bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing spatio-temporal approach to predict economic dynamics based on online news
Authors: Zhang Yuzhou , Sun Hua , Gao Guang , Shou Lidan , Wu Dun 
Published: September 2022

AbstractEconomic forecasting is a scientific decision-making tool, and it is one of the important basis for the government to formulate economic plans, predict the implementation of the plan, and guide the implementation of the plan. Current knowledge about the use of online news in the prediction of economic patterns in China is limited, especially considering the spatio-temporal dynamics over time. This study explored the spatio-temporal patterns of economic output values in Yinzhou, Ningbo, China between 2018 and 2021, and proposed generalized linear model (GLM) and Geographically weighted regression (GWR) model to predict the dynamics using online news data. The results indicated that there were spatio-temporal variations in the economic dynamics in the study area. The online news showed a great potential to predict economic dynamics, with better performance in the GWR model. The findings suggested online news combining with spatio-temporal approach can better forecast economic dynamics, which can be seen as a pre-requisite for developing an online news-based surveillance system The advanced spatio-temporal analysis enables governments to garner insights about the patterns of economic dynamics over time, which may enhance the ability of government to formulate economic plans and to predict the implementation of the plan. The proposed model may be extended to greater geographic area to validate such approach.
Irrigation and warming drive the decreases in surface albedo over High Mountain Asia
Authors: Maina Fadji Z. , Kumar Sujay V. , Gangodagamage Chandana 
Published: September 2022

AbstractHuman and climate induced land surface changes resulting from irrigation, snow cover decreases, and greening impact the surface albedo over High Mountain Asia (HMA). Here we use a partial information decomposition approach and remote sensing data to quantify the effects of the changes in leaf area index, soil moisture, and snow cover on the surface albedo in HMA, home to over a billion people, from 2003 to 2020. The study establishes strong evidence of anthropogenic agricultural water use over irrigated lands (e.g., Ganges–Brahmaputra) which causes the highest surface albedo decreases (≤ 1%/year). Greening and decreased snow cover from warming also drive changes in visible and near-infrared surface albedo in different areas of HMA. The significant role of irrigation and greening in influencing albedo suggests the potential of a positive feedback cycle where albedo decreases lead to increased evaporative demand and increased stress on water resources.
Blind-noise image denoising with block-matching domain transformation filtering and improved guided filtering
Authors: Jia Hongbin , Yin Qingbo , Lu Mingyu 
Published: September 2022

AbstractThe adaptive block size processing method in different image areas makes block-matching and 3D-filtering (BM3D) have a very good image denoising effect. Based on these observation, in this paper, we improve BM3D in three aspects: adaptive noise variance estimation, domain transformation filtering and nonlinear filtering. First, we improve the noise-variance estimation method of principle component analysis using multilayer wavelet decomposition. Second, we propose compressive sensing based Gaussian sequence Hartley domain transform filtering to reduce noise. Finally, we perform edge-preserving smoothing on the preprocessed image using the guided filtering based on total variation. Experimental results show that the proposed denoising method can be competitive with many representative denoising methods on the evaluation criteria of PSNR. However, it is worth further research on the visual quality of denoised images.
Introduction of an adhesion factor to cube in cube models and its effect on calculated moduli of particulate composites
Authors: Rech Julian , Dorp Esther Ramakers–van , Michels Patrick , Möginger Bernhard , Hausnerova Berenika 
Published: September 2022

AbstractThe cube in cube approach was used by Paul and Ishai-Cohen to model and derive formulas for filler content dependent Young’s moduli of particle filled composites assuming perfect filler matrix adhesion. Their formulas were chosen because of their simplicity, and recalculated using an elementary volume approach which transforms spherical inclusions to cubic inclusions. The EV approach led to expression of the composites moduli that allows introducing an adhesion factor kadh ranging from 0 and 1 to take into account reduced filler matrix adhesion. This adhesion factor scales the edge length of the cubic inclusions, thus reducing the stress transfer area between matrix and filler. Fitting the experimental data with the modified Paul model provides reasonable kadh for PA66, PBT, PP, PE-LD and BR which are in line with their surface energies. Further analysis showed that stiffening only occurs if kadh exceeds $$\sqrt{{E}_{\mathrm{M}}/{E}_{\mathrm{F}}}$$

E
M

/

E
F

and depends on the ratio of matrix modulus and filler modulus. The modified model allows for a quick calculation of any particle filled composites for known matrix modulus EM, filler modulus EF, filler volume content vF and adhesion factor kadh. Thus, finite element analysis (FEA) simulations of any particle filled polymer parts as well as materials selection are significantly eased. FEA of cubic and hexagonal EV arrangements show that stress distributions within the EV exhibit more shear stresses if one deviates from the cubic arrangement. At high filler contents the assumption that the property of the EV is representative for the whole composite, holds only for filler volume contents up to 15 or 20% (corresponding to 30 to 40 weight %). Thus, for vast majority of commercially available particulate composites, the modified model can be applied. Furthermore, this indicates that the cube in cube approach reaches two limits: (i) the occurrence of increasing shear stresses at filler contents above 20% due to deviations of EV arrangements or spatial filler distribution from cubic arrangements (singular), and (ii) increasing interaction between particles with the formation of particle network within the matrix violating the EV assumption of their homogeneous dispersion.
The degradation of performance of a state-of-the-art skin image classifier when applied to patient-driven internet search
Authors: Han Seung Seog , Navarrete-Dechent Cristian , Liopyris Konstantinos , Kim Myoung Shin , Park Gyeong Hun , Woo Sang Seok , Park Juhyun , Shin Jung Won , Kim Bo Ri , Kim Min Jae , Donoso Francisca , Villanueva Francisco , Ramirez Cristian , Chang Sung Eun , Halpern Allan , Kim Seong Hwan , Na Jung-Im 
Published: September 2022

AbstractModel Dermatology (https://modelderm.com; Build2021) is a publicly testable neural network that can classify 184 skin disorders. We aimed to investigate whether our algorithm can classify clinical images of an Internet community along with tertiary care center datasets. Consecutive images from an Internet skin cancer community (‘RD’ dataset, 1,282 images posted between 25 January 2020 to 30 July 2021; https://reddit.com/r/melanoma) were analyzed retrospectively, along with hospital datasets (Edinburgh dataset, 1,300 images; SNU dataset, 2,101 images; TeleDerm dataset, 340 consecutive images). The algorithm’s performance was equivalent to that of dermatologists in the curated clinical datasets (Edinburgh and SNU datasets). However, its performance deteriorated in the RD and TeleDerm datasets because of insufficient image quality and the presence of out-of-distribution disorders, respectively. For the RD dataset, the algorithm’s Top-1/3 accuracy (39.2%/67.2%) and AUC (0.800) were equivalent to that of general physicians (36.8%/52.9%). It was more accurate than that of the laypersons using random Internet searches (19.2%/24.4%). The Top-1/3 accuracy was affected by inadequate image quality (adequate = 43.2%/71.3% versus inadequate = 32.9%/60.8%), whereas participant performance did not deteriorate (adequate = 35.8%/52.7% vs. inadequate = 38.4%/53.3%). In this report, the algorithm performance was significantly affected by the change of the intended settings, which implies that AI algorithms at dermatologist-level, in-distribution setting, may not be able to show the same level of performance in with out-of-distribution settings.
Multiscale modeling of collective cell migration elucidates the mechanism underlying tumor–stromal interactions in different spatiotemporal scales
Authors: Heidary Zarifeh , Haghjooy Javanmard Shaghayegh , Izadi Iman , Zare Nasrin , Ghaisari Jafar 
Published: September 2022

AbstractMetastasis is the pathogenic spread of cancer cells from a primary tumor to a secondary site which happens at the late stages of cancer. It is caused by a variety of biological, chemical, and physical processes, such as molecular interactions, intercellular communications, and tissue-level activities. Complex interactions of cancer cells with their microenvironment components such as cancer associated fibroblasts (CAFs) and extracellular matrix (ECM) cause them to adopt an invasive phenotype that promotes tumor growth and migration. This paper presents a multiscale model for integrating a wide range of time and space interactions at the molecular, cellular, and tissue levels in a three-dimensional domain. The modeling procedure starts with presenting nonlinear dynamics of cancer cells and CAFs using ordinary differential equations based on TGFβ, CXCL12, and LIF signaling pathways. Unknown kinetic parameters in these models are estimated using hybrid unscented Kalman filter and the models are validated using experimental data. Then, the principal role of CAFs on metastasis is revealed by spatial–temporal modeling of circulating signals throughout the TME. At this stage, the model has evolved into a coupled ODE–PDE system that is capable of determining cancer cells’ status in one of the quiescent, proliferating or migratory conditions due to certain metastasis factors and ECM characteristics. At the tissue level, we consider a force-based framework to model the cancer cell proliferation and migration as the final step towards cancer cell metastasis. The ability of the multiscale model to depict cancer cells’ behavior in different levels of modeling is confirmed by comparing its outputs with the results of RT PCR and wound scratch assay techniques. Performance evaluation of the model indicates that the proposed multiscale model can pave the way for improving the efficiency of therapeutic methods in metastasis prevention.
Developing a hybrid FRP-concrete composite beam
Authors: Garnevičius Mantas , Gribniak Viktor 
Published: September 2022

AbstractCurrent materials engineering trends put forward the development of efficient structural solutions. The steel replacement with fiber-reinforced polymers (FRP) exemplifies the key to the corrosion problem. However, the relatively low deformation modulus of typical FRP materials raises the deformations of the structural components. Together with the self-weight reduction increasing the kinematic displacements, the latter issue makes developing hybrid structures comprising compression-resistant concrete and high-performance in tension FRP profiles important. Although such hybrid systems are applicable for bridge engineering, the uncertainty of the inter-component bonding properties complicates developing these innovative structures, including the design models. The typical solution focuses on the local bond improvement, e.g., employing FRP profile perforation and mechanical anchorage systems. However, this study introduces an alternative solution, using the stress-ribbon bridge structural system for creating the hybrid beam prototype, which combines the synthetic fiber-reinforced concrete slab and pultruded FRP profile fixed on the supports. This work exemplifies the structural development concept when the finite element (FE) modeling outcome defines the target reference of the design procedure. Thus, on the one hand, this innovative structure simplifies the corresponding numerical (FE) model, which assumes the perfect bond between the components of the hybrid beam system. On the other hand, the solution to the support problem (resulting from a low resistance of pultruded FRP profiles to transverse loads) improves the structural performance of the bridge prototype, doubling the structure’s flexural stiffness and load-bearing capacity regarding the weak concrete supports’ system. The bending tests proved the adequacy of this solution in describing the design reference for further development of the proposed structural concept.
Task-related, intrinsic oscillatory and aperiodic neural activity predict performance in naturalistic team-based training scenarios
Authors: Cross Zachariah R. , Chatburn Alex , Melberzs Lee , Temby Philip , Pomeroy Diane , Schlesewsky Matthias , Bornkessel-Schlesewsky Ina 
Published: September 2022

AbstractEffective teams are essential for optimally functioning societies. However, little is known regarding the neural basis of two or more individuals engaging cooperatively in real-world tasks, such as in operational training environments. In this exploratory study, we recruited forty individuals paired as twenty dyads and recorded dual-EEG at rest and during realistic training scenarios of increasing complexity using virtual simulation systems. We estimated markers of intrinsic brain activity (i.e., individual alpha frequency and aperiodic activity), as well as task-related theta and alpha oscillations. Using nonlinear modelling and a logistic regression machine learning model, we found that resting-state EEG predicts performance and can also reliably differentiate between members within a dyad. Task-related theta and alpha activity during easy training tasks predicted later performance on complex training to a greater extent than prior behaviour. These findings complement laboratory-based research on both oscillatory and aperiodic activity in higher-order cognition and provide evidence that theta and alpha activity play a critical role in complex task performance in team environments.
The ability of trimethylamine N-oxide to resist pressure induced perturbations to water structure
Authors: Laurent Harrison , Youngs Tristan G. A. , Headen Thomas F. , Soper Alan K. , Dougan Lorna 
Published: September 2022

AbstractTrimethylamine N-oxide (TMAO) protects organisms from the damaging effects of high pressure. At the molecular level both TMAO and pressure perturb water structure but it is not understood how they act in combination. Here, we use neutron scattering coupled with computational modelling to provide atomistic insight into the structure of water under pressure at 4 kbar in the presence and absence of TMAO. The data reveal that TMAO resists pressure-induced perturbation to water structure, particularly in retaining a clear second solvation shell, enhanced hydrogen bonding between water molecules and strong TMAO – water hydrogen bonds. We calculate an ‘osmolyte protection’ ratio at which pressure and TMAO-induced energy changes effectively cancel out. Remarkably this ratio translates across scales to the organism level, matching the observed concentration dependence of TMAO in the muscle tissue of organisms as a function of depth. Osmolyte protection may therefore offer a molecular mechanism for the macroscale survival of life in extreme environments.
A genetically encoded BRET-based SARS-CoV-2 Mpro protease activity sensor
Authors: Geethakumari Anupriya M. , Ahmed Wesam S. , Rasool Saad , Fatima Asma , Nasir Uddin S. M. , Aouida Mustapha , Biswas Kabir H. 
Published: September 2022

AbstractThe main protease, Mpro, is critical for SARS-CoV-2 replication and an appealing target for designing anti-SARS-CoV-2 agents. Therefore, there is a demand for the development of improved sensors to monitor its activity. Here, we report a pair of genetically encoded, bioluminescence resonance energy transfer (BRET)-based sensors for detecting Mpro proteolytic activity in live cells as well as in vitro. The sensors were generated by sandwiching peptides containing the Mpro N-terminal autocleavage sites, either AVLQSGFR (short) or KTSAVLQSGFRKME (long), in between the mNeonGreen and NanoLuc proteins. Co-expression of the sensors with Mpro in live cells resulted in their cleavage while mutation of the critical C145 residue (C145A) in Mpro completely abrogated their cleavage. Additionally, the sensors recapitulated the inhibition of Mpro by the well-characterized pharmacological agent GC376. Further, in vitro assays with the BRET-based Mpro sensors revealed a molecular crowding-mediated increase in the rate of Mpro activity and a decrease in the inhibitory potential of GC376. The sensors developed here will find direct utility in studies related to drug discovery targeting the SARS-CoV-2 Mpro and functional genomics application to determine the effect of sequence variation in Mpro.
LIDA: The Leiden Ice Database for Astrochemistry
Authors: Rocha W. R. M. , Rachid M. G. , Olsthoorn B. , van Dishoeck E. F. , McClure M. K. , Linnartz H. 
Published: September 2022

Context. High-quality vibrational spectra of solid-phase molecules in ice mixtures and for temperatures of astrophysical relevance are needed to interpret infrared observations toward protostars and background stars. Such data are collected worldwide by several laboratory groups in support of existing and upcoming astronomical observations. Over the last 25 yr, the Laboratory for Astrophysics at Leiden Observatory has provided more than 1100 (high-resolution) spectra of diverse ice samples.
Aims. In time with the recent launch of the James Webb Space Telescope, we have fully upgraded the Leiden Ice Database for Astrochemistry (LIDA) adding recently measured spectra. The goal of this paper is to describe what options exist regarding accessing and working with a large collection of infrared (IR) spectra, and the ultraviolet-visible (UV/vis) to the mid-infrared refractive index of H2O ice. This also includes astronomy-oriented online tools to support the interpretation of IR ice observations.
Methods. This ice database is based on open-source Python software, such as Flask and Bokeh, used to generate the web pages and graph visualization, respectively. Structured Query Language (SQL) is used for searching ice analogs within the database and Jmol allows for three-dimensional molecule visualization. The database provides the vibrational modes of molecules known and expected to exist as ice in space. These modes are characterized using density functional theory with the orca software. The IR data in the database are recorded via transmission spectroscopy of ice films condensed on cryogenic substrates. The real UV/vis refractive indices of H2O ice are derived from interference fringes created from the simultaneous use of a monochromatic HeNe laser beam and a broadband Xe-arc lamp, whereas the real and imaginary mid-IR values are theoretically calculated. LIDA not only provides information on fundamental ice properties, but it also offers online tools. The first tool, SPECFY, is directly linked to the data in the database to create a synthetic spectrum of ices towards protostars. The second tool allows the uploading of external files and the calculation of mid-infrared refractive index values.
Results. LIDA provides an open-access and user-friendly platform to search, download, and visualize experimental data of astrophysically relevant molecules in the solid phase. It also provides the means to support astronomical observations; in particular, those that will be obtained with the James Webb Space Telescope. As an example, we analysed the Infrared Space Observatory spectrum of the protostar AFGL 989 using the resources available in LIDA and derived the column densities of H2O, CO and CO2 ices.
Cholestatic Hepatitis Secondary to Epstein–Barr Virus Infection in Children: Case Series and Review of the Literature
Authors: Erbaş Irem Ceren , Özlü Canan , Asrak Hatice Karaoğlu , Güzin Ayşe Çakıl , Belet Nurşen 
Published: September 2022

Abstract
Objective Cholestatic hepatitis (CH) secondary to Epstein–Barr virus (EBV) infection is a rare clinical condition in children and adolescents, which was reported in 5% of patients. Herein, we aimed to present our pediatric cases of EBV-induced CH with a comprehensive review of previously reported patients.
Methods Three cases of EBV-induced CH diagnosed in our department were included in the study. EBV diagnosis was established with positive viral capsid antigen immunoglobulin M and immunoglobulin G. A search of English literature on pediatric cases with EBV-induced CH was conducted on common search engines (PubMed and GoogleScholar).
Results We report three novel cases (aged 6, 15, and 16 years) presented with infectious mononucleosis and jaundice. The physical examination revealed mild hepatomegaly in all of them. In our cases, a total of 21 pediatric patients with EBV-induced CH were reported until now (52.4% male), with a median age of 15 (1–18) years. Regarding all patients, the most common symptoms were fever (94.1%) and jaundice (84.6%). In laboratory results, aspartate aminotransferase [308 (62–2,148) IU/L], alanine aminotransferase [312 (79–899) IU/L], and γ-glutamyl transpeptidase [328.9 ± 198.6 IU/L] levels were elevated. Hyperbilirubinaemia was observed in all cases with a median direct bilirubin level of 3.9 (1.9–21.8) mg/dL. A thickened gallbladder wall was the most common (50%) finding in the abdominal ultrasonography of the cases. Most of the reported patients recovered under supportive treatment without any further morbidity.
Conclusions Although EBV-induced CH is an atypical presentation for children, it should be considered in the differential diagnosis of cholestasis. Supportive therapies are the mainstay of treatment in most of these cases.
Assessment of Serum Zinc and Selenium Levels in Children with COVID-19
Authors: Kirankaya Aysegul , Ozel Abdulrahman , Gayret Ozlem Bostan , Atici Adem , Tenekecigil Aslihan , Erol Meltem 
Published: September 2022

Abstract
Objective Zinc and selenium levels are being investigated with increasing frequency in adult patients with coronavirus disease 2019 (COVID-19). However, levels of zinc and selenium in children with COVID-19 have not been adequately studied to date.
Methods This prospective, observational study was conducted on 146 pediatric patients diagnosed with COVID-19 and 49 healthy controls between 2020 and 2021. Normal serum zinc reference values were 0.60 to 1.20 µg/mL for children 0 to 10 years old and 0.66 to 1.10 µg/mL for children ≥11 years old. The normal range for serum selenium concentration was considered between 70 and 150 µg/L. Deficiencies were defined for values below the reference range.
Results Zinc and selenium levels were significantly lower in the COVID-19 (+) group compared with the controls (zinc: 0.7 ± 0.2 vs 0.9 ± 0.2 µg/mL, p < 0.001; selenium: 57.1 ± 9.1 vs 66.5 ± 11.4 µg/L, p < 0.01, respectively). Also, zinc and selenium levels were found to be statistically significantly lower in the hospitalized group compared with the outpatient group (zinc: 0.6 ± 0.2 vs 0.8 ± 0.2 µg/mL, p < 0.001; selenium: 52.1 ± 9.6 vs 58.8 ± 8.3 µg/L, p < 0.001). In the receiver operating characteristic curve analysis, selenium levels with a cutoff value of 55.50 µg/L, with 75% sensitivity and 70% specificity, and zinc levels with a cutoff value of 0.7 µg/mL, with 56% sensitivity and 53% specificity, predicted hospitalization.
Conclusion Our data showed that serum zinc and selenium levels were significantly lower in patients with COVID-19 compared with healthy control group. Also, zinc and selenium levels were found to be lower in the hospitalized group compared with the outpatient COVID-19 group.