for neural tissue engineering. In particular, we obtain conductive and nano-modulated poly(D,L-lactic acid) (PLA) and poly(lactic-co-glycolic acid) free-standing films by simply iterating a replica moulding process and coating the polymer with a thin layer of poly(3,4-ethylendioxythiophene) polystyrene sulfonate. The capability of the substrates to retain both surface patterning and electrical properties when exposed to a liquid environment has been evaluated by atomic force microscopy, electrochemical impedance spectroscopy and thermal characterizations. In particular, we show that PLA-based films maintain their surface nano-modulation for up to three weeks of exposure to a liquid environment, a time sufficient for promoting axonal anisotropic sprouting and growth during neuronal cell differentiation. In conclusion, the developed substrates represent a novel and easily-tunable platform to design bioresorbable implantable devices featuring both topographic and electrical cues.
Current-carrying properties of REBCO multi-filamentary tapes prepared by reel-to-reel ultraviolet picosecond laser cutting
Authors: Li Zuoguang , Zhang Zhan , Bu Yuhu , Wang Donghu , Xiao Guanyu , Jin Huan , Qin Jinggang , Zhou Chao 
Published: September 2024

Abstract
REBCO high-temperature superconductors have been widely used in high magnetic field applications, because of their excellent critical current properties and high critical temperature. However, REBCO tape has an extremely large width-to-thickness ratio (typically in the range of 1000–10 000) which causes power dissipation to be too high in applications. One of the most effective ways to reduce AC loss is to divide the superconducting layer in the REBCO tape into filaments. The current-carrying properties degradation behavior of the prepared multi-filamentary tape is the focus of attention. In this study, REBCO multi-filamentary tapes are tested with different numbers of filaments (2-filament, 6-filament, and 10-filament) by cutting the copper-stabilizing layers and superconducting layers through a self-developed reel-to-reel ultraviolet picosecond laser cutting device. The findings indicate that the cut groove has a depth of approximately 30 μm and the width of the groove in the superconducting layer measures around 15 μm. Meanwhile, the research found that ultraviolet picosecond laser cutting does not cause substantial degradation in the I
C. Under self-field conditions at 77 K, the I
C decreases with the number of filaments, and the degradation of I
C is less than 13% in a 10-filament tape, the corresponding decrease of J
C is only about 10%. Similarly, at 4.2 K and under various high-field (10 T, 12 T, 14 T) conditions, the I
C variation trends of REBCO high-temperature superconducting (HTS) multi-filamentary tapes and non-striated tapes are fundamentally similar, both decreasing as the applied vertical magnetic field increases. Furthermore, it was found that a constant tension of 50 MPa, 100 MPa, and 200 MPa had no effect on the current-carrying properties of the REBCO HTS multi-filamentary tapes.
The influence of spin-orbit coupling on the electronic structure and optical properties of α-BiB3O6 crystals: a first-principles investigation
Authors: Yang Guang , Hu Peng , Chen Hongheng , Jing Qun 
Published: September 2024

Abstract
The α-BiB3O6 (BIBO) crystal is highly regarded in the field of nonlinear optical (NLO) due to its excellent ultraviolet (UV) NLO properties, including a short ultraviolet cutoff edge (270 nm), appropriate birefringence (Δn = 0.175@546 nm), and strong second harmonic generation (deff = 3.20 pm V−1). In this study, the first-principles computational software PWmat was employed to conduct theoretical calculations on BIBO crystals, including the non-negligible spin-orbit coupling (SOC) effects induced by the presence of the heavy element bismuth (Bi). The results show that after the inclusion of the SOC, the calculated birefringence (0.173@546 nm) and effective second harmonic generation (SHG) coefficient (deff = 3.16 pm V−1) are in good agreement with experimental data. Furthermore, comparative analysis has demonstrated that the changes in the band structure induced by the SOC effect are significant factors influencing the optical properties of the material.
Synchronization of Euler-Lagrangian networks: a periodic intermittent dynamic event-triggered control strategy
Authors: Wang Shuangao , Wei Bo , Yin Huan 
Published: September 2024

Abstract
In this paper, the synchronization problem of Euler–Lagrange networks is studied by designing periodic intermittent dynamic event-triggered control strategy. The synchronization of all agents is successfully realized by injecting negative comments into the discontinuous interval of the agents described by the Euler–Lagrange systems. Different from the traditional intermittent event-triggered control strategy, the control strategy proposed in this study introduces internal dynamic variables to expand the sampling interval of the event-triggered mechanism, thereby improving resource utilization efficiency. In addition, the event trigger instants of each agent are asynchronous. By using graph theory and Lyapunov method, the synchronization criteria of Euler–Lagrange networks are derived. The effectiveness of the proposed control scheme is verified by simulations for the Euler-Lagrangian network composed of six two-link rotating manipulators.