bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Multi‐Proxy Approach Using Zircon (U‐Th)/He Thermochronometry and Biomarker Thermal Maturity to Robustly Capture Earthquake Temperature Rise Along the Punchbowl Fault, California
Authors: Armstrong E. M. , Ault A. K. , Bradbury K. K. , Savage H. M. , Polissar P. J. , Thomson S. N. 
Published: April 2022

AbstractDuring an earthquake, work done to overcome fault friction is dissipated as heat. Coseismic temperature rise, critical for identifying and constraining the magnitude of past earthquakes, is difficult to accurately quantify. To address this issue, we compare two temperature‐sensitive geochemical systems, zircon (U‐Th)/He (ZHe) thermochronometry and thermal maturity of organic matter (biomarkers), which respond to short‐duration, high temperatures. Models of prior biomarker data from the Punchbowl fault (PF), CA, indicate coseismic temperatures of ∼465–1,065°C in the principal slip zone (PSZ; Savage & Polissar, 2019, https://doi.org/10.1029/2019gc008225) depending on prescribed thickness of the deforming zone. We resampled two PF sample sites and acquired high‐spatial resolution ZHe data (n = 45 individual analyses) from the PSZ and fault core gouge, together with adjacent crystalline basement and Punchbowl Formation rocks. Results define a positive ZHe date‐effective U (eU) trend from ∼10 to 60 Ma and ∼20–700 ppm eU with a plateau at ∼65 Ma at >700 ppm eU. This pattern suggests the PSZ and fault core gouge share a similar thermal history to material outside the PF. Individual apatite (U‐Th)/He dates (n = 5) from an undeformed Punchbowl Formation sample are ∼4 Ma for grains with ∼30–150 ppm eU, implying rapid cooling and exhumation at that time due to PF activity. Zircon damage‐diffusivity relationships inform a suite of numerical models that collectively bracket coseismic temperatures on the PF to <725–800°C for 90% He loss. Results support general compatibility between ZHe and biomarker‐derived temperature rise estimates, and spatio‐temporal variability in coseismic temperatures along the PF.
Reconciling Roles of External Forcing and Internal Variability in Indian Ocean Decadal Variability Since 1920
Authors: Hua Wenjian , Dai Aiguo , Qin Minhua 
Published: April 2022

AbstractOn decadal time scales, Indian Ocean sea surface temperatures (SSTs) exhibit coherent basin‐wide changes, but their origins are not well understood. Here we analyze observations and model simulations from Coupled Model Intercomparison Project Phase 6 and Community Earth System Model Version 1 to quantify the roles of external forcing and internal climate variability in causing Indian Ocean decadal SST variations. Results show that both external forcing and internal variability since 1920 have contributed to the observed decadal variations in linearly detrended Indian Ocean SSTs, and they exhibit an out‐of‐phase relationship since the 1950s. The internally‐generated variations arise from remote influences from the tropical Pacific and possible contributions from internal local processes, while the influence from the Atlantic Multidecadal Oscillation is opposite to that of the Interdecadal Pacific Oscillation. Decadal SST changes caused by nonlinear variations in greenhouse gases and aerosols are roughly out‐of‐phase with the internal variability, thus dampening observed SST variations since the 1950s.
Response of Eurasian Temperature to Barents–Kara Sea Ice: Evaluation by Multi‐Model Seasonal Predictions
Authors: Komatsu Kensuke K. , Takaya Yuhei , Toyoda Takahiro , Hasumi Hiroyasu 
Published: April 2022

AbstractBarents–Kara (BK) sea ice conditions are considered a potential source of seasonal predictability in the midlatitudes, but confirmation or refutation of this possibility remains elusive. We focused on the Warm Arctic–Cold Eurasia (WACE) pattern and evaluated whether BK sea ice is related to winter conditions over Eurasia in multi‐model seasonal predictions. In the models analyzed, the WACE index is strongly depended on the BK temperature and did not rigorously reflect a sea ice–Eurasia causal link. The autumn BK sea ice anomaly was not a precursor of winter atmospheric conditions over Eurasia. Rather, the winter atmospheric circulation likely drives both winter BK sea ice and Eurasian temperature. However, the predicted winter sea ice–Eurasia links are likely weaker than the observed. This contrast is speciously related to the modest sea ice variance, but the actual influence of BK sea ice variation on Eurasian temperature anomalies is still questionable.
Increased Large‐Scale Convective Aggregation in CMIP5 Projections: Implications for Tropical Precipitation Extremes
Authors: Bläckberg C. P. O. , Singh M. S. 
Published: April 2022

AbstractConvective aggregation refers to the clustering of convective events and occurs on a wide range of spatial scales. It has been suggested that the behavior of convective aggregation may change under global warming, with potential implications for future changes in precipitation extremes. Here, convective regions of the tropics are defined from a percentile threshold on gridded daily precipitation data and used to quantify large‐scale convective aggregation in an ensemble of global climate models. Applying three separate indices for aggregation, it is found that large‐scale convective aggregation increases in 17 of the 19 analyzed models under future warming. However, aggregation is found not to be correlated with tropical‐mean precipitation extremes, either climatologically or with respect to the sensitivity to warming. The large model spread in aggregation indices across the ensemble suggests the possible utility of large‐scale convective aggregation as a target for model evaluation.
The Influence of the Vietnam Offshore Current and Kuroshio Intrusion on Net Community Production in the Oligotrophic South China Sea in Summer
Authors: Qin Chuan , Zhang Guiling , Han Yu , Zhang Guicheng , Sun Jun 
Published: April 2022

AbstractIn summer, the Vietnam Offshore Current (VOC) and the Kuroshio intrusion are two important processes inducing considerable environmental fluctuations in the surface water of the South China Sea (SCS). Net community production (NCP) is an important proxy of the biological pump strength and can be estimated based on the dissolved oxygen to argon ratio (O2/Ar) in the mixed layer. To quantify the influence of the VOC and Kuroshio intrusion on NCP in the oligotrophic SCS, we obtained continuous measurements of O2/Ar in the upper mixed layer using membrane inlet mass spectrometry (MIMS) in the northeastern SCS in summer 2017. Average NCP was 2.6 ± 1.5 mmol C m−2 d−1 in Phase 1 (from 13 to 20 July), 5.3 ± 1.7 mmol C m−2 d−1 in Phase 2 (from 20 to 25 July), and 13.9 ± 2.3 mmol C m−2 d−1 in Phase 3 (from 30 July to 9 August). Data from the region influenced by vertical mixing were excluded. A strong correlation between the surface dissolved inorganic nitrogen (DIN) and NCP was obtained, indicating that DIN was an essential factor controlling NCP in the SCS surface water. During our sampling, the Kuroshio intruded into the northeastern SCS in a loop pattern through the Luzon Strait, approximately halving the surface DIN concentration and reducing NCP by 36%. In addition, the VOC drove Vietnam coastal upwelled water to extend northeastward, causing an increase of 140% in the surface DIN concentration and an associated elevation of 160% in NCP in the northeastern SCS.
Automatic Spread‐F Detection Using Deep Learning
Authors: Luwanga Christopher , Fang Tzu‐Wei , Chandran Amal , Lee Yu‐Ju 
Published: April 2022

AbstractSpread‐F (SF) is a feature that can be visually observed on ionograms when the ionosonde signals are significantly impacted by plasma irregularities in the ionosphere. Depending on the scale of the plasma irregularities, radio waves of different frequencies are impacted differently when the signals pass through the ionosphere. An automated method for detecting SF in ionograms is presented in this study. Through detecting the existence of SF in ionograms, we can help identify instances of plasma irregularities that are potentially affecting the high‐frequency radio‐wave systems. The ionogram images from Jicamarca observatory in Peru, during the years 2008–2019, are used in this study. Three machine learning approaches have been carried out: supervised learning using Support Vector Machines, and two neural network‐based learning methods: autoencoder and transfer learning. Of these three methods, the transfer learning approach, which uses convolutional neural network architectures, demonstrates the best performance. The best existing architecture that is suitable for this problem appears to be the ResNet50. With respect to the training epoch number, the ResNet50 showed the greatest change in the metric values for the key metrics that we were tracking. Furthermore, on a test set of 2050 ionograms, the model based on the ResNet50 architecture provides an accuracy of 89%, recall of 87%, precision of 95%, as well as Area Under the Curve of 96%. The work also provides a labeled data set of around 28,000 ionograms, which is extremely useful for the community for future machine learning studies.
A Rapid Localized Deceleration of Earth's Radiation Belt Relativistic Electrons Driven by Storm Proton Injection
Authors: Chen Zewen , Su Zhenpeng , He Zhaoguo , Wu Zhiyong , Dai Guyue , Wang Bin , Xiong Ying , Zheng Huinan , Cui Jun , Wang Yuming 
Published: April 2022

AbstractEarth's radiation belt relativistic electron dropouts frequently occur during the main phase of geomagnetic storms, which have been partially attributed to the ring current ion enhancements causing geomagnetic field reconfigurations. In contrast to early studies on the global radiation belt response to the ring current buildup on a timescale of several hours, we here describe a rapid, localized, significant decay of relativistic electrons driven by freshly injected energetic protons during the 27 May 2017 geomagnetic storm. Near the proton injection front, the magnetic field lines were stretched outward, with an inferred migration of their equatorial crossings by ∼0.5 RE during several minutes. Because of the betatron and Fermi decelerations, the relativistic electron fluxes decreased by 2–3 orders of magnitude with pitch‐angle distributions evolving from pancake/flat‐top type to cigar type. This rapid localized response pattern of relativistic electrons could be general over strong particle injections during both geomagnetic storms and substorms.
Carbon Dioxide Ice Glaciers at the South Pole of Mars
Authors: Smith I. B. , Schlegel N.‐J. , Larour E. , Isola I. , Buhler P. B. , Putzig N. E. , Greve R. 
Published: April 2022

AbstractMassive, kilometer thick deposits of carbon dioxide (CO2) ice have been detected at the south polar cap of Mars by radar investigations. These deposits are divided into several units that are separated by thin water ice bounding layers. Recent studies investigated the accumulation history of CO2 ice and found that the deposits most likely formed during several episodes in the past, when Martian obliquity was much lower than now. Those studies, while able to predict total volumes of CO2 ice consistent with those observed, did not attempt to explain the anomalous three‐dimensional distribution (thickness or extent) of CO2 ice or the ice's offset from the topographic high of the polar cap. In this paper we use a combination of feature analysis and numerical modeling to demonstrate that the CO2 deposits flow as glaciers and that glacial flow distributes the ice into its current position. Further, this distribution allows the ice to survive during high obliquity excursions.
ToxPi*GIS Toolkit: creating, viewing, and sharing integrative visualizations for geospatial data using ArcGIS
Authors: Fleming Jonathon , Marvel Skylar W. , Supak Stacy , Motsinger-Reif Alison A. , Reif David M. 
Published: April 2022

Abstract
Background
Presenting a comprehensive picture of geographic data comprising multiple factors is an inherently integrative undertaking. Visualizing such data in an interactive form is essential for public sharing and geographic information systems (GIS) analysis. The Toxicological Prioritization Index (ToxPi) framework offers a visual analytic integrating data that is compatible with geographic data. ArcGIS is a predominant geospatial software available for presenting and communicating geographic data, yet to our knowledge there is no methodology for integrating ToxPi profiles into ArcGIS maps.

Objective
We introduce an actively developed suite of software, the ToxPi*GIS Toolkit, for creating, viewing, sharing, and analyzing interactive ToxPi profiles in ArcGIS to allow for new GIS analysis and an avenue for providing geospatial results to the public.

Methods
The ToxPi*GIS Toolkit is a collection of methods for creating interactive feature layers that contain ToxPi profiles. It currently includes an ArcGIS Toolbox (ToxPiToolbox.tbx) for drawing location-specific ToxPi profiles in a single feature layer, a collection of modular Python scripts that create predesigned layer files containing ToxPi feature layers from the command line, and a collection of Python routines for useful data manipulation and preprocessing. We present workflows documenting ToxPi feature layer creation, sharing, and embedding for both novice and advanced users looking for additional customizability.

Results
Map visualizations created with the ToxPi*GIS Toolkit can be made freely available on public URLs, allowing users without ArcGIS Pro access or expertise to view and interact with them. Novice users with ArcGIS Pro access can create de novo custom maps, and advanced users can exploit additional customization options. The ArcGIS Toolbox provides a simple means for generating ToxPi feature layers. We illustrate its usage with current COVID-19 data to compare drivers of pandemic vulnerability in counties across the United States.

Significance
The integration of ToxPi profiles with ArcGIS provides new avenues for geospatial analysis, visualization, and public sharing of multi-factor data. This allows for comparison of data across a region, which can support decisions that help address issues such as disease prevention, environmental health, natural disaster prevention, chemical risk, and many others. Development of new features, which will advance the interests of the scientific community in many fields, is ongoing for the ToxPi*GIS Toolkit, which can be accessed from www.toxpi.org.
Increased apoptotic sensitivity of glioblastoma enables therapeutic targeting by BH3-mimetics
Authors: Koessinger Anna L. , Cloix Catherine , Koessinger Dominik , Heiland Dieter Henrik , Bock Florian J. , Strathdee Karen , Kinch Kevin , Martínez-Escardó Laura , Paul Nikki R. , Nixon Colin , Malviya Gaurav , Jackson Mark R. , Campbell Kirsteen J. , Stevenson Katrina , Davis Sandeep , Elmasry Yassmin , Ahmed Asma , O’Prey Jim , Ichim Gabriel , Schnell Oliver , Stewart William , Blyth Karen , Ryan Kevin M. , Chalmers Anthony J. , Norman Jim C. , Tait Stephen W. G. 
Published: April 2022

AbstractGlioblastoma (GBM) is the most prevalent malignant primary brain tumour in adults. GBM typically has a poor prognosis, mainly due to a lack of effective treatment options leading to tumour persistence or recurrence. We investigated the therapeutic potential of targeting anti-apoptotic BCL-2 proteins in GBM. Levels of anti-apoptotic BCL-xL and MCL-1 were consistently increased in GBM compared with non-malignant cells and tissue. Moreover, we found that relative to their differentiated counterparts, patient-derived GBM stem-like cells also displayed higher expression of anti-apoptotic BCL-2 family members. High anti-apoptotic BCL-xL and MCL-1 expression correlated with heightened susceptibility of GBM to BCL-2 family protein-targeting BH3-mimetics. This is indicative of increased apoptotic priming. Indeed, GBM displayed an obligate requirement for MCL-1 expression in both tumour development and maintenance. Investigating this apoptotic sensitivity, we found that sequential inhibition of BCL-xL and MCL-1 led to robust anti-tumour responses in vivo, in the absence of overt toxicity. These data demonstrate that BCL-xL and MCL-1 pro-survival function is a fundamental prerequisite for GBM survival that can be therapeutically exploited by BH3-mimetics.
Long noncoding RNA CASC7 is a novel regulator of glycolysis in oesophageal cancer via a miR-143-3p-mediated HK2 signalling pathway
Authors: Sun Wei , Wang Dao , Zu Yukun , Deng Yu 
Published: April 2022

AbstractLong noncoding RNAs have been proven to play a crucial role in many tumours. Here, we explored the role of the lncRNA cancer susceptibility candidate 7 (CASC7) in oesophageal cancer. LncRNA CASC7 was identified in our database analysis, and we found that it was significantly higher in oesophageal tumour tissue than in normal tissue and that high expression of lncRNA CASC7 predicted a poor prognosis. Furthermore, we verified through cell experiments that low expression of lncRNA CASC7 in oesophageal cancer cells significantly inhibited tumour proliferation, which could be explained by the effect of lncRNA CASC7 on aerobic glycolysis. Next, we found that the expression of CASC7 and hexokinase 2 (HK2) in oesophageal cancer was positively correlated in database analysis, and this conclusion was further verified in cell experiments. To determine the mechanism, we found that miR-143-3p can bind to both lncRNA CASC7 and HK2. In clinical specimens, we also found high expression of lncRNA CASC7 in tumours, and the expression levels of lncRNA CASC7 and HK2 were positively correlated. In conclusion, downregulating lncRNA CASC7 could inhibit tumour proliferation by reducing glycolysis through the miR-143-3p/HK2 axis.
Coacervation in polyzwitterion-polyelectrolyte systems and their potential applications for gastrointestinal drug delivery platforms
Authors: Margossian Khatcher O. , Brown Marcel U. , Emrick Todd , Muthukumar Murugappan 
Published: April 2022

AbstractTraditionally, complex coacervation is regarded as a process whereby two oppositely charged polyelectrolytes self-assemble into spherical droplets. Here, we introduce the polyzwitterionic complex, “pZC”, formed by the liquid-liquid phase separation of a polyzwitterion and a polyelectrolyte, and elucidate a mechanism by which such complexes can assemble using theory and experimental evidence. This system exhibits orthogonal phase behavior-it remains intact in acidic conditions, but disassembles as the pH increases, a process governed by the acid-base equilibria of the constituent chains. We relate the observed phase behavior to physiological conditions within the gastrointestinal tract with a simulation of the gastroduodenal junction, and demonstrate using video microscopy the viability of polyzwitterionic coacervates as technologies for the pH-triggered release of cargo. Such a system is envisaged to tackle imminent problems of drug transport via the oral route and serve as a packaging solution to increase uptake efficiency.
Bloom helicase mediates formation of large single–stranded DNA loops during DNA end processing
Authors: Xue Chaoyou , Salunkhe Sameer J. , Tomimatsu Nozomi , Kawale Ajinkya S. , Kwon Youngho , Burma Sandeep , Sung Patrick , Greene Eric C. 
Published: April 2022

AbstractBloom syndrome (BS) is associated with a profoundly increased cancer risk and is caused by mutations in the Bloom helicase (BLM). BLM is involved in the nucleolytic processing of the ends of DNA double–strand breaks (DSBs), to yield long 3′ ssDNA tails that serve as the substrate for break repair by homologous recombination (HR). Here, we use single–molecule imaging to demonstrate that BLM mediates formation of large ssDNA loops during DNA end processing. A BLM mutant lacking the N–terminal domain (NTD) retains vigorous in vitro end processing activity but fails to generate ssDNA loops. This same mutant supports DSB end processing in cells, however, these cells do not form RAD51 DNA repair foci and the processed DSBs are channeled into synthesis–dependent strand annealing (SSA) instead of HR–mediated repair, consistent with a defect in RAD51 filament formation. Together, our results provide insights into BLM functions during homologous recombination.
Spontaneous chiral symmetry breaking in a random driven chemical system
Authors: Piñeros William D. , Tlusty Tsvi 
Published: April 2022

AbstractLiving systems have evolved to efficiently consume available energy sources using an elaborate circuitry of chemical reactions which, puzzlingly, bear a strict restriction to asymmetric chiral configurations. While autocatalysis is known to promote such chiral symmetry breaking, whether a similar phenomenon may also be induced in a more general class of configurable chemical systems—via energy exploitation—is a sensible yet underappreciated possibility. This work examines this question within a model of randomly generated complex chemical networks. We show that chiral symmetry breaking may occur spontaneously and generically by harnessing energy sources from external environmental drives. Key to this transition are intrinsic fluctuations of achiral-to-chiral reactions and tight matching of system configurations to the environmental drives, which together amplify and sustain diverged enantiomer distributions. These asymmetric states emerge through steep energetic transitions from the corresponding symmetric states and sharply cluster as highly-dissipating states. The results thus demonstrate a generic mechanism in which energetic drives may give rise to homochirality in an otherwise totally symmetrical environment, and from an early-life perspective, might emerge as a competitive, energy-harvesting advantage.
A novel belief rule base expert system with interval-valued references
Authors: Sun Chao , Yang Ruohan , He Wei , Zhu Hailong 
Published: April 2022

AbstractAs an essential parameter in the belief rule base (BRB), referential values refer to evaluation criteria for describing attributes using quantitative data or linguistic terms, the rationality and preciseness of which are important to the modeling accuracy. At present, the studies on referential values of BRB are mainly related to single-valued data. However, due to the inherent uncertainty, ambiguity, and vagueness of expert knowledge, the single-valued references provided by experts cannot represent qualitative information adequately. In this paper, a novel BRB with interval-valued references (BRB-IR) is proposed, in which qualitative knowledge and quantitative data can be integrated to construct models. First, the interval-valued referential values provided by experts are optimized by a nonlinear optimization algorithm to obtain the optimal referential values. Furthermore, other model parameters are optimized by the projection covariance matrix adaptation evolutionary strategy (P-CMA-ES) algorithm. Finally, a case study for pipeline leak detection is constructed to verify the model's effectiveness, and the results show that the proposed BRB-IR is more effective and characterizes expert knowledge better than the classical BRB using single-valued references.
Learning aerodynamics with neural network
Authors: Peng Wenhui , Zhang Yao , Laurendeau Eric , Desmarais Michel C. 
Published: April 2022

AbstractWe propose a neural network (NN) architecture, the Element Spatial Convolution Neural Network (ESCNN), towards the airfoil lift coefficient prediction task. The ESCNN outperforms existing state-of-the-art NNs in terms of prediction accuracy, with two orders of less parameters. We further investigate and explain how the ESCNN succeeds in making accurate predictions with standard convolution layers. We discover that the ESCNN has the ability to extract physical patterns that emerge from aerodynamics, and such patterns are clearly reflected within a layer of the network. We show that the ESCNN is capable of learning the physical laws and equation of aerodynamics from simulation data.