bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mating pattern, duration and multiple mating inChondromorpha severiniSilvestri (Diplopoda: Polydesmida)
Authors: Bhakat S. 
Published: August 2020

AbstractMating behaviour ofChondromorpha severini, a polydesmid millipede was studied in the field and in the laboratory condition. Copulating pair follows the general rule of love play before actual act of coitus. Mating duration varied from one to 25 minute with an average of eight minute. Mating frequency was maximum in early and late hours of day. In the multiple mate preference experiment, 10 pairs of male and female were used to calculate preference index (Pi) of individual sex. Preference index varies from 0.65 to 0.91. The implication of multiple mating has been discussed in detail. The study confirmed that i) the species belongs to polygynandrous mating system where males are the pursuers and females are the accomplishers ii) short and long duration mating is related to mate acquisition and mate guarding respectively
Sleep and sleep deprivation differentially alter white matter microstructure: a mixed model design utilising advanced diffusion modelling
Authors: Voldsbekk Irene , Groote Inge , Zak Nathalia , Roelfs Daniël , Geier Oliver , Due-Tønnessen Paulina , Løkken Lise-Linn , Strømstad Marie , Blakstvedt Taran Y. , Kuiper Yvonne S. , Elvsåshagen Torbjørn , Westlye Lars T. , Bjørnerud Atle , Maximov Ivan I. 
Published: August 2020

AbstractSleep deprivation influences several critical functions, yet how it affects human brain white matter (WM) is not well understood. The aim of the present work was to investigate the effect of 32 hours of sleep deprivation on WM microstructure compared to changes observed in a normal sleep-wake cycle (SWC). To this end, we utilised diffusion weighted imaging (DWI) including the diffusion tensor model, diffusion kurtosis imaging and the spherical mean technique, a novel biophysical diffusion model. 46 healthy adults (23 sleep deprived vs 23 with normal SWC) underwent DWI across 4 time points (morning, evening, next day morning and next day afternoon, after a total of 32 hours). Linear mixed models revealed significant group × time interaction effects, indicating that sleep deprivation and normal SWC differentially affect WM microstructure. Voxel-wise comparisons showed that these effects spanned large, bilateral WM regions. These findings provide important insight into how sleep deprivation affects the human brain.
Solving musculoskeletal biomechanics with machine learning
Authors: Smirnov Yaroslav , Smirnov Denis , Popov Anton , Yakovenko Sergiy 
Published: August 2020

AbstractDeep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be approximated by relatively simple functions, which opens the opportunity for machine learning applications. In this study, we challenged general machine learning algorithms with the problem of approximating the posture-dependent moment arm and muscle length relationships of the human arm and hand muscles. We used two types of algorithms, light gradient boosting machine (LGB) and fully connected artificial neural network (ANN) solving the wrapping kinematics of 33 muscles spanning up to six degrees of freedom (DOF) each for the arm and hand model with 18 DOFs. The input-output training and testing datasets were generated by our previous phenomenological model based on the autogenerated polynomial structures (Sobinov et al., 2019). Both models achieved a similar level of errors: ANN model errors were 0.08±0.05% for muscle lengths and 0.53±0.29% for moment arms, and LGB model made similar errors—0.18±0.06% and 0.13±0.07%, respectively. LGB model reached the training goal with only 10^3 samples, while ANN required 10^6 samples; however, LGB models were about 39 slower than ANN models in the evaluation. The sufficient performance of developed models demonstrates the future applicability of machine learning for musculoskeletal transformations in a variety of applications, such as in advanced powered prosthetics.Author SummaryThe accurate decoding of arm and hand motor intent from biological signals remains a key challenge. Solving this task with machine learning requires vast posture- and task-dependent data for identifying structural and functional parameters within dynamic musculoskeletal relationships. This problem is related tothe curse of dimensionalitywhere the processing complexity grows exponentially with the number of degrees of freedom described by the model. Here, we developed a tool based on artificial neural networks (ANN) to solve the kinematic transformation from posture to muscle path length and muscle moment arms. We used an accurate model of posture-dependent muscle moment arms and length to train and test the ability of ANN to solve this high-dimensional and computationally intense transformation and compare it to the boosted decision tree approach. We demonstrated that model-driven training is an efficient method to handle the encoding of high-dimensional musculoskeletal relationships. Adding muscles to the transformation, which increases the input-output complexity, does not reduce the prediction accuracy and does not require the increase in the number of elements within the network demonstrating the viability of this approach for applications using musculoskeletal biomechanics.
Disinfection chemicals mode of action on the bacterial spore structure and their Raman spectra
Authors: Malyshev Dmitry , Dahlberg Tobias , Wiklund Krister , Andersson Per Ola , Henriksson Sara , Andersson Magnus 
Published: August 2020

AbstractContamination of toxic spore-forming bacteria is problematic since spores can survive a plethora of disinfection chemicals. It is also problematic to rapidly detect if the disinfection chemical was active, leaving spores dead. Robust decontamination strategies, as well as reliable detection methods to identify dead from viable spores, are thus critical. Vibrational detection methods such as Raman spectroscopy has been suggested for rapid diagnostics and differentiation of live and dead spores. We investigate in this work, using laser tweezers Raman spectroscopy, the changes in Raman spectra ofBacillus thuringiensisspores treated with sporicidal agents such as chlorine dioxide, peracetic acid, and sodium hypochlorite. We also imaged treated spores using SEM and TEM to verify if any changes to the spore structure can be correlated to the Raman spectra. We found that chlorine dioxide did not change the Raman spectrum or the spore structure; peracetic acid shows a time-dependent decrease in the characteristic DNA/DPA peaks and ∼20 % of the spores were degraded and collapsed; spores treated with sodium hypochlorite show an abrupt drop in DNA and DPA peaks within 20 minutes all though the spore structure was overall intact, however, the exosporium layer was reduced. Structural changes appeared over several minutes, compared to the inactivation time of the spores, which is less than a minute. We conclude that vibrational spectroscopy provides powerful means to detect changes in spores but it might be problematic to identify if spores are live or dead after a decontamination procedure.
Study of In silico Anti-Inflammatory Potential of Tyramine-Fe complex in Brotowali Plants
Authors: Wimbuh T.W. , Sutiman B.S. , Widyarti Sri , Djoko H. S 
Published: August 2020

AbstractTyramine-Fe complex is a bioinorganic complex that is formed in brotowali. The complex is thought to play a role in the anti-inflammatory activity of brotowali. The mechanism of anti-inflammatory drugs such as aspirin and ibuprofen is to inhibit the reaction of prostaglandin formation from COX2 and arachidonic acid. In this study, a docking simulation was performed between COX2 with single tyramine, tyramine-Fe complex ibuprofen, and aspirin. The results showed that the COX2-single tyramine bond overlapped with COX2-aspirin, while COX2-tyramine-Fe complex bond overlapped with COX2-ibuprofen. The energy required to form COX2-tyramine-Fe complex bond was smaller than that of single tyramine, aspirin, and ibuprofen. The number of bonds of COX2-tyramine-Fe complex bond is higher than that of single tyramine. These results proved that COX2-tyramine-Fe complex bond had anti-inflammatory activity and greater activity than single tyramine.
Discovery of NOvel CIP2A VAriant (NOCIVA) and its clinical relevance in myeloid leukemias
Authors: Mäkelä Eleonora , Pavic Karolina , Varila Taru , Salmenniemi Urpu , Löyttyniemi Eliisa , Nagelli Srikar , Kähäri Veli-Matti , Clark Richard E , Bachanaboyina Venkata Kumari , Lucas Claire , Itälä-Remes Maija , Westermarck Jukka 
Published: August 2020

AbstractCancerous inhibitor of PP2A (CIP2A) is a prevalent human oncoprotein that inhibits tumor suppressor PP2A-B56a. However,CIP2AmRNA and protein variants remain uncharacterized. Here, we report discovery of aCIP2Asplicing variantNOCIVA(NOvel CIp2a VAriant).NOCIVAcontainsCIP2Aexons 1-13 fused to a continuous stretch of 349 nucleotide fromCIP2Aintron 13. Intriguingly, the first 39 nucleotides of theNOCIVAspecific sequence are in coding frame with exon 13 ofCIP2A, and codes for a 13 amino acid peptide tail unhomologous to any known human protein sequence. Therefore, NOCIVA translates to a unique human protein. NOCIVA retains the capacity to bind to B56a, but whereas CIP2A is predominantly a cytoplasmic protein, NOCIVA translocates to nucleus. Indicative of prevalent alternative splicing fromCIP2AtoNOCIVAin myeloid malignancies, acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) patient samples overexpressNOCIVA, but notCIP2AmRNA. In AML, highNOCIVAmRNA expression is a marker for adverse overall survival. In CML, highNOCIVAexpression associates with inferior event free survival among imatinib treated patients, but not among patients treated with dasatinib or nilotinib. Collectively, we describe discovery of a novel variant of oncoprotein CIP2A, and its clinical relevance in myeloid leukemias.Key PointsDiscovery and characterization of a first mRNA variant of one of the most prevalently deregulated human oncoproteins CIP2AUnlike CIP2A, NOCIVA mRNA is overexpressed in AML and CML patient samples and associates with poor clinical response in both myeloid cancers
Determinants of consumers’ intention to use credit card: a perspective of multifaceted perceived risk
Authors: Trinh Hoang Nam , Tran Hong Ha , Vuong Duc Hoang Quan 
Published: August 2020

Purpose
The purpose of this study is to develop a theoretical model for consumer behavioral intention by integrating the technology acceptance model (TAM) and the theory of perceived risk, which is tested on the intended use of credit cards in Vietnam.

Design/methodology/approach
The data were collected from 485 bank customers through a nationwide online survey. An exploratory and confirmatory factor analyzes were performed to validate the factor structure of the measurement items while structural equation modeling was used to validate the proposed model and testing the hypotheses.

Findings
The results of structural equation modeling reveal that perceived risk, perceived usefulness, social influence and perceived ease of use were significant determinants of consumer intention to use a credit card. Of them, only perceived risk discouraged the intended use of a credit card, which was synthesized from psychological, financial, performance, privacy, time, social and security risk.

Research limitations/implications
This study measured the first-order risk dimensions based on the payment function of the credit card only; these measurements missed potential losses relevant to credit function of credit cards.

Practical implications
This study can be beneficial to banks enacting policies to attract more consumers and to help decide how to allocate resources to retain and expand their customer base.

Originality/value
The study adds value to the literature on consumer behavior by confirming the impact of second-order perceived risk on the intended use of credit cards, which most previous studies have not demonstrated. The research also provides an empirical evidence to the academic research platform on e-banking services in Vietnam, especially related to the credit card industry.
The undergraduate simulation framework: standardising design and delivery
Authors: Irwin Pauletta Marie , Brown Robin A. , Butler Sonia 
Published: August 2020

PurposeHigher education organisations have the need and capacity to meet the expectations of industry partners to develop practice-ready graduates. Altered social and fiscal constraints have implications for the health workforce and, as such, decisions about how best to prepare undergraduate students should be considered. The purpose of this (conceptual) paper is to present the undergraduate simulation framework that has been designed to promote a standardised delivery of simulation, enabling graduates to have a higher capacity to meet workforce needs.Design/methodology/approachEducation and simulation literature were explored to inform the development of the undergraduate simulation framework. A working knowledge of accreditation standards of health professions was key in designing a framework that could be applied across disciplines.FindingsThe framework encompasses tenets of a learner-centred pedagogy as well as professional and simulation standards. Experiential learning, behaviourism and social constructivism are viewed as foundational pillars when developing and delivering a simulation and have been considered in developing the framework. Other constructs of the undergraduate simulation framework are prebriefing, debriefing, repetition in the form of simulation cycles and the roles of student and educator.Practical implicationsIt is recommended that this framework be incorporated into undergraduate health programmes to enhance student learning and potentiate the transference of knowledge and skills to the clinical setting.Originality/valueThe undergraduate simulation framework was developed to contribute to education and simulation research literature specific to health programmes to enhance student learning in readiness for the clinical environment.
Overreaction effect: evidence from an emerging market (Shanghai stock market)
Authors: Reddy Krishna , Qamar Muhammad Ali Jibran , Mirza Nawazish , Shi Fangwei 
Published: August 2020

PurposeThe purpose of the study is to examine overreaction effect in the Chinese stock market after the global financial crisis (GFC) of 2007 for all the stocks listed in Shanghai Stock Exchange (SSE) Composite 50 index.Design/methodology/approachTo capture overreaction effect in the stock listed at SSE 50 Index, a time series analysis of average cumulative abnormal return within a unified framework is applied for the period of January 2009 to December 2015. From these loser and winner portfolios, contrarian strategy is applied to build arbitrage portfolio, which is the difference of mean reversions between loser and winner portfolios. The portfolio construction is based on a 12-month formation period and 6-month testing period for intermediate-term analysis and. for short-term analysis, 6 month formation and 3 month testing periods. The authors also applied regression analysis to test a return reversal effect for the sampled period.FindingsResults show that contrarian strategy yields positive excess returns for the arbitrage portfolio for most of the testing periods. The intermediate baseline case shows the arbitrage portfolio producing an average excess return of 14.1%, while even the short-term one produces 4%, which is statistically significant at the 5% level. The study finds asymmetrical overreactions in the SSE especially for loser portfolios. The biggest winner and loser portfolios follow the mean reversal effect. Moreover, before-after test for the biggest winner and loser portfolios shows that the losers recovered and beat the market immediately.Practical implicationsThe study could benefit government, policy makers and regulators by studying how presence of more individual investors than institutional investors of China stock market leads to more irrational decisions giving rise to volatility. The regulators could build favourable policies for institutional investors to give them incentive to invest more than individual investors through which market volatility could be controlled.Originality/valueThis research contributes to market behaviour research, showing how working under hypotheses of overreaction; gains can be made with contrarian investment strategy through arbitrage portfolios. The authors provide specific additional support for the short and medium-term overreaction in the SSE for the period 2009–2015 using regression analysis.Contribution to ImpactThis research contributes to market behaviour research, showing how working under hypotheses of overreaction; gains can be made with contrarian investment strategy through arbitrage portfolios. We provide specific additional support for the short and medium-term overreaction in the SSE for the period 2009–2015 using regression analysis.
A study of job involvement prediction using machine learning technique
Authors: Choi Youngkeun , Choi Jae Won 
Published: August 2020

PurposeJob involvement can be linked with important work outcomes. One way for organizations to increase job involvement is to use machine learning technology to predict employees’ job involvement, so that their leaders of human resource (HR) management can take proactive measures or plan succession for preservation. This paper aims to develop a reliable job involvement prediction model using machine learning technique.Design/methodology/approachThis study used the data set, which is available at International Business Machines (IBM) Watson Analytics in IBM community and applied a generalized linear model (GLM) including linear regression and binomial classification. This study essentially had two primary approaches. First, this paper intends to understand the role of variables in job involvement prediction modeling better. Second, the study seeks to evaluate the predictive performance of GLM including linear regression and binomial classification.FindingsIn these results, first, employees’ job involvement with a lot of individual factors can be predicted. Second, for each model, this model showed the outstanding predictive performance.Practical implicationsThe pre-access and modeling methodology used in this paper can be viewed as a roadmap for the reader to follow the steps taken in this study and to apply procedures to identify the causes of many other HR management problems.Originality/valueThis paper is the first one to attempt to come up with the best-performing model for predicting job involvement based on a limited set of features including employees’ demographics using machine learning technique.
Deformation prediction based on an adaptive GA-BPNN and the online compensation of a 5-DOF hybrid robot
Authors: Sun YuBo , Xiao Juliang , Liu Haitao , Huang Tian , Wang Guodong 
Published: August 2020

Purpose
The purpose of this paper is to accurately obtain the deformation of a hybrid robot and rapidly enable real-time compensation in friction stir welding (FSW). In this paper, a prediction algorithm based on the back-propagation neural network (BPNN) optimized by the adaptive genetic algorithm (GA) is presented.

Design/methodology/approach
Via the algorithm, the deformations of a five-degree-of-freedom (5-DOF) hybrid robot TriMule800 at a limited number of positions are taken as the training set. The current position of the robot and the axial force it is subjected to are used as the input; the deformation of the robot is taken as the output to construct a BPNN; and an adaptive GA is adopted to optimize the weights and thresholds of the BPNN.

Findings
This algorithm can quickly predict the deformation of a robot at any point in the workspace. In this study, a force-deformation experiment bench is built, and the experiment proves that the correspondence between the simulated and actual deformations is as high as 98%; therefore, the simulation data can be used as the actual deformation. Finally, 40 sets of data are taken as examples for the prediction, the errors of predicted and simulated deformations are calculated and the accuracy of the prediction algorithm is verified.

Practical implications
The entire algorithm is verified by the laboratory-developed 5-DOF hybrid robot, and it can be applied to other hybrid robots as well.

Originality/value
Robots have been widely used in FSW. Traditional series robots cannot bear the large axial force during welding, and the deformation of the robot will affect the machining quality. In some research studies, hybrid robots have been used in FSW. However, the deformation of a hybrid robot in thick-plate welding applications cannot be ignored. Presently, there is no research on the deformation of hybrid robots in FSW, let alone the analysis and prediction of their deformation. This research provides a feasible methodology for analysing the deformation and compensation of hybrid robots in FSW. This makes it possible to calculate the deformation of the hybrid robot in FSW without external sensors.
An empirical examination of elder abuse through the lens of mens rea
Authors: Bajpai Naval , Sharma Gunjan , Dubey Prince , Kulshreshtha Kushagra 
Published: August 2020

PurposeThe purpose of this paper is to examine elder abuse (EA) tendency in the context of social integration and caregiver stress (CGS). Afterward, the attempt has been made to examine the role of mens rea or intention in the incidence of EA.Design/methodology/approachFor examining the opinion on EA through the perspective of the elderly and caregiver, a questionnaire was developed using exploratory factor analysis and confirmatory factor analysis. Afterward, a z-test was used for analyzing the results obtained from the elderly and caregivers.FindingsThe results showed that in the context of EA, the opinion of caregivers differs from the elderly. This finding was attributed to the theory of ignorance. Based on this, the legal action for every incidence of EA was discouraged by seeking endorsement from the attachment theory especially for the EA incidences arising due to factors such as social isolation or CGS.Research limitations/implicationsThis research addresses the presence and non-presence of mens rea or intention in the incidence of EA. Future studies may be conducted by taking a sample from two more developed and developing economies. Moreover, based on the findings the recommended framework can be empirically examined by future researchers.Practical implicationsUnderstanding the study through the perspective of the caregiver may facilitate the academicians and practitioners in keeping the fabric of relationships stronger among the elderly and caregiver.Originality/valueBased on the results obtained from the elderly and caregiver, this study proposes a conceptual framework for examining the EA through the lens of mens rea/intention of the caregiver. It is recommended that initiating legal action for every incidence of EA must be discouraged. However, the incidences such as physical abuse (assault), financial abuse, sexual abuse and alike for which the law itself assumes the presence of intention must not be exempted.