bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Parkes Pulsar Timing Array third data release
Authors: Zic Andrew , Reardon Daniel J. , Kapur Agastya , Hobbs George , Mandow Rami , Curyło Małgorzata , Shannon Ryan M. , Askew Jacob , Bailes Matthew , Bhat N. D. Ramesh , Cameron Andrew , Chen Zu-Cheng , Dai Shi , Di Marco Valentina , Feng Yi , Kerr Matthew , Kulkarni Atharva , Lower Marcus E. , Luo Rui , Manchester Richard N. , Miles Matthew T. , Nathan Rowina S. , Osłowski Stefan , Rogers Axl F. , Russell Christopher J. , Sarkissian John M. , Shamohammadi Mohsen , Spiewak Renée , Thyagarajan Nithyanandan , Toomey Lawrence , Wang Shuangqiang , Zhang Lei , Zhang Songbo , Zhu Xing-Jiang 
Published: July 2023

Abstract
We present the third data release from the Parkes Pulsar Timing Array (PPTA) project. The release contains observations of 32 pulsars obtained using the 64-m Parkes ‘Murriyang’ radio telescope. The data span is up to 18 yr with a typical cadence of 3 weeks. This data release is formed by combining an updated version of our second data release with

$\sim$

3 yr of more recent data primarily obtained using an ultra-wide-bandwidth receiver system that operates between 704 and 4032 MHz. We provide calibrated pulse profiles, flux density dynamic spectra, pulse times of arrival, and initial pulsar timing models. We describe methods for processing such wide-bandwidth observations and compare this data release with our previous release.
A new species of Pisione (Annelida, Sigalionidae) from Palk Bay, India
Authors: Murugesan Perumal , Miranda Vinícius da Rocha , Punniyamoorthy Rengasamy , Mahadevan Gopalan 
Published: July 2023

Abstract
Pisione Grube, 1857 was considered rare animals, but its numbers have increased in recent decades. New species have been described, mainly from the Indo-Pacific region. In this study, we described Pisione balasubramaniani sp. nov., from Tamil Nadu, southeastern coast of India. The new species resembles other species in the ‘remota complex’ in the shape of the copulatory organs, elongated ventral cirri on the first segment and a short dorsal cirrus on the second segment. It can be distinguished from members of this group by having protruding notoacicula from the anteriormost to the very last chaetigers, three pairs of copulatory organs on chaetigers 15–17 and variances in the morphology of the copulatory organ. We also provide an updated comparison between all known species of Pisione based on key morphological characters. We also present a discussion of species of this genus in India and nearby regions.
How Credibly Do CMIP6 Simulations Capture Historical Mean and Extreme Precipitation Changes?
Authors: Donat Markus G. , Delgado‐Torres Carlos , De Luca Paolo , Mahmood Rashed , Ortega Pablo , Doblas‐Reyes Francisco J. 
Published: July 2023

AbstractFuture precipitation changes are typically estimated from climate model simulations, while the credibility of such projections needs to be assessed by their ability to capture observed precipitation changes. Here we evaluate how skillfully historical climate simulations contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) capture observed changes in mean and extreme precipitation. We find that CMIP6 historical simulations skillfully represent observed precipitation changes over large parts of Europe, Asia, northeastern North America, parts of South America and western Australia, whereas a lack of skill is apparent in western North America and parts of Africa. In particular in regions with moderate skill the availability of very large ensembles can be beneficial to improve the simulation accuracy. CMIP6 simulations are regionally skillful where they capture observed (positive or negative) trends, whereas a lack of skill is found in regions characterized by negative observed precipitation trends where CMIP6 simulates increases.
Impacts of Uncontrolled Operator Splitting Methods on Parameter Identification, Prediction Uncertainty, and Subsurface Flux Representation in Conceptual Hydrological Models
Authors: Woldegiorgis Befekadu Taddesse , Baulch Helen , Wheater Howard , Crossman Jill , Clark Martyn , Stadnyk Tricia , Bajracharya Ajay 
Published: July 2023

AbstractThe proper numerical representation of physical processes in mechanistic hydrological models is essential to produce robust predictions. A common problem with numerical schemes in hydrological models is that multiple concurrent fluxes are calculated sequentially. Although the importance of errors introduced by inappropriate numerical schemes is well recognized in the literature, many hydrological models calculate concurrent fluxes sequentially. Here, two versions of the HYPE model are used to investigate the limitations of sequential calculations. A fourth order Gear‐Nordsieck solution of the continuous state‐space formulation of HYPE (I‐HYPE) is developed to provide a robust solution, and a fixed‐step implicit Euler scheme (IE‐HYPE) is implemented to provide a computationally efficient and robust approximation of the I‐HYPE simulations. In contrast to I‐HYPE, results show that the original HYPE and the sequential calculation implemented in the continuous state‐space formulation of HYPE (SQ‐HYPE) typically simulate no interflow when soil moisture levels exceed the field capacity. The discrepancy between SQ‐HYPE and I‐HYPE grows with the size of the computation time step, and this implies a compromised representation of flow paths by sequential schemes. IE‐HYPE provides responses comparable with I‐HYPE for both daily and hourly time steps. IE‐HYPE and SQ‐HYPE are compared in terms of their groundwater representation, parameter identifiability, and predictive skills for two catchments. The sequential models have larger groundwater contributions to flow than IE‐HYPE because the splitting errors in SQ‐HYPE limit the interflow flux. IE‐HYPE estimates of the groundwater flux are more consistent with literature values of groundwater contributions to flow for the basins studied.
DNN‐Based Retrieval of Arctic Sea Ice Concentration From GNSS‐R and Its Effects on the Synoptic‐Scale Forecasting as Supplementary Observation Source
Authors: Yang Lu , Guo Bofeng , Zhang Zhaoyi , Zhang Xuefeng 
Published: July 2023

AbstractUsing delay‐Doppler maps of Global Navigation Satellite Systems Reflectometry (GNSS‐R) from the TechDemoSat‐1 satellite and considering sea ice and ocean interaction, an innovative method for retrieval of Arctic sea ice concentration (SIC) based on a deep neural network is proposed. This retrieval method shows the potential of future GNSS‐R applications for Arctic missions. Compared with SIC products from Hamburg University, the root mean square errors (RMSE) of retrieved results in March and June 2016 are 0.0284 and 0.0415, respectively. When the retrieved GNSS‐R SIC data are added into the assimilation as supplementary passive microwave remote‐sensing data, it has a positive influence on improving the accuracy of the Arctic SIC forecast. Especially in some edge regions of sea ice, when compared to only assimilating the remote‐sensing data, the regional RMSE of joint assimilation has a maximum decrease of approximately 17% in the 24‐hr forecast time, and over 5% in 72‐hr.
Measuring the Emission Changes and Meteorological Dependence of Source‐Specific BC Aerosol Using Factor Analysis Coupled With Machine Learning
Authors: Dai Tianjiao , Dai Qili , Ding Jing , Liu Baoshuang , Bi Xiaohui , Wu Jianhui , Zhang Yufen , Feng Yinchang 
Published: July 2023

AbstractReducing ambient black carbon (BC) relies on the targeted control of anthropogenic emissions. Measuring emission changes in source‐specific BC aerosol is essential to assess the effectiveness of regulatory policies but is difficult due to the presence of meteorology and multiple co‐existing emissions. Herein, we propose a data‐driven approach, combining dispersion‐normalized factor analysis (DN‐PMF) with a machine learning weather adjustment (deweathering) technique, to decompose ambient BC into source emissions and meteorological drivers. Six refined BC sources were extracted from the factor analysis of aethalometer multi‐wavelength BC and concurrent observational datasets. In addition to the widely reported dominant sources, such as vehicular emissions (VE) and coal/biomass burning (BB), a discernible port and shipping emission source were identified with potential impacts on coastal air quality. The source‐specific BC showed abrupt changes in response to interventions (e.g., holidays) after separating weather‐related confounders. Significant reductions in deweathered coal and BB, VE, and local dust verified the effectiveness of policies, such as clean winter‐heating and support for the Clean Air Actions. As revealed by a post‐hoc model explanation technique, the evolution of the boundary layer was the predominant meteorological driver exerting the opposite impact on local sources with respect to distant regional‐wide sources, that is, the port and shipping emissions.
Bright Na‐Carbonate Exposures Reveal Recent, Widespread Mobilization of Material in Ceres' Shallow Subsurface
Authors: Stein N. , Ehlmann B. L. , Stevenson D. J. , Castillo‐Rogez J. , Raymond C. A. 
Published: July 2023

AbstractStriking exposures of Na‐carbonate‐bearing bright materials are found in Ahuna Mons and the central landforms of Occator and other impact craters on Ceres; however, most bright materials on its surface occur as excavated material on the rims and walls of impact craters. The source of the widespread excavated Na‐carbonate is uncertain. We map the distribution and extent of rim/wall Na‐carbonate with Dawn's Visible and Infrared Imaging Spectrometer and Dawn Framing Camera. We find a strong spatial correlation between domes and shallow subsurface Na‐carbonate deposits, suggesting that they form by the same or related processes. Using Monte Carlo impact models, we constrain the timing of emplacement of the shallow subsurface Na‐carbonate to be within the last ∼1 Ga and probably within the last few hundred Ma. We examine the likelihood that the Na‐carbonate was mobilized from Ceres' deep interior and the plausibility of brine upwelling or solid‐state processes. We demonstrate that a combination of processes, including diapirism driven by Rayleigh‐Taylor instabilities and solid‐state deformation due to differential loading, could mobilize Na‐carbonate‐bearing ices to the shallow subsurface without requiring the recent presence of brines.
Implant-to-implant wireless networking with metamaterial textiles
Authors: Tian Xi , Zeng Qihang , Kurt Selman A. , Li Renee R. , Nguyen Dat T. , Xiong Ze , Li Zhipeng , Yang Xin , Xiao Xiao , Wu Changsheng , Tee Benjamin C. K. , Nikolayev Denys , Charles Christopher J. , Ho John S. 
Published: July 2023

AbstractImplanted bioelectronic devices can form distributed networks capable of sensing health conditions and delivering therapy throughout the body. Current clinically-used approaches for wireless communication, however, do not support direct networking between implants because of signal losses from absorption and reflection by the body. As a result, existing examples of such networks rely on an external relay device that needs to be periodically recharged and constitutes a single point of failure. Here, we demonstrate direct implant-to-implant wireless networking at the scale of the human body using metamaterial textiles. The textiles facilitate non-radiative propagation of radio-frequency signals along the surface of the body, passively amplifying the received signal strength by more than three orders of magnitude (>30 dB) compared to without the textile. Using a porcine model, we demonstrate closed-loop control of the heart rate by wirelessly networking a loop recorder and a vagus nerve stimulator at more than 40 cm distance. Our work establishes a wireless technology to directly network body-integrated devices for precise and adaptive bioelectronic therapies.
Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis
Authors: Zhang Ting , Zeng Qingdong , Ji Fan , Wu Honghong , Ledesma-Amaro Rodrigo , Wei Qingshan , Yang Hao , Xia Xuhan , Ren Yao , Mu Keqing , He Qiang , Kang Zhensheng , Deng Ruijie 
Published: July 2023

AbstractMolecular diagnostics for crop diseases can guide the precise application of pesticides, thereby reducing pesticide usage while improving crop yield, but tools are lacking. Here, we report an in-field molecular diagnostic tool that uses a cheap colorimetric paper and a smartphone, allowing multiplexed, low-cost, rapid detection of crop pathogens. Rapid nucleic acid amplification-free detection of pathogenic RNA is achieved by combining toehold-mediated strand displacement with a metal ion-mediated urease catalysis reaction. We demonstrate multiplexed detection of six wheat pathogenic fungi and an early detection of wheat stripe rust. When coupled with a microneedle for rapid nucleic acid extraction and a smartphone app for results analysis, the sample-to-result test can be completed in ~10 min in the field. Importantly, by detecting fungal RNA and mutations, the approach allows to distinguish viable and dead pathogens and to sensitively identify mutation-carrying fungicide-resistant isolates, providing fundamental information for precision crop disease management.
Targeting an allosteric site in dynamin-related protein 1 to inhibit Fis1-mediated mitochondrial dysfunction
Authors: Rios Luis , Pokhrel Suman , Li Sin-Jin , Heo Gwangbeom , Haileselassie Bereketeab , Mochly-Rosen Daria 
Published: July 2023

AbstractThe large cytosolic GTPase, dynamin-related protein 1 (Drp1), mediates both physiological and pathological mitochondrial fission. Cell stress triggers Drp1 binding to mitochondrial Fis1 and subsequently, mitochondrial fragmentation, ROS production, metabolic collapse, and cell death. Because Drp1 also mediates physiological fission by binding to mitochondrial Mff, therapeutics that inhibit pathological fission should spare physiological mitochondrial fission. P110, a peptide inhibitor of Drp1-Fis1 interaction, reduces pathology in numerous models of neurodegeneration, ischemia, and sepsis without blocking the physiological functions of Drp1. Since peptides have pharmacokinetic limitations, we set out to identify small molecules that mimic P110’s benefit. We map the P110-binding site to a switch I-adjacent grove (SWAG) on Drp1. Screening for SWAG-binding small molecules identifies SC9, which mimics P110’s benefits in cells and a mouse model of endotoxemia. We suggest that the SWAG-binding small molecules discovered in this study may reduce the burden of Drp1-mediated pathologies and potentially pathologies associated with other members of the GTPase family.
Dual-color DNA-PAINT single-particle tracking enables extended studies of membrane protein interactions
Authors: Niederauer Christian , Nguyen Chikim , Wang-Henderson Miles , Stein Johannes , Strauss Sebastian , Cumberworth Alexander , Stehr Florian , Jungmann Ralf , Schwille Petra , Ganzinger Kristina A. 
Published: July 2023

AbstractDNA-PAINT based single-particle tracking (DNA-PAINT-SPT) has recently significantly enhanced observation times in in vitro SPT experiments by overcoming the constraints of fluorophore photobleaching. However, with the reported implementation, only a single target can be imaged and the technique cannot be applied straight to live cell imaging. Here we report on leveraging this technique from a proof-of-principle implementation to a useful tool for the SPT community by introducing simultaneous live cell dual-color DNA-PAINT-SPT for quantifying protein dimerization and tracking proteins in living cell membranes, demonstrating its improved performance over single-dye SPT.
Architecture of the human G-protein-methylmalonyl-CoA mutase nanoassembly for B12 delivery and repair
Authors: Mascarenhas Romila , Ruetz Markus , Gouda Harsha , Heitman Natalie , Yaw Madeline , Banerjee Ruma 
Published: July 2023

AbstractG-proteins function as molecular switches to power cofactor translocation and confer fidelity in metal trafficking. The G-protein, MMAA, together with MMAB, an adenosyltransferase, orchestrate cofactor delivery and repair of B12-dependent human methylmalonyl-CoA mutase (MMUT). The mechanism by which the complex assembles and moves a >1300 Da cargo, or fails in disease, are poorly understood. Herein, we report the crystal structure of the human MMUT-MMAA nano-assembly, which reveals a dramatic 180° rotation of the B12 domain, exposing it to solvent. The complex, stabilized by MMAA wedging between two MMUT domains, leads to ordering of the switch I and III loops, revealing the molecular basis of mutase-dependent GTPase activation. The structure explains the biochemical penalties incurred by methylmalonic aciduria-causing mutations that reside at the MMAA-MMUT interfaces we identify here.
Identify hidden spreaders of pandemic over contact tracing networks
Authors: Huang Shuhong , Sun Jiachen , Feng Ling , Xie Jiarong , Wang Dashun , Hu Yanqing 
Published: July 2023

AbstractThe COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Due to the continuous emergence of new virus variants, even if vaccines have been widely used, the detection of asymptomatic infected persons is still important in the epidemic control. Based on the unique characteristics of COVID-19 spreading dynamics, here we propose a theoretical framework capturing the transition probabilities among different infectious states in a network, and extend it to an efficient algorithm to identify asymptotic individuals. We find that using pure physical spreading equations, the hidden spreaders of COVID-19 can be identified with remarkable accuracy, even with incomplete information of the contract-tracing networks. Furthermore, our framework can be useful for other epidemic diseases that also feature asymptomatic spreading.
Design of ion channel blocking, toxin-like Kunitz inhibitor peptides from the tapeworm, Echinococcus granulosus, with potential anti-cancer activity
Authors: Rashno Zahra , Rismani Elham , Ghasemi Jahan B. , Mansouri Mehdi , Shabani Mohammad , Afgar Ali , Dabiri Shahriar , Rezaei Makhouri Farahnaz , Hatami Abbas , Harandi Majid Fasihi 
Published: July 2023

AbstractOver-expression of K+ channels has been reported in human cancers and is associated with the poor prognosis of several malignancies. EAG1, a particular potassium ion channel, is widely expressed in the brain but poorly expressed in other normal tissues. Kunitz proteins are dominant in metazoan including the dog tapeworm, Echinococcus granulosus. Using computational analyses on one A-type potassium channel, EAG1, and in vitro cellular methods, including major cancer cell biomarkers expression, immunocytochemistry and whole-cell patch clamp, we demonstrated the anti-tumor activity of three synthetic small peptides derived from E. granulosus Kunitz4 protease inhibitors. Experiments showed induced significant apoptosis and inhibition of proliferation in both cancer cell lines via disruption in cell-cycle transition from the G0/G1 to S phase. Western blotting showed that the levels of cell cycle-related proteins including P27 and P53 were altered upon kunitz4-a and kunitz4-c treatment. Patch clamp analysis demonstrated a significant increase in spontaneous firing frequency in Purkinje neurons, and exposure to kunitz4-c was associated with an increase in the number of rebound action potentials after hyperpolarized current. This noteworthy component in nature could act as an ion channel blocker and is a potential candidate for cancer chemotherapy based on potassium channel blockage.
Deep joint learning of pathological region localization and Alzheimer’s disease diagnosis
Authors: Park Changhyun , Jung Wonsik , Suk Heung-Il 
Published: July 2023

AbstractThe identification of Alzheimer’s disease (AD) using structural magnetic resonance imaging (sMRI) has been studied based on the subtle morphological changes in the brain. One of the typical approaches is a deep learning-based patch-level feature representation. For this approach, however, the predetermined patches before learning the diagnostic model can limit classification performance. To mitigate this problem, we propose the BrainBagNet with a position-based gate (PG), which applies position information of brain images represented through the 3D coordinates. Our proposed method represents the patch-level class evidence based on both MR scan and position information for image-level prediction. To validate the effectiveness of our proposed framework, we conducted comprehensive experiments comparing it with state-of-the-art methods, utilizing two publicly available datasets: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle (AIBL) dataset. Furthermore, our experimental results demonstrate that our proposed method outperforms the existing competing methods in terms of classification performance for both AD diagnosis and mild cognitive impairment conversion prediction tasks. In addition, we performed various analyses of the results from diverse perspectives to obtain further insights into the underlying mechanisms and strengths of our proposed framework. Based on the results of our experiments, we demonstrate that our proposed framework has the potential to advance deep-learning-based patch-level feature representation studies for AD diagnosis and MCI conversion prediction. In addition, our method provides valuable insights, such as interpretability, and the ability to capture subtle changes, into the underlying pathological processes of AD and MCI, benefiting both researchers and clinicians.
Active learning for prediction of tensile properties for material extrusion additive manufacturing
Authors: Nasrin Tahamina , Pourali Masoumeh , Pourkamali-Anaraki Farhad , Peterson Amy M. 
Published: July 2023

AbstractMachine learning techniques were used to predict tensile properties of material extrusion-based additively manufactured parts made with Technomelt PA 6910, a hot melt adhesive. An adaptive data generation technique, specifically an active learning process based on the Gaussian process regression algorithm, was employed to enable prediction with limited training data. After three rounds of data collection, machine learning models based on linear regression, ridge regression, Gaussian process regression, and K-nearest neighbors were tasked with predicting properties for the test dataset, which consisted of parts fabricated with five processing parameters chosen using a random number generator. Overall, linear regression and ridge regression successfully predicted output parameters, with < 10% error for 56% of predictions. K-nearest neighbors performed worse than linear regression and ridge regression, with < 10% error for 32% of predictions and 10–20% error for 60% of predictions. While Gaussian process regression performed with the lowest accuracy (< 10% error for 32% of prediction cases and 10–20% error for 40% of predictions), it benefited most from the adaptive data generation technique. This work demonstrates that machine learning models using adaptive data generation techniques can efficiently predict properties of additively manufactured structures with limited training data.