bluuebunny/crossref_metadata_2025_split huggingface dataset:
Changes in Daytime Cirrus Properties From the ISCCP‐H Data Set and Their Impacts on the Radiation Energy Budget
Authors: Wang Meihua , Su Jing , Han Xinyi , Deng Xingzhu , Peng Nan , Liu Lei 
Published: August 2024

AbstractThe change in clouds during the day is critical to the Earth's energy balance and climatic evolution. However, there have been relatively few studies on cloud variations at daily timescales, owing to limitations of ground‐ and satellite‐observations, especially for cirrus clouds. In this study, we examined the daytime cirrus variation (DCV) at the global scales and its associated effects on radiation budgets based on the International Satellite Cloud Climatology Project H data set. The changes in continental cirrus cover are more significant than that over the ocean, reaching a maximum of 17.3% in the afternoon. Over the tropical deep convection regions, cirrus cloud cover and optical depth exhibit large amplitudes during the daytime, closely linked to average properties of cirrus clouds. Using a process‐based radiative transfer model, we calculated the variations in daytime cirrus cloud radiative forcing (CRF). After noon, cirrus clouds over both land and ocean generate the strongest shortwave (SW) cooling and longwave (LW) warming effects at the top of the atmosphere (TOA). At the global scale, daytime cirrus clouds cause an average net CRF of 3.6 W/m2 at the TOA. If the DCV is neglected in the model, the SW cooling and LW warming effects are overestimated by 2.5 and 1.8 W/m2 at the TOA, leading to a net radiation bias of 0.7 W/m2. The findings provide key information for targeting specific aspects of the cirrus parameterization scheme in climate models.
Constraining Present‐Day Anthropogenic Total Iron Emissions Using Model and Observations
Authors: Rathod Sagar D. , Hamilton Douglas S. , Nino Lance , Kreidenweis Sonia M. , Bian Qijing , Mahowald Natalie M. , Alastuey Andres , Querol Xavier , Paytan Adina , Artaxo Paulo , Herut Barak , Gaston Cassandra , Prospero Joseph , Chellam Shankararaman , Hueglin Christoph , Varrica Daniela , Dongarra Gaetano , Cohen David D. , Smichowski Patricia , Gomez Dario , Lambert Fabrice , Barraza Francisco , Bergametti Gilles , Rodríguez Sergio , Gonzalez‐Ramos Yenny , Hand Jenny , Kyllönen Katriina , Hakola Hannele , Chuang Patrick , Hopke Philip K. , Harrison Roy M. , Martin Randall V. , Walsh Brenna , Weagle Crystal , Maenhaut Willy , Morera‐Gómez Yasser , Chen Yu‐Cheng , Pierce Jeffrey R. , Bond Tami C. 
Published: August 2024

AbstractIron emissions from human activities, such as oil combustion and smelting, affect the Earth's climate and marine ecosystems. These emissions are difficult to quantify accurately due to a lack of observations, particularly in remote ocean regions. In this study, we used long‐term, near‐source observations in areas with a dominance of anthropogenic iron emissions in various parts of the world to better estimate the total amount of anthropogenic iron emissions. We also used a statistical source apportionment method to identify the anthropogenic components and their sub‐sources from bulk aerosol observations in the United States. We find that the estimates of anthropogenic iron emissions are within a factor of 3 in most regions compared to previous inventory estimates. Under‐ or overestimation varied by region and depended on the number of sites, interannual variability, and the statistical filter choice. Smelting‐related iron emissions are overestimated by a factor of 1.5 in East Asia compared to previous estimates. More long‐term iron observations and the consideration of the influence of dust and wildfires could help reduce the uncertainty in anthropogenic iron emissions estimates.
Investigating the 17 March 2013 Geomagnetic Storm Impacts on the Wholly Coupled Solar Wind‐Magnetosphere‐Ionosphere‐Thermosphere System‐Of‐Systems
Authors: Horvath Ildiko , Lovell Brian C. 
Published: August 2024

AbstractIn this study, we investigate the impacts of the 17 March 2013 strong geomagnetic storm on the wholly coupled Solar Wind‐Magnetosphere‐Ionosphere‐Thermosphere system‐of‐systems. Obtained from multipoint observations, our new results show (1) the solar‐wind Alfven waves propagating antisunward in the sheath region and (2) oscillating solar wind interplanetary magnetic field (IMF) and electric (E) field (IEF EY) that powered (3) rigorous dayside and nightside flux transfer events (FTEs) when (4) the nightside‐reconnection‐related short circuiting led to fast‐time Subauroral Ion Drifts (SAID) and Subauroral Polarization Streams (SAPS) E field development across the inner‐magnetosphere plasmapause where the solar‐wind Alfven waves (4) transitioned into kinetic Alfven waves (5) fueling the hot zone. Also, the antisunward solar‐wind Alfven waves (6) drove enhanced large‐scale region‐1 field‐aligned currents creating (7) undershielding conditions (8) allowing the dawn‐to‐dusk convection E field's earthward penetration, and (9) generated increased solar‐wind kinetic energy, which became deposited (10) to the ionosphere increasing the ionospheric electron temperature (by the downward flowing suprathermal electron fluxes) and (11) to the thermosphere oscillating the neutral winds and increasing the neutral temperature, and finally leading to (12) the development of bright stable auroral red (SAR) arcs in (13) the enhanced SAID/SAPS flow channels (FCs) developed during FTEs, (14) demonstrated with FC‐2 and FC‐3 events, in the enhanced polar convection that (15) the Rice Convection Model could reproduce. Finally, we conclude the antisunward‐propagating large‐amplitude solar‐wind Alfven waves' ultimate significant role in creating the favorable conditions for the various phenomena documented with the new observational results (1–14).
Time‐Lagged Effects of Ionospheric Response to Severe Geomagnetic Storms on GNSS Kinematic Precise Point Positioning
Authors: Yang Zhe , Morton Y. T. Jade 
Published: August 2024

AbstractThis paper investigates time‐lag effects of ionospheric response to two severe geomagnetic storms (Kp = 8) on the degradation of kinematic precise point positioning (PPP) solutions, utilizing over 5500 Global Navigation Satellite Systems (GNSS) stations distributed worldwide. Focusing on these two severe geomagnetic storms that occurred during solar cycle 24, the study employs an open‐source positioning software package, namely RTKLIB, to derive the PPP solutions. The findings reveal significant variations in time lags across different magnetic latitudes. These variations are driven by ionospheric responses to a southward interplanetary magnetic field and subsequent decreases in the SMY‐H index during the 2015 St. Patrick's Day Storm and the 2017 September 7–8 Storm. Specifically, at high latitudes, PPP degradation primarily manifests during the main phase of the storm, resulting in delays spanning from several minutes to 1–2 hr after the sudden onset of the storm. In contrast, mid‐ and low latitudes exhibit a wider range of delays extending up to tens of hours. Notably, rapid positioning degradation is observed predominantly at the magnetic local time noon and midnight sectors. The study discusses these time lag effects concerning the intensity of various ionospheric disturbances triggered by the interactions among the solar wind, magnetosphere, and ionosphere during geomagnetic storms. The insights obtained from this research have the potential to be integrated into physics‐based and machine‐learning models to enhance forecasting capabilities of space weather impacts.
IGF2BP3 promotes mRNA degradation through internal m7G modification
Authors: Liu Chang , Dou Xiaoyang , Zhao Yutao , Zhang Linda , Zhang Lisheng , Dai Qing , Liu Jun , Wu Tong , Xiao Yu , He Chuan 
Published: August 2024

Abstract
Recent studies have suggested that mRNA internal m7G and its writer protein METTL1 are closely related to cell metabolism and cancer regulation. Here, we identify that IGF2BP family proteins IGF2BP1-3 can preferentially bind internal mRNA m7G. Such interactions, especially IGF2BP3 with m7G, could promote the degradation of m7G target transcripts in cancer cells. IGF2BP3 is more responsive to changes of m7G modification, while IGF2BP1 prefers m6A to stabilize the bound transcripts. We also demonstrate that p53 transcript, TP53, is m7G-modified at its 3’UTR in cancer cells. In glioblastoma, the methylation level and the half lifetime of the modified transcript could be modulated by tuning IGF2BP3, or by site-specific targeting of m7G through a dCas13b-guided system, resulting in modulation of cancer progression and chemosensitivity.
CaV1.1 voltage-sensing domain III exclusively controls skeletal muscle excitation-contraction coupling
Authors: Pelizzari Simone , Heiss Martin C. , Fernández-Quintero Monica L. , El Ghaleb Yousra , Liedl Klaus R. , Tuluc Petronel , Campiglio Marta , Flucher Bernhard E. 
Published: August 2024

AbstractSkeletal muscle contractions are initiated by action potentials, which are sensed by the voltage-gated calcium channel (CaV1.1) and are conformationally coupled to calcium release from intracellular stores. Notably, CaV1.1 contains four separate voltage-sensing domains (VSDs), which activate channel gating and excitation-contraction (EC-) coupling at different voltages and with distinct kinetics. Here we show that a single VSD of CaV1.1 controls skeletal muscle EC-coupling. Whereas mutations in VSDs I, II and IV affect the current properties but not EC-coupling, only mutations in VSD III alter the voltage-dependence of depolarization-induced calcium release. Molecular dynamics simulations reveal comprehensive, non-canonical state transitions of VSD III in response to membrane depolarization. Identifying the voltage sensor that activates EC-coupling and detecting its unique conformational changes opens the door to unraveling the downstream events linking VSD III motion to the opening of the calcium release channel, and thus resolving the signal transduction mechanism of skeletal muscle EC-coupling.
Fine-tuning protein language models boosts predictions across diverse tasks
Authors: Schmirler Robert , Heinzinger Michael , Rost Burkhard 
Published: August 2024

AbstractPrediction methods inputting embeddings from protein language models have reached or even surpassed state-of-the-art performance on many protein prediction tasks. In natural language processing fine-tuning large language models has become the de facto standard. In contrast, most protein language model-based protein predictions do not back-propagate to the language model. Here, we compare the fine-tuning of three state-of-the-art models (ESM2, ProtT5, Ankh) on eight different tasks. Two results stand out. Firstly, task-specific supervised fine-tuning almost always improves downstream predictions. Secondly, parameter-efficient fine-tuning can reach similar improvements consuming substantially fewer resources at up to 4.5-fold acceleration of training over fine-tuning full models. Our results suggest to always try fine-tuning, in particular for problems with small datasets, such as for fitness landscape predictions of a single protein. For ease of adaptability, we provide easy-to-use notebooks to fine-tune all models used during this work for per-protein (pooling) and per-residue prediction tasks.
Fibrin drives thromboinflammation and neuropathology in COVID-19
Authors: Ryu Jae Kyu , Yan Zhaoqi , Montano Mauricio , Sozmen Elif G. , Dixit Karuna , Suryawanshi Rahul K. , Matsui Yusuke , Helmy Ekram , Kaushal Prashant , Makanani Sara K. , Deerinck Thomas J. , Meyer-Franke Anke , Rios Coronado Pamela E. , Trevino Troy N. , Shin Min-Gyoung , Tognatta Reshmi , Liu Yixin , Schuck Renaud , Le Lucas , Miyajima Hisao , Mendiola Andrew S. , Arun Nikhita , Guo Brandon , Taha Taha Y. , Agrawal Ayushi , MacDonald Eilidh , Aries Oliver , Yan Aaron , Weaver Olivia , Petersen Mark A. , Meza Acevedo Rosa , Alzamora Maria del Pilar S. , Thomas Reuben , Traglia Michela , Kouznetsova Valentina L. , Tsigelny Igor F. , Pico Alexander R. , Red-Horse Kristy , Ellisman Mark H. , Krogan Nevan J. , Bouhaddou Mehdi , Ott Melanie , Greene Warner C. , Akassoglou Katerina 
Published: August 2024

AbstractLife-threatening thrombotic events and neurological symptoms are prevalent in COVID-19 and are persistent in patients with long COVID experiencing post-acute sequelae of SARS-CoV-2 infection1–4. Despite the clinical evidence1,5–7, the underlying mechanisms of coagulopathy in COVID-19 and its consequences in inflammation and neuropathology remain poorly understood and treatment options are insufficient. Fibrinogen, the central structural component of blood clots, is abundantly deposited in the lungs and brains of patients with COVID-19, correlates with disease severity and is a predictive biomarker for post-COVID-19 cognitive deficits1,5,8–10. Here we show that fibrin binds to the SARS-CoV-2 spike protein, forming proinflammatory blood clots that drive systemic thromboinflammation and neuropathology in COVID-19. Fibrin, acting through its inflammatory domain, is required for oxidative stress and macrophage activation in the lungs, whereas it suppresses natural killer cells, after SARS-CoV-2 infection. Fibrin promotes neuroinflammation and neuronal loss after infection, as well as innate immune activation in the brain and lungs independently of active infection. A monoclonal antibody targeting the inflammatory fibrin domain provides protection from microglial activation and neuronal injury, as well as from thromboinflammation in the lung after infection. Thus, fibrin drives inflammation and neuropathology in SARS-CoV-2 infection, and fibrin-targeting immunotherapy may represent a therapeutic intervention for patients with acute COVID-19 and long COVID.
Probing plant signal processing optogenetically by two channelrhodopsins
Authors: Ding Meiqi , Zhou Yang , Becker Dirk , Yang Shang , Krischke Markus , Scherzer Sönke , Yu-Strzelczyk Jing , Mueller Martin J. , Hedrich Rainer , Nagel Georg , Gao Shiqiang , Konrad Kai R. 
Published: August 2024

AbstractEarly plant responses to different stress situations often encompass cytosolic Ca2+ increases, plasma membrane depolarization and the generation of reactive oxygen species1–3. However, the mechanisms by which these signalling elements are translated into defined physiological outcomes are poorly understood. Here, to study the basis for encoding of specificity in plant signal processing, we used light-gated ion channels (channelrhodopsins). We developed a genetically engineered channelrhodopsin variant called XXM 2.0 with high Ca2+ conductance that enabled triggering cytosolic Ca2+ elevations in planta. Plant responses to light-induced Ca2+ influx through XXM 2.0 were studied side by side with effects caused by an anion efflux through the light-gated anion channelrhodopsin ACR1 2.04. Although both tools triggered membrane depolarizations, their activation led to distinct plant stress responses: XXM 2.0-induced Ca2+ signals stimulated production of reactive oxygen species and defence mechanisms; ACR1 2.0-mediated anion efflux triggered drought stress responses. Our findings imply that discrete Ca2+ signals and anion efflux serve as triggers for specific metabolic and transcriptional reprogramming enabling plants to adapt to particular stress situations. Our optogenetics approach unveiled that within plant leaves, distinct physiological responses are triggered by specific ion fluxes, which are accompanied by similar electrical signals.
The potential of aptamers for the analysis of ceramic bound proteins found within pottery
Authors: Kosel Janez , Ropret Polonca 
Published: August 2024

AbstractArchaeological pottery are the most numerous objects found during excavations and reflect the culinary practices of the past. However, their functionality for cooking/storing specific foods or drinks cannot be deduced solely from comparing their shapes and sizes. Analysis of protein residues bound to ceramics can reveal the protein/animal type through their amino acid sequence, thus enabling direct identification of food types. Therefore, the aim of our experimental study was to test sixteen aptamers for the analysis of proteinaceous organic residues found within the porous structure of pottery. Traditionally prepared archaeological ceramic replicas were cooked for 5 days in various food/protein suspensions, were UV aged, buried for a year, excavated, and extensively cleaned. Their shards were analysed using immunofluorescence microscopy with aptamers. Results show that eight aptamers (Clone1 and Kirby for egg residuals; seqU5 and BLG14 for milk residuals; HA for blood residuals; Gli4 for gluten residuals; Par1 for fish residuals; and D1 for collagen residuals) produced a successful/specific immunofluorescence microscopy result when they were hybridised to shards containing target protein residuals. Interestingly, on whole egg control samples, when the egg lysozyme-targeting aptamer Kirby was used, fluorescence intensity was 3.1 times greater compared to that observed with anti-ovalbumin antibodies.
Critical metrology of minimally accessible anisotropic spin chains
Authors: Adani Marco , Cavazzoni Simone , Teklu Berihu , Bordone Paolo , Paris Matteo G. A. 
Published: August 2024

AbstractWe address quantum metrology in critical spin chains with anisotropy and Dzyaloshinskii–Moriya (DM) interaction, and show how local and quasi-local measurements may be exploited to characterize global properties of the systems. In particular, we evaluate the classical (magnetization) and quantum Fisher information of the relevant parameters for the density matrix of a single spin and that of a pair of spins ranging from nearest to sixth-nearest neighbors, to the limiting case of very distant spins. Our results allow us to elucidate the role of the different parameters and to individuate the optimal working regimes for the precise characterization of the system, also clarifying the effects of correlations on the estimation precision.
Action observation perspective influences the effectiveness of combined action observation and motor imagery training for novices learning an Osoto Gari judo throw
Authors: Chye Samantha , Valappil Ashika Chembila , Knight Ryan , Greene Andrew , Shearer David , Frank Cornelia , Diss Ceri , Bruton Adam 
Published: August 2024

AbstractCombined action observation and motor imagery (AOMI) training improves motor skill performance, but limited research has investigated possible moderating factors for this intervention. This study examined the influence of action observation (AO) perspective on the effectiveness of AOMI training for novices learning a ‘shadow’ Osoto Gari judo throw. Thirty novice participants were randomly assigned to AOMI training that displayed egocentric footage (AOMIEGO) or allocentric footage (AOMIALLO) of the Osoto Gari, or Control training. A motor learning design incorporating pre-test (Day 1), acquisition (Days 2–6), post-test (Day 7), and retention-test (Day 14) was adopted. Motor skill performance, self-efficacy, and mental representation structures were recorded as measures of learning. There were mixed effects for motor skill performance across the three training conditions utilized in this study, with AOMIALLO training significantly reducing error scores for final right hip flexion angle and peak right ankle velocity compared to AOMIEGO training. Self-efficacy increased for all training conditions over time. Both AOMIEGO and AOMIALLO training led to improved functional changes in mental representation structures over time compared to Control training. The findings suggest AOMI training led to improved perceptual-cognitive scaffolding, irrespective of AO perspective, and offer some support for the use of AOMIALLO training to facilitate novice learning of complex, serial motor skills in sport.
Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study
Authors: Yoon Soo Bin , Lee Jeong-Moo , Jung Chul-Woo , Suh Kyung-Suk , Lee Kwang-Woong , Yi Nam-Joon , Hong Suk Kyun , Choi YoungRok , Hong Su young , Lee Hyung-Chul 
Published: August 2024

AbstractTitrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retrospective cohort study aimed to develop and validate a machine-learning algorithm to predict tacrolimus concentration. Data from 443 patients undergoing liver transplantation between 2017 and 2020 at an academic hospital in South Korea were collected to train machine-learning models. Long short-term memory (LSTM) and gradient-boosted regression tree (GBRT) models were developed using time-series doses and concentrations of tacrolimus with covariates of age, sex, weight, height, liver enzymes, total bilirubin, international normalized ratio, albumin, serum creatinine, and hematocrit. We conducted performance comparisons with linear regression and populational pharmacokinetic models, followed by external validation using the eICU Collaborative Research Database collected in the United States between 2014 and 2015. In the external validation, the LSTM outperformed the GBRT, linear regression, and populational pharmacokinetic models with median performance error (8.8%, 25.3%, 13.9%, and − 11.4%, respectively; P < 0.001) and median absolute performance error (22.3%, 33.1%, 26.8%, and 23.4%, respectively; P < 0.001). Dosing based on the LSTM model’s suggestions achieved therapeutic concentrations more frequently on the chi-square test (P < 0.001). Patients who received doses outside the suggested range were associated with longer ICU stays by an average of 2.5 days (P = 0.042). In conclusion, machine learning models showed excellent performance in predicting tacrolimus concentration in liver transplantation recipients and can be useful for concentration titration in these patients.
Leverage points for the uptake of organic food production and consumption in the United Kingdom
Authors: Staton Tom , Davison Nicholas , Westaway Sally , Arguile Lisa , Adams Nina , Aguilera Victor , Bellamy Lillie , Bolger Alysoun , Gantlett Richard , Jacobs Steven , Noone Niamh , Staley Joanna T. , Smith Laurence G. 
Published: August 2024

AbstractOrganic food systems are recognised as an important component in meeting United Nations’ (UN) Sustainable Development Goals. A leverage points perspective can help to identify approaches which have the potential to facilitate transformative systemic change towards organic and sustainable farming. Using fuzzy cognitive maps developed from expert stakeholder opinions, we modelled a system of drivers of organic food production and consumption in the United Kingdom, according to the UN Sustainability Assessment of Food and Agriculture systems framework. The most influential concepts in the uptake of organic systems were related to system norms and values and social structures, such as short-term economic thinking, landowner engagement, and relationships with certification bodies. However, in a scenario analysis, organic stakeholders identified relatively shallower leverage points as more likely to change under a sustainable future, resulting in limited systemic change. This demonstrates the need for policies targeting system norms, values and social structures relating to food systems to facilitate the transition to organic and sustainable farming.