bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning
Authors: Corcoran Forrest , Parrish Christopher E. , Magruder Lori A. , Swinski J. P. 
Published: October 2024

AbstractSince the 2018 launch of NASA's ICESat‐2 satellite, numerous studies have documented the bathymetric measurement capabilities of the space‐based laser altimeter. However, a commonly identified limitation of ICESat‐2 bathymetric point clouds is that they lack accompanying spectral reflectance attributes, or even intensity values, which have been found useful for benthic habitat mapping with airborne bathymetric lidar. We present a novel method for extracting bathymetry from ICESat‐2 data and automatically adding spectral reflectance values from Sentinel‐2 imagery to each detected bathymetric point. This method, which leverages the cloud computing systems Google Earth Engine and NASA's SlideRule Earth, is ideally suited for “big data” projects with ICESat‐2 data products. To demonstrate the scalability of our workflow, we collected 3,500 ICESat‐2 segments containing approximately 1.4 million spectrally‐attributed bathymetric points. We then used this data set to facilitate training of a deep recurrent neural network for classifying benthic habitats at the ICESat‐2 photon level. We trained two identical models, one with and one without the spectral attributes, to investigate the benefits of fusing ICESat‐2 photons with Sentinel‐2. The results show an improvement in model performance of 18 percentage points, based on F1 score. The procedures and source code are publicly available and will enhance the value of the new ICESat‐2 bathymetry data product, ATL24, which is scheduled for release in Fall 2024. These procedures may also be applicable to data from NASA's upcoming CASALS mission.
Lithospheric Structure and Strength Variations in Antarctica From Joint Modeling of Elevation, Geoid and Seismic Data
Authors: Ji Fei , Xu Mingju , Zhang Qiao , Liu Xiaochun , Zhou Xin 
Published: October 2024

AbstractAntarctica is renowned for its ancient cratons, difficult‐to‐observe sutures and active continental rifts. Detailed lithospheric structure and strength estimates are crucial for understanding the potential distribution, long‐term geological evolution, and deformation patterns of this continent. The lithospheric structure of the Antarctic continent is investigated based on joint modeling of elevation and geoid data with the incorporation of seismic data and thermal constraints. Moreover, these results are used to infer the yield strength envelopes across Antarctica. The laterally variable lithospheric strength is finally generated by vertically integrating the above envelopes. Our results show that the variations in the depth of the lithosphere‐asthenosphere boundary (LAB) are analogous to the variations in the integrated lithospheric strength; these parameters vary from 60 to 220 km and from 0.5 × 1013 Pa m to 4.5 × 1013 Pa m, respectively. Several regions of East Antarctica exhibit thick and strong lithospheric mantle, suggesting the presence of scattered cratonic blocks. In contrast, a thin and weak lithosphere is observed in the tectonically active West Antarctica and East Antarctic orogenic belts. Generally, our new LAB model correlates well with previous estimates, but there is a significant inconsistency beneath the Gamburtsev Subglacial Mountains, where a shallow LAB (∼120 km), high Moho temperature, and low strength (<1.0 × 1013 Pa m) are estimated. We speculate that this anomaly may be similar to the thinned lithosphere in Dronning Maud Land and reflects the removal of the mantle lithosphere during or after the orogenesis associated with the assembly of the Gondwana supercontinent.
PACAP regulates neuroendocrine and behavioral stress responses via CRF-containing neurons of the rat hypothalamic paraventricular nucleus
Authors: Ebner Karl , Fontebasso Veronica , Ferro Federico , Singewald Nicolas , Hannibal Jens 
Published: October 2024

Abstract
Pituitary adenylate cyclase-activating polypeptide (PACAP) is a neuropeptide widely distributed in the brain including the hypothalamic paraventricular nucleus (PVN) implying a regulatory role in stress function. Recent evidence indicates that one of the main targets of PACAP within the PVN are corticotropin-releasing factor (CRF) neurons, which are key regulators of the hypothalamic-pituitary-adrenal (HPA) axis. However, the neural correlates that mediate PACAP effects on stress function are not fully understood. In the present study, we characterized the neuronal mechanism by which PACAP regulates neuroendocrine and behavioral stress responses in rats. We found that intracerebroventricular administration of PACAP increased the swim stress-induced c-Fos expression in distinct brain areas of the stress and anxiety circuitry including the parvocellular part of the PVN and changed behavioral stress coping during forced swimming to a more passive coping style (i.e., indicated by increased floating and reduced struggling behavior). Subsequently, PACAP administration directly into the PVN mimicked these behavioral effects and potentiated the plasma ACTH response to forced swim stress suggesting an excitatory role of PACAP on HPA stress axis reactivity. In addition, immunohistochemical analysis revealed a considerable portion of stress-activated CRF neurons in the medial parvocellular part of the PVN that co-localized PAC1 receptors suggesting that PACAP-induced effects on stress function are likely mediated directly by activation of CRF neurons in the PVN. Thus, these findings suggest that the PVN may represent one of the key areas where PACAP regulates the neuroendocrine and behavioral stress response.
METTL3 confers oxaliplatin resistance through the activation of G6PD-enhanced pentose phosphate pathway in hepatocellular carcinoma
Authors: Jin Xiaohan , Lv Yongrui , Bie Fengjie , Duan Jinling , Ma Chao , Dai Miaomiao , Chen Jiewei , Lu Lianghe , Xu Shuidan , Zhou Jie , Li Si , Bi Jiong , Wang Fengwei , Xie Dan , Cai Muyan 
Published: October 2024

AbstractOxaliplatin-based therapeutics is a widely used treatment approach for hepatocellular carcinoma (HCC) patients; however, drug resistance poses a significant clinical challenge. Epigenetic modifications have been implicated in the development of drug resistance. In our study, employing siRNA library screening, we identified that silencing the m6A writer METTL3 significantly enhanced the sensitivity to oxaliplatin in both in vivo and in vitro HCC models. Further investigations through combined RNA-seq and non-targeted metabolomics analysis revealed that silencing METTL3 impeded the pentose phosphate pathway (PPP), leading to a reduction in NADPH and nucleotide precursors. This disruption induced DNA damage, decreased DNA synthesis, and ultimately resulted in cell cycle arrest. Mechanistically, METTL3 was found to modify E3 ligase TRIM21 near the 3’UTR with N6-methyladenosine, leading to reduced RNA stability upon recognition by YTHDF2. TRIM21, in turn, facilitated the degradation of the rate-limiting enzyme of PPP, G6PD, through the ubiquitination-proteasome pathway. Importantly, high expression of METTL3 was significantly associated with adverse prognosis and oxaliplatin resistance in HCC patients. Notably, treatment with the specific METTL3 inhibitor, STM2457, significantly improved the efficacy of oxaliplatin. These findings underscore the critical role of the METTL3/TRIM21/G6PD axis in driving oxaliplatin resistance and present a promising strategy to overcome chemoresistance in HCC.
Dirichlet latent modelling enables effective learning and sampling of the functional protein design space
Authors: Lobzaev Evgenii , Stracquadanio Giovanni 
Published: October 2024

AbstractEngineering proteins with desired functions and biochemical properties is pivotal for biotechnology and drug discovery. While computational methods based on evolutionary information are reducing the experimental burden by designing targeted libraries of functional variants, they still have a low success rate when the desired protein has few or very remote homologous sequences. Here we propose an autoregressive model, called Temporal Dirichlet Variational Autoencoder (TDVAE), which exploits the mathematical properties of the Dirichlet distribution and temporal convolution to efficiently learn high-order information from a functionally related, possibly remotely similar, set of sequences. TDVAE is highly accurate in predicting the effects of amino acid mutations, while being significantly 90% smaller than the other state-of-the-art models. We then use TDVAE to design variants of the human alpha galactosidase enzymes as potential treatment for Fabry disease. Our model builds a library of diverse variants which retain sequence, biochemical and structural properties of the wildtype protein, suggesting they could be suitable for enzyme replacement therapy. Taken together, our results show the importance of accurate sequence modelling and the potential of autoregressive models as protein engineering and analysis tools.
Collective buoyancy-driven dynamics in swarming enzymatic nanomotors
Authors: Chen Shuqin , Peetroons Xander , Bakenecker Anna C. , Lezcano Florencia , Aranson Igor S. , Sánchez Samuel 
Published: October 2024

AbstractEnzymatic nanomotors harvest kinetic energy through the catalysis of chemical fuels. When a drop containing nanomotors is placed in a fuel-rich environment, they assemble into ordered groups and exhibit intriguing collective behaviour akin to the bioconvection of aerobic microorganismal suspensions. This collective behaviour presents numerous advantages compared to individual nanomotors, including expanded coverage and prolonged propulsion duration. However, the physical mechanisms underlying the collective motion have yet to be fully elucidated. Our study investigates the formation of enzymatic swarms using experimental analysis and computational modelling. We show that the directional movement of enzymatic nanomotor swarms is due to their solutal buoyancy. We investigate various factors that impact the movement of nanomotor swarms, such as particle concentration, fuel concentration, fuel viscosity, and vertical confinement. We examine the effects of these factors on swarm self-organization to gain a deeper understanding. In addition, the urease catalysis reaction produces ammonia and carbon dioxide, accelerating the directional movement of active swarms in urea compared with passive ones in the same conditions. The numerical analysis agrees with the experimental findings. Our findings are crucial for the potential biomedical applications of enzymatic nanomotor swarms, ranging from enhanced diffusion in bio-fluids and targeted delivery to cancer therapy.
Late-life psychiatric factors and life satisfaction are associated with cognitive errors: evidence from an experimental module of a large-scale survey in India
Authors: Irshad C. V. , Padma Sri Lekha P. , Abdul Azeez E. P. , Muhammed T. 
Published: October 2024

AbstractOlder adults are at risk of committing cognitive and decision-making errors due to the decline in cognitive functions. However, the understanding of the determining factors of cognitive errors among ageing adults is limited. In this study, we explored the role of various psychiatric factors, life satisfaction, and other socioeconomic, health and behavioural risk factors of committing cognitive errors among middle-aged and older adults in India. The study utilized the data from the experimental module of the Longitudinal Ageing Study in India (LASI) Wave-1 (2017–2018) with a sample of 12,754 adults aged 45 years and above. The cognitive error was measured using logical fallacies committed in the activity-based experiments. The study employed descriptive, bivariate statistics and multivariable logistic regression models to identify the factors associated with cognitive errors among the study participants. Depression (aOR = 1.28, 99%, CI: 1.16–1.41), life satisfaction (aOR = 0.99, 99%, CI: 0.98–1.00), and cognitive impairment (aOR = 1.13, 90% CI: 1.00–1.30) were significantly associated with higher odds of committing cognitive errors among the middle-aged and older adults. Also, ageing adults with low educational levels, functional limitations, sleep disturbances, smoking history, living in rural areas and belonging to scheduled tribes had a higher probability of committing cognitive errors. However, involvement in physical activity, reading habits and social interactions reduced the odds of cognitive errors among this sample. Mental health and well-being indicators, including depression, life satisfaction, cognitive impairment, and other health and behavioural health factors, determine cognitive errors among ageing adults. Programs and policies should be initiated to address these factors, reduce cognitive errors, and ensure active ageing.
Adaptation biases the parallel perception of subitized numerosities
Authors: Liu Wei , Zhao Xiaoke , Liu Ying , Li Yating , Li Jingguang 
Published: October 2024

AbstractNumerosity adaptation is a phenomenon in which prolonged exposure to a stimulus of greater numerosity makes subsequent stimuli appear less numerous, and vice versa. It has been confined to moderated numerosities outside the subitizing range (> 4). This study investigated whether the estimation of small numerosities (1–4), which is performed rapidly and accurately due to the mechanism of subitizing, is susceptible to adaptation. After adapting to a 50-dot stimulus, participants were presented with stimuli consisting of 1–5 color sets. In some trials, participants were informed of the target color-set before the presentation of stimuli, while in others, they were instructed afterwards. When estimating 1–4 dots in the single-color set or superset (the total dots), no adaptation effect was observed. The coefficient of variation (CV) was below 0.05, indicating the effective function of subitizing. However, when enumerating subsets in parallel, adaptation biased the estimation. The CV in estimating subitized numerosities was comparable to and correlated with that of estimating moderate numerosities (5–12), suggesting that subitizing was superseded by numerosity estimation. Greater effects arise when the targets were probed afterwards, with elevated CV. The prior adaptor may be more weighted to optimize detection of number deviations, especially under higher perceptual uncertainty.
Antigen targeting and anti-tumor activity of a novel anti-CD146 212Pb internalizing alpha-radioimmunoconjugate against malignant peritoneal mesothelioma
Authors: Lindland Kim , Malenge Marion Masitsa , Li Ruth Gong , Wouters Roxanne , Bønsdorff Tina Bjørnlund , Juzeniene Asta , Dragovic Srdan M. 
Published: October 2024

AbstractMalignant mesothelioma, a highly aggressive cancer that primarily affects the serosal membranes, has limited therapeutic options, particularly for cavitary tumors, such as peritoneal and pleural malignant mesothelioma. Intracavitary administration of a radioimmunoconjugate to locally target mesothelioma cancer cells has been proposed as a treatment. CD146, upregulated in mesothelioma but not in healthy tissues, is a promising therapeutic target. This study characterized CD146 expression and binding/internalization kinetics of the CD146-targeting antibody OI-3 coupled with 212Pb (212Pb-TCMC-OI-3) in human mesothelioma cells. Flow cytometry showed that both chimeric (chOI-3) and murine (mOI-3) antibodies rapidly bound and internalized within 1–6 h in MSTO-211H cells. 212Pb-TCMC-chOI-3 exhibited 3.1- to 13.7-fold and 3.1- to 8.5-fold increased internalized 212Pb and 212Bi atoms per cell at 2 and 24 h, respectively, compared to isotype control, underscoring enhanced internalization efficiency. Intraperitoneal administration of 212Pb-TCMC-mOI-3 to mice with intraperitoneal MSTO-211H xenografts improved median survival by a ratio of 1.3 compared to non-binding 212Pb-TCMC-mIgG1. The ability of 212Pb-TCMC-mOI-3 to target and inhibit the growth of intraperitoneal mesothelioma xenografts supports targeted radionuclide therapy’s efficacy for metastatic peritoneal mesothelioma. This study highlights the potential of localized CD146-targeted radioimmunotherapy for malignant mesothelioma, offering a new avenue for improving patient outcomes.
Evaluation of tolvaptan-associated hepatic disorder using different national pharmacovigilance databases
Authors: Uno Takaya , Hosomi Kouichi , Yokoyama Satoshi 
Published: October 2024

AbstractTolvaptan-associated hepatic disorder is a rare, but lethal adverse event; however, the precise risk and time of onset remain unclear. This study aimed to characterize the severity, time‑to‑onset, and outcomes of hepatic disorder based on patient age and sex. Patient data were acquired from the Japanese Adverse Drug Event Report database (JADER) and the JAPIC AERS database, which consists of the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) processed by the Japan Pharmaceutical Information Center. Hepatic disorder was classified as severe or nonsevere. Tolvaptan use was associated with hepatic disorder in analyses using the FAERS [Severe hepatic disorder: reporting odds ratio (ROR) 4.93, 95% confidence interval (CI) 4.33‒5.61; information component (IC) 2.11, 95% CI 1.92‒2.29; nonsevere hepatic disorder: ROR 6.78, 95% CI 6.01‒7.65; IC 2.51, 95% CI 2.33‒2.68] and the JADER (severe hepatic disorder: ROR 4.21, 95% CI 3.57‒4.97; IC 1.86, 95% CI 1.63‒2.10; nonsevere hepatic disorder: ROR 4.27, 95% CI 3.68‒4.95; IC 1.83, 95% CI 1.62‒2.04). A time‑to‑onset analysis revealed that the median onset time was significantly longer in patients aged < 60 years compared with patients aged ≥ 60, regardless of the severity (FAERS: severe hepatic disorder 7 vs. 58 days, p < 0.0001; nonsevere hepatic disorder 8 vs. 52.5 days, p < 0.0001; JADER: severe hepatic disorder 9.5 vs. 32 days, p = 0.0017; nonsevere hepatic disorder 9 vs. 89 days, p < 0.0001). Severe outcomes were observed, regardless of the severity of hepatic disorder. Patients should be monitored for liver function based on age to prevent fatal outcomes.
Chest compression quality decreases in hypoxic conditions simulating an airliner cabin at cruising altitude: a randomized, controlled, double-blind Manikin Study
Authors: Schmitz Jan , Aeschbach Daniel , Beccard Inga , Frings Nina , Hinkelbein Jochen , Jordan Jens , Kammerer Tobias , Liebold Felix , Limper Ulrich , Post Titiaan , Schick Volker , Tank Jens , Elmenhorst Eva-Maria 
Published: October 2024

AbstractAir traveler numbers are predicted to reach 4.0 billion in 2024. Between 1/15,000–50,000 passengers will experience acute medical problems inflight with cardiac arrests requiring cardiopulmonary resuscitation (CPR) accounting for 0.3% of medical emergencies. Hypoxia in airplane cabins could impair oxygenation and physical performance of caregivers. We conducted a randomized controlled, double-blind study to test the hypothesis that hypoxia decreases the effectiveness in performing CPR. We randomized 24 healthcare professionals to two different study arms, each consisting of two conditions: arm (1) ‘hypoxia (FiO2 15%, equivalent to 2400 m altitude)’ versus ‘normoxia’; arm (2) ‘hypoxia + supplemental oxygen’ versus ‘normoxia + supplemental oxygen’. The order of conditions was counterbalanced and a minimum wash-out period of 24 h was granted between conditions. In each condition participants performed a 5-min cardiac compression only CPR (CCO-CPR) using a full-body manikin after one, three and six hours in an altitude chamber. Mixed ANOVAs with post-hoc false-discovery-rate adjusted pairwise comparisons indicated that although compression frequency was maintained, the number of compressions with correct depth was decreased at all times during hypoxia compared to normoxia (all p < 0.002). After 6 h hypoxia exposure, mean compression depth was below the recommended compression depth defined by ERC/AHA guidelines and reduced compared to normoxia (42.4 ± 12.6 mm vs. 54.6 ± 4.3 mm, p < 0.0001). Supplemental oxygen during CCO-CPR in hypoxia prevented the decrease of compression-depth (55.3 ± 3 mm). Extended hypoxia exposure akin to conditions in airplane cabins can reduce quality of chest compressions during CPR. Supplemental oxygen for healthcare providers is an effective countermeasure.
Reverse engineering of feedforward cortical-Hippocampal microcircuits for modelling neural network function and dysfunction
Authors: Hanssen Katrine Sjaastad , Winter-Hjelm Nicolai , Niethammer Salome Nora , Kobro-Flatmoen Asgeir , Witter Menno P. , Sandvig Axel , Sandvig Ioanna 
Published: October 2024

AbstractEngineered biological neural networks are indispensable models for investigation of neural function and dysfunction from the subcellular to the network level. Notably, advanced neuroengineering approaches are of significant interest for their potential to replicate the topological and functional organization of brain networks. In this study, we reverse engineered feedforward neural networks of primary cortical and hippocampal neurons, using a custom-designed multinodal microfluidic device with Tesla valve inspired microtunnels. By interfacing this device with nanoporous microelectrodes, we show that the reverse engineered multinodal neural networks exhibit capacity for both segregated and integrated functional activity, mimicking brain network dynamics. To advocate the broader applicability of our model system, we induced localized perturbations with amyloid beta to study the impact of pathology on network functionality. Additionally, we demonstrate long-term culturing of subregion- and layer specific neurons extracted from the entorhinal cortex and hippocampus of adult Alzheimer’s-model mice and rats. Our results thus highlight the potential of our approach for reverse engineering of anatomically relevant multinodal neural networks to study dynamic structure-function relationships in both healthy and pathological conditions.
Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques
Authors: Basthikodi Mustafa , Chaithrashree M. , Ahamed Shafeeq B. M. , Gurpur Ananth Prabhu 
Published: October 2024

AbstractIn the field of medical imaging, accurately classifying brain tumors remains a significant challenge because of the visual similarities among different tumor types. This research addresses the challenge of multiclass categorization by employing Support Vector Machine (SVM) as the core classification algorithm and analyzing its performance in conjunction with feature extraction techniques such as Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP), as well as the dimensionality reduction technique, Principal Component Analysis (PCA). The study utilizes a dataset sourced from Kaggle, comprising MRI images classified into four classes, with images captured from various anatomical planes. Initially, the SVM model alone attained an accuracy(acc_val) of 86.57% on unseen test data, establishing a baseline for performance. To enhance this, PCA was incorporated for dimensionality reduction, which improved the acc_val to 94.20%, demonstrating the effectiveness of reducing feature dimensionality in mitigating overfitting and enhancing model generalization. Further performance gains were realized by applying feature extraction techniques—HOG and LBP—in conjunction with SVM, resulting in an acc_val of 95.95%. The most substantial improvement was observed when combining SVM with both HOG, LBP, and PCA, achieving an impressive acc_val of 96.03%, along with an F1 score(F1_val) of 96.00%, precision(prec_val) of 96.02%, and recall(rec_val) of 96.03%. This approach will not only improves categorization performance but also improves efficacy of computation, making it a robust and effective method for multiclass brain tumor prediction.