bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comprehensive 3D epigenomic maps define limbal stem/progenitor cell function and identity
Authors: Li Mingsen , Huang Huaxing , Wang Bofeng , Jiang Shaoshuai , Guo Huizhen , Zhu Liqiong , Wu Siqi , Liu Jiafeng , Wang Li , Lan Xihong , Zhang Wang , Zhu Jin , Li Fuxi , Tan Jieying , Mao Zhen , Liu Chunqiao , Ji Jianping , Ding Junjun , Zhang Kang , Yuan Jin , Liu Yizhi , Ouyang Hong 
Published: March 2022

AbstractThe insights into how genome topology couples with epigenetic states to govern the function and identity of the corneal epithelium are poorly understood. Here, we generate a high-resolution Hi-C interaction map of human limbal stem/progenitor cells (LSCs) and show that chromatin multi-hierarchical organisation is coupled to gene expression. By integrating Hi-C, epigenome and transcriptome data, we characterize the comprehensive 3D epigenomic landscapes of LSCs. We find that super-silencers mediate gene repression associated with corneal development, differentiation and disease via chromatin looping and/or proximity. Super-enhancer (SE) interaction analysis identified a set of SE interactive hubs that contribute to LSC-specific gene activation. These active and inactive element-anchored loop networks occur within the cohesin-occupied CTCF-CTCF loops. We further reveal a coordinated regulatory network of core transcription factors based on SE-promoter interactions. Our results provide detailed insights into the genome organization principle for epigenetic regulation of gene expression in stratified epithelia.
The methyl phosphate capping enzyme Bmc1/Bin3 is a stable component of the fission yeast telomerase holoenzyme
Authors: Porat Jennifer , El Baidouri Moaine , Grigull Jorg , Deragon Jean-Marc , Bayfield Mark A. 
Published: March 2022

AbstractThe telomerase holoenzyme is critical for maintaining eukaryotic genome integrity. In addition to a reverse transcriptase and an RNA template, telomerase contains additional proteins that protect the telomerase RNA and promote holoenzyme assembly. Here we report that the methyl phosphate capping enzyme (MePCE) Bmc1/Bin3 is a stable component of the S. pombe telomerase holoenzyme. Bmc1 associates with the telomerase holoenzyme and U6 snRNA through an interaction with the recently described LARP7 family member Pof8, and we demonstrate that these two factors are evolutionarily linked in fungi. Our data suggest that the association of Bmc1 with telomerase is independent of its methyltransferase activity, but rather that Bmc1 functions in telomerase holoenzyme assembly by promoting TER1 accumulation and Pof8 recruitment to TER1. Taken together, this work yields new insight into the composition, assembly, and regulation of the telomerase holoenzyme in fission yeast as well as the breadth of its evolutionary conservation.
Damage-tolerant material design motif derived from asymmetrical rotation
Authors: Wang Wei , Chen Shu Jian , Chen Weiqiang , Duan Wenhui , Lai Jia Zie , Sagoe-Crentsil Kwesi 
Published: March 2022

AbstractMotifs extracted from nature can lead to significant advances in materials design and have been used to tackle the apparent exclusivity between strength and damage tolerance of brittle materials. Here we present a segmental design motif found in arthropod exoskeleton, in which asymmetrical rotational degree of freedom is used in damage control in contrast to the conventional interfacial shear failure mechanism of existing design motifs. We realise this design motif in a compression-resisting lightweight brittle material, demonstrating a unique progressive failure behaviour that preserves material integrity with 60–80% of load-bearing capacity at >50% of compressive strain. This rotational degree of freedom further enables a periodic energy absorbance pattern during failure yielding 200% higher strength than the corresponding cellular structure and up to 97.9% reduction of post-damage residual stress compared with ductile materials. Fifty material combinations covering 27 types of materials analysed display potential progressive failure behaviour by this design motif, thereby establishing a broad spectrum of potential applications of the design motif for advanced materials design, energy storage/conversion and architectural structures.
Complementary Operando Spectroscopy identification of in-situ generated metastable charge-asymmetry Cu2-CuN3 clusters for CO2 reduction to ethanol
Authors: Su Xiaozhi , Jiang Zhuoli , Zhou Jing , Liu Hengjie , Zhou Danni , Shang Huishan , Ni Xingming , Peng Zheng , Yang Fan , Chen Wenxing , Qi Zeming , Wang Dingsheng , Wang Yu 
Published: March 2022

AbstractCopper-based materials can reliably convert carbon dioxide into multi-carbon products but they suffer from poor activity and product selectivity. The atomic structure-activity relationship of electrocatalysts for the selectivity is controversial due to the lacking of systemic multiple dimensions for operando condition study. Herein, we synthesized high-performance CO2RR catalyst comprising of CuO clusters supported on N-doped carbon nanosheets, which exhibited high C2+ products Faradaic efficiency of 73% including decent ethanol selectivity of 51% with a partial current density of 14.4 mA/cm−2 at −1.1 V vs. RHE. We evidenced catalyst restructuring and tracked the variation of the active states under reaction conditions, presenting the atomic structure-activity relationship of this catalyst. Operando XAS, XANES simulations and Quasi-in-situ XPS analyses identified a reversible potential-dependent transformation from dispersed CuO clusters to Cu2-CuN3 clusters which are the optimal sites. This cluster can’t exist without the applied potential. The N-doping dispersed the reduced Cun clusters uniformly and maintained excellent stability and high activity with adjusting the charge distribution between the Cu atoms and N-doped carbon interface. By combining Operando FTIR and DFT calculations, it was recognized that the Cu2-CuN3 clusters displayed charge-asymmetric sites which were intensified by CH3* adsorbing, beneficial to the formation of the high-efficiency asymmetric ethanol.
Efficient and universal characterization of atomic structures through a topological graph order parameter
Authors: Chapman James , Goldman Nir , Wood Brandon C. 
Published: March 2022

AbstractA graph-based order parameter, based on the topology of the graph itself, is introduced for the characterization of atomistic structures. The order parameter is universal to any material/chemical system and is transferable to all structural geometries. Four sets of data are used to validate both the generalizability and accuracy of the algorithm: (1) liquid lithium configurations spanning up to 300 GPa, (2) condensed phases of carbon along with nanotubes and buckyballs at ambient and high temperature, (3) a diverse set of aluminum configurations including surfaces, compressed and expanded lattices, point defects, grain boundaries, liquids, nanoparticles, all at nonzero temperatures, and (4) eleven niobium oxide crystal phases generated with ab initio molecular dynamics. We compare our proposed method to existing, state-of-the-art methods for the cases of aluminum and niobium oxide. Our order parameter uniquely classifies every configuration and outperforms all studied existing methods, opening the door for its use in a multitude of complex application spaces that can require fine structure-level characterization of atomistic graphs.
Practical quantum tokens without quantum memories and experimental tests
Authors: Kent Adrian , Lowndes David , Pitalúa-García Damián , Rarity John 
Published: March 2022

AbstractUnforgeable quantum money tokens were the first invention of quantum information science, but remain technologically challenging as they require quantum memories and/or long-distance quantum communication. More recently, virtual “S-money” tokens were introduced. These are generated by quantum cryptography, do not require quantum memories or long-distance quantum communication, and yet in principle guarantee many of the security advantages of quantum money. Here, we describe implementations of S-money schemes with off-the-shelf quantum key distribution technology, and analyse security in the presence of noise, losses, and experimental imperfection. Our schemes satisfy near-instant validation without cross-checking. We show that, given standard assumptions in mistrustful quantum cryptographic implementations, unforgeability and user privacy could be guaranteed with attainable refinements of our off-the-shelf setup. We discuss the possibilities for unconditionally secure (assumption-free) implementations.
High spatial resolution dataset of La Mobilière insurance customers
Authors: Battiston Alice , Massaro Emanuele , Binder Claudia R. , Schifanella Rossano 
Published: March 2022

AbstractWe present the La Mobilière insurance customers dataset: a 12-year-long longitudinal collection of data on policies of customers of the Swiss insurance company La Mobilière. To preserve the privacy of La Mobilière customers, we propose the data aggregated at two geographical levels, based on the place of residence of the customer: postal areas and municipalities. For each geographical area, the data provides summary statistics on: (i) the demographic characteristics of the customer base, (ii) characteristics of vehicles insurance policies and (iii) characteristics of housing and building insurance policies. To assess the validity of the data, we investigate the temporal consistency of the data and the representativeness of La Mobilière customer base along several dimensions (total population, percentage of foreigners, etc.). We also show how the insurance data can reliably model the spatial patterns of socio-economic indicators at a high geographical resolution. We believe that the reuse of this data provides an opportunity for researchers to broaden the socio-economic characterization of Swiss areas beyond the use of official data sources.
1024-channel electrophysiological recordings in macaque V1 and V4 during resting state
Authors: Chen Xing , Morales-Gregorio Aitor , Sprenger Julia , Kleinjohann Alexander , Sridhar Shashwat , van Albada Sacha J. , Grün Sonja , Roelfsema Pieter R. 
Published: March 2022

AbstractCo-variations in resting state activity are thought to arise from a variety of correlated inputs to neurons, such as bottom-up activity from lower areas, feedback from higher areas, recurrent processing in local circuits, and fluctuations in neuromodulatory systems. Most studies have examined resting state activity throughout the brain using MRI scans, or observed local co-variations in activity by recording from a small number of electrodes. We carried out electrophysiological recordings from over a thousand chronically implanted electrodes in the visual cortex of non-human primates, yielding a resting state dataset with unprecedentedly high channel counts and spatiotemporal resolution. Such signals could be used to observe brain waves across larger regions of cortex, offering a temporally detailed picture of brain activity. In this paper, we provide the dataset, describe the raw and processed data formats and data acquisition methods, and indicate how the data can be used to yield new insights into the ‘background’ activity that influences the processing of visual information in our brain.
MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks
Authors: Nandy Aditya , Terrones Gianmarco , Arunachalam Naveen , Duan Chenru , Kastner David W. , Kulik Heather J. 
Published: March 2022

AbstractWe report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.
China’s environmental policy intensity for 1978–2019
Authors: Zhang Guoxing , Gao Yang , Li Jiexun , Su Bin , Chen Zhanglei , Lin Weichun 
Published: March 2022

AbstractImproving the measurement of environmental policy intensity would affect not only the selection of variables in environmental policy research but also the research conclusions when evaluating policy effects. Because direct evaluation is lacking, the existing research usually applies data such as pollutant emission data, or the number of policies to construct proxy variables. However, these proxy variables are affected by many assumptions and different selection criteria, and they are inevitably accompanied by endogeneity problems. In this study, China’s environmental policy is comprehensively collected for the first time, and a machine learning algorithm is applied to evaluate the policy intensity. We provide all the policies issued by the Chinese government from 1978 to 2019 and the quantified intensity for each policy. We also distinguish all policies into three types according to their attributes. This dataset can help researchers to further understand China’s environmental policy system. In addition, it provides a valuable dataset for related research on evaluating environmental policy and recommending actions for further improvement.
VIB5 database with accurate ab initio quantum chemical molecular potential energy surfaces
Authors: Zhang Lina , Zhang Shuang , Owens Alec , Yurchenko Sergei N. , Dral Pavlo O. 
Published: March 2022

AbstractHigh-level ab initio quantum chemical (QC) molecular potential energy surfaces (PESs) are crucial for accurately simulating molecular rotation-vibration spectra. Machine learning (ML) can help alleviate the cost of constructing such PESs, but requires access to the original ab initio PES data, namely potential energies computed on high-density grids of nuclear geometries. In this work, we present a new structured PES database called VIB5, which contains high-quality ab initio data on 5 small polyatomic molecules of astrophysical significance (CH3Cl, CH4, SiH4, CH3F, and NaOH). The VIB5 database is based on previously used PESs, which, however, are either publicly unavailable or lacking key information to make them suitable for ML applications. The VIB5 database provides tens of thousands of grid points for each molecule with theoretical best estimates of potential energies along with their constituent energy correction terms and a data-extraction script. In addition, new complementary QC calculations of energies and energy gradients have been performed to provide a consistent database, which, e.g., can be used for gradient-based ML methods.
A building height dataset across China in 2017 estimated by the spatially-informed approach
Authors: Yang Chen , Zhao Shuqing 
Published: March 2022

AbstractAs a fundamental aspect of the urban form, building height is a key attribute for reflecting human activities and human-environment interactions in the urban context. However, openly accessible building height maps covering the whole China remain sorely limited, particularly for spatially informed data. Here we developed a 1 km × 1 km resolution building height dataset across China in 2017 using Spatially-informed Gaussian process regression (Si-GPR) and open-access Sentinel-1 data. Building height estimation was performed using the spatially-explicit Gaussian process regression (GPR) in 39 major Chinese cities where the spatially explicit and robust cadastral data are available and the spatially-implicit GPR for the remaining 304 cities, respectively. The cross-validation results indicated that the proposed Si-GPR model overall achieved considerable estimation accuracy (R2 = 0.81, RMSE = 4.22 m) across the entire country. Because of the implementation of local modelling, the spatially-explicit GPR outperformed (R2 = 0.89, RMSE = 2.82 m) the spatially-implicit GPR (R2 = 0.72, RMSE = 6.46 m) for all low-rise, mid-rise, and high-rise buildings. This dataset, with extensive-coverage and high-accuracy, can support further studies on the characteristics, causes, and consequences of urbanization.
The combination of pre-neoadjuvant chemoradiotherapy inflammation biomarkers could be a prognostic marker for rectal cancer patients
Authors: Zhang Jing , Zhang Lin , Gou Yuanyuan , Diao Panya , Hu Yi 
Published: March 2022

AbstractThe neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) have a strong association with prognosis in patients with Stage II/III rectal cancer (RC). We attempted to explore a new system combining these two ratios, named the NLM score, and examine its prognostic value in Stage II/III RC patients undergoing neoadjuvant chemoradiotherapy (NCRT). We retrospectively analyzed data of 237 stage II/III RC patients who underwent NCRT followed by standard TME in our hospital and defined the NLM score as follows: Score 2: pre-NCRT NLR > 2.565 and pre-NCRT LMR < 2.410. Score 1: (pre-NCRT NLR > 2.565 and pre-NCRT LMR > 2.410) OR (pre-NCRT NLR < 2.565 and pre-NCRT LMR < 2.410). Score 0: pre-NCRT NLR < 2.565 and pre-NCRT LMR > 2.410. Multivariate analyses implied that lower ypTNM stage (stage 0–I vs. II–III) (hazard ratio [HR] 0.420, 95% confidence interval [CI] 0.180–0.980 for OS; HR 0.375, 95% CI 0.163–0.862 for DFS) and an NLM score ≤ 1 (HR 0.288, 95% CI 0.134–0.619 for OS; HR 0.229, 95% CI 0.107–0.494 for DFS) could independently predict better overall survival (OS) and disease-free survival (DFS). The novel scoring system, which integrated pre-NCRT NLR and pre-NCRT LMR, was an independent prognostic factor in stage II/III RC patients undergoing NRCT and had better predictive values than these ratios alone.
Brain conditions mediate the association between aging and happiness
Authors: Kokubun Keisuke , Nemoto Kiyotaka , Yamakawa Yoshinori 
Published: March 2022

AbstractAs the population ages, the realization of a long and happy life is becoming an increasingly important issue in many societies. Therefore, it is important to clarify how happiness and the brain change with aging. In this study, which was conducted with 417 healthy adults in Japan, the analysis showed that fractional anisotropy (FA) correlated with happiness, especially in the internal capsule, corona radiata, posterior thalamic radiation, cingulum, and superior longitudinal fasciculus. According to previous neuroscience studies, these regions are involved in emotional regulation. In psychological studies, emotional regulation has been associated with improvement in happiness. Therefore, this study is the first to show that FA mediates the relationship between age and subjective happiness in a way that bridges these different fields.
Effects of oxygen fertilization on damage reduction in flooded snap bean (Phaseolus vulgaris L.)
Authors: Liu Danyang , Paul Anna-Lisa , Morgan Kelly T. , Liu Guodong 
Published: March 2022

AbstractFlooding is one of the major abiotic stresses for vegetable production in Florida. Hydroponic and pot trials were conducted with snap bean to evaluate the effects of oxygen fertilization on the biochemical and physiological status of flooded snap bean plants. There were three treatments in the hydroponic trials were: (1) flooded (control), (2) bubble aeration with ambient air, and (3) hydrogen peroxide (H2O2) applied at the beginning of the trial. Plant health was evaluated by determining nitrogen (N) and phosphorus (P) uptake rates. The greenhouse pot trials were used to quantify the effects of three different application rates of solid oxygen fertilizers as calcium peroxide (CaO2) and magnesium peroxide (MgO2). The results showed that plant N and P uptake rates were significantly greater (p < 0.05) with H2O2 than without H2O2. The N uptake rates with H2O2 were like that of those with bubbling. The uptake rate of NH4+ was significantly greater than that of NO3− with the bubbling and H2O2 conditions, but the uptake rate of NO3− was significantly greater than that of NH4+ in the flooding condition. The plant height, leaf greenness, shoot biomass, and yield were all significantly greater with CaO2 or MgO2 than without either solid oxygen fertilizer. The minimum damage of flooded snap bean was found with 2 g CaO2 or 4 g MgO2 per pot. These results indicated that oxygen fertilization may potentially improve yield of flooded snap bean plants.
Reconstructing high fidelity digital rock images using deep convolutional neural networks
Authors: Bizhani Majid , Ardakani Omid Haeri , Little Edward 
Published: March 2022

AbstractImaging methods have broad applications in geosciences. Scanning electron microscopy (SEM) and micro-CT scanning have been applied for studying various geological problems. Despite significant advances in imaging capabilities, and image processing algorithms, acquiring high-quality data from images is still challenging and time-consuming.
Obtaining a 3D representative volume for a tight rock sample takes days to weeks. Image artifacts such as noise further complicate the use of imaging methods for the determination of rock properties. In this study, we present applications of several convolutional neural networks (CNN) for rapid image denoising, deblurring and super-resolving digital rock images. Such an approach enables rapid imaging of larger samples, which in turn improves the statistical relevance of the subsequent analysis. We demonstrate the application of several CNNs for image restoration applicable to scientific imaging. The results show that images can be denoised without a priori knowledge of the noise with great confidence. Furthermore, we show how attaching several CNNs in an end-to-end fashion can improve the final quality of reconstruction. Our experiments with SEM and CT scan images of several rock types show image denoising, deblurring and super-resolution can be performed simultaneously.