bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accurate descriptions of molecule-surface interactions in electrocatalytic CO2 reduction on the copper surfaces
Authors: Chen Zheng , Liu Zhangyun , Xu Xin 
Published: February 2023

AbstractCopper-based catalysts play a pivotal role in many industrial processes and hold a great promise for electrocatalytic CO2 reduction reaction into valuable chemicals and fuels. Towards the rational design of catalysts, the growing demand on theoretical study is seriously at odds with the low accuracy of the most widely used functionals of generalized gradient approximation. Here, we present results using a hybrid scheme that combines the doubly hybrid XYG3 functional and the periodic generalized gradient approximation, whose accuracy is validated against an experimental set on copper surfaces. A near chemical accuracy is established for this set, which, in turn, leads to a substantial improvement for the calculated equilibrium and onset potentials as against the experimental values for CO2 reduction to CO on Cu(111) and Cu(100) electrodes. We anticipate that the easy use of the hybrid scheme will boost the predictive power for accurate descriptions of molecule-surface interactions in heterogeneous catalysis.
Impact of host genetic polymorphisms on response to inactivated influenza vaccine in children
Authors: Tsang Tim K. , Wang Can , Tsang Nicole N. Y. , Fang Vicky J. , Perera Ranawaka A. P. M. , Malik Peiris J. S. , Leung Gabriel M. , Cowling Benjamin J. , Ip Dennis K. M. 
Published: February 2023

AbstractIn randomized controlled trials of influenza vaccination, 550 children received trivalent-inactivated influenza vaccine, permitting us to explore relationship between vaccine response and host single nucleotide polymorphisms (SNPs) in 23 candidate genes with adjustment of multiple testing. For host SNPs in TLR7–1817G/T (rs5741880), genotype GT was associated with lower odds (OR: 0.22, 95% CI: 0.09, 0.53) of have post-vaccination hemagglutination-inhibiting (HAI) titers ≥40, compared with genotype GG and TT combined under the over-dominant model. For host SNPs in TLR8–129G/C (rs3764879), genotype GT was associated with lower odds (OR: 0.47; 95% CI: 0.28, 0.80) of have post vaccination HAI titers ≥40 compared with genotype GG and AA combined under the over-dominant model. Our results could contribute to the development of better vaccines that may offer improved protection to all recipients.
The determinants of household water consumption: A review and assessment framework for research and practice
Authors: Cominola A. , Preiss L. , Thyer M. , Maier H. R. , Prevos P. , Stewart R. A. , Castelletti A. 
Published: February 2023

AbstractAchieving a thorough understanding of the determinants of household water consumption is crucial to support demand management strategies. Yet, existing research on household water consumption determinants is often limited to specific case studies, with findings that are difficult to generalize and not conclusive. Here, we first contribute an updated framework for review, classification, and analysis of the literature on the determinants of household water consumption. Our framework allows trade-off analysis of different criteria that account for the representation of a potential water consumption determinant in the literature, its impact across heterogeneous case studies, and the effort required to collect information on it. We then review a comprehensive set of 48 publications with our proposed framework. The results of our trade-off analysis show that distinct groups of determinants exist, allowing for the formulation of recommendations for practitioners and researchers on which determinants to consider in practice and prioritize in future research.
Antarctic shelf ocean warming and sea ice melt affected by projected El Niño changes
Authors: Cai Wenju , Jia Fan , Li Shujun , Purich Ariaan , Wang Guojian , Wu Lixin , Gan Bolan , Santoso Agus , Geng Tao , Ng Benjamin , Yang Yun , Ferreira David , Meehl Gerald A. , McPhaden Michael J. 
Published: February 2023

AbstractAntarctic shelf ocean warming affects melt of ice shelf/sheets and sea ice but projected changes vary vastly across climate models. A projected increase in El Niño variability has been found to slow future mid-latitude Southern Ocean warming but how this impacts the Antarctic shelf ocean is unknown. Here we show that a projected increase in El Niño variability accelerates Antarctic shelf ocean warming, hastening ice shelf/sheet melt but slowing sea ice reduction.
A meta-analysis of genetic effects associated with neurodevelopmental disorders and co-occurring conditions
Authors: Gidziela Agnieszka , Ahmadzadeh Yasmin I. , Michelini Giorgia , Allegrini Andrea G. , Agnew-Blais Jessica , Lau Lok Yan , Duret Megan , Procopio Francesca , Daly Emily , Ronald Angelica , Rimfeld Kaili , Malanchini Margherita 
Published: February 2023

AbstractA systematic understanding of the aetiology of neurodevelopmental disorders (NDDs) and their co-occurrence with other conditions during childhood and adolescence remains incomplete. In the current meta-analysis, we synthesized the literature on (1) the contribution of genetic and environmental factors to NDDs, (2) the genetic and environmental overlap between different NDDs, and (3) the co-occurrence between NDDs and disruptive, impulse control and conduct disorders (DICCs). Searches were conducted across three platforms: Web of Science, Ovid Medline and Ovid Embase. Studies were included only if 75% or more of the sample consisted of children and/or adolescents and the studies had measured the aetiology of NDDs and DICCs using single-generation family designs or genomic methods. Studies that had selected participants on the basis of unrelated diagnoses or injuries were excluded. We performed multilevel, random-effects meta-analyses on 296 independent studies, including over four million (partly overlapping) individuals. We further explored developmental trajectories and the moderating roles of gender, measurement, geography and ancestry. We found all NDDs to be substantially heritable (family-based heritability, 0.66 (s.e. = 0.03); SNP heritability, 0.19 (s.e. = 0.03)). Meta-analytic genetic correlations between NDDs were moderate (grand family-based genetic correlation, 0.36 (s.e. = 0.12); grand SNP-based genetic correlation, 0.39 (s.e. = 0.19)) but differed substantially between pairs of disorders. The genetic overlap between NDDs and DICCs was strong (grand family-based genetic correlation, 0.62 (s.e. = 0.20)). While our work provides evidence to inform and potentially guide clinical and educational diagnostic procedures and practice, it also highlights the imbalance in the research effort that has characterized developmental genetics research.
A longitudinal microstructural MRI dataset in healthy C57Bl/6 mice at 9.4 Tesla
Authors: Rahman Naila , Xu Kathy , Budde Matthew D. , Brown Arthur , Baron Corey A. 
Published: February 2023

AbstractMultimodal microstructural MRI has shown increased sensitivity and specificity to changes in various brain disease and injury models in the preclinical setting. Here, we present an in vivo longitudinal dataset, including a subset of ex vivo data, acquired as control data and to investigate microstructural changes in the healthy mouse brain. The dataset consists of structural T2-weighted imaging, magnetization transfer ratio and saturation imaging, and advanced quantitative diffusion MRI (dMRI) methods. The dMRI methods include oscillating gradient spin echo (OGSE) dMRI and microscopic anisotropy (μA) dMRI, which provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The technical skills required to analyze microstructural MRI data are complex and include MRI sequence development, acquisition, and computational neuroimaging expertise. Here, we share unprocessed and preprocessed data, and scalar maps of quantitative MRI metrics. We envision utility of this dataset in the microstructural MRI field to develop and test biophysical models, methods that model temporal brain dynamics, and registration and preprocessing pipelines.
Individual structure mapping over six million trees for New York City USA
Authors: Ma Qin , Lin Jian , Ju Yang , Li Wenkai , Liang Lu , Guo Qinghua 
Published: February 2023

AbstractIndividual tree structure mapping in cities is important for urban environmental studies. Despite mapping products for tree canopy cover and biomass are reported at multiple spatial scales using various approaches, spatially explicit mapping of individual trees and their three-dimensional structure is sparse. Here we produced an individual tree dataset including tree locations, height, crown area, crown volume, and biomass over the entire New York City, USA for 6,005,690 trees. Individual trees were detected and mapped from remotely sensed datasets along with their height and crown size information. Tree biomass in 296 field plots was measured and modelled using i-Tree Eco. Wall-to-wall tree biomass was mapped using relationships between field measurements and remotely sensed datasets and downscaled to individual trees. Validation using field-plot measurements indicated that our mapping products overestimated tree number, mean tree height and maximum tree height by 11.1%, 8.6%, and 5.3%, respectively. These overestimations were mainly due to the spatial and temporal mis-match between field measurements and remote sensing observations and uncertainties in tree segmentation algorithms. This dataset enables the evaluation of urban forest ecosystem services including regulating urban heat and promoting urban health, which can provide valuable insights for urban forest management and policy making.
Chromosome-scale genome assembly of a natural diploid kiwifruit (Actinidia chinensis var. deliciosa)
Authors: Xia Hui , Deng Honghong , Li Mingzhang , Xie Yue , Lin Lijin , Zhang Huifen , Luo Xian , Lv Xiulan , Wang Jin , Liang Dong 
Published: February 2023

AbstractThe most commercialized kiwifruit, Actinidia chinensis var. deliciosa (Acd), is an allohexaploid (2n = 6x = 174), making high-quality assemblage genome challenging. We previously discovered a rare naturally occurring diploid Acd plant. Here, chromosome-level de novo genome assembly for this diploid Acd was reported, reaching approximately 621.98 Mb in length with contig and scaffold N50 values of 10.08 and 21.09 Mb, respectively, 99.66% of the bases anchored to 29 pseudochromosomes, and 38,990 protein-coding genes and 42.29% repetitive elements annotated. The divergence time of A. chinensis cv. ‘Red5’ and ‘Hongyang’ (11.1–27.7 mya) was more recent compared with the divergence time of them and Acd (19.9–41.2 mya), with the divergence time of A. eriantha cv. ‘White’ being the earliest (22.9–45.7 mya) among that of the four Actinidia species. The 4DTv distance distribution highlighted three recent whole-genome duplication events in Acd. This is the first high-quality diploid Acd genome, which lays an important foundation for not only kiwifruit functional genomics studies but also further elucidating genome evolution of allohexaploid Acd.
The necessity of incorporating non-genetic risk factors into polygenic risk score models
Authors: van Dam Sipko , Folkertsma Pytrik , Castela Forte Jose , de Vries Dylan H. , Herrera Cunillera Camila , Gannamani Rahul , Wolffenbuttel Bruce H. R. 
Published: February 2023

AbstractThe growing public interest in genetic risk scores for various health conditions can be harnessed to inspire preventive health action. However, current commercially available genetic risk scores can be deceiving as they do not consider other, easily attainable risk factors, such as sex, BMI, age, smoking habits, parental disease status and physical activity. Recent scientific literature shows that adding these factors can improve PGS based predictions significantly. However, implementation of existing PGS based models that also consider these factors requires reference data based on a specific genotyping chip, which is not always available. In this paper, we offer a method naïve to the genotyping chip used. We train these models using the UK Biobank data and test these externally in the Lifelines cohort. We show improved performance at identifying the 10% most at-risk individuals for type 2 diabetes (T2D) and coronary artery disease (CAD) by including common risk factors. Incidence in the highest risk group increases from 3.0- and 4.0-fold to 5.8 for T2D, when comparing the genetics-based model, common risk factor-based model and combined model, respectively. Similarly, we observe an increase from 2.4- and 3.0-fold to 4.7-fold risk for CAD. As such, we conclude that it is paramount that these additional variables are considered when reporting risk, unlike current practice with current available genetic tests.
Circular active noise barrier using theoretical control filter considering interaction between speaker and barrier
Authors: Lee Sanghyeon , Park Youngjin 
Published: February 2023

AbstractA circular active noise barrier using a theoretically calculated control filter without real-time adaptation was proposed for noise reduction in a specific outdoor space. A compact circular barrier is used for the movable system to cope with changes in the location of the workspace, and noise in a wide frequency band can be reduced by conducting active noise control through control speakers arranged around a barrier. However, there was a significant performance gap compared with the maximum performance achieved by using the experimental fixed filter due to an extremely simplified theoretical model which ignores the interaction between the control speakers and the barrier. Therefore, this study tried to minimize the performance degradation when applying the theoretically calculated control filter. Another theoretical model is introduced to improve the noise reduction performance by considering the interaction between the control speaker and the barrier. Through experiment, it is confirmed that noise reduction performance is improved by about 2.6 dB in the frequency of interest.
The association of ODF4 with AK1 and AK2 in mice is essential for fertility through its contribution to flagellar shape
Authors: Ito Chizuru , Makino Tsukasa , Mutoh Tohru , Kikkawa Masahide , Toshimori Kiyotaka 
Published: February 2023

AbstractNormal sperm flagellar shape and movement are essential for fertilization. The integral protein outer dense fiber 4 (ODF4) localizes to ODFs, but its function remains unclear. Adenylate kinase (AK) is a phosphotransferase that catalyzes the interconversion and controls the concentration equilibrium of adenine nucleotides. AK shuttles ATP to energy-consuming sites. Here, we report on the relationship of flagellar shape and movement with ODF4, AK1 and AK2 by using Odf4-deletion (Odf4−/−) mice. Soluble ODF4 is coimmunoprecipitated with AK1 and AK2 in Odf4+/+ spermatozoa. ODF4, AK1 and AK2 localize to whole flagella (plasmalemma, mitochondria, ODFs, and residual cytoplasmic droplets (CDs)), principal pieces, and midpieces, respectively. Odf4−/− sperm flagella lose ODF4 and reduce AK1 and AK2 but produce ATP. The flagellum is bent (hairpin flagellum) with a large CD in the midpiece. There is no motility in the midpiece, but the principal piece is motile. Odf4−/− spermatozoa progress backward and fail to ascend in the uterus. Thus, Odf4−/− males are infertile owing to abnormal flagellar shape and movement caused mainly by the loss of ODF4 with AK1 and AK2. This study is supported by the rescue experiment; the abnormalities and male infertility caused by Odf4 deletion were reversed by Odf4 restoration.
Full-visibility 3D imaging of oxygenation and blood flow by simultaneous multispectral photoacoustic fluctuation imaging (MS-PAFI) and ultrasound Doppler
Authors: Godefroy Guillaume , Arnal Bastien , Bossy Emmanuel 
Published: February 2023

AbstractWe present a method and setup that provide complementary three-dimensional (3D) images of blood oxygenation (via quantitative photoacoustic imaging) and blood flow dynamics (via ultrasound Doppler). The proposed approach is label-free and exploits blood-induced fluctuations, and is implemented on a sparse array with only 256 elements, driven with a commercially available ultrasound electronics. We first implement 3D photoacoustic fluctuation imaging (PAFI) to image chicken embryo, and obtain full-visibility images of the vascular morphology. We obtain simultaneously 3D ultrasound power Doppler with a comparable image quality. We then introduce multispectral photoacoustic fluctuation imaging (MS-PAFI), and demonstrate that it can provide quantitative measurements of the absorbed optical energy density with full visibility and enhanced contrast, as compared to conventional delay-and-sum multispectral photoacoustic imaging. We finally showcase the synergy and complementarity between MS-PAFI, which provides 3D quantitative oxygenation (SO$$_2$$

2

) imaging, and 3D ultrasound Doppler, which provides quantitative information on blood flow dynamics. MS-PAFI represents a promising alternative to model-based inversions with the advantage of resolving all the visibility artefacts without prior and regularization, by use of a straightforward processing scheme.
Symplectic encoders for physics-constrained variational dynamics inference
Authors: Bacsa Kiran , Lai Zhilu , Liu Wei , Todd Michael , Chatzi Eleni 
Published: February 2023

AbstractWe propose a new variational autoencoder (VAE) with physical constraints capable of learning the dynamics of Multiple Degree of Freedom (MDOF) dynamic systems. Standard variational autoencoders place greater emphasis on compression than interpretability regarding the learned latent space. We propose a new type of encoder, based on the recently developed Hamiltonian Neural Networks, to impose symplectic constraints on the inferred a posteriori distribution. In addition to delivering robust trajectory predictions under noisy conditions, our model is capable of learning an energy-preserving latent representation of the system. This offers new perspectives for the application of physics-informed neural networks on engineering problems linked to dynamics.
In silico assessment on TdP risks of drug combinations under CiPA paradigm
Authors: Qauli Ali Ikhsanul , Marcellinus Aroli , Setiawan Muhammad Aldo , Zain Andi Faiz Naufal , Pinandito Azka Muhammad , Lim Ki Moo 
Published: February 2023

AbstractResearchers have recently proposed the Comprehensive In-vitro Proarrhythmia Assay (CiPA) to analyze medicines’ TdP risks. Using the TdP metric known as qNet, numerous single-drug effects have been studied to classify the medications as low, intermediate, and high-risk. Furthermore, multiple medication therapies are recognized as a potential method for curing patients, mainly when limited drugs are available. This work expands the TdP risk assessment of drugs by introducing a CiPA-based in silico analysis of the TdP risk of combined drugs. The cardiac cell model was simulated using the population of models approach incorporating drug-drug interactions (DDIs) models on several ion channels for various drug pairs. Action potential duration (APD90), qNet, and calcium duration (CaD90) were computed and analyzed as biomarker features. The drug combination maps were also used to illustrate combined medicines' TdP risk. We found that the combined drugs alter cell responses in terms of biomarkers such as APD90, qNet, and CaD90 in a highly nonlinear manner. The results also revealed that combinations of high-risk with low-risk and intermediate-risk with low-risk drugs could result in compounds with varying TdP risks depending on the drug concentrations.
Identifying inhibitors of β-haematin formation with activity against chloroquine-resistant Plasmodium falciparum malaria parasites via virtual screening approaches
Authors: Amod Leah , Mohunlal Roxanne , Teixeira Nicole , Egan Timothy J. , Wicht Kathryn J. 
Published: February 2023

AbstractThe biomineral haemozoin, or its synthetic analogue β-haematin (βH), has been the focus of several target-based screens for activity against Plasmodium falciparum parasites. Together with the known βH crystal structure, the availability of this screening data makes the target amenable to both structure-based and ligand-based virtual screening. In this study, molecular docking and machine learning techniques, including Bayesian and support vector machine classifiers, were used in sequence to screen the in silico ChemDiv 300k Representative Compounds library for inhibitors of βH with retained activity against P. falciparum. We commercially obtained and tested a prioritised set of inhibitors and identified the coumarin and iminodipyridinopyrimidine chemotypes as potent in vitro inhibitors of βH and whole cell parasite growth.
Deep learning ensemble 2D CNN approach towards the detection of lung cancer
Authors: Shah Asghar Ali , Malik Hafiz Abid Mahmood , Muhammad AbdulHafeez , Alourani Abdullah , Butt Zaeem Arif 
Published: February 2023

AbstractIn recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model. For this work, an Ensemble approach has been developed to address the issue of Lung Nodule Detection. Instead of using only one Deep Learning model, we combined the performance of two or more CNNs so they could perform and predict the outcome with more accuracy. The LUNA 16 Grand challenge dataset has been utilized, which is available online on their website. The dataset consists of a CT scan with annotations that better understand the data and information about each CT scan. Deep Learning works the same way our brain neurons work; therefore, deep learning is based on Artificial Neural Networks. An extensive CT scan dataset is collected to train the deep learning model. CNNs are prepared using the data set to classify cancerous and non-cancerous images. A set of training, validation, and testing datasets is developed, which is used by our Deep Ensemble 2D CNN. Deep Ensemble 2D CNN consists of three different CNNs with different layers, kernels, and pooling techniques. Our Deep Ensemble 2D CNN gave us a great result with 95% combined accuracy, which is higher than the baseline method.