bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Decrease to Low Carbonate Clumped Isotope Temperatures in Cryogenian Strata
Authors: Mackey T. J. , Jost A. B. , Creveling J. R. , Bergmann K. D. 
Published: September 2020

AbstractPreglacial and synglacial low‐latitude carbonate sediments of the Elbobreen Formation, NE Svalbard, preserve facies changes associated with low‐latitude glacial advance in Cryogenian “Snowball Earth” episodes (717–635 Ma). We present the first application of carbonate clumped (Δ47) isotope thermometry on synglacial Snowball Earth carbonates and combine results with sedimentologic and petrographic observations and stable isotope (δ13C and δ18O) geochemistry to assess Neoproterozoic environmental change. We find elevated calcite Δ47 temperatures, which likely reflect solid‐state reordering during burial. Dolomites, however, record lower temperatures that vary with facies and stratigraphy. Preglacial dolomite Δ47 temperatures range from 48–77°C, with a reconstructed fluid δ18OVSMOW value of +0.6‰ in the coldest sample. Glacial diamictites and dolomicrites comprise (1) reworked detrital clasts similar to preglacial strata in stable isotope composition and petrographic textures and (2) autochthonous dolomicrite with more positive δ18O values than those of preglacial dolomites or cooccurring detrital clasts. Mean glacial autochthonous dolomicrite Δ47 temperatures are 26 ± 10°C (95% CL) cooler than preglacial strata, with four samples <25°C. All dolomite Δ47 temperatures reflect diagenesis associated with lithification, yet observed stratigraphic and textural Δ47 temperature differences indicate that this occurred early and only contributes to part of the preserved temperature signal. Alteration trends within populations are consistent with low water/rock ratio diagenesis or partial solid‐state reordering; either possibility supports the likelihood of preserved δ18O trends. We postulate that the preserved temperature and δ18O differences between low‐latitude preglacial Tonian and synglacial Cryogenian dolomites are an imperfect reflection of primary temperature change and ice sheet expansion.
New Insights Into Crustal and Mantle Flow Beneath the Red River Fault Zone and Adjacent Areas on the Southern Margin of the Tibetan Plateau Revealed by a 3‐D Magnetotelluric Study
Authors: Yu Nian , Unsworth Martyn , Wang Xuben , Li Dewei , Wang Enci , Li Ruiheng , Hu Yuanbang , Cai Xuelin 
Published: September 2020

AbstractA number of geodynamic models have been proposed for the southeastern margin of the Tibetan plateau and include a range of deformation processes. One unresolved issue is whether crustal and mantle flow occurs, and if it does, how flow contributes to the mass balance of the India‐Asia collision. To address this question, new magnetotelluric data were collected and used to derive a three‐dimensional electrical resistivity model of the crust and upper mantle beneath the Red River Fault (RRF) zone and adjacent areas. The most prominent features of the model are (1) a resistor in the upper‐middle crust directly beneath the trace of the RRF; (2) a major change in upper mantle resistivity across the RRF; and (3) a significant conductor in the upper mantle northeast of the RRF, which extends upward into the crust, and which requires a melt fraction of up to 3%. The model suggests that the lower crustal conductors may be due to melt/fluids derived from the mantle, rather than from outward flow from Tibet. The most likely source of fluids and melts could be upwelling mantle flow related to the Hainan mantle plume. The change in resistivity across the RRF implies a change in lithospheric strength may explain the present‐day localization of deformation and uplift at this location. The resistivity model may also give insights into the distribution of ore deposits in Ailao Shan, since many mineral deposits are derived from magmatic fluids generated in the mantle at the edges of regions of thick lithosphere.
Temporal Variability of Internal Wave‐Driven Mixing in Two Distinct Regions of the Arctic Ocean
Authors: Chanona Melanie , Waterman Stephanie 
Published: September 2020

AbstractThis work investigates how internal wave‐driven turbulence varies in time, from hourly to yearly timescales, and in space, across two distinct regions of the Arctic Ocean. We apply a shear‐based fine‐scale parameterization to mooring records in Nares Strait and on the Beaufort Sea shelf‐slope that sampled the upper stratified water column every 30–45 min and span 2003–2006 and 2003–2004, respectively. In doing so, we generate over 600,000 estimates of the internal wave‐driven dissipation rate. These estimates exhibit large temporal variability in both regions, spanning over 3 orders of magnitude. Despite these wide ranges, we find distinct distributions at each site. In Nares Strait, the time series of dissipation shows systematic variation at tidal frequencies, and tidal forcing appears to influence dissipation more strongly than winds, sea ice, and stratification on daily timescales. On longer timescales, dissipation exhibits a weak seasonal cycle, being elevated when the stratification is high and during the ice melt season. In the Beaufort Sea, we detect no dominant timescales or significant relationships with forcing metrics, but note that the dissipation rate is typically 2 orders of magnitude lower than that in Nares Strait. This region is characterized as being in a turbulent mixing regime for only 2% of the record, compared to 73% of the Nares Strait record, implying that turbulence here is rarely energetic enough relative to the stratification to drive a turbulent heat flux. Inferred Beaufort Sea heat fluxes are an order of magnitude lower than the O(1) W m−2 average value found in Nares Strait.
Global Irrigation Characteristics and Effects Simulated by Fully Coupled Land Surface, River, and Water Management Models in E3SM
Authors: Zhou Tian , Leung L. Ruby , Leng Guoyong , Voisin Nathalie , Li Hong‐Yi , Craig Anthony P. , Tesfa Teklu , Mao Yuna 
Published: September 2020

AbstractIrrigation supports agricultural production, but widespread use of irrigation can perturb the regional and global water cycle. The one‐way coupled irrigation scheme used in some land surface models and Earth system models assumes that surface water demand is always met and ignores the surface water constraints, leading to overestimation of surface water usage, underestimation of groundwater pumping, and unrealistic simulation of their seasonal variability. To better represent the irrigation processes, a two‐way coupled irrigation scheme is developed within the Energy Exascale Earth System Model (E3SM). The new irrigation scheme simulates irrigation water demand and applies irrigation water in E3SM Land Model (ELM), which is coupled to a river routing model and a water management model (MOSART‐WM) that simulate streamflow, reservoir operations, and irrigation water supply. With two‐way coupling, surface water irrigation is constrained by the available runoff, streamflow, and reservoir storage. Simulations were performed for 1979–2008 at 0.5° spatial resolution to estimate irrigation surface water and groundwater use and their seasonality in global and large river basin scales. Compared to one‐way coupling, the two‐way coupling scheme (1) estimates less surface water withdrawal and less return flow due to the surface water constraint; (2) better represents groundwater recharge and groundwater level decline at global scale; and (3) is able to capture the seasonal dynamics of irrigation water allocations which reflect the local water conditions. The new development is an important step to more realistically account for the interactions between human water use and the terrestrial water cycle in an Earth system model.
Improving Spatial Patterns Prior to Land Surface Data Assimilation via Model Calibration Using SMAP Surface Soil Moisture Data
Authors: Zhou Jianhong , Wu Zhiyong , Crow Wade T. , Dong Jianzhi , He Hai 
Published: September 2020

AbstractPrior to their application in a land surface data assimilation (DA) system, remotely sensed (RS) soil moisture (SM) products are typically rescaled to remove systematic differences with respect to comparable SM estimates obtained from a land surface model (LSM). This preprocessing of RS SM data—commonly referred to as bias correction—implicitly discards spatial information present in the RS SM retrievals. Here, based on dense SM network measurements obtained within the Huai River Basin of China, we demonstrate that L‐band Soil Moisture Active Passive (SMAP) L3 surface SM retrievals provide a better representation of spatial variability in time‐averaged SM fields than a LSM—suggesting that, as typically applied, bias correction is neglecting important spatial information present in RS SM products. To maximally use RS information, we propose an alternative bias correction approach for SM DA that provides the benefits of traditional rescaling while also improving modeled SM spatial patterns. Specifically, the regional mean of SMAP SM is first corrected using modeled SM of the Variable Infiltration Capacity (VIC) model. Then, two key VIC soil parameters (EXPT and BULKD) are calibrated to match the relative subregional spatial variability captured by the SMAP SM product. Results show that our model calibration method successfully improves VIC SM spatial patterns and pixel‐wise time series. Furthermore, these SM improvements translate into enhanced VIC streamflow estimates. Overall, results suggest that the proposed bias correction framework can improve current land surface DA systems by maximally utilizing spatial information contained in RS SM products.
The membrane-associated form of cyclin D1 enhances cellular invasion
Authors: Chen Ke , Jiao Xuanmao , Ashton Anthony , Di Rocco Agnese , Pestell Timothy G. , Sun Yunguang , Zhao Jun , Casimiro Mathew C. , Li Zhiping , Lisanti Michael P. , McCue Peter A. , Shen Duanwen , Achilefu Samuel , Rui Hallgeir , Pestell Richard G. 
Published: September 2020

AbstractThe essential G1-cyclin, CCND1, is a collaborative nuclear oncogene that is frequently overexpressed in cancer. D-type cyclins bind and activate CDK4 and CDK6 thereby contributing to G1–S cell-cycle progression. In addition to the nucleus, herein cyclin D1 was also located in the cytoplasmic membrane. In contrast with the nuclear-localized form of cyclin D1 (cyclin D1NL), the cytoplasmic membrane-localized form of cyclin D1 (cyclin D1MEM) induced transwell migration and the velocity of cellular migration. The cyclin D1MEM was sufficient to induce G1–S cell-cycle progression, cellular proliferation, and colony formation. The cyclin D1MEM was sufficient to induce phosphorylation of the serine threonine kinase Akt (Ser473) and augmented extranuclear localized 17β-estradiol dendrimer conjugate (EDC)-mediated phosphorylation of Akt (Ser473). These studies suggest distinct subcellular compartments of cell cycle proteins may convey distinct functions.
Coupling physics in machine learning to predict properties of high-temperatures alloys
Authors: Peng Jian , Yamamoto Yukinori , Hawk Jeffrey A. , Lara-Curzio Edgar , Shin Dongwon 
Published: September 2020

AbstractHigh-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.
Extracting bulk defect parameters in silicon wafers using machine learning models
Authors: Buratti Yoann , Le Gia Quoc Thong , Dick Josef , Zhu Yan , Hameiri Ziv 
Published: September 2020

AbstractThe performance of high-efficiency silicon solar cells is limited by the presence of bulk defects. Identification of these defects has the potential to improve cell performance and reliability. The impact of bulk defects on minority carrier lifetime is commonly measured using temperature- and injection-dependent lifetime spectroscopy and the defect parameters, such as its energy level and capture cross-section ratio, are usually extracted by fitting the Shockley-Read-Hall equation. We propose an alternative extraction approach by using machine learning trained on more than a million simulated lifetime curves, achieving coefficient of determinations between the true and predicted values of the defect parameters above 99%. In particular, random forest regressors, show that defect energy levels can be predicted with a high precision of ±0.02 eV, 87% of the time. The traditional approach of fitting to the Shockley-Read-Hall equation usually yields two sets of defect parameters, one in each half bandgap. The machine learning model is trained to predict the half bandgap location of the energy level, and successfully overcome the traditional approach’s limitation. The proposed approach is validated using experimental measurements, where the machine learning predicts defect energy level and capture cross-section ratio within the uncertainty range of the traditional fitting method. The successful application of machine learning in the context of bulk defect parameter extraction paves the way to more complex data-driven physical models which have the potential to overcome the limitation of traditional approaches and can be applied to other materials such as perovskite and thin film.
Achieving the ultimate precision limit with a weakly interacting quantum probe
Authors: Cohen D. , Gefen T. , Ortiz L. , Retzker A. 
Published: September 2020

AbstractThe ultimate precision limit in estimating the Larmor frequency of N unentangled qubits is well established, and is highly important for magnetometers, gyroscopes, and other types of quantum sensors. However, this limit assumes perfect projective measurements of the quantum registers. This requirement is not practical in many physical systems, such as NMR spectroscopy, where a weakly interacting external probe is used as a measurement device. Here, we show that in the framework of quantum nano-NMR spectroscopy, in which these limitations are inherent, the ultimate precision limit is still achievable using control and a finely tuned measurement.
Strongly correlated superconductor with polytypic 3D Dirac points
Authors: Borisenko Sergey , Bezguba Volodymyr , Fedorov Alexander , Kushnirenko Yevhen , Voroshnin Vladimir , Sturza Mihai , Aswartham Saicharan , Yaresko Alexander 
Published: September 2020

AbstractTopological superconductors should be able to provide essential ingredients for quantum computing, but are very challenging to realize. Spin–orbit interaction in iron-based superconductors opens the energy gap between the p-states of pnictogen and d-states of iron very close to the Fermi level, and such p-states have been recently experimentally detected. Density-functional theory predicts existence of topological surface states within this gap in FeTe1−xSex making it an attractive candidate material. Here we use synchrotron-based angle-resolved photoemission spectroscopy and band structure calculations to demonstrate that FeTe1−xSex (x = 0.45) is a superconducting 3D Dirac semimetal hosting type-I and type-II Dirac points and that its electronic structure remains topologically trivial. We show that the inverted band gap in FeTe1−xSex can possibly be realized by further increase of Te content, but strong correlations reduce it to a sub-meV size, making the experimental detection of this gap and corresponding topological surface states very challenging, not to mention exact matching with the Fermi level. On the other hand, the p–d and d–d interactions are responsible for the formation of extremely flat band at the Fermi level pointing to its intimate relation with the mechanism of high-Tc superconductivity in IBS.
The potential of Gantry beamline large momentum acceptance for real time tumour tracking in pencil beam scanning proton therapy
Authors: Fattori Giovanni , Zhang Ye , Meer David , Weber Damien Charles , Lomax Antony John , Safai Sairos 
Published: September 2020

AbstractTumour tracking is an advanced radiotherapy technique for precise treatment of tumours subject to organ motion. In this work, we addressed crucial aspects of dose delivery for its realisation in pencil beam scanning proton therapy, exploring the momentum acceptance and global achromaticity of a Gantry beamline to perform continuous energy regulation with a standard upstream degrader. This novel approach is validated on simulation data from three geometric phantoms of increasing complexity and one liver cancer patient using 4D dose calculations. Results from a standard high-to-low beamline ramping scheme were compared to alternative energy meandering schemes including combinations with rescanning. Target coverage and dose conformity were generally well recovered with tumour tracking even though for particularly small targets, large variations are reported for the different approaches. Meandering in energy while rescanning has a positive impact on target homogeneity and similarly, hot spots outside the targets are mitigated with a relatively fast convergence rate for most tracking scenarios, halving the volume of hot spots after as little as 3 rescans. This work investigates the yet unexplored potential of having a large momentum acceptance in medical beam line, and provides an alternative to take tumour tracking with particle therapy closer to clinical translation.
An IMiD-induced SALL4 degron system for selective degradation of target proteins
Authors: Yamanaka Satoshi , Shoya Yuki , Matsuoka Saya , Nishida-Fukuda Hisayo , Shibata Norio , Sawasaki Tatsuya 
Published: September 2020

AbstractRegulating the amount of proteins in living cells is a powerful approach for understanding the functions of the proteins. Immunomodulatory drugs (IMiDs) induce the degradation of neosubstrates by interacting with celebron (CRBN) in the cullin E3 ubiquitin ligase complex (CRL4CRBN). Here, we developed the IMiD-dependent Sal-like protein 4 (SALL4) degron (S4D) system for chemical protein knockdown. In transient assays, an N- or C-terminal S4D tag induced the degradation of proteins localized to various subcellular compartments, including the plasma membrane. The activity of luciferase-S4D was reduced by 90% within 3 h of IMiD treatment. IMiD treatment reduced the expression of endogenous S4D-fused RelA and IκBα in knock-in (KI) experiments. Interestingly, the IκBα knockdown suggested that there may be another, unknown mechanism for RelA translocation to the nucleus. Furthermore, 5-hydroxythalidomide as a thalidomide metabolite specifically degradated S4D-tagged protein. These results indicate that the S4D system is a useful tool for cellular biology.
Flatfishes colonised freshwater environments by acquisition of various DHA biosynthetic pathways
Authors: Matsushita Yoshiyuki , Miyoshi Kaho , Kabeya Naoki , Sanada Shuwa , Yazawa Ryosuke , Haga Yutaka , Satoh Shuichi , Yamamoto Yoji , Strüssmann Carlos Augusto , Luckenbach John Adam , Yoshizaki Goro 
Published: September 2020

AbstractThe colonisation of freshwater environments by marine fishes has historically been considered a result of adaptation to low osmolality. However, most marine fishes cannot synthesise the physiologically indispensable fatty acid, docosahexaenoic acid (DHA), due to incomplete DHA biosynthetic pathways, which must be adapted to survive in freshwater environments where DHA is poor relative to marine environments. By analysing DHA biosynthetic pathways of one marine and three freshwater-dependent species from the flatfish family Achiridae, we revealed that functions of fatty acid metabolising enzymes have uniquely and independently evolved by multi-functionalisation or neofunctionalisation in each freshwater species, such that every functional combination of the enzymes has converged to generate complete and functional DHA biosynthetic pathways. Our results demonstrate the elaborate patchwork of fatty acid metabolism and the importance of acquiring DHA biosynthetic function in order for fish to cross the nutritional barrier at the mouth of rivers and colonise freshwater environments.
Dengue virus dominates lipid metabolism modulations in Wolbachia-coinfected Aedes aegypti
Authors: Koh Cassandra , Islam M. Nurul , Ye Yixin H. , Chotiwan Nunya , Graham Barbara , Belisle John T. , Kouremenos Konstantinos A. , Dayalan Saravanan , Tull Dedreia L. , Klatt Stephan , Perera Rushika , McGraw Elizabeth A. 
Published: September 2020

AbstractCompetition between viruses andWolbachiafor host lipids is a proposed mechanism ofWolbachia-mediated virus blocking in insects. Yet, the metabolomic interaction between virus and symbiont within the mosquito has not been clearly defined. We compare the lipid profiles ofAedes aegyptimosquitoes bearing mono- or dual-infections of theWolbachia wMel strain and dengue virus serotype 3 (DENV3). We found metabolic signatures of infection-induced intracellular events but little evidence to support direct competition betweenWolbachiaand virus for host lipids. Lipid profiles of dual-infected mosquitoes resemble those of DENV3 mono-infected mosquitoes, suggesting virus-driven modulation dominates over that ofWolbachia. Interestingly, knockdown of key metabolic enzymes suggests cardiolipins are host factors for DENV3 andWolbachiareplication. These findings define theWolbachia-DENV3 metabolic interaction as indirectly antagonistic, rather than directly competitive, and reveal new research avenues with respect to mosquito × virus interactions at the molecular level.
Emergent charge order from correlated electron-phonon physics in cuprates
Authors: Banerjee S. , Atkinson W. A. , Kampf A. P. 
Published: September 2020

AbstractCharge-density wave order is now understood to be a widespread feature of underdoped cuprate high-temperature superconductors, although its origins remain unclear. While experiments suggest that the charge-ordering wavevector is determined by Fermi-surface nesting, the relevant sections of the Fermi surface are featureless and provide no clue as to the underlying mechanism. Here, focusing on underdoped YBa2Cu3O6+x, we propose that charge-density waves form from the incipient softening of a bond-buckling phonon. The momentum dependence of its coupling to itinerant electrons favourably selects the wavevector found in experiments. But, it requires quasiparticle renormalization by strong electronic correlations to enable a unique enhancement of the charge susceptibility near the B1g-phonon selected wavevector. The B1g phonon frequency softens by a few percent, and finite-range charge-density wave correlations will form locally, if nucleated by defects or dopant disorder. These results suggest that underdoped cuprates cannot be understood in the context of strong electronic correlations alone.