bluuebunny/crossref_metadata_2025_split huggingface dataset:
HEXACO‐60 Personality Trait Inventory: Psychometric characteristics in a sample of medical students from Serbia
Authors: Ristic Ivan , Dotlic Jelena , Ignjatovic‐Ristic Dragana , Gazibara Tatjana 
Published: September 2021

AbstractThe 60‐item HEXACO (HEXACO‐60) Personality Trait Inventory may be strategically more important for researchers compared to the 100‐item HEXACO. The aim of this study was to assess the psychometric properties of the 60‐item HEXACO questionnaire in the Serbian language. This cross‐sectional study, conducted at three of five Faculties of Medicine during the summer semester of the 2016/2017 academic year, included 617 students. Relevant data were collected by an anonymous questionnaire. Cronbach's α and McDonald's ω coefficients for the total scale were .71 and .72, respectively. Apart from Agreeableness, all other domains showed an adequate level of internal consistency (both Cronbach's α and McDonald's ω coefficients were >.70). Random data eigenvalues obtained on the parallel analysis had higher values than initial exploratory factor analysis eigenvalues up to the sixth factor. Accordingly, it was concluded that six factors should be extracted from the Serbian HEXACO‐60. These factors corresponded in item distribution with the original ones. Based on the confirmatory factor analysis, using both 60 items and 24 facets, overall goodness of fit of the Serbian HEXACO‐60 was acceptable. Moreover, there were no domains with completely invalid indices, as there were at least two indices that were acceptable for each domain. The Serbian version of the HEXACO‐60 has acceptable psychometric properties. This shorter version with 60 items may be more suitable for brief surveys or when the time allocated to fill in a questionnaire is limited.
The effect of physical activity on anhedonia in individuals with depressive symptoms
Authors: Sun Ching‐wen , Wang Ya‐jing , Fang Yu‐qi , He Yu‐qian , Wang Xuan , So Billy C. L. , Shum David H. K. , Yan Chao 
Published: September 2021

AbstractThe therapeutic effect of antidepressants has been demonstrated for anhedonia in patients with depression. However, antidepressants may cause side‐effects, such as cardiovascular dysfunction. Although physical activity has minor side‐effects, it may serve as an alternative for improving anhedonia and depression. We sought to investigate whether physical activity reduces the level of anhedonia in individuals with depression. Fifty‐six university students with moderate depressive symptoms (Beck Depression Inventory total score > 16) were divided into three training groups: the Running Group (RG, n = 19), the Stretching Group (SG, n = 19), and the Control Group (n = 18). We employed the Monetary Incentive Delay (MID) task and the Temporal Experience of Pleasure Scale (TEPS) to evaluate hedonic capacity. All participants in the RG and SG received 8 weeks of jogging and stretching training, respectively. The RG experienced an increase in the level of arousal during anticipation of a future reward and recalled less negativity towards the loss condition. The SG exhibited enhanced scores on the Anticipatory and Consummatory Pleasure subscales of the TEPS after training. Moreover, in the RG, greater improvements in anticipatory arousal ratings for pleasure and remembered valence ratings for negative affect were associated with longer training duration, lower maximum heart rate, and higher consumed calories during training. To conclude, physical activity is effective in improving anticipatory anhedonia in individuals with depressive symptoms.
S100B in maternal circulation of pregnancies complicated by FGR and brain sparing
Authors: Swissa Shani S. , Baron Joel , Tirosh Dan , Yaniv‐Salem Shimrit , Shelef Ilan , Hershkovitz Reli , Beharier Ofer 
Published: September 2021

AbstractObjectiveTo determine whether the presence of brain sparing in fetal growth restricted (FGR) fetuses involves elevation of the cerebral injury biomarker S100B in maternal circulation.MethodsWe included 63 women with suspected small for gestational age (SGA) fetuses between 24 and 35 +6/7 weeks of gestation. Maternal plasma angiogenic factors measurements and sonographic evaluation were performed at recruitment. Next, we subdivided our SGA cohort into three groups: SGA fetuses, FGR fetuses without brain‐sparing, and FGR fetuses with brain‐sparing (FGR‐BS). Serum S100B concentration was calculated as S100B µg/L, S100B MoM, and the ratio S100B/ estimated fetal weight (EFW). We also report one case of S100B concentration surge in maternal serum following the diagnosis of fetal intraventricular hemorrhage (IVH).ResultsThe FGR‐BS group had higher maternal S100B µg/L (p < 0.01, p < 0.05, respectively), S100B MoM (p < 0.001, p < 0.001, respectively), and S100B/EFW (p < 0.001, p < 0.01, respectively), compared to the SGA and FGR groups. In the case report, maternal serum S100B concentrations were 0.0346 µg/L before, and 0.0874 µg/L after IVH occurrence.ConclusionsS100B concentration in maternal serum increased in pregnancies complicated by FGR and brain sparing. These results may substantiate in‐utero cerebral injury and may explain the adverse neurocognitive outcomes reported for this group.
Effect of C‐terminal and N‐terminal dimerization and alanine scanning on antibacterial activity of the analogs of the peptide p‐BthTX‐I
Authors: Santos‐Filho Norival Alves , Righetto Gabriela Marinho , Pereira Marina Rodrigues , Piccoli Julia Pinto , Almeida Larissa Mathias Teizen , Leal Thainá Cristina , Camargo Ilana Lopes Baratella Cunha , Cilli Eduardo Maffud 
Published: September 2021

AbstractThe peptide (p‐BthTX‐I)2 [(KKYRYHLKPFCKK)2] and its analog des‐Lys12,Lys13‐(p‐BthTX‐I)2 [(KKYRYHLKPFC)2] showed activity against bacteria and potential specificity against prokaryotic cells. In this study, we synthesized the peptide des‐Cys11,Lys12,Lys13‐(p‐BthTX‐I)2K [(KKYRYHLKPF)2K] with a Lys instead of a Cys residue in the dimerization step, beginning the SPPS with Fmoc‐Lys(Fmoc)‐OH. This change avoided Cys oxidation, decreasing one step in the original peptide synthesis and obtaining a smaller and more stable peptide. The antimicrobial activity of the peptide des‐Cys11,Lys12,Lys13‐(p‐BthTX‐I)2K was superior to that of the (p‐BthTX‐I)2 peptide against the bacterial strains tested. Additionally, to evaluate the impact of the linker position on peptide dimerization, we synthesized peptide E(p‐BthTX‐I)2 [E(KKYRYHLKPFCKK)2] using Fmoc‐Glu‐OH at the end of the synthesis. This N‐terminal dimeric peptide did not increase the antibacterial activity, indicating that the free N‐terminal is essential for (p‐BthTX‐I)2 activity. Additionally, we observed lower antimicrobial activity by substituting positive and aromatic residues with Ala in the alanine scanning assay, irrespective of the amino acid change, indicating that each amino acid is essential for the mechanism of action of the peptide. Therefore, we demonstrated that the (p‐BthTX‐I)2 analog, which is shorter and synthesized by an easier process leading to a more stable peptide, is the most antibacterial active peptide against multidrug‐resistant bacteria and does not increase hemolysis activity.
A fluorescent polyurethane foam based on rhodamine derivative as Fe(III) sensor in pure water
Authors: Chen Yinyan , Wu Yiban , Zhu Yifan , Tian Saiqi 
Published: September 2021

AbstractRhodamine salicylaldehyde was synthesized, and its structure was determined using Fourier transform infrared spectroscopy, 1H NMR analysis as well as electrospray ionization mass spectrometry. Then a fluorescent polyurethane foam was obtained via incorporation of the prepared rhodamine salicylaldehyde, and the fluorescence sensing behavior for Fe3+ was systematically investigated. Results showed that the fluorescent polyurethane foam produced higher fluorescence in the presence of Fe3+ in pure water, due to the coordination for Fe3+ binding leading to ring‐opened amide form. The limit of detection was calculated to be 1.64 × 10−6 mol L–1. This fluorescent polyurethane foam can also recognize Fe3+ with good selectivity. © 2021 Society of Industrial Chemistry.
An experimental study to identify key psychological mechanisms that promote and predict resilience in the aftermath of treatment for breast cancer
Authors: Gordon Rola , Fawson Sophie , Moss‐Morris Rona , Armes Jo , Hirsch Colette R. 
Published: September 2021

AbstractObjectiveWomen living with and beyond breast cancer (BC) frequently encounter cancer‐related information in day‐to‐day life. The extent they are emotionally resilient to this information differs between women. Identifying key modifiable psychological mechanisms predicting resilience in these women could highlight targets for interventions to improve resilience in others. This study investigates resilience over time in women living beyond BC and how this relates to individual differences in the way the brain processes information.MethodsSeventy women within a year of finishing first‐line treatment for BC (clinical and community recruitment) completed computerised tasks to assess the tendency to attend to cancer information (dot‐probe task), the tendency to draw negative cancer‐related interpretations from ambiguous information (ambiguous scenarios task) and extent of executive functioning (attentional network task). Questionnaires were completed assessing resilience, and other clinically relevant psychological variables (fear of cancer recurrence, distress, quality of life, and worry) at the time of the laboratory tasks (T1) and again 6 months later (T2).ResultsThe only cognitive process associated with self‐reported resilience was interpretation bias. Generating more negative cancer‐related interpretations of ambiguous information at T1 significantly predicted resilience at T2, whilst controlling for T1 resilience and other clinically relevant variables. Furthermore, resilience scores were relatively stable over time and moderately correlated with other clinically relevant variables.ConclusionsThis study is the first to identify a key cognitive mechanism that predicts resilience in women living beyond BC. This finding suggests interventions to reduce cancer‐related interpretations of ambiguous information could promote resilience in women living beyond BC.
The evolution of the antimicrobial peptide database over 18 years: Milestones and new features
Authors: Wang Guangshun , Zietz C. Michael , Mudgapalli Ashok , Wang Shuona , Wang Zhe 
Published: September 2021

AbstractThe antimicrobial peptide database (APD) has served the antimicrobial peptide field for 18 years. Because it is widely used in research and education, this article documents database milestones and key events that have transformed it into the current form. A comparison is made for the APD peptide statistics between 2010 and 2020, validating the major database findings to date. We also describe new additions ranging from peptide entries to search functions. Of note, the APD also contains antimicrobial peptides from host microbiota, which are important in shaping immune systems and could be linked to a variety of human diseases. Finally, the database has been re‐programmed to the web branding and latest security compliance of the University of Nebraska Medical Center. The reprogrammed APD can be accessed at https://aps.unmc.edu.
Python‐based Helix Indexer: A graphical user interface program for finding symmetry of helical assembly through Fourier–Bessel indexing of electron microscopic data
Authors: Zhang Xuewu 
Published: September 2021

AbstractMany macromolecules form helical assemblies to carry out their functions. Helical reconstruction from electron microscopic images is a powerful approach for solving high‐resolution structures of such assemblies. Determination of the symmetry parameters of the helical assemblies is a prerequisite step in helical reconstruction. The most widely used method for deducing the symmetry is through Fourier–Bessel indexing the diffraction pattern of the helical assemblies. This method, however, often leads to incorrect solutions, due to intrinsic ambiguities in indexing helical diffraction patterns. Here, we present Python‐based Helix Indexer (PyHI), which provides a graphical user interface (GUI) to guide the users through the process of symmetry determination. Diffraction patterns can be read into the program directly or calculated on the fly from two‐dimensional class averages of helical assemblies. PyHI allows deducing the Bessel orders of diffraction peaks by using both the amplitudes and phases of the diffraction data. Based on the Bessel orders of two unit vectors, the Fourier space lattice is constructed with minimal user inputs. The program then uses a refinement algorithm to optimize the Fourier space lattice, and subsequently generate the helical assembly in real space. The program provides both a publication‐quality graphic representation of the helical assembly and the symmetry parameters required for subsequent helical reconstruction steps.
Cosmological Models with Big Rip and Pseudo Rip Scenarios in Extended Theory of Gravity
Authors: Ray Pratik P , Tarai Sankarsan , Mishra B. , Tripathy S. K. 
Published: September 2021

AbstractIn this paper, we have presented the big rip and pseudo rip cosmological models in an extended theory of gravity. The matter field is considered to be that of perfect fluid. The geometrical parameters are adjusted in such a manner that it matches the prescriptions given by cosmological observations, to be specific to the H0 range. The models favour phantom behaviour. The violation of strong energy conditions are shown in both the models, as it has become essential in an extended gravity theory to provide late time cosmic acceleration. We show that, the choice of the coupling parameter has significant effects on the evolution of the universe.
ANOVA‐particle swarm optimization‐based feature selection and gradient boosting machine classifier for improved protein–protein interaction prediction
Authors: Mahapatra Satyajit , Sahu Sitanshu Sekhar 
Published: September 2021

AbstractFeature fusion and selection strategies have been applied to improve accuracy in the prediction of protein–protein interaction (PPI). In this paper, an embedded feature selection framework is developed by integrating a cost function based on analysis of variance (ANOVA) with the particle swarm optimization (PSO), termed AVPSO. Initially, the features of the protein sequences extracted using pseudo‐amino acid composition (PseAAC), conjoint triad composition, and local descriptor are fused. Then, AVPSO is employed to select the optimal set of features. The light gradient boosting machine (LGBM) classifier is used to predict the PPIs using the optimal feature subset. On the five‐fold cross‐validation analysis, the proposed model (AVPSO‐LGBM) achieved an average accuracy of 97.12% and 95.09%, respectively, on the intraspecies PPI datasets Saccharomyces cerevisiae and Helicobacter pylori. On the interspecies, PPI datasets of the Human‐Bacillus and Human‐Yersinia, an average accuracy of 95.20% and 93.44%, are achieved. Results obtained on independent test datasets, and network datasets show that the prediction accuracy of the AVPSO‐LGBM is better than the existing methods, demonstrating its generalization ability. The improved prediction performance obtained by the proposed model makes it a reliable and effective PPI prediction model.
Probability bound analysis: A novel approach for quantifying parameter uncertainty in decision‐analytic modeling and cost‐effectiveness analysis
Authors: Iskandar Rowan 
Published: September 2021

AbstractDecisions about health interventions are often made using limited evidence. Mathematical models used to inform such decisions often include uncertainty analysis to account for the effect of uncertainty in the current evidence base on decision‐relevant quantities. However, current uncertainty quantification methodologies, including probabilistic sensitivity analysis (PSA), require modelers to specify a precise probability distribution to represent the uncertainty of a model parameter. This study introduces a novel approach for representing and propagating parameter uncertainty, probability bounds analysis (PBA), where the uncertainty about the unknown probability distribution of a model parameter is expressed in terms of an interval bounded by lower and upper bounds on the unknown cumulative distribution function (p‐box) and without assuming a particular form of the distribution function. We give the formulas of the p‐boxes for common situations (given combinations of data on minimum, maximum, median, mean, or standard deviation), describe an approach to propagate p‐boxes into a black‐box mathematical model, and introduce an approach for decision‐making based on the results of PBA. We demonstrate the characteristics and utility of PBA vs PSA using two case studies. In sum, this study provides modelers with practical tools to conduct parameter uncertainty quantification given the constraints of available data and with the fewest assumptions.
Catalyzing Bond‐Dissociation in Graphene via Alkali‐Iodide Molecules
Authors: Vats Nilesh , Negi Devendra S. , Singh Deobrat , Sigle Wilfried , Abb Sabine , Sen Suman , Szilagyi Sven , Ochner Hannah , Ahuja Rajeev , Kern Klaus , Rauschenbach Stephan , van Aken Peter A. 
Published: September 2021

AbstractAtomic design of a 2D‐material such as graphene can be substantially influenced by etching, deliberately induced in a transmission electron microscope. It is achieved primarily by overcoming the threshold energy for defect formation by controlling the kinetic energy and current density of the fast electrons. Recent studies have demonstrated that the presence of certain species of atoms can catalyze atomic bond dissociation processes under the electron beam by reducing their threshold energy. Most of the reported catalytic atom species are single atoms, which have strong interaction with single‐layer graphene (SLG). Yet, no such behavior has been reported for molecular species. This work shows by experimentally comparing the interaction of alkali and halide species separately and conjointly with SLG, that in the presence of electron irradiation, etching of SLG is drastically enhanced by the simultaneous presence of alkali and iodine atoms. Density functional theory and first principles molecular dynamics calculations reveal that due to charge‐transfer phenomena the CC bonds weaken close to the alkali‐iodide species, which increases the carbon displacement cross‐section. This study ascribes pronounced etching activity observed in SLG to the catalytic behavior of the alkali‐iodide species in the presence of electron irradiation.
Synergistic Dissociation‐and‐Trapping Effect to Promote Li‐Ion Conduction in Polymer Electrolytes via Oxygen Vacancies
Authors: Song Yongli , Yang Luyi , Li Jiawen , Zhang Mingzheng , Wang Yaohui , Li Shunning , Chen Shiming , Yang Kai , Xu Kang , Pan Feng 
Published: September 2021

AbstractDespite their promised safety and mechanical flexibility, solvent‐free polymer electrolytes suffer from low Li‐ion conductivities due to poor dissociation of conducting salts and low Li+‐transference numbers due to Li+‐trapping by ether‐linkages. In this work, the authors found that oxygen vacancies carried by nanosized Al2O3 fillers preferentially promotes Li+‐conduction in poly(ethylene oxide) (PEO). These vacancies and free electrons therein, whose concentration can be tuned, effectively break up the ion pairs by weakening the Coulombic attraction within them, while simultaneously interacting with the anions, thus preferentially constraining the movement of anions. This synergistic dissociation‐and‐trapping effect leads to the significant and selective improvement in Li‐ion conductivity. Solid state batteries built on such PEO‐based electrolytes exhibits superior performance at high current density. This discovery reveals a molecular‐level rationale for the long‐observed phenomenon that certain inorganic nano‐fillers improve ion conduction in PEO, and provides a universal approach to tailor superior polymer‐based electrolytes for the next generation solid‐state batteries.
High Performance Air Breathing Flexible Lithium–Air Battery
Authors: Jaradat Ahmad , Zhang Chengji , Singh Sachin Kumar , Ahmed Junaid , Ahmadiparidari Alireza , Majidi Leily , Rastegar Sina , Hemmat Zahra , Wang Shuxi , Ngo Anh T. , Curtiss Larry A. , Daly Matthew , Subramanian Arunkumar , Salehi‐khojin Amin 
Published: September 2021

AbstractLithium–oxygen (Li–O2) batteries possess the highest theoretical energy density (3500 Wh kg−1), which makes them attractive candidates for modern electronics and transportation applications. In this work, an inexpensive, flexible, and wearable Li–O2 battery based on the bifunctional redox mediator of InBr3, MoS2 cathode catalyst, and Fomblin‐based oxygen permeable membrane that enable long‐cycle‐life operation of the battery in pure oxygen, dry air, and ambient air is designed, fabricated, and tested. The battery operates in ambient air with an open system air‐breathing architecture and exhibits excellent cycling up to 240 at the high current density of 1 A g−1 with a relative humidity of 75%. The electrochemical performance of the battery including deep‐discharge capacity, and rate capability remains almost identical after 1000 cycle in a bending fatigue test. This finding opens a new direction for utilizing high performance Li–O2 batteries for applications in the field of flexible and wearable electronics.
Controllable Frontal Polymerization and Spontaneous Patterning Enabled by Phase‐Changing Particles
Authors: Gao Yuan , Dearborn Mason A. , Hemmer Julie , Wang Zhao , Esser‐Kahn Aaron P. , Geubelle Philippe H. 
Published: September 2021

AbstractFrontal polymerization provides a rapid, economic, and environmentally friendly methodology to manufacture thermoset polymers and composites. Despite its efficiency and reduced environmental impact, the manufacturing method is underutilized due to the limited fundamental understanding of its dynamic control. This work reports the control and patterning of the front propagation in a dicyclopentadiene resin by immersion of phase‐changing polycaprolactone particles. Predictive and designed patterning is enabled by multiphysical numerical analyses, which reveal that the interplay between endothermic phase transition, exothermic chemical reaction, and heat exchange govern the temperature, velocity, and propagation path of the front via two different interaction regimes. To pattern the front, one can vary the size and spacing between the particles and increase the number of propagating fronts, resulting in tunable physical patterns formed due to front separation and merging near the particles. Both single‐ and double‐frontal polymerization experiments in an open mold are performed. The results confirm the front–particle interaction mechanisms and the shapes of the patterns explored numerically. The present study offers a fundamental understanding of frontal polymerization in the presence of heat‐absorbing second‐phase materials and proposes a potential one‐step manufacturing method for precisely patterned polymeric and composite materials without masks, molds, or printers.