bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ground‐Observed Snow Albedo Changes During Rain‐On‐Snow Events in Northern Alaska
Authors: Xie Yan , Pettersen Claire , Flanner Mark , Shates Julia 
Published: August 2024

AbstractRain‐on‐snow (ROS) events occur when rain falls on snowpack and can have substantial ecological and social impacts. During ROS events, liquid water in the snowpack can decrease the surface albedo, which contributes to the positive snow‐albedo feedback and further accelerates snowmelt. In a warming climate, the frequency and spatial coverage of ROS events are projected to increase in the high‐latitude regions, especially in northern Alaska. Multi‐year ground observations at two northern Alaska sites are utilized to evaluate 59 ROS events from 2012 to 2022. Results show that ROS events lead to dramatic snow albedo changes with a mean decline of −0.04 per day, which is considerably larger than the multi‐year mean of −0.005 in May and −0.008 in June. A snow albedo model is used to simulate the daily snow albedo changes due to snowpack liquid water content. The simulated impact of liquid water content accounts for only 10% of the observed snow albedo changes. In addition, composite synoptic conditions from reanalysis products reveal different moisture sources for ROS events. ROS events in May are associated with anomalous high pressure systems over the site and meridional transport of warm and moist air from lower latitudes. While the June synoptic conditions for ROS events show little deviation from the climatological mean and suggest local moisture contributions. ROS events in June show comparable snow albedo changes as in May despite the difference in moisture sources, which implies a prolonged impact of ROS events on rapid snow deterioration during late spring.
Meta Shack–Hartmann wavefront sensor with large sampling density and large angular field of view: phase imaging of complex objects
Authors: Go Gi-Hyun , Lee Dong-gu , Oh Jaeyeon , Song Gookho , Lee Doeon , Jang Mooseok 
Published: August 2024

AbstractShack–Hartmann wavefront sensors measure the local slopes of an incoming wavefront based on the displacement of focal spots created by a lenslet array, serving as key components for adaptive optics for astronomical and biomedical imaging. Traditionally, the challenges in increasing the density and the curvature of the lenslet have limited the use of such wavefront sensors in characterizing slowly varying wavefront structures. Here, we develop a metasurface-enhanced Shack–Hartmann wavefront sensor (meta SHWFS) to break this limit, considering the interplay between the lenslet parameters and the performance of SHWFS. We experimentally validate the meta SHWFS with a sampling density of 5963 per mm2 and a maximum acceptance angle of 8° which outperforms the traditional SFWFS by an order of magnitude. Furthermore, to the best of our knowledge, we demonstrate the first use of a wavefront sensing scheme in single-shot phase imaging of highly complex patterns, including biological tissue patterns. The proposed approach opens up new opportunities in incorporating exceptional light manipulation capabilities of the metasurface platform in complex wavefront characterization.
Current advance of nanotechnology in diagnosis and treatment for malignant tumors
Authors: Wang Bilan , Hu Shiqi , Teng Yan , Chen Junli , Wang Haoyuan , Xu Yezhen , Wang Kaiyu , Xu Jianguo , Cheng Yongzhong , Gao Xiang 
Published: August 2024

AbstractCancer remains a significant risk to human health. Nanomedicine is a new multidisciplinary field that is garnering a lot of interest and investigation. Nanomedicine shows great potential for cancer diagnosis and treatment. Specifically engineered nanoparticles can be employed as contrast agents in cancer diagnostics to enable high sensitivity and high-resolution tumor detection by imaging examinations. Novel approaches for tumor labeling and detection are also made possible by the use of nanoprobes and nanobiosensors. The achievement of targeted medication delivery in cancer therapy can be accomplished through the rational design and manufacture of nanodrug carriers. Nanoparticles have the capability to effectively transport medications or gene fragments to tumor tissues via passive or active targeting processes, thus enhancing treatment outcomes while minimizing harm to healthy tissues. Simultaneously, nanoparticles can be employed in the context of radiation sensitization and photothermal therapy to enhance the therapeutic efficacy of malignant tumors. This review presents a literature overview and summary of how nanotechnology is used in the diagnosis and treatment of malignant tumors. According to oncological diseases originating from different systems of the body and combining the pathophysiological features of cancers at different sites, we review the most recent developments in nanotechnology applications. Finally, we briefly discuss the prospects and challenges of nanotechnology in cancer.
Future sea ice weakening amplifies wind-driven trends in surface stress and Arctic Ocean spin-up
Authors: Muilwijk Morven , Hattermann Tore , Martin Torge , Granskog Mats A. 
Published: August 2024

AbstractArctic sea ice mediates atmosphere-ocean momentum transfer, which drives upper ocean circulation. How Arctic Ocean surface stress and velocity respond to sea ice decline and changing winds under global warming is unclear. Here we show that state-of-the-art climate models consistently predict an increase in future (2015–2100) ocean surface stress in response to increased surface wind speed, declining sea ice area, and a weaker ice pack. While wind speeds increase most during fall (+2.2% per decade), surface stress rises most in winter (+5.1% per decade) being amplified by reduced internal ice stress. This is because, as sea ice concentration decreases in a warming climate, less energy is dissipated by the weaker ice pack, resulting in more momentum transfer to the ocean. The increased momentum transfer accelerates Arctic Ocean surface velocity (+31–47% by 2100), leading to elevated ocean kinetic energy and enhanced vertical mixing. The enhanced surface stress also increases the Beaufort Gyre Ekman convergence and freshwater content, impacting Arctic marine ecosystems and the downstream ocean circulation. The impacts of projected changes are profound, but different and simplified model formulations of atmosphere-ice-ocean momentum transfer introduce considerable uncertainty, highlighting the need for improved coupling in climate models.
Variant-proof high affinity ACE2 antagonist limits SARS-CoV-2 replication in upper and lower airways
Authors: Gagne Matthew , Flynn Barbara J. , Honeycutt Christopher Cole , Flebbe Dillon R. , Andrew Shayne F. , Provost Samantha J. , McCormick Lauren , Van Ry Alex , McCarthy Elizabeth , Todd John-Paul M. , Bao Saran , Teng I-Ting , Marciano Shir , Rudich Yinon , Li Chunlin , Jain Shilpi , Wali Bushra , Pessaint Laurent , Dodson Alan , Cook Anthony , Lewis Mark G. , Andersen Hanne , Zahradník Jiří , Suthar Mehul S. , Nason Martha C. , Foulds Kathryn E. , Kwong Peter D. , Roederer Mario , Schreiber Gideon , Seder Robert A. , Douek Daniel C. 
Published: August 2024

AbstractSARS-CoV-2 has the capacity to evolve mutations that escape vaccine- and infection-acquired immunity and antiviral drugs. A variant-agnostic therapeutic agent that protects against severe disease without putting selective pressure on the virus would thus be a valuable biomedical tool that would maintain its efficacy despite the ongoing emergence of new variants. Here, we challenge male rhesus macaques with SARS-CoV-2 Delta—the most pathogenic variant in a highly susceptible animal model. At the time of challenge, we also treat the macaques with aerosolized RBD-62, a protein developed through multiple rounds of in vitro evolution of SARS-CoV-2 RBD to acquire 1000-fold enhanced ACE2 binding affinity. RBD-62 treatment equivalently suppresses virus replication in both upper and lower airways, a phenomenon not previously observed with clinically approved vaccines. Importantly, RBD-62 does not block the development of virus-specific T- and B-cell responses and does not elicit anti-drug immunity. These data provide proof-of-concept that RBD-62 can prevent severe disease from a highly virulent variant.
Defective mitochondrial COX1 translation due to loss of COX14 function triggers ROS-induced inflammation in mouse liver
Authors: Aich Abhishek , Boshnakovska Angela , Witte Steffen , Gall Tanja , Unthan-Fechner Kerstin , Yousefi Roya , Chowdhury Arpita , Dahal Drishan , Methi Aditi , Kaufmann Svenja , Silbern Ivan , Prochazka Jan , Nichtova Zuzana , Palkova Marcela , Raishbrook Miles , Koubkova Gizela , Sedlacek Radislav , Tröder Simon E. , Zevnik Branko , Riedel Dietmar , Michanski Susann , Möbius Wiebke , Ströbel Philipp , Lüchtenborg Christian , Giavalisco Patrick , Urlaub Henning , Fischer Andre , Brügger Britta , Jakobs Stefan , Rehling Peter 
Published: August 2024

AbstractMitochondrial oxidative phosphorylation (OXPHOS) fuels cellular ATP demands. OXPHOS defects lead to severe human disorders with unexplained tissue specific pathologies. Mitochondrial gene expression is essential for OXPHOS biogenesis since core subunits of the complexes are mitochondrial-encoded. COX14 is required for translation of COX1, the central mitochondrial-encoded subunit of complex IV. Here we describe a COX14 mutant mouse corresponding to a patient with complex IV deficiency. COX14M19I mice display broad tissue-specific pathologies. A hallmark phenotype is severe liver inflammation linked to release of mitochondrial RNA into the cytosol sensed by RIG-1 pathway. We find that mitochondrial RNA release is triggered by increased reactive oxygen species production in the deficiency of complex IV. Additionally, we describe a COA3Y72C mouse, affected in an assembly factor that cooperates with COX14 in early COX1 biogenesis, which displays a similar yet milder inflammatory phenotype. Our study provides insight into a link between defective mitochondrial gene expression and tissue-specific inflammation.
Dehydration regulates structural reorganization of dynamic hydrogels
Authors: Xu Dan , Meng Xintong , Liu Siyuan , Poisson Jade , Vana Philipp , Zhang Kai 
Published: August 2024

AbstractThe dehydration process is widely recognized as a significant phenomenon in nature. Hydrogels, which are important functional materials with high water content and crosslinked networks, encounter the issue of dehydration in their practical applications. Here, we report the distinctive anisotropic dehydration modality of dynamic hydrogels, which is fundamentally different from the more commonly observed isotropic dehydration of covalent hydrogels. Xerogels derived from dynamic hydrogel dehydration will fully cover a curved substrate surface and exhibit hollow structures with internal knots, in contrast to the bulk xerogels produced by covalent hydrogel dehydration. Depending on the competing cohesion of polymer chains and the adhesion at the hydrogel-substrate interface, the previously overlooked reorganization of polymer networks within dynamic hydrogels, triggered by dehydration-induced stress, has been discovered to regulate such macroscopic structural reconstruction for dynamic hydrogel dehydration. With the attached hydrogel-substrate interface, the surface microstructures of substrates can also be engraved onto xerogels with high resolution and on a large scale. This work will greatly enhance our understanding of the soft matter dehydration process and broaden the applications of dehydration technologies using water-containing materials.
Synchronized crystallization in tin-lead perovskite solar cells
Authors: Zhang Yao , Li Chunyan , Zhao Haiyan , Yu Zhongxun , Tang Xiaoan , Zhang Jixiang , Chen Zhenhua , Zeng Jianrong , Zhang Peng , Han Liyuan , Chen Han 
Published: August 2024

AbstractTin-lead halide perovskites with a bandgap near 1.2 electron-volt hold great promise for thin-film photovoltaics. However, the film quality of solution-processed Sn-Pb perovskites is compromised by the asynchronous crystallization behavior between Sn and Pb components, where the crystallization of Sn-based perovskites tends to occur faster than that of Pb. Here we show that the rapid crystallization of Sn is rooted in its stereochemically active lone pair, which impedes coordination between the metal ion and Lewis base ligands in the perovskite precursor. From this perspective, we introduce a noncovalent binding agent targeting the open metal site of coordinatively unsaturated Sn(II) solvates, thereby synchronizing crystallization kinetics and homogenizing Sn-Pb alloying. The resultant single-junction Sn-Pb perovskite solar cells achieve a certified power conversion efficiency of 24.13 per cent. The encapsulated device retains 90 per cent of the initial efficiency after 795 h of maximum power point operation under simulated one-sun illumination.
Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning
Authors: Pathrudkar Shashank , Thiagarajan Ponkrshnan , Agarwal Shivang , Banerjee Amartya S. , Ghosh Susanta 
Published: August 2024

AbstractThe ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources.
Feasibility of peak temperature targets in light of institutional constraints
Authors: Bertram Christoph , Brutschin Elina , Drouet Laurent , Luderer Gunnar , van Ruijven Bas , Aleluia Reis Lara , Baptista Luiz Bernardo , de Boer Harmen-Sytze , Cui Ryna , Daioglou Vassilis , Fosse Florian , Fragkiadakis Dimitris , Fricko Oliver , Fujimori Shinichiro , Hultman Nate , Iyer Gokul , Keramidas Kimon , Krey Volker , Kriegler Elmar , Lamboll Robin D. , Mandaroux Rahel , Rochedo Pedro , Rogelj Joeri , Schaeffer Roberto , Silva Diego , Tagomori Isabela , van Vuuren Detlef , Vrontisi Zoi , Riahi Keywan 
Published: August 2024

AbstractDespite faster-than-expected progress in clean energy technology deployment, global annual CO2 emissions have increased from 2020 to 2023. The feasibility of limiting warming to 1.5 °C is therefore questioned. Here we present a model intercomparison study that accounts for emissions trends until 2023 and compares cost-effective scenarios to alternative scenarios with institutional, geophysical and technological feasibility constraints and enablers informed by previous literature. Our results show that the most ambitious mitigation trajectories with updated climate information still manage to limit peak warming to below 1.6 °C (‘low overshoot’) with around 50% likelihood. However, feasibility constraints, especially in the institutional dimension, decrease this maximum likelihood considerably to 5–45%. Accelerated energy demand transformation can reduce costs for staying below 2 °C but have only a limited impact on further increasing the likelihood of limiting warming to 1.6 °C. Our study helps to establish a new benchmark of mitigation scenarios that goes beyond the dominant cost-effective scenario design.
Self-organization of mortal filaments and its role in bacterial division ring formation
Authors: Vanhille-Campos Christian , Whitley Kevin D. , Radler Philipp , Loose Martin , Holden Séamus , Šarić Anđela 
Published: August 2024

AbstractFilaments in the cell commonly treadmill. Driven by energy consumption, they grow on one end while shrinking on the other, causing filaments to appear motile even though individual proteins remain static. This process is characteristic of cytoskeletal filaments and leads to collective filament self-organization. Here we show that treadmilling drives filament nematic ordering by dissolving misaligned filaments. Taking the bacterial FtsZ protein involved in cell division as an example, we show that this mechanism aligns FtsZ filaments in vitro and drives the organization of the division ring in living Bacillus subtilis cells. We find that ordering via local dissolution also allows the system to quickly respond to chemical and geometrical biases in the cell, enabling us to quantitatively explain the ring formation dynamics in vivo. Beyond FtsZ and other cytoskeletal filaments, our study identifies a mechanism for self-organization via constant birth and death of energy-consuming filaments.
Basin-scale study of CO2 storage in stacked sequence of geological formations
Authors: Wijaya Nur , Morgan David , Vikara Derek , Grant Timothy , Cunha Luciane , Liu Guoxiang 
Published: August 2024

AbstractCommercial scale decarbonization through carbon capture and storage may likely involve many CO2 storage projects located in close proximity. The close proximity could raise concerns over caprock integrity associated with reservoir pressure buildup and interference among adjacent projects. Commercial-scale injection will also require large prospective CO2 storage resource and high injectivity in the targeted storage formations. To accommodate the need for both large resource and high injectivity, project operators could consider injecting CO2 into a stacked sequence of formations. This analysis investigates the benefits of injecting CO2 into a vertically stacked sequence of saline formations, over injecting the same amount of CO2 into a single saline formation, in addressing these challenges. Our analysis shows that injecting into the stacked sequence mitigates the extent of pressure buildup among the stacked formations, while still achieving the same or greater target CO2 storage volumes. Among cases modeled, the resulting pressure buildup front is most reduced when each storage site distributes injection volumes over several wells, each of which injects a portion of the total CO2 mass across the stacked sequence. This favorable case not only results in the smallest CO2 aerial footprint, but also shows the largest reduction in the pressure buildup at the top of perforation at the injection wells (upwards of approximately 46% compared to the single-formation storage), the result of which is crucial to maintain caprock integrity. This analysis provides insights into required decision-making when considering multi-project deployment in a shared basin.
Spatial distribution of active compounds in stratum corneum—partitioning between corneocytes and lipid matrix
Authors: Sjövall Peter , Gregoire Sebastien , Wargniez William , Skedung Lisa , Detroyer Ann , Luengo Gustavo S. 
Published: August 2024

AbstractThe interaction of active substances with molecular structures in stratum corneum (SC) is crucial for the efficacy and safety of cosmetic formulations and topical drugs. However, the molecular architecture of SC is highly complex and methods to unambiguously localize exogenous molecules within SC are lacking. Consequently, little is known about the distribution of actives within SC, and proposed penetration mechanisms through SC are typically limited to simple diffusion via a tortuous (lipid only) or transverse (across corneocytes and lipid matrix) pathway. In this work, 3D mass spectrometry imaging is used to determine the spatial distributions of four active substances at subcellular resolution in SC, including partitioning between the corneocytes and the intercellular lipid matrix. The results indicate that caffeine, 2-methyl resorcinol and oxybenzone are homogeneously distributed in the corneocytes but largely absent in the lipid matrix, despite considerable differences in lipophilicity. In contrast, the distribution- of jasmonic acid derivative is more inhomogeneous and indicates considerable localization to both the lipid phase and the corneocytes.
Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain
Authors: A. Abdel Hady Doaa , Abd El-Hafeez Tarek 
Published: August 2024

AbstractThis paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. The study aims to identify the most important features related to low back pain and to develop accurate models for predicting low back pain. Machine learning approaches showed promise for analyzing biomechanical factors related to postnatal low back pain (LBP). This study applied regression and classification algorithms to the trunk movement proposed dataset from 100 postpartum women, 50 with LBP and 50 without. The Optimized optuna Regressor achieved the best regression performance with a mean squared error (MSE) of 0.000273, mean absolute error (MAE) of 0.0039, and R2 score of 0.9968. In classification, the Basic CNN and Random Forest Classifier both attained near-perfect accuracy of 1.0, the area under the receiver operating characteristic curve (AUC) of 1.0, precision of 1.0, recall of 1.0, and F1-score of 1.0, outperforming other models. Key predictive features included pain (correlation of -0.732 with flexion range of motion), range of motion measures (flexion and extension correlation of 0.662), and average movements (correlation of 0.957 with flexion). Feature selection consistently identified pain, flexion, extension, lateral flexion, and average movement as influential across methods. While limited to this initial dataset and constrained by generalizability, machine learning offered quantitative insight. Models accurately regressed (MSE < 0.01, R2 > 0.95) and classified (accuracy > 0.94) trunk biomechanics distinguishing LBP. Incorporating additional demographic, clinical, and patient-reported factors may enhance individualized risk prediction and treatment personalization. This preliminary application of advanced analytics supported machine learning's potential utility for both LBP risk determination and outcome improvement. This study provides valuable insights into the use of machine learning techniques for analyzing trunk movement in women with postnatal low back pain and can potentially inform the development of more effective treatments.Trial registration: The trial was designed as an observational and cross-section study. The study was approved by the Ethical Committee in Deraya University, Faculty of Pharmacy, (No: 10/2023). According to the ethical standards of the Declaration of Helsinki. This study complies with the principles of human research. Each patient signed a written consent form after being given a thorough description of the trial. The study was conducted at the outpatient clinic from February 2023 till June 30, 2023.