bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assimilation of NEON Observations Into a Process‐Based Carbon Cycle Model Reveals Divergent Mechanisms of Carbon Dynamics in Temperate Deciduous Forests
Authors: Jung Chang Gyo , Hararuk Oleksandra 
Published: February 2022

AbstractTerrestrial ecosystems can potentially alleviate or exacerbate climate change by regulating atmospheric CO2 concentration. Divergent predictions of the terrestrial C sink by the Earth System Models (ESMs) indicate no unified mechanism regarding abiotic and biotic response to climate change. The amount and diversity of observations of the terrestrial C cycle create an opportunity to improve the predictive capacity of C‐cycle models. Modeling study with observations could provide valuable insights into the controls on interannual variability of the terrestrial C sink. In this study, we used data from three deciduous forest sites in the National Ecological Observatory Network (NEON) to do site‐specific parameterizations in the Terrestrial ECOsystem model (TECO), and explore controls of the net C uptake. Calibrated TECO explained 58%–83% of variation in leaf area index and 35%–40% in net ecosystem C exchange. Root mean square (percentage) errors were 131–436 gC/m2 (0.9%–3.3%) for wood C pool, 3–73 gC/m2 (0.5%–11.5%) for fine root C, and 170–763 gC/m2 (1.9%–8.0%) for soil C pool. Calibrated parameters revealed site‐specific processes in phenology and turnover of leaves, fine roots, slow‐decomposing soil C, and temperature sensitivity of organic matter decomposition among the three deciduous forest sites. These findings imply the presence of bias in models using parameters at plant functional type (PFT) level, which is the case for many ESMs relying on land surface components (i.e., PFTs), rather than site‐specific parameters. Lastly, the magnitude of the terrestrial C sink in deciduous forests increased with temperature and this increase was caused by the temperature‐driven stimulation of the gross primary production.
An Improved D8‐LTD for the Extraction of Total Contributing Area (TCA) by Adopting the Strategies of Path Independency and Local Dispersion
Authors: Li Zhenya , Shi Pengfei , Yang Tao , Wang Chao , Yong Bin , Song Ying 
Published: February 2022

AbstractD8‐LTD is a path‐based flow direction algorithm for estimating total contributing area (TCA). However, the precision of D8‐LTD TCA is seriously challenged by the incorporation of flow paths and basic hypothesis of allowing only one receiving pixel. To solve above problems, this work proposes an improved D8‐LTD by adopting the strategies of path independency and local dispersion (i.e., LTD‐PI‐LD). Two indicators are adopted to quantify size and extent precisions of estimated TCAs relative to theoretical TCA. Then, LTD‐PI‐LD is compared with several typical single‐flow‐direction (SFD) algorithms (i.e., D8, D8‐LTD, LTD‐PI) and multiple‐flow‐direction (MFD) algorithms (i.e., MFD‐Quinn, D∞) on synthetic surfaces. Results indicate that (1) TCA maps of D8 and D8‐LTD contain too many singular high‐TCA tubes and source pixels, which are caused by the incorporation of flow paths and converging characteristics of SFD algorithms. (2) In MFD algorithms, the problem of artificial dispersion triggers the mechanisms of cross compensation and mutual amplification, leading to high size precision but low extent precision in estimated TCAs. (3) Strategy of path independency prevents the occurrence of high‐TCA tubes and effectively reduces the number of source pixels. (4) Both size and extent precisions of D8‐LTD TCAs are largely improved by introducing the strategies of path independency and local dispersion. Of all algorithms, LTD‐PI‐LD obtains the highest/second‐highest size precision and the highest extent precision in estimated TCAs. As a whole, LTD‐PI‐LD shows advantages of high precision in estimated TCAs, low computation cost, high efficiency and robustness to DEM (Digital elevation model) resolution and terrain type.
A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds
Authors: Li Kailong , Huang Guohe , Wang Shuo , Baetz Brian , Xu Weihuang 
Published: February 2022

AbstractStreamflow simulations at daily time steps are vital to water resources management, especially in arid regions. Previously, data‐driven models have been used as an effective tool for daily streamflow simulation. However, the accuracy of conventional data‐driven approaches is affected by the temporal autocorrelation of daily streamflow, especially in irrigated watersheds where the persistence of saturated flows dominates irrigation seasons. This study presents a Stepwise Clustered Regression Tree Ensemble (SCRTE) to address the streamflow autocorrelation. With the provision of a state‐of‐the‐art data‐driven model Stepwise Cluster Analysis (SCA), the SCRTE enables both single‐ and multi‐output settings (i.e., model predictand can be either a scalar or a vector), which can thus address interactions among streamflow values over multiple consecutive days. The autocorrelation effect of daily streamflow is evaluated based on single‐ and multi‐output SCA ensembles, which can then be aggregated according to their performance for various streamflow quantile ranges. To facilitate the irrigation scheduling decision‐making under rigorous transboundary water regulations, the SCRTE is applied to three interconnected watersheds with mixed land use, located in a floodplain of the Yellow River basin in China. The results show that the SCRTE outperforms seven well‐known benchmark models across seven evaluation metrics. Our findings reveal that the SCRTE can reflect the varying effects of autocorrelation over different streamflow quantile ranges, thereby improving the streamflow simulation. The multi‐output SCA ensembles are more capable of addressing the medium flows, while the single‐output one can better simulate the low and high flows.
Representing individual electronic states for machine learning GW band structures of 2D materials
Authors: Knøsgaard Nikolaj Rørbæk , Thygesen Kristian Sommer 
Published: February 2022

AbstractChoosing optimal representation methods of atomic and electronic structures is essential when machine learning properties of materials. We address the problem of representing quantum states of electrons in a solid for the purpose of machine leaning state-specific electronic properties. Specifically, we construct a fingerprint based on energy decomposed operator matrix elements (ENDOME) and radially decomposed projected density of states (RAD-PDOS), which are both obtainable from a standard density functional theory (DFT) calculation. Using such fingerprints we train a gradient boosting model on a set of 46k G0W0quasiparticle energies. The resulting model predicts the self-energy correction of states in materials not seen by the model with a mean absolute error of 0.14 eV. By including the material’s calculated dielectric constant in the fingerprint the error can be further reduced by 30%, which we find is due to an enhanced ability to learn the correlation/screening part of the self-energy. Our work paves the way for accurate estimates of quasiparticle band structures at the cost of a standard DFT calculation.
Phagocytosis by an HIV antibody is associated with reduced viremia irrespective of enhanced complement lysis
Authors: Spencer David A. , Goldberg Benjamin S. , Pandey Shilpi , Ordonez Tracy , Dufloo Jérémy , Barnette Philip , Sutton William F. , Henderson Heidi , Agnor Rebecca , Gao Lina , Bruel Timothée , Schwartz Olivier , Haigwood Nancy L. , Ackerman Margaret E. , Hessell Ann J. 
Published: February 2022

AbstractIncreasingly, antibodies are being used to treat and prevent viral infections. In the context of HIV, efficacy is primarily attributed to dose-dependent neutralization potency and to a lesser extent Fc-mediated effector functions. It remains unclear whether augmenting effector functions of broadly neutralizing antibodies (bNAbs) may improve their clinical potential. Here, we use bNAb 10E8v4 targeting the membrane external proximal region (MPER) to examine the role of antibody-mediated effector and complement (C’) activity when administered prophylactically against SHIV challenge in rhesus macaques. With sub-protective dosing, we find a 78–88% reduction in post-acute viremia that is associated with 10E8v4-mediated phagocytosis acting at the time of challenge. Neither plasma nor tissue viremic outcomes in vivo is improved with an Fc-modified variant of 10E8v4 enhanced for C’ functions as determined in vitro. These results suggest that effector functions inherent to unmodified 10E8v4 contribute to efficacy against SHIVSF162P3 in the absence of plasma neutralizing titers, while C’ functions are dispensable in this setting, informing design of bNAb modifications for improving protective efficacy.
Assortative mating biases marker-based heritability estimators
Authors: Border Richard , O’Rourke Sean , de Candia Teresa , Goddard Michael E. , Visscher Peter M. , Yengo Loic , Jones Matt , Keller Matthew C. 
Published: February 2022

AbstractMany traits are subject to assortative mating, with recent molecular genetic findings confirming longstanding theoretical predictions that assortative mating induces long range dependence across causal variants. However, all marker-based heritability estimators implicitly assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments and likelihood-based estimators are biased in the presence of assortative mating and derive corrected heritability estimators for traits subject to assortment. Finally, we demonstrate that the empirical patterns of estimates across methods and sample sizes for real traits subject to assortative mating are congruent with expected assortative mating-induced biases. For example, marker-based heritability estimates for height are 14% – 23% higher than corrected estimates using UK Biobank data.
Multiple expansions of globally uncommon SARS-CoV-2 lineages in Nigeria
Authors: Ozer Egon A. , Simons Lacy M. , Adewumi Olubusuyi M. , Fowotade Adeola A. , Omoruyi Ewean C. , Adeniji Johnson A. , Olayinka Oluseyi A. , Dean Taylor J. , Zayas Janet , Bhimalli Pavan P. , Ash Michelle K. , Maiga Almoustapha I. , Somboro Anou M. , Maiga Mamoudou , Godzik Adam , Schneider Jeffrey R. , Mamede João I. , Taiwo Babafemi O. , Hultquist Judd F. , Lorenzo-Redondo Ramon 
Published: February 2022

AbstractDisparities in SARS-CoV-2 genomic surveillance have limited our understanding of the viral population dynamics and may delay identification of globally important variants. Despite being the most populated country in Africa, Nigeria has remained critically under sampled. Here, we report sequences from 378 SARS-CoV-2 isolates collected in Oyo State, Nigeria between July 2020 and August 2021. In early 2021, most isolates belonged to the Alpha “variant of concern” (VOC) or the Eta lineage. Eta outcompeted Alpha in Nigeria and across West Africa, persisting in the region even after expansion of an otherwise rare Delta sub-lineage. Spike protein from the Eta variant conferred increased infectivity and decreased neutralization by convalescent sera in vitro. Phylodynamic reconstructions suggest that Eta originated in West Africa before spreading globally and represented a VOC in early 2021. These results demonstrate a distinct distribution of SARS-CoV-2 lineages in Nigeria, and emphasize the need for improved genomic surveillance worldwide.
Genomic insights into the evolution of Echinochloa species as weed and orphan crop
Authors: Wu Dongya , Shen Enhui , Jiang Bowen , Feng Yu , Tang Wei , Lao Sangting , Jia Lei , Lin Han-Yang , Xie Lingjuan , Weng Xifang , Dong Chenfeng , Qian Qinghong , Lin Feng , Xu Haiming , Lu Huabing , Cutti Luan , Chen Huajun , Deng Shuiguang , Guo Longbiao , Chuah Tse-Seng , Song Beng-Kah , Scarabel Laura , Qiu Jie , Zhu Qian-Hao , Yu Qin , Timko Michael P. , Yamaguchi Hirofumi , Merotto Aldo , Qiu Yingxiong , Olsen Kenneth M. , Fan Longjiang , Ye Chu-Yu 
Published: February 2022

AbstractAs one of the great survivors of the plant kingdom, barnyard grasses (Echinochloa spp.) are the most noxious and common weeds in paddy ecosystems. Meanwhile, at least two Echinochloa species have been domesticated and cultivated as millets. In order to better understand the genomic forces driving the evolution of Echinochloa species toward weed and crop characteristics, we assemble genomes of three Echinochloa species (allohexaploid E. crus-galli and E. colona, and allotetraploid E. oryzicola) and re-sequence 737 accessions of barnyard grasses and millets from 16 rice-producing countries. Phylogenomic and comparative genomic analyses reveal the complex and reticulate evolution in the speciation of Echinochloa polyploids and provide evidence of constrained disease-related gene copy numbers in Echinochloa. A population-level investigation uncovers deep population differentiation for local adaptation, multiple target-site herbicide resistance mutations of barnyard grasses, and limited domestication of barnyard millets. Our results provide genomic insights into the dual roles of Echinochloa species as weeds and crops as well as essential resources for studying plant polyploidization, adaptation, precision weed control and millet improvements.
Addressing materials’ microstructure diversity using transfer learning
Authors: Goetz Aurèle , Durmaz Ali Riza , Müller Martin , Thomas Akhil , Britz Dominik , Kerfriden Pierre , Eberl Chris 
Published: February 2022

AbstractMaterials’ microstructures are signatures of their alloying composition and processing history. Automated, quantitative analyses of microstructural constituents were lately accomplished through deep learning approaches. However, their shortcomings are poor data efficiency and domain generalizability across data sets, inherently conflicting the expenses associated with annotating data through experts, and extensive materials diversity. To tackle both, we propose to apply a sub-class of transfer learning methods called unsupervised domain adaptation (UDA). UDA addresses the task of finding domain-invariant features when supplied with annotated source data and unannotated target data, such that performance on the latter is optimized. Exemplarily, this study is conducted on a lath-shaped bainite segmentation task in complex phase steel micrographs. Domains to bridge are selected to be different metallographic specimen preparations and distinct imaging modalities. We show that a state-of-the-art UDA approach substantially fosters the transfer between the investigated domains, underlining this technique’s potential to cope with materials variance.
Photonic resource state generation from a minimal number of quantum emitters
Authors: Li Bikun , Economou Sophia E. , Barnes Edwin 
Published: February 2022

AbstractMulti-photon entangled graph states are a fundamental resource in quantum communication networks, distributed quantum computing, and sensing. These states can in principle be created deterministically from quantum emitters such as optically active quantum dots or defects, atomic systems, or superconducting qubits. However, finding efficient schemes to produce such states has been a long-standing challenge. Here, we present an algorithm that, given a desired multi-photon graph state, determines the minimum number of quantum emitters and precise operation sequences that can produce it. The algorithm itself and the resulting operation sequence both scale polynomially in the size of the photonic graph state, allowing one to obtain efficient schemes to generate graph states containing hundreds or thousands of photons.
Sequence-dependent surface condensation of a pioneer transcription factor on DNA
Authors: Morin Jose A. , Wittmann Sina , Choubey Sandeep , Klosin Adam , Golfier Stefan , Hyman Anthony A. , Jülicher Frank , Grill Stephan W. 
Published: February 2022

AbstractBiomolecular condensates are dense assemblies of proteins that form distinct biochemical compartments without being surrounded by a membrane. Some, such as P granules and stress granules, behave as droplets and contain many millions of molecules. Others, such as transcriptional condensates that form on the surface of DNA, are small and contain thousands of molecules. The physics behind the formation of small condensates on DNA surfaces is still under discussion. Here we investigate the nature of transcription factor condensates using the pioneer transcription factor Krüppel-like factor 4 (Klf4). We show that Klf4 can phase separate on its own at high concentrations, but at low concentrations, Klf4 only forms condensates on DNA. Using optical tweezers, we demonstrate that these Klf4 condensates form on DNA as a type of surface condensation. This surface condensation involves a switch-like transition from a thin adsorbed layer to a thick condensed layer, which shows hallmarks of a prewetting transition. The localization of condensates on DNA correlates with sequence, suggesting that the condensate formation of Klf4 on DNA is a sequence-dependent form of surface condensation. Prewetting together with sequence specificity can explain the size and position control of surface condensates. We speculate that a prewetting transition of pioneer transcription factors on DNA underlies the formation and positioning of transcriptional condensates and provides robustness to transcriptional regulation.
Assessing central serous chorioretinopathy with deep learning and multiple optical coherence tomography images
Authors: Ko Junseo , Han Jinyoung , Yoon Jeewoo , Park Ji In , Hwang Joon Seo , Han Jeong Mo , Park Kyu Hyung , Hwang Daniel Duck-Jin 
Published: February 2022

AbstractCentral serous chorioretinopathy (CSC) is one of the most common macular diseases that can reduce the quality of life of patients. This study aimed to build a deep learning-based classification model using multiple spectral domain optical coherence tomography (SD-OCT) images together to diagnose CSC. Our proposed system contains two modules: single-image prediction (SIP) and a final decision (FD) classifier. A total of 7425 SD-OCT images from 297 participants (109 acute CSC, 106 chronic CSC, 82 normal) were included. In the fivefold cross validation test, our model showed an average accuracy of 94.2%. Compared to other end-to-end models, for example, a 3D convolutional neural network (CNN) model and a CNN-long short-term memory (CNN-LSTM) model, the proposed system showed more than 10% higher accuracy. In the experiments comparing the proposed model and ophthalmologists, our model showed higher accuracy than experts in distinguishing between acute, chronic, and normal cases. Our results show that an automated deep learning-based model could play a supplementary role alongside ophthalmologists in the diagnosis and management of CSC. In particular, the proposed model seems clinically applicable because it can classify CSCs using multiple OCT images simultaneously.
Development and user experience of an innovative multi-mode stroke rehabilitation system for the arm and hand for patients with stroke
Authors: Hsieh Yu-Wei , Lee Meng-Ta , Chen Chih-Chi , Hsu Fu-Lin , Wu Ching-Yi 
Published: February 2022

AbstractMany individuals with stroke experience upper-limb motor deficits, and a recent trend is to develop novel devices for enhancing their motor function. This study aimed to develop a new upper-limb rehabilitation system with the integration of two rehabilitation therapies into one system, digital mirror therapy (MT) and action observation therapy (AOT), and to test the usability of this system. In the part I study, the new system was designed to operate in multiple training modes of digital MT (i.e., unilateral and bilateral modes) and AOT (i.e., pre-recorded and self-recorded videos) with self-developed software. In the part II study, 4 certified occupational therapists and 10 stroke patients were recruited for evaluating usability. The System Usability Scale (SUS) (maximum score = 100) and a self-designed questionnaire (maximum score = 50) were used. The mean scores of the SUS were 79.38 and 80.00, and those of the self-designed questionnaire were 41.00 and 42.80, respectively, for the therapists and patients after using this system, which indicated good usability and user experiences. This novel upper-limb rehabilitation system with good usability might be further used to increase the delivery of two emerging rehabilitation therapies, digital AOT and MT, to individuals with stroke.
Slow waves on long helices
Authors: Barr Lauren E. , Ward Gareth P. , Hibbins Alastair P. , Hendry Euan , Sambles J. Roy 
Published: February 2022

AbstractSlowing light in a non-dispersive and controllable fashion opens the door to many new phenomena in photonics. As such, many schemes have been put forward to decrease the velocity of light, most of which are limited in bandwidth or incur high losses. In this paper we show that a long metallic helix supports a low-loss, broadband slow wave with a mode index that can be controlled via geometrical design. For one particular geometry, we characterise the dispersion of the mode, finding a relatively constant mode index of $$\sim$$
∼
45 between 10 and 30 GHz. We compare our experimental results to both a geometrical model and full numerical simulation to quantify and understand the limitations in bandwidth. We find that the bandwidth of the region of linear dispersion is associated with the degree of hybridisation between the fields of a helical mode that travels around the helical wire and an axial mode that disperses along the light line. Finally, we discuss approaches to broaden the frequency range of near-constant mode index: we find that placing a straight wire along the axis of the helix suppresses the interaction between the axial and high index modes supported by the helix, leading to both an increase in bandwidth and a more linear dispersion.
Therapeutic response to leflunomide in combo therapy and monotherapy is associated to serum teriflunomide (A77 1726) levels
Authors: Fajardo-Robledo Nicte S. , Jacobo-Cuevas Heriberto , Perez-Guerrero Edsaul E. , Corona-Sanchez Esther G. , Saldaña-Cruz A. Miriam , Romero-Tejeda Elba M. , Rodriguez-Jimenez N. Alejandra , Totsuka-Sutto Sylvia E. , Lopez-Roa Rocio I. , Ponce-Guarneros Juan M. , Alcaraz-Lopez M. Fabiola , Cerpa-Cruz Sergio , Muñoz-Valle J. Francisco , Cardona-Muñoz E. German , Gonzalez-Lopez Laura , Gamez-Nava Jorge I. ,   Research Group for Factors Related to Therapeutic Outcomes in Autoimmune Diseases, Fajardo-Robledo Nicte Selene , Jacobo-Cuevas Heriberto , Saldaña-Cruz Ana Miriam , Rodriguez-Jimenez Norma Alejandra , Ponce-Guarneros Juan Manuel , Alcaraz-Lopez Miriam Fabiola , Cardona-Muñoz Ernesto German , Gonzalez-Lopez Laura , Gamez-Nava Jorge Ivan 
Published: February 2022

AbstractThere is a significant rate of therapeutic failure in rheumatoid arthritis (RA) patients treated with leflunomide (LEF). This study investigates the utility values of teriflunomide levels (A77 1726) in identifying RA patients who remained with moderate or severe disease activity after the treatment with LEF. In this cross-sectional study, we compared: (a) RA patients who achieved a DAS28-ESR ≤ 3.2, and (b) RA patients who maintained a DAS28-ESR > 3.2 after treatment. ROC curves determined the cut-off of A77 1726 with the better performance to identify patients achieving a DAS28-ESR ≤ 3.2. Of the 115 patients treated with LEF, 69 (60%) remained with moderate/severe disease activity and 46 (40%) achieved low disease activity/remission. Higher A77 1726 levels showed a negative correlation with DAS28-ESR (r = − 0.42, p < 0.001) and other parameters of disease activity. We obtained the following utility values with the cut-off of A77 1726 > 10 µg/mL to identify RA patients who achieved a DAS28-ESR ≤ 3.2: sensitivity of 91.31%; specificity of 73.91%; positive predictive value of 70.00%; and negative predictive value of 92.73%. Serum A77 1726 discriminated between RA patients who remained with moderate/severe disease activity despite the treatment with LEF both as monotherapy and LEF as combo therapy.