bluuebunny/crossref_metadata_2025_split huggingface dataset:
T cell exhaustion and a failure in antigen presentation drive resistance to the graft-versus-leukemia effect
Authors: Zhou Meng , Sacirbegovic Faruk , Zhao Kai , Rosenberger Sarah , Shlomchik Warren D. 
Published: August 2020

AbstractIn hematopoietic cell transplants, alloreactive T cells mediate the graft-versus-leukemia (GVL) effect. However, leukemia relapse accounts for nearly half of deaths. Understanding GVL failure requires a system in which GVL-inducing T cells can be tracked. We used such a model wherein GVL is exclusively mediated by T cells that recognize the minor histocompatibility antigen H60. Here we report that GVL fails due to insufficient H60 presentation and T cell exhaustion. Leukemia-derived H60 is inefficiently cross-presented whereas direct T cell recognition of leukemia cells intensifies exhaustion. The anti-H60 response is augmented by H60-vaccination, an agonist αCD40 antibody (FGK45), and leukemia apoptosis. T cell exhaustion is marked by inhibitory molecule upregulation and the development of TOX+and CD39−TCF-1+cells. PD-1 blockade diminishes exhaustion and improves GVL, while blockade of Tim-3, TIGIT or LAG3 is ineffective. Of all interventions, FGK45 administration at the time of transplant is the most effective at improving memory and naïve T cell anti-H60 responses and GVL. Our studies define important causes of GVL failure and suggest strategies to overcome them.
High-temperature-resistant silicon-polymer hybrid modulator operating at up to 200 Gbit s−1 for energy-efficient datacentres and harsh-environment applications
Authors: Lu Guo-Wei , Hong Jianxun , Qiu Feng , Spring Andrew M. , Kashino Tsubasa , Oshima Juro , Ozawa Masa-aki , Nawata Hideyuki , Yokoyama Shiyoshi 
Published: August 2020

AbstractTo reduce the ever-increasing energy consumption in datacenters, one of the effective approaches is to increase the ambient temperature, thus lowering the energy consumed in the cooling systems. However, this entails more stringent requirements for the reliability and durability of the optoelectronic components. Herein, we fabricate and demonstrate silicon-polymer hybrid modulators which support ultra-fast single-lane data rates up to 200 gigabits per second, and meanwhile feature excellent reliability with an exceptional signal fidelity retained at extremely-high ambient temperatures up to 110 °C and even after long-term exposure to high temperatures. This is achieved by taking advantage of the high electro-optic (EO) activities (in-devicen3r33 = 1021 pm V−1), low dielectric constant, low propagation loss (α, 0.22 dB mm−1), and ultra-high glass transition temperature (Tg, 172 °C) of the developed side-chain EO polymers. The presented modulator simultaneously fulfils the requirements of bandwidth, EO efficiency, and thermal stability for EO modulators. It could provide ultra-fast and reliable interconnects for energy-hungry and harsh-environment applications such as datacentres, 5G/B5G, autonomous driving, and aviation systems, effectively addressing the energy consumption issue for the next-generation optical communication.
Molecular basis for DNA repair synthesis on short gaps by mycobacterial Primase-Polymerase C
Authors: Brissett Nigel C. , Zabrady Katerina , Płociński Przemysław , Bianchi Julie , Korycka-Machała Małgorzata , Brzostek Anna , Dziadek Jarosław , Doherty Aidan J. 
Published: August 2020

AbstractCells utilise specialized polymerases from the Primase-Polymerase (Prim-Pol) superfamily to maintain genome stability. Prim-Pol’s function in genome maintenance pathways including replication, repair and damage tolerance. Mycobacteria contain multiple Prim-Pols required for lesion repair, including Prim-PolC that performs short gap repair synthesis during excision repair. To understand the molecular basis of Prim-PolC’s gap recognition and synthesis activities, we elucidated crystal structures of pre- and post-catalytic complexes bound to gapped DNA substrates. These intermediates explain its binding preference for short gaps and reveal a distinctive modus operandi called Synthesis-dependent Template Displacement (STD). This mechanism enables Prim-PolC to couple primer extension with template base dislocation, ensuring that the unpaired templating bases in the gap are ushered into the active site in an ordered manner. Insights provided by these structures establishes the molecular basis of Prim-PolC’s gap recognition and extension activities, while also illuminating the mechanisms of primer extension utilised by closely related Prim-Pols.
Ecological uncertainty favours the diversification of host use in avian brood parasites
Authors: Antonson Nicholas D. , Rubenstein Dustin R. , Hauber Mark E. , Botero Carlos A. 
Published: August 2020

AbstractAdaptive responses to ecological uncertainty may affect the dynamics of interspecific interactions and shape the course of evolution within symbioses. Obligate avian brood parasites provide a particularly tractable system for understanding how uncertainty, driven by environmental variability and symbiont phenology, influences the evolution of species interactions. Here, we use phylogenetically-informed analyses and a comprehensive dataset on the behaviour and geographic distribution of obligate avian brood parasites and their hosts to demonstrate that increasing uncertainty in thermoregulation and parental investment of parasitic young are positively associated with host richness and diversity. Our findings are consistent with the theoretical expectation that ecological risks and environmental unpredictability should favour the evolution of bet-hedging. Additionally, these highly consistent patterns highlight the important role that ecological uncertainty is likely to play in shaping the evolution of specialisation and generalism in complex interspecific relationships.
Transparent near-infrared perovskite light-emitting diodes
Authors: Xie Chenchao , Zhao Xiaofei , Ong Evon Woan Yuann , Tan Zhi-Kuang 
Published: August 2020

AbstractMobile and wearable devices are increasingly reliant on near-infrared (NIR) covert illumination for facial recognition, eye-tracking or motion and depth sensing functions. However, these small devices offer limited spatial real estate that is typically already occupied by their full-area electronic color displays. Here, we report a transparent perovskite light-emitting diode (LED) that could be overlaid across a color display to provide an efficient and high-intensity NIR illumination. Our transparent devices are constructed with an ITO/AZO/PEIE/FAPbI3/poly-TPD/MoO3/Al/ITO/Ag/ITO architecture, and offer a high average transmittance of more than 55% across the visible spectral region. In particular, our Al/ITO/Ag/ITO top transparent electrode was designed to offer a combination low sheet resistance and low plasma damage upon electrode deposition. The devices emit at 799 nm with a high total external quantum efficiency of 5.7% at a current density of 5.3 mA cm−2 and a high radiance of 1.5 W sr−1 m−2, and possess a large functional device area of 120 mm2. The efficient performance is ideal for battery-powered wearable devices, and could enable advanced security and sensing features on future smart-watches, phones, gaming consoles and augmented or virtual reality headsets.
Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data
Authors: Ziatdinov Maxim , Nelson Christopher T. , Zhang Xiaohang , Vasudevan Rama K. , Eliseev Eugene , Morozovska Anna N. , Takeuchi Ichiro , Kalinin Sergei V. 
Published: August 2020

AbstractMachine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy, with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models. However, the fundamental limitation of machine learning methods is their correlative nature, leading to extreme susceptibility to confounding factors. Here, we implement the workflow for causal analysis of structural scanning transmission electron microscopy (STEM) data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions. The combinatorial library of the Sm-doped BiFeO3 is grown to cover the composition range from pure ferroelectric BFO to orthorhombic 20% Sm-doped BFO. Atomically resolved STEM images are acquired for selected compositions and are used to create a set of local compositional, structural, and polarization field descriptors. The information-geometric causal inference (IGCI) and additive noise model (ANM) analysis are used to establish the pairwise causal directions between the descriptors, ordering the data set in the causal direction. The causal chain for IGCI and ANM across the composition is compared and suggests the presence of common causal mechanisms across the composition series. Ultimately, we believe that the causal analysis of the multimodal data will allow exploring the causal links between multiple competing mechanisms that control the emergence of unique functionalities of morphotropic materials and ferroelectric relaxors.
Design rules for strong electro-optic materials
Authors: Hamze Ali K. , Reynaud Marc , Geler-Kremer Jacqueline , Demkov Alexander A. 
Published: August 2020

AbstractThe explosive rise of silicon photonics has led to renewed interest in the electro-optic (EO) or Pockels effect due to its potential uses in many next generation device applications. To find materials with a strong EO response in thin film form, which are essential for low power and small footprint devices, one needs to find a general design rule for strong Pockels materials. To elucidate what makes the Pockels effect strong, we study the effect in LiB3O5 (LBO) and CsB3O5 (CBO) and use these materials as prototypical examples of where conventional wisdom breaks down. We find the Pockels tensor components to be extremely small in both materials, despite the large degree of anharmonicity in the crystals, which has been used as a proxy for the presence of nonlinear electronic effects. We relate the lack of EO response to the large optical phonon frequencies (despite the relatively large Raman susceptibility) in LBO and to the small Raman susceptibility (despite the low phonon frequencies) in CBO, respectively. We shed light on the underlying physical phenomena behind the Raman susceptibility, which we find to be intimately linked to the electron–phonon coupling strength of the near-edge electronic states, and identify a route to discovering new strong EO materials.
Temporal Wheeler’s delayed-choice experiment based on cold atomic quantum memory
Authors: Dong Ming-Xin , Ding Dong-Sheng , Yu Yi-Chen , Ye Ying-Hao , Zhang Wei-Hang , Li En-Ze , Zeng Lei , Zhang Kan , Li Da-Chuang , Guo Guang-Can , Shi Bao-Sen 
Published: August 2020

AbstractNowadays the most intriguing features of wave-particle complementarity of single-photons are exemplified by the famous Wheeler’s delayed-choice experiment with linear optics, nuclear magnetic resonance, and integrated photonic device systems in the optical platform. Until now, the delayed-choice experiments are demonstrated by either massless photons or massive particles, such as atoms, however, there is no report demonstrating Wheeler’s ideas in a hybrid system which consists of photons and atoms simultaneously. Here, we demonstrate a Wheeler’s delayed-choice experiment in an interface of light and atomic memory, in which the cold atomic memory makes the heralded single-photon divided into a superposition of atomic collective excitation and leaked pulse, thus acting as memory-based beam-splitters. We observe the intermediate states between particle and wave behavior by changing the relative proportion of the quantum random number generator, the second memory efficiency, and the relative storage time of two memories. The reported results confirm Bohr’s view that it makes no sense to illustrate the wave-like or particle-like behavior of light and matter before the measurement happens, and are helpful for improving our comprehension of the complementarity principle under the interface of light-atom interaction.
Multiomics data integration unveils core transcriptional regulatory networks governing cell-type identity
Authors: Jung Sascha , del Sol Antonio 
Published: August 2020

AbstractA plethora of computational approaches have been proposed for reconstructing gene regulatory networks (GRNs) from gene expression data. However, gene regulatory processes are often too complex to predict from the transcriptome alone. Here, we present a computational method, Moni, that systematically integrates epigenetics, transcriptomics, and protein–protein interactions to reconstruct GRNs among core transcription factors and their co-factors governing cell identity. We applied Moni to 57 datasets of human cell types and lines and demonstrate that it can accurately infer GRNs, thereby outperforming state-of-the-art methods.
From data to decisions: understanding information flows within regulatory water quality monitoring programs
Authors: Kumpel Emily , MacLeod Clara , Stuart Kara , Cock-Esteb Alicea , Khush Ranjiv , Peletz Rachel 
Published: August 2020

AbstractMost countries maintain regulatory requirements for testing of drinking water supplies to guide treatment procedures and ensure safe water delivery to consumers. It is unclear, however, if water quality data are always used effectively, particularly in low-resource settings. Efforts to improve the use of water quality data will benefit from a comprehensive understanding of existing systems for managing and sharing information. This study evaluates the methods used to organize, analyze, and transmit drinking water quality data among 26 water supplier or surveillance institutions and two regulatory agencies in six countries of sub-Saharan Africa. Following extensive qualitative and quantitative data collection, we developed data flow diagrams to map formal and informal water quality networks. We found high levels of similarities between the information systems established by different institutions operating under different regulatory structures. We determined that the key barriers to information flows were the limited aggregation and analysis of data and the poor enforcement of data sharing requirements. Our results suggest that broad reforms are necessary to improve the use of these water quality data to manage water safety. These measures could include strengthening enforcement of testing and reporting, building staff capacity for managing and using data, and integrating collection of water quality data with other information systems.
Seventy-year long record of monthly water balance estimates for Earth’s largest lake system
Authors: Do Hong X. , Smith Joeseph P. , Fry Lauren M. , Gronewold Andrew D. 
Published: August 2020

AbstractWe develop new estimates of monthly water balance components from 1950 to 2019 for the Laurentian Great Lakes, the largest surface freshwater system on Earth. For each of the Great Lakes, lake storage changes and water balance components were estimated using the Large Lakes Statistical Water Balance Model (L2SWBM). Multiple independent data sources, contributed by a binational community of research scientists and practitioners, were assimilated into the L2SWBM to infer feasible values of water balance components through a Bayesian framework. A conventional water balance model was used to constrain the new estimates, ensuring that the water balance can be reconciled over multiple time periods. The new estimates are useful for investigating changes in water availability, or benchmarking new hydrological models and data products developed for the Laurentian Great Lakes Region. The source code and inputs of the L2SWBM model are also made available, and can be adapted to include new data sources for the Great Lakes, or to address water balance problems on other large lake systems.
Evaluating the integrity of forested riparian buffers over a large area using LiDAR data and Google Earth Engine
Authors: Zurqani Hamdi A. , Post Christopher J. , Mikhailova Elena A. , Cope Michael P. , Allen Jeffery S. , Lytle Blake A. 
Published: August 2020

AbstractSpatial and temporal changes in land cover have direct impacts on the hydrological cycle and stream quality. Techniques for accurately and efficiently mapping these changes are evolving quickly, and it is important to evaluate how useful these techniques are to address the environmental impact of land cover on riparian buffer areas. The objectives of this study were to: (1) determine the classes and distribution of land cover in the riparian areas of streams; (2) examine the discrepancies within the existing land cover data from National Land Cover Database (NLCD) using high-resolution imagery of the National Agriculture Imagery Program (NAIP) and a LiDAR canopy height model; and (3) develop a technique using LiDAR data to help characterize riparian buffers over large spatial extents. One-meter canopy height models were constructed in a high-throughput computing environment. The machine learning algorithm Support Vector Machine (SVM) was trained to perform supervised land cover classification at a 1-m resolution on the Google Earth Engine (GEE) platform using NAIP imagery and LiDAR-derived canopy height models. This integrated approach to land cover classification provided a substantial improvement in the resolution and accuracy of classifications with F1 Score of each land cover classification ranging from 64.88 to 95.32%. The resulting 1-m land cover map is a highly detailed representation of land cover in the study area. Forests (evergreen and deciduous) and wetlands are by far the dominant land cover classes in riparian zones of the Lower Savannah River Basin, followed by cultivated crops and pasture/hay. Stress from urbanization in the riparian zones appears to be localized. This study demonstrates a method to create accurate high-resolution riparian buffer maps which can be used to improve water management and provide future prospects for improving buffer zones monitoring to assess stream health.
Multicentric evidence of emotional impairments in hypertensive heart disease
Authors: Yoris Adrián , Legaz Agustina , Abrevaya Sofía , Alarco Sofía , López Peláez Jéssica , Sánchez Ramiro , García Adolfo M. , Ibáñez Agustín , Sedeño Lucas 
Published: August 2020

AbstractThe mechanisms underlying emotional alterations constitute a key research target in neuroscience. Emerging evidence indicates that these disruptions can be related to abnormal interoception (i.e., the sensing of visceral feelings), as observed in patients with cardiodynamic deficits. To directly assess these links, we performed the first multicenter study on emotion recognition and interoception in patients with hypertensive heart disease (HHD). Participants from two countries completed a facial emotion recognition test, and a subsample additionally underwent an interoception protocol based on a validated heartbeat detection task. HHD patients from both countries presented deficits in the recognition of overall and negative emotions. Moreover, interoceptive performance was impaired in the HHD group. In addition, a significant association between interoceptive performance and emotion recognition was observed in the control group, but this relation was abolished in the HHD group. All results survived after covariance with cognitive status measures, suggesting they were not biased by general cognitive deficits in the patients. Taken together, these findings suggest that emotional recognition alterations could represent a sui generis deficit in HHD, and that it may be partially explained by the disruption of mechanisms subserving the integration of neuro-visceral signals.
Predictions of melatonin suppression during the early biological night and their implications for residential light exposures prior to sleeping
Authors: Rea Mark S. , Nagare Rohan , Figueiro Mariana G. 
Published: August 2020

AbstractThe magnitude of nocturnal melatonin suppression depends upon the spectrum, amount, and duration of light exposure. The functional relationship between melatonin suppression and the light spectrum and amount have been previously described. Only one duration-dependent parameter was needed to extend this functional relationship to predict nocturnal melatonin suppression during the early biological night from a variety of published studies. Those predictions suggest that ambient lighting commonly found in North American homes will not suppress melatonin for durations up to 3 h, whereas extended use of self-luminous displays in the home prior to sleep can.
Impact of coupling topology upon noise robustness of small optical reservoirs
Authors: Cheng Tian-You , Liu Ching-Chuan , Jhou Da-Ya , Chen Chii-Chang 
Published: August 2020

AbstractIn this work, we perform the numerical investigation of the performance of the small optical reservoir computing (RC) systems with four neurons using the commercial software for optical fiber communication system. The small optical RC system consists of the components of the optical fiber communication. The nonlinear function which is required in RC is provided by the erbium-doped optical fiber amplifiers (EDFA). We demonstrate that the EDFA should be operated in the saturated or non-linear regime to obtain a better performance of the small optical RC system. The performance of the small optical RC systems for different topological neuron structures is investigated. The results show that the interconnection between the neurons could offer a better performance than the systems without interconnection between the neurons. Moreover, the input signals with different noise levels are launched into the systems. The results show that the small optical RC system can classify the noisy input optical waveforms even when the signal-to-noise ratio is as low as − 2.55 dB.
Machine learning for RNA sequencing-based intrinsic subtyping of breast cancer
Authors: Cascianelli Silvia , Molineris Ivan , Isella Claudio , Masseroli Marco , Medico Enzo 
Published: August 2020

AbstractStratification of breast cancer (BC) into molecular subtypes by multigene expression assays is of demonstrated clinical utility. In principle, global RNA-sequencing (RNA-seq) should enable reconstructing existing transcriptional classifications of BC samples. Yet, it is not clear whether adaptation to RNA-seq of classifiers originally developed using PCR or microarrays, or reconstruction through machine learning (ML) is preferable. Hence, we focused on robustness and portability of PAM50, a nearest-centroid classifier developed on microarray data to identify five BC “intrinsic subtypes”. We found that standard PAM50 is profoundly affected by the composition of the sample cohort used for reference construction, and we propose a strategy, named AWCA, to mitigate this issue, improving classification robustness, with over 90% of concordance, and prognostic ability; we also show that AWCA-based PAM50 can even be applied as single-sample method. Furthermore, we explored five supervised learners to build robust, single-sample intrinsic subtype callers via RNA-seq. From our ML-based survey, regularized multiclass logistic regression (mLR) displayed the best performance, further increased by ad-hoc gene selection on the global transcriptome. On external test sets, mLR classifications reached 90% concordance with PAM50-based calls, without need of reference sample; mLR proven robustness and prognostic ability make it an equally valuable single-sample method to strengthen BC subtyping.