bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unraveling hidden rules behind the wet-to-dry transition of bubble array by glass-box physics rule learner
Authors: Cho In Ho , Yeom Sinchul , Sarkar Tanmoy , Oh Tae-Sik 
Published: February 2022

AbstractA liquid–gas foam, here called bubble array, is a ubiquitous phenomenon widely observed in daily lives, food, pharmaceutical and cosmetic products, and even bio- and nano-technologies. This intriguing phenomenon has been often studied in a well-controlled environment in laboratories, computations, or analytical models. Still, real-world bubble undergoes complex nonlinear transitions from wet to dry conditions, which are hard to describe by unified rules as a whole. Here, we show that a few early-phase snapshots of bubble array can be learned by a glass-box physics rule learner (GPRL) leading to prediction rules of future bubble array. Unlike the black-box machine learning approach, the glass-box approach seeks to unravel expressive rules of the phenomenon that can evolve. Without known principles, GPRL identifies plausible rules of bubble prediction with an elongated bubble array data that transitions from wet to dry states. Then, the best-so-far GPRL-identified rule is applied to an independent circular bubble array, demonstrating the potential generality of the rule. We explain how GPRL uses the spatio-temporal convolved information of early bubbles to mimic the scientist’s perception of bubble sides, shapes, and inter-bubble influences. This research will help combine foam physics and machine learning to better understand and control bubbles.
Temporal and spatial variability of prehistoric aquatic resource procurement: a case study from Mesolithic Northern Iberia
Authors: Milano Stefania , Schöne Bernd R. , González-Morales Manuel R. , Gutiérrez-Zugasti Igor 
Published: February 2022

AbstractPrehistoric shell middens hold valuable evidence of past human–environment interactions. In this study, we used carbon (δ13C) and oxygen (δ18O) stable isotopes of Mytilus galloprovincialis shells excavated from El Perro, La Fragua and La Chora, three Mesolithic middens in Cantabria, Northern Spain, to examine hunter-gatherer subsistence strategies in terms of seasonality and collection areas. Furthermore, we used shell δ18O to reconstruct water temperature during the early Holocene. Stable isotopes reveal a shellfish harvesting diversification trend represented by the gradual establishment of the upper estuaries as new procurement areas and an increase of harvesting mobility in both coastal and in-land sites. These innovations in subsistence strategies during the Mesolithic coincided with major changes in the surrounding environment as attested by the water temperature reconstructions based on δ18O and backed by several global and regional records. Overall, our results show that shell δ13C and δ18O stable isotopes have an underexplored potential as provenance proxies which stimulates their application to the archaeological record to further understand prehistoric human resource procurement and diet.
Predicting plant growth response under fluctuating temperature by carbon balance modelling
Authors: Seydel Charlotte , Biener Julia , Brodsky Vladimir , Eberlein Svenja , Nägele Thomas 
Published: February 2022

AbstractQuantification of system dynamics is a central aim of mathematical modelling in biology. Defining experimentally supported functional relationships between molecular entities by mathematical terms enables the application of computational routines to simulate and analyse the underlying molecular system. In many fields of natural sciences and engineering, trigonometric functions are applied to describe oscillatory processes. As biochemical oscillations occur in many aspects of biochemistry and biophysics, Fourier analysis of metabolic functions promises to quantify, describe and analyse metabolism and its reaction towards environmental fluctuations. Here, Fourier polynomials were developed from experimental time-series data and combined with block diagram simulation of plant metabolism to study heat shock response of photosynthetic CO2 assimilation and carbohydrate metabolism in Arabidopsis thaliana. Simulations predicted a stabilising effect of reduced sucrose biosynthesis capacity and increased capacity of starch biosynthesis on carbon assimilation under transient heat stress. Model predictions were experimentally validated by quantifying plant growth under such stress conditions. In conclusion, this suggests that Fourier polynomials represent a predictive mathematical approach to study dynamic plant-environment interactions.
In-depth comparison of the metabolic and pharmacokinetic behaviour of the structurally related synthetic cannabinoids AMB-FUBINACA and AMB-CHMICA in rats
Authors: Fabregat-Safont David , Mata-Pesquera María , Barneo-Muñoz Manuela , Martinez-Garcia Ferran , Mardal Marie , Davidsen Anders B. , Sancho Juan V. , Hernández Félix , Ibáñez María 
Published: February 2022

AbstractSynthetic cannabinoids receptor agonists (SCRAs) are often almost completely metabolised, and hence their pharmacokinetics should be carefully evaluated for determining the most adequate biomarker in toxicological analysis. Two structurally related SCRAs, AMB-FUBINACA and AMB-CHMICA, were selected to evaluate their in vivo metabolism and pharmacokinetics using male Sprague-Dawley rats. Brain, liver, kidney, blood (serum) and urine samples were collected at different times to assess the differences in metabolism, metabolic reactions, tissue distribution and excretion. Both compounds experimented O-demethyl reaction, which occurred more rapidly for AMB-FUBINACA. The parent compounds and O-demethyl metabolites were highly bioaccumulated in liver, and were still detected in this tissue 48 h after injection. The different indazole/indole N-functionalisation produced diverse metabolic reactions in this moiety and thus, different urinary metabolites were formed. Out of the two compounds, AMB-FUBINACA seemed to easily cross the blood-brain barrier, presenting higher brain/serum concentrations ratio than AMB-CHMICA.
Peptide transporter structure reveals binding and action mechanism of a potent PEPT1 and PEPT2 inhibitor
Authors: Stauffer Mirko , Jeckelmann Jean-Marc , Ilgü Hüseyin , Ucurum Zöhre , Boggavarapu Rajendra , Fotiadis Dimitrios 
Published: February 2022

AbstractInhibitors for membrane transporters have been shown to be indispensable as drugs and tool compounds. The proton-dependent oligopeptide transporters PEPT1 and PEPT2 from the SLC15 family play important roles in human and mammalian physiology. With Lys[Z(NO2)]-Val (LZNV), a modified Lys-Val dipeptide, a potent transport inhibitor for PEPT1 and PEPT2 is available. Here we present the crystal structure of the peptide transporter YePEPT in complex with LZNV. The structure revealed the molecular interactions for inhibitor binding and a previously undescribed mostly hydrophobic pocket, the PZ pocket, involved in interaction with LZNV. Comparison with a here determined ligand-free structure of the transporter unveiled that the initially absent PZ pocket emerges through conformational changes upon inhibitor binding. The provided biochemical and structural information constitutes an important framework for the mechanistic understanding of inhibitor binding and action in proton-dependent oligopeptide transporters.
Ancient and recent collisions revealed by phosphate minerals in the Chelyabinsk meteorite
Authors: Walton Craig R. , Shorttle Oliver , Hu Sen , Rae Auriol S. P. , Jianglong Ji , Černok Ana , Williams Helen , Liu Yu , Tang Guoqiang , Li Qiuli , Anand Mahesh 
Published: February 2022

AbstractThe collision history of asteroids is an important archive of inner Solar System evolution. Evidence for these collisions is brought to Earth by meteorites. However, as meteorites often preserve numerous impact-reset mineral ages, interpretation of their collision histories is controversial. Here, we combine analysis of phosphate U-Pb ages and microtextures to interpret the collision history of Chelyabinsk—a highly shocked meteorite. We show that phosphate U-Pb ages correlate with phosphate microtextural state. Pristine phosphate domain U-Pb compositions are generally concordant, whereas fracture-damaged domains universally display discordance. Combining both populations best constrains upper (4473 ± 11 Ma) and lower intercept (−9 ± 55 Ma, i.e., within error of present) U-Pb ages. All phosphate U-Pb ages were completely reset during an ancient high energy collision, whilst fracture-damaged domains experienced further Pb-loss during mild and recent collisional re-heating. Targeting textural sub-populations of phosphate grains permits more robust reconstruction of asteroidal collision histories.
Single infrared image super‐resolution based on lightweight multi‐path feature fusion network
Authors: Mo Fei , Wu Heng , Qu Shuo , Luo Shaojuan , Cheng Lianglun 
Published: February 2022

AbstractSingle infrared (IR) image super‐resolution methods can help to reduce the cost and difficulty in manufacturing IR sensors for the imaging system. However, the deep learning‐based image SR methods need to build a complex network and thus consume a lot of computational power, which limits the application of SR technology on devices with low computing resources in practice. To solve this problem, the authors present a lightweight multi‐path feature fusion network (MFFN) for the single infrared (IR) image SR. A multi‐path feature fusion block (MFFB) is developed to extract and fuse multiple and discriminative features in a recursive feedback way. Specifically, the multiple features are refined via the linear feature extraction branch, shared‐source residual feature extraction branch, and channel attention branch in MFFB. Finally, the authors reconstruct the high‐resolution IR images from the low‐resolution counterpart based on the refined multiple features. The experimental results demonstrate that MFFN achieves high‐quality single infrared image SR and shows superiority over previous methods for several scale factors (e.g. ×2, ×3, and ×4). MFFN has potential applications in the mobile infrared imaging system.
Dynamic exploitation Gaussian bare‐bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power system
Authors: Qu Zhaoyang , Dong Yunchang , Mugemanyi Sylvère , Yu Tong , Bo Xiaoyong , Li Huashun , Li Yang , Rugema François Xavier , Bananeza Christophe 
Published: February 2022

AbstractHere, a novel Gaussian bare‐bones bat algorithm (GBBBA) and its modified version named as dynamic exploitation Gaussian bare‐bones bat algorithm (DeGBBBA) are proposed for solving optimal reactive power dispatch (ORPD) problem. The optimal reactive power dispatch (ORPD) plays a fundamental role in ensuring stable, secure, reliable as well as economical operation of the power system. The proposed GBBBA employs the Gaussian distribution in updating the bat positions in an effort to mitigate the premature convergence problem associated with the standard BA. DeGBBBA is an advanced variant of GBBBA in which a modified Gaussian distribution is introduced so as to allow the dynamic adaptation of exploitation and exploitation in the proposed algorithm. Both GBBBA and DeGBBBA are used to determine the optimal settings of generator bus voltages, tap setting transformers and shunt reactive sources in order to minimize the active power loss, total voltage deviations and voltage stability index. The performance and effectiveness of the proposed algorithms are demonstrated on three power system test bus including the standard IEEE 14‐bus, IEEE 57‐bus and IEEE 118‐bus. Simulation results show that GBBBA and DeGBBBA are robust and effective in solving the ORPD problem.
Few‐shot learning based multi‐weather‐condition impedance identification for MPPT‐controlled PV converters
Authors: Peng Yang , Wang Yue , Liu Yonghui , Gao Kaijie , Yin Taiyuan , Yu Hanqiao 
Published: February 2022

AbstractThe broadband impedance of converters is an essential feature for the stability analysis of new energy sources. However, obtaining the impedance for photovoltaic (PV) converters with Maximum Power Point Tracking (MPPT) control is challenging because of their non‐linear control schemes and real‐time changing operating points. In this case, conventional linearized impedance modelling methods are not applicable, and conventional direct measurement approaches are highly time‐consuming. This paper proposes a few‐shot learning approach for quick access to PV converters’ impedance. The proposed method is based on the model agnostic meta‐learning (MAML) algorithm, suitable for MPPT‐controlled converters whose impedance changes with time, temperature, and irradiation. In the training process, it adjusts the initial model of the machine learning algorithm under different weather conditions. After completing the training, the initial model can adapt to a new condition with very few samples. Under this approach, with only a few data measured at several frequency points, broadband impedance for MPPT‐controlled converters under any weather conditions can be accurately predicted, avoiding time‐consuming measurements and inaccurate prediction of existing methods. Contrast simulation results show the effectiveness and superiority of the proposed method.
Exploring underlying elements of the motivational self system among learners in two instructional contexts
Authors: Ady Fatima , Kartchava Eva , Rodgers Michael 
Published: February 2022

AbstractThrough the lens ofDörnyei’s (2005)Second Language Motivational Self System, this exploratory study focused on two groups of Canadian newcomers learning English in the traditional classroom setting (English as a Second Language [ESL];n = 37) and the workplace (Workplace Language Training [WLT];n = 29) to determine the role of motivation in their integration into Canadian society and development of the ‘Canadian self’. The results, collected by way of a questionnaire and follow-up interviews, show newcomers holding positive attitudes towards English learning and building their Canadian identity in the process. Notably, beliefs concerning the value of employment to fulfill personal obligations and duties promoted the WLT learners’ motivation and willingness to engage with language learning significantly more than those of their ESL counterparts. Pedagogical implications are discussed.
Shifting perspective on indexicals
Authors: Bowker Mark 
Published: February 2022

Abstract
The debate over the meanings of indexical expressions has relied heavily on the method of counterexamples. This paper challenges that method by showing that purported counterexamples can often be explained away by appeal to perspective shifts. For these counterexamples to establish anything about indexical reference, we must identify the conditions under which theorists can legitimately appeal to perspective shifts. Some tests for semantic content are considered and it is argued that none of them can tell us when appeal to perspective shift is admissible. The paper then considers how we should proceed if we become convinced that there is no way to identify the content of indexical expressions, offering reasons in favour of a nihilist conception of character over an epistemicist or pluralist conception.
Particulate ECM biomaterial ink is 3D printed and naturally crosslinked to form structurally-layered and lubricated cartilage tissue mimics
Authors: Barthold Jeanne E , McCreery Kaitlin P , Martinez Jaylene , Bellerjeau Charlotte , Ding Yifu , Bryant Stephanie J , Whiting Gregory L , Neu Corey P 
Published: February 2022

Abstract
Articular cartilage is a layered tissue with a complex, heterogeneous structure and lubricated surface which is challenging to reproduce using traditional tissue engineering methods. Three-dimensional printing techniques have enabled engineering of complex scaffolds for cartilage regeneration, but constructs fail to replicate the unique zonal layers, and limited cytocompatible crosslinkers exist. To address the need for mechanically robust, layered scaffolds, we developed an extracellular matrix particle-based biomaterial ink (pECM biomaterial ink) which can be extruded, polymerizes via disulfide bonding, and restores layered tissue structure and surface lubrication. Our cartilage pECM biomaterial ink utilizes functionalized hyaluronan (HA), a naturally occurring glycosaminoglycan, crosslinked directly to decellularized tissue particles (ø40–100 µm). We experimentally determined that HA functionalized with thiol groups (t-HA) forms disulfide bonds with the ECM particles to form a 3D network. We show that two inks can be co-printed to create a layered cartilage scaffold with bulk compressive and surface (friction coefficient, adhesion, and roughness) mechanics approaching values measured on native cartilage. We demonstrate that our printing process enables the addition of macropores throughout the construct, increasing the viability of introduced cells by 10%. The delivery of these 3D printed scaffolds to a defect is straightforward, customizable to any shape, and adheres to surrounding tissue.
Effect of olefin block copolymer on the toughness of microcellular polypropylene composite
Authors: Mao Huajie , Li Hao , Guo Wei , Wu Min , Zeng Fankun 
Published: February 2022

Abstract
Microcellular polypropylene (PP) materials have wide applications in the automotive industry and other fields due to their low density, high heat resistance, and environmental friendliness. To improve the toughness of foamed PP materials, the olefin block copolymer (OBC) toughened foamed PP materials, PP-G-MAH as compatibilizer, were prepared by foaming injection molding (FIM) to study the influence of OBC elastomer on the toughness of foamed PP composites. The crystallization behavior showed that the addition of OBC could increase the crystallinity and reduce the spherulite size of PP0, which benefited the improvement of toughness. The rheological measurement demonstrated that the melt viscosity of various PP0/OBC samples was significantly improved in contrast to those of PP0, which was good for the formation of the cell. The microstructure images showed that the cell sizes of the core layer and transition layer in the vertical section reached respectively 40.4μm and 27.3μm, as the content of OBC was 20 wt%. When 20 wt% OBC was added, the impact toughness of PP composites increased by 2.3 times, and the plastic deformation of the cell wall in the impact profile increased maximum, which could absorb a lot of energy and benefit for toughening. The representative volume element (RVE) model of PP/OBC foamed materials was established to simulate its impact and tensile process, taking into account the effect of elastomers and cells. The simulation results showed that the stress concentration factor of foamed PP under impact load increased from 1.80 to 1.93 by adding 20 wt% OBC content and 15 vol% void fraction, while the stress concentration factor of foamed PP under tensile load increased from 1.6 to 2.07 by adding 15 wt% OBC content and 15 vol% void fraction. The impact toughness of foamed PP composites with 20 wt% OBC and the tensile toughness of that with 15 wt% OBC reached the maximum with a 14.2% void fraction.
Thiophene–furan oligomers: beyond-DFT study of electronic and optical properties
Authors: Bastos V A , da Silva T J , Caldas M J 
Published: February 2022

Abstract
Thiophene oligomers are an important class of organic materials for photovoltaic applications, owing to their unique optoelectronic properties. Recently it has been suggested that incorporation of furan units to the thiophene chains, maintaining the chain structure, namely thienylfuran linear oligomers, can bring improvements to the final material. In this work, we present a theoretical study of thiophene, furan and thienylfuran short chains, up to 4 units. Structural and electronic properties were obtained using Hartree–Fock (HF) and density functional theory (DFT) calculations plus beyond mean-field methodologies, specifically second-order Møller–Plesset perturbation theory on HF (HF-MP2) and many-body perturbation theory by the G0W0 approximation on DFT (G0W0@DFT). The optical properties were calculated on top of G0W0@DFT data using the Bethe–Salpeter equation. We investigate properties from the monomers T and F to tetramers with different sequencing of units TT, TF or FF, always bonded through the usual carbon atoms. As well known for the uniform oligothiophene chains, also here for any TT sequencing we find a torsion angle of 150°, while all other sequencing of units result planar, which can be relevant for film producing. Also we find that the first optical transitions for the oligomers reaches a promising threshold of

∼
3

eV at the tetramer length, combined with ionization potentials around ∼6.5–7 eV compatible with usual semiconductor/substrate band alignments, which confirms the relevance of these organic compounds for photovoltaic applications.
Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images
Authors: Wang Fengsheng , Wei Leyi 
Published: February 2022

AbstractMotivationThe development of microscopic imaging techniques enables us to study protein subcellular locations from the tissue level down to the cell level, contributing to the rapid development of image-based protein subcellular location prediction approaches. However, existing methods suffer from intrinsic limitations, such as poor feature representation ability, data imbalanced issue, and multi-label classification problem, greatly impacting the model performance and generalization.ResultsIn this study, we propose MSTLoc, a novel multi-scale end-to-end deep learning model to identify protein subcellular locations in the imbalanced multi-label immunohistochemistry (IHC) images dataset. In our MSTLoc, we deploy a deep convolution neural network to extract multi-scale features from the IHC images, aggregate the high-level features and low-level features via feature fusion to sufficiently exploit the dependencies amongst various subcellular locations, and utilize Vision Transformer (ViT) to model the relationship amongst the features and enhance the feature representation ability. We demonstrate that the proposed MSTLoc achieves better performance than current state-of-the-art models in multi-label subcellular location prediction. Through feature visualization and interpretation analysis, we demonstrate that as compared with the hand-crafted features, the multi-scale deep features learnt from our model exhibit better ability in capturing discriminative patterns underlying protein subcellular locations, and the features from different scales are complementary for the improvement in performance. Finally, case study results indicate that our MSTLoc can successfully identify some biomarkers from proteins that are closely involved with cancer development.Availability and implementationFor the convenient use of our method, we establish a user-friendly webserver available at http://server.wei-group.net/MSTLoc.Supplementary informationSupplementary data are available at Bioinformatics online.