bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi Algorithmic Approach to Galactic Swarm Optimization (MAGSO)
Authors: Kathirvelu Vasanthakumar , Muthiah-Nakarajan Venkataraman
Published: March 2021

Multi population algorithms have noted advantages over single population algorithms when optimizing over multimodal function space. The existence of multiple attractors allows exploration on multiple optimality regions which in turn provide more exploration compared with single population algorithms. This paper is a novel utilization of Galactic Swarm Optimization (GSO) framework in which the subpopulations are evolved with multiple variants of Particle Swarm Optimizers (PSOs) rather than single PSO. The proposed algorithm is called Multi Algorithmic Galactic Swarm Optimization (MAGSO). MAGSO utilizing multivariant PSOs in subswarm level combined with the unique superswarm tailored for exploitation has shown significant improvement of performance over GSO. Experiments done on Congress on Evolutionary Computation 2013 (CEC 2013) and other standard general benchmark suite indicate that the proposed MAGSO algorithm outperforms the previously proposed state-of-the-art swarm algorithms such as Bollinger bands approach on boosting ABC (ABCBB) and Dynamic Neighbourhood Learning Particle Swarm Optimizer (DNLPSO) besides GSO. Detailed statistical evidence over 51 independent trials on the benchmark functions indicate the best performance of MAGSO. The main research goal is to propose a superior performing algorithm by exploiting the GSO framework. In order to show its efficacy, the algorithm is compared with state-of-the-art swarm algorithms such as ABCBB, DNLPSO and unmodified GSO. The method proposed here is quite general and demonstrates that more ability and extension work possible from the basic GSO framework.
Methodological Approaches to Data Pre-Processing Formalization for Statistical Analysis
Authors: Pisareva D A , Pakhomova E A , Rozhkova O V
Published: March 2021

The topic of data processing is not accidental for us. Initially, we were engaged in the development of mathematical tools for assessing the impact of the University of Science city on the effectiveness of Regional development (USR). The theme turned out to be similar to the Triple Helix (TH) model of H. Etzkowitz. The main difference of the TH from the USR: the TH gives deep processes interpretations, the USR – the economic-mathematical modeling tools. In our opinion both models supplement each other: the USR interpretation results become brighter by means of the TH formulations. Then we began to develop a toolkit for the TH model with its adaptation to the conditions of Russia. Our articles are available on the Official Website of the Triple Helix Association Russian Chapter. It was important to adapt the apparatus of continuous functions to discrete functions, since official statistics are discrete in nature. Working with statistics revealed methodological problems. The paper offers methodological approaches to formalizing data pre-processing (the method of time series linking) in order to unify them and to further process them by modeling and forecasting. It seems that this formalization eliminates the appropriate methodological flaw in the literature.
Social Welfare Maximization in Smart Grid: Review
Authors: Ramnath Gaikwad Sachin , Harikrishnan R.
Published: March 2021

The modernized power grid system intelligently integrates various advanced Information and Communication Technologies (ICT’s) with various renewable energy sources. Moreover, recently huge developments are happening in renewable sources, with enhancement in technologies eras, the role of consumers is shifting towards producers so-called “prosumers”. Furthermore, main challenges are enlarged energy demands, uncertainty in weather conditions and varied occupant’s behavioural interventions in energy consumption. So, residential user-end is having potential for energy optimization by energy efficiency, conservation, and active participation. Besides, the dynamic fair energy pricing with respect to generation cost and load demands is motivational parameters to users. On the same, this paper focuses on analysing a novel, attractive business model that can actively participate prosumers through dynamic pricing and incentive based demand response programs for the maximization of social welfare. Further this review paper focusses on various optimization models to achieve social welfare maximization. And also bibliometric analysis has been done through the Scopus database.
A Comparative Study of Laplace Transform and Sumudu Transform on

H¯

-Function
Authors: Bhargava Alok , Jain Ravi Kumar , Agarwal Garima
Published: March 2021

In this work, we have done the comparative study of Laplace Transform and Sumudu Transform on

H
¯

-function and derived certain new results. Some corollaries are also mentioned to illustrate the main results.
Machine Learning and Prophecy of Behavior: A Breakthrough in Artificial Intelligence
Authors: Saini G , Seema  , Mor K
Published: March 2021

This paper addresses how the cognitive theory and machine learning methods are combined for predicting human behavior. Prediction of human behavior is a complex task. Researchers addressing this task proposes behavior models using statistical methods and rationality theory such as machine learning algorithms. This paper addresses how the potential cognitive theory and machine learning methods are combined in using by providing and predicting human behavior. We discussed two theory focal points theory and aspiration adaptation theory can be used in combination with machine learning algorithm to simplify the task and understanding the behavior. These two approaches are combined and compared in two domains. In simple domain, both machine learning models and psychological models give clear predictions and results. While in complex domains, the results states that psychological models’ results are not cleared and in learning models are also inaccurate. To overcome this problem, the hybrid model is introduced which helps in understanding of how people behave and at the same time how they wouldn’t behave reducing the space helping machine learning algorithms in searching the accurate behavior models. Hybrid model with cognitive models and machine learning have contributed in models which predicted behavior accurately as compared to machine learning models/cognitive models alone. The future researches must work in these directions using hybrid models for successful prediction of behavior.
On the treatment of zero returns in the estimation of log-GARCH model : Empirical study
Authors: Settar Abdeljalil , Badaoui Mohammed
Published: March 2021

This paper proposes a QML estimation of log-GARCH model in the presence of zero returns. Our approach consists in treating zeros within the data as missing values which are objects of imputation based on the EM algorithm and information filter. Empirical applications to the Crude Oil WTI, CAC40 and the FTSE100 return series are presented and show that through the proposed imputation, the log-GARCH model fits better the data compared to the traditional approach whereby the estimation is performed after removing the zeros from the data.
Data Stream Clustering for Big Data Sets: A comparative Analysis
Authors: Dubey Ankit Kumar , Gupta Rajendra , Mishra Satanand
Published: March 2021

The world is growing rapidly with constantly increasing the data. There are innumerable people around the world who use different types of applications, whether it is for reservation, marketing, shopping or knowledge in the form of text, image, audio and video. Only data is being generated everywhere and this growing data which is large and high dimensional is in nature is generally known as “big data”. For the organizations, it is a big task to cluster streaming big data successfully. In this paper, we are presenting a survey of data stream clustering algorithms applied over big data and big datasets. The paper shows the comparative analysis of all the studied methods and also review the evolution and progression of data stream clustering algorithm for big datasets. The paper also analyses the proposed and implemented algorithms in recent years.
Role of Machine Learning in Sustainable Engineering: A Review
Authors: Bhatnagar Vaibhav , Sharma Shefali , Bhatnagar Anurag , Kumar Lov
Published: March 2021

Sustainable engineering is the method of modelling or running systems that allow efficient use of products and resources, that is, at a scale which would not endanger the natural habitat or the potential of subsequent generations to reach their own needs. Machine learning has large number of applications such as healthcare, agriculture, security and even in day to day life. In sustainable computing, machine learning also plays a crucial role. The emergence of Machine learning and even its exponentially greater implications on many markets require an evaluation of its implications on sustainable innovation achievement. In this paper, a review of supervised and unsupervised machine algorithms is done which are used in sustainable engineering. Different engineering disciplines such as mechanical, civil, chemical engineering are covered that working on sustainability. As the interpretation of the review it can be stated that there is a wide scope of working on sustainable development though machine learning. Specific machine learning algorithms are required to work on sustainable engineering. This review is helpful for engineers which are working in the field of sustainable development.
Mathematical Analysis of Radiating Viscoelastic Unsteady MHD Fluid Flow through an Absorbent Media between Upstanding Equidistant plates with Joule Heating Impact
Authors: Mehta T , Mehta R , Kumar M
Published: March 2021

Focus of the present study is to explore Joule heating effect on incompressible, radiating, electrically conducting viscoelastic unsteady magnetohydrodynamic fluid and heat transfer through an absorbent media between upstanding equidistant plates with constant suction and slip condition. Lorentz force and Joule heating is discussed by momentum and energy equation respectively. Electric conductivity and the electromagnetic force both are in very small case in fluid flow. A consistent magnetic field is taking to perpendicular to the plane of the plates. The oscillatory time-dependent coupled equations (non-linear) are simplified to provide result for the fluid velocity and heat transfer by using structured perturbation execution. It has been noticed that velocity and heat transfer rise due to rise in Grashof number, Eckert number, heat source parameter or slip parameter while decrease as permeability parameter, radiation parameter or wavelength rises. As Hartmann number rises the fluid velocity diminishes, while opposite behaviour is noted in case of heat transfer effect. The velocity and heat transfer impact of the fluid rise with cross flow Reynolds parameter or frequency of the oscillation. Numerical outcomes for the motion and heat transpose effect profiles for multiples physical numbers also the local skin friction coefficient and spot Nusselt number are argued numerically, presented graphically along with.
Measuring Accuracy of Stock Price Prediction Using Machine Learning Based Classifiers
Authors: Kaur Ranjeet , Sharma Dr. Yogesh Kumar , Bhatt Devershi Pallavi
Published: March 2021

Stock market prediction means to decide the future development of the stock estimation of a budgetary trade. The exact forecast of offer value development will prompt more benefit speculators can make. The investigation dependent on the past gathered enormous information with the utilization of the AI strategies is appropriate for different fields. The basic aim is to generate the analysis for driving good information which will be useful for the purpose of decision making. The quality of the decisions will definitely be enhanced. There are various machine learning techniques lies with different accuracies. The selection of the best technique such that the highest level of accuracy can be achieved. In the current research there are three techniques with different variant are tested for showing the relative accuracy of the specific technique. The all these techniques are based on supervised learning will requires training for the better accuracy. In current research paper all the techniques with different variants are trained with the different sizes of the training sets. These training sizes are 70:30,50:50 and 30:70. The best variant is the 70:30 for the KNN. The given variant shows the highest accuracy in terms of the prediction.
New Paradigm in Healthcare Industry Using Big Data Analytics
Authors: Luthra Hiya , Arun Sai Nihith T. , Sri Sai Pravallika V. , Raghuram Shree R , Chaurasia Ankur , Bansal Hina
Published: March 2021

New scientific methods, such as genome sequencing for the next decade, produce enormous amounts of biological data that leads us to scientific breakthroughs through careful study and interpretation, However, scholars fail to keep up with all the enormous data blocks. As the generation of salient Information is rising with each day, we can use it in a productive way. The medical data can be useful in fields of personalized medicine whose demand is growing exponentially. The term emerged in 2012, along with the “Industry 4.0” concept, Famous, and refers to big data, in the marketing of IT, in which industrial equipment-generated data could hold more Potential benefit for sector. Large Data Industrials refers to many Diverse produced time series by industrial equipment at a high speed. Preventive medicine for individual patients with chronic conditions, Big Data and healthcare are critical to address the risk of hospitalization. The current review discusses about application of big data in gene sequencing, healthcare, electronic health reports (EHRs), medical scans, genomic sequencing, reports of payers, pharmacy studies, wearables, and medical devices, gathered ample health data, to name a few. This paper mainly talks about the ways in which big data can be used and the tools for analyzing big data. Big data analytics aims to provide innovations that optimize patient care and generate value for healthcare institutions with improvements in medicine, infrastructure and funding.
A Comparative Study for Efficient Covid-19 Detecting Machine Learning Models on CT Images
Authors: Chaturvedi Ankur , Mishra Divyansh , Vikram Rajpoot Dr. , Gupta Janvi , Sharma Aditi
Published: March 2021

Covid19 has impacted the whole world drastically, Lakhs of people are getting infected every day and thus it’s getting difficult to detect Covid infection in huge population. Main cause of death due to Covid19 is Covid infection in lungs. Infection in lungs is detected by CT-Scans of Lungs, but since doctors need time analyze CT-Scan and also, they can’t do it 24 into 7. Hence, many people who need extra medical care for Covid infection in lungs, need to wait for analysis of CT-Scan by doctors that whether their lungs are infected or not. Here in the paper, it is tried to make an efficient Machine Learning Model which could detect Covid19 infection in Lungs, in order to reduce human resource time (time taken by doctors to analyze lung CT-Scan) so that needy patients would be getting started with corresponding medical treatments more quickly. It may save many lives. Here in the paper, it’s tried to detect Covid19 infection in lungs analyzing CT-scans using SVM classifier, Logistic regressions classifier and Machine learning perceptron with two approaches of preprocessing of pixel values and then results have been compared. Dataset used for the purpose, is kaggle dataset (Covid-19 Lung CT Scan.
Advanced Glaucoma Detection Using Hybrid Approach and Singular Value Decomposition from Fundus Images
Authors: Kirar B S , Ahmed G , Sharma S , Agrawal D K
Published: March 2021

Glaucoma is a known to be an of eye disease that is accountable for impaired vision worldwide. For medical images, image decomposition has high impact for diagnosis of diseases. This work proposes an intelligent hybrid approach-based image decomposition method for advanced glaucoma detection. The proposed approach employs Discrete Wavelet Transform (DWT) hybridisation with Empirical Wavelet Transform (EWT). Texture features are used and extracted from decomposed image components as these captures subtle variation. To discover effective features, extracted features are added to Singular Value Decomposition (SVD). In addition, efficient features are added to the Support Vector Machine (SVM) algorithm and 95.83 percent accuracy, 91.67 percent sensitivity and 100 percent specificity has been achieved for twelve-fold cross validation. Hence, the approach proposed demonstrates superior efficiency of decomposition.
Design and Analysis of an Efficient Multi-Relational Decision Tree Learning Algorithm
Authors: Yadav Chhutten Singh , Kumar Abhishek , Kumar Ankit , Dadheech Pankaj
Published: March 2021

In today’s day of modern era when the data handling objectives are getting bigger and bigger with respect to volume, learning and inferring knowledge from complex data becomes the utmost problem. The research in Knowledge Discovery in Databases has been primarily directed to attribute-value learning in which one is described through a fixed set tuple given with their values. Database or dataset is seen in the form of table relation in which every row corresponds to an instance and column represents an attribute respectively. In this paper a New framework is introduced a much more sophisticated and deserving approach i.e., Hybrid Multi-Relational Decision Tree Learning Algorithm which overcomes with Exiting technology drawbacks and other anomalies. Result show that Hybrid Multi- Relational Decision Tree Learning Algorithm provides certain methods which reduces its execution time. Experimental results on different datasets provide a clear indication that Hybrid Multi-Relational Decision Tree Learning Algorithm is comprehensively a better approach.
A Secure Image Watermarking Scheme Based on DWT, SVD and Arnold Transform
Authors: Kumar Lalan , Singh Kamred Udham
Published: March 2021

The rapid advancement and extensive growth in communication technology is creating a critical need to develop novel methods and techniques to protect copyright, ownership, authentication and content integrity of digital media. An efficient solution to this kind of problem is digital watermarking of multimedia. A watermark refers to a visible or invisible digital data to be embedded in a cover data so as to provide copyright protection at time when need arises. Numerous watermarking methods have been presented in the literature and several watermarking software packages have been developed in past decades. Still robustness and security of watermark against a variety of attacks is one of the most important issues to be solved. In this paper, techniques are presented for watermarking of 8-bit images. The watermark is subjected to Arnold Transform to introduce randomness, thus enhancing security and improving PSNR. Embedding is done in low frequency LL band of original image. Inverse Discrete wavelet Transform is then computed to obtain the watermarked image. The results are evaluated by applying number of attacks on the watermarked image. The experimental results show that the proposed watermarking technique is robust and efficient.
Classification of Imbalanced Data:Review of Methods and Applications
Authors: Kumar Pradeep , Bhatnagar Roheet , Gaur Kuntal , Bhatnagar Anurag
Published: March 2021

Imbalance in dataset enforces numerous challenges to implement data analytic in all existing real world applications using machine learning. Data imbalance occurs when sample size from a class is very small or large then another class. Performance of predicted models is greatly affected when dataset is highly imbalanced and sample size increases. Overall, Imbalanced training data have a major negative impact on performance. Leading machine learning technique combat with imbalanced dataset by focusing on avoiding the minority class and reducing the inaccuracy for the majority class. This article presents a review of different approaches to classify imbalanced dataset and their application areas.
A Secure and Efficient Data Migration Over Cloud Computing
Authors: Madhukar Rao G. , Srinivas K. , Samee Sayyad , Venkatesh K , Dadheech Pankaj , Raja Linesh , Yagnik Garvit
Published: March 2021

Migrations are complex and depend on the project and its requirements, so security is essential to ensure a safe migration to the cloud. There are many things to consider, such as compliance with industry regulations, performance, or internal security policies. When migrating to a cloud environment, security is a shared responsibility. The cloud is based on a shared responsibility model in which the Internet service provider is responsible for cloud security and the customer is responsible for cloud security. In this paper we proposed a high-security for transferring large scale data files to the cloud database management system and an inter-cloud knowledge migration process that provides quick intervals. Measuring the effectiveness of the proposed work we have compare the performance with exiting cloud migration services. In our proposed work communication plays an essential role in its success. It is necessary to ensure clear and efficient communication between the parties involved in the migration, from decision makers to IT experts, legal teams and security officials. By sharing the needs, goals, and threats among the parties involved, our prosed work creates a successful migration strategy for the business, thereby avoiding disruptions, data loss, and other potential risks.
Plasma-assisted Co/Zr-metal organic framework catalysis of CO2 hydrogenation: influence of Co precursors
Authors: LI Yanqin , ZHAO Jing , BU Decai , ZHANG Xulei , PENG Teng , DI Lanbo , ZHANG Xiuling
Published: March 2021

In this study, Co/Zr-metal organic framework (MOF) precursors were obtained by a room-temperature liquid-phase precipitation method and the equivalent-volume impregnation method, respectively, using a Zr-MOF as the support, and Co/Zr-MOF-M and Co/Zr-MOF-N catalysts were prepared after calcination in a hydrogen–argon mixture gases (

V

Ar

:

V

H

2

=
9
:
1

) at 350 °C for 2 h. The catalytic activities of the prepared samples for CO2 methanation under atmospheric-pressure cold plasma were studied. The results showed that Co/Zr-MOF-M had a good synergistic effect with cold plasma. At a discharge power of 13.0 W,

V

H

2

:

V

CO

2

=
4
:
1

and a gas flow rate of 30 ml·min−1, the CO2 conversion was 58.9% and the CH4 selectivity reached 94.7%, which was higher than for Co/Zr-MOF-N under plasma (CO2 conversion 24.8%, CH4 selectivity 9.8%). X-ray diffraction, scanning electron microscopy, transmission electron microscopy, N2 adsorption and desorption (Brunauer–Emmett–Teller) and x-ray photoelectron spectroscopy analysis results showed that Co/Zr-MOF-M and Co/Zr-MOF-N retained a good Zr-MOF framework structure, and the Co oxide was uniformly dispersed on the surface of the Zr-MOF. Compared with Co/Zr-MOF-N, the Co/Zr-MOF-M catalyst has a larger specific surface area and higher Co2+/Cototal and Co/Zr ratios. Additionally, the Co oxide in Co/Zr-MOF-M is distributed on the surface of the Zr-MOF in the form of porous particles, which may be the main reason why the catalytic activity of Co/Zr-MOF-M is higher than that of Co/Zr-MOF-N.
Motional heating of spatially extended ion crystals
Authors: Kalincev D , Dreissen L S , Kulosa A P , Yeh C-H , Fürst H A , Mehlstäubler T E
Published: March 2021

We study heating of motional modes of a single ion and of extended ion crystals trapped in a linear radio frequency (rf) Paul trap with a precision of

Δ

n

̄

̇

≈
0.1

phonons s−1. Single-ion axial and radial heating rates are consistent and electric field noise has been stable over the course of four years. At a secular frequency of ω
sec = 2π × 620 kHz, we measure

n

̄

̇

=
0.56

(

6

)

phonons s−1 per ion for the center-of-mass (com) mode of linear chains of up to 11 ions and observe no significant heating of the out-of-phase (oop) modes. By displacing the ions away from the nodal line, inducing excess micromotion, rf noise heats the com mode quadratically as a function of radial displacement r by

n

̄

̇

(

r

)

/

r

2

=
0.89

(

4

)

phonons s−1
μm−2 per ion, while the oop modes are protected from rf-noise induced heating in linear chains. By changing the quality factor of the resonant rf circuit from Q = 542 to Q = 204, we observe an increase of rf noise by a factor of up to 3. We show that the rf-noise induced heating of motional modes of extended crystals also depends on the symmetry of the crystal and of the mode itself. As an example, we consider several 2D and 3D crystal configurations. Heating rates of up to 500 ph s−1 are observed for individual modes, giving rise to a total kinetic energy increase and thus a fractional time dilation shift of up to −0.3 × 10−18 s−1 of the total system. In addition, we detail how the excitation probability of the individual ions is reduced and decoherence is increased due to the Debye–Waller effect.
Tumor IsomiR Encyclopedia (TIE): a pan-cancer database of miRNA isoforms
Authors: Bofill-De Ros Xavier , Luke Brian , Guthridge Robert , Mudunuri Uma , Loss Michael , Gu Shuo
Published: March 2021

MicroRNAs (miRNAs) are master regulators of gene expression in cancers. Their sequence variants or isoforms (isomiRs) are highly abundant and possess unique functions. Given their short sequence length and high heterogeneity, mapping isomiRs can be challenging; without adequate depth and data aggregation, low frequency events are often disregarded. To address these challenges, we present the Tumor IsomiR Encyclopedia (TIE): a dynamic database of isomiRs from over 10 000 adult and pediatric tumor samples in The Cancer Genome Atlas (TCGA) and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects. A key novelty of TIE is its ability to annotate heterogeneous isomiR sequences and aggregate the variants obtained across all datasets. Results can be browsed online or downloaded as spreadsheets. Here, we show analysis of isomiRs of miR-21 and miR-30a to demonstrate the utility of TIE.

Availability and implementation

TIE search engine and data are freely available to use at https://isomir.ccr.cancer.gov/.

Supplementary information

Supplementary data are available at Bioinformatics online.
Mild traumatic brain injury is associated with dysregulated neural network functioning in children and adolescents
Authors: Safar Kristina , Zhang Jing , Emami Zahra , Gharehgazlou Avideh , Ibrahim George , Dunkley Benjamin T
Published: March 2021

Mild traumatic brain injury is highly prevalent in paediatric populations, and can result in chronic physical, cognitive and emotional impairment, known as persistent post-concussive symptoms. Magnetoencephalography has been used to investigate neurophysiological dysregulation in mild traumatic brain injury in adults; however, whether neural dysrhythmia persists in chronic mild traumatic brain injury in children and adolescents is largely unknown. We predicted that children and adolescents would show similar dysfunction as adults, including pathological slow-wave oscillations and maladaptive, frequency-specific, alterations to neural connectivity. Using magnetoencephalography, we investigated regional oscillatory power and distributed brain-wide networks in a cross-sectional sample of children and adolescents in the chronic stages of mild traumatic brain injury. Additionally, we used a machine learning pipeline to identify the most relevant magnetoencephalography features for classifying mild traumatic brain injury and to test the relative classification performance of regional power versus functional coupling. Results revealed that the majority of participants with chronic mild traumatic brain injury reported persistent post-concussive symptoms. For neurophysiological imaging, we found increased regional power in the delta band in chronic mild traumatic brain injury, predominantly in bilateral occipital cortices and in the right inferior temporal gyrus. Those with chronic mild traumatic brain injury also showed dysregulated neuronal coupling, including decreased connectivity in the delta range, as well as hyper-connectivity in the theta, low gamma and high gamma bands, primarily involving frontal, temporal and occipital brain areas. Furthermore, our multivariate classification approach combined with functional connectivity data outperformed regional power in terms of between-group classification accuracy. For the first time, we establish that local and large-scale neural activity are altered in youth in the chronic phase of mild traumatic brain injury, with the majority presenting persistent post-concussive symptoms, and that dysregulated interregional neural communication is a reliable marker of lingering paediatric ‘mild’ traumatic brain injury.
Leg or antenna injury inCataglyphisants impairs survival but does not hinder searching for food
Authors: Gilad Tomer , Dorfman Arik , Subach Aziz , Scharf Inon
Published: March 2021

Injury is common in nature resulting, for example, from fighting, partial predation, or the wear of body parts. Injury is costly, expressed in impaired performance, failure in competition, and a shorter life span. A survey of the literature revealed the frequent occurrence of injury in ants and its various causes. We examined whether leg or antenna injury impacts food-discovery time and reduces the likelihood of reaching food in workers of the desert ant Cataglyphis niger. We examined the search-related consequences of injury in groups of either 4 or 8 workers searching for food in a short arena, a long arena, and a maze. We conducted a small field survey to evaluate the prevalence of injury in the studied population. Finally, we compared the survival rates of injured versus uninjured workers in the laboratory. Injury was common in the field, with almost 9% of the workers collected out of the nest, found to be injured. Injured workers survived shorter than uninjured ones and there was a positive link between injury severity and survival. However, we could not detect an effect of injury on any of the searching-related response variables, neither in the arenas nor in the mazes tested. We suggest that workers that survive such injury are only moderately affected by it.
Results of the Seventh Scientific Workshop of ECCO: Precision Medicine in IBD—Prediction and Prevention of Inflammatory Bowel Disease
Authors: Torres Joana , Halfvarson Jonas , Rodríguez-Lago Iago , Hedin Charlotte R H , Jess Tine , Dubinsky Marla , Croitoru Kenneth , Colombel Jean-Frédéric
Published: March 2021

Inflammatory bowel disease [IBD] is a complex chronic disorder with no clear aetiology and no known cure. Despite recent advances in overall disease management and improved therapeutics, patients with IBD still experience a substantial burden. Furthermore, as the incidence continues to increase in developing areas of the world, it is expected that the burden of IBD to society will increase and exert tremendous pressure on health care systems worldwide. Therefore, new strategies to prevent the global increase of IBD are urgently required. Data are being progressively acquired on the period preceding disease diagnosis, which support the concept that IBD has a preclinical period that may reveal the triggers of disease and may be amenable to early intervention. Having a better knowledge of this preclinical period will increase the potential not only for improved understanding of disease pathogenesis and improved therapeutics, but also for disease prediction and prevention.
Iranian and Iraqi torture survivors in Finland and Sweden: findings from two population-based studies
Authors: Garoff Ferdinand , Tinghög Petter , Suvisaari Jaana , Lilja Eero , Castaneda Anu E
Published: March 2021

Background

Most refugees and other forced migrants have experienced potentially traumatic events (PTEs). Torture and other traumatic experiences, as well as various daily stressors, impact the mental health and psychosocial well-being of war-affected populations.

Methods

The study includes two population-based samples of Iranian and Iraqi men living in Finland and Sweden. The Finnish Migrant Health and Well-being Study (Maamu) was conducted in 2010–2012. The Linköping study was conducted in Sweden in 2005. In both samples, health and well-being measures, social and economic outcomes as well as health service utilization were reported.

Results

The final sample for analysis consisted of two groups of males of Iranian or Iraqi origin: 278 residents in Finland and 267 residents in Sweden. Both groups were subdivided according to the reported PTEs: Torture survivors; Other PTEs; No PTEs. Migrants that reported PTEs, torture survivors in particular, had significantly poorer social and health outcomes. Torture survivors also reported lower trust and confidence in authorities and public service providers, as well as more loneliness, social isolation and experiences of discrimination.

Conclusions

Torture and other PTEs prevalent in refugee and migrant populations create a wide-ranging and long-term impact in terms of increased risk of various types of adverse social and health conditions. Early identification through systematic and effective screening should be the first step in guiding migrants and refugees suffering from experiences of torture and other PTEs to flexible, multidisciplinary services.
Heritability of fat distributions in male mice from the founder strains of the Diversity Outbred mouse population
Authors: Keenan Brendan T , Webster Jeanette C , Wiemken Andrew S , Lavi-Romer Nir , Nguyen Teresa , Svenson Karen L , Galante Raymond J , Churchill Gary A , Pickup Stephen , Pack Allan I , Schwab Richard J
Published: March 2021

Specific fat distributions are risk factors for complex diseases, including coronary heart disease and obstructive sleep apnea. To demonstrate the utility of high-diversity mouse models for elucidating genetic associations, we describe the phenotyping and heritability of fat distributions within the five classical inbred and three wild-derived founder mouse strains of the Collaborative Cross and Diversity Outbred mice. Measurements of subcutaneous and internal fat volumes in the abdomen, thorax and neck, and fat volumes in the tongue and pericardium were obtained using magnetic resonance imaging in male mice from the A/J (n = 12), C57BL/6J (n = 17), 129S1/SvlmJ (n = 12), NOD/LtJ (n = 14), NZO/HILtJ (n = 12), CAST/EiJ (n = 14), PWK/PhJ (n = 12), and WSB/EiJ (n = 15) strains. Phenotypes were compared across strains using analysis of variance and heritability estimated as the proportion of phenotypic variability attributable to strain. Heritability ranged from 44 to 91% across traits, including >70% heritability of tongue fat. A majority of heritability estimates remained significant controlling for body weight, suggesting genetic influences independent of general obesity. Principal components analysis supports genetic influences on overall obesity and specific to increased pericardial and intra-neck fat. Thus, among the founder strains of the Collaborative Cross and Diversity Outbred mice, we observed significant heritability of subcutaneous and internal fat volumes in the neck, thorax and abdomen, pericardial fat volume and tongue fat volume, consistent with genetic architecture playing an important role in explaining trait variability. Findings pave the way for studies utilizing high-diversity mouse models to identify genes affecting fat distributions and, in turn, influencing risk for associated complex disorders.
Genomic region associated with pod color variation in pea (Pisum sativum)
Authors: Shirasawa Kenta , Sasaki Kazuhiro , Hirakawa Hideki , Isobe Sachiko
Published: March 2021

Pea (Pisum sativum) was chosen as the research material by Gregor Mendel to discover the laws of inheritance. Out of seven traits studied by Mendel, genes controlling three traits including pod shape, pod color, and flower position have not been identified to date. With the aim of identifying the genomic region controlling pod color, we determined the genome sequence of a pea line with yellow pods. Genome sequence reads obtained using a Nanopore sequencing technology were assembled into 117,981 contigs (3.3 Gb), with an N50 value of 51.2 kb. A total of 531,242 potential protein-coding genes were predicted, of which 519,349 (2.8 Gb) were located within repetitive sequences (2.8 Gb). The assembled sequences were ordered using a reference as a guide to build pseudomolecules. Subsequent genetic and association analyses led to the identification of a genomic region that controls pea pod color. DNA sequences at this genomic location and transcriptome profiles of green and yellow pod lines were analyzed, and genes encoding 3' exoribonucleases were selected as potential candidates controlling pod color. The results presented in this study are expected to accelerate pan-genome studies in pea and facilitate the identification of the gene controlling one of the traits studied by Mendel.
High resolution reconstructions of the Southwest Indian Ridge, 52 Ma to present: implications for the breakup and absolute motion of the Africa plate
Authors: DeMets C , Merkouriev S , Sauter D
Published: March 2021

We reconstruct the post-52 Ma seafloor spreading history of the Southwest Indian Ridge at 44 distinct times from inversions of ≈20 000 magnetic reversal, fracture zone and transform fault crossings, spanning major regional tectonic events such as the Arabia–Eurasia continental collision, the Arabia Peninsula’s detachment from Africa, the arrival of the Afar mantle plume below eastern Africa and the initiation of rifting in eastern Africa. Best-fitting and noise-reduced rotation sequences for the Nubia–Antarctic, Lwandle–Antarctic and Somalia–Antarctic Plate pairs indicate that spreading rates everywhere along the ridge declined gradually by ≈50 per cent from ≈31 to 19–18 Ma. A concurrent similar-magnitude slowdown in the component of the Africa Plate’s absolute motion parallel to Southwest Indian Ridge spreading suggests that both were caused by a 31–18 Ma change in the forces that drove and resisted Africa’s absolute motion. Possible causes for this change include the effects of the Afar mantle plume on eastern Africa or the Arabia Peninsula’s detachment from the Somalia Plate, which culminated at 20–18 Ma with the onset of seafloor spreading in the Gulf of Aden. At earlier times, an apparently robust but previously unknown ≈6-Myr-long period of rapid kinematic change occurred from 43 to 37 Ma, consisting of a ≈50 per cent spreading rate slowdown from 43 to 40 Ma followed by a full spreading rate recovery and 30–40° clockwise rotation of the plate slip direction from 40 to 37 Ma. Although these kinematic changes coincided with a reconfiguration of the palaeoridge geometry, their underlying cause is unknown. Southwest Indian Ridge abyssal hill azimuths are consistent with the slip directions estimated with our newly derived Somalia–Antarctic and Lwandle–Antarctic angular velocities, adding confidence in their reliability. Lwandle–Antarctica Plate motion has closely tracked Somalia–Antarctic Plate motion since 50 Ma, consistent with slow-to-no motion between the Lwandle and Somalia plates for much of that time. In contrast, Nubia–Somalia rotations estimated from our new Southwest Indian Ridge rotations indicate that 189 ± 34 km of WNW–ESE divergence between Nubia and Somalia has occurred in northern Africa since 40 Ma, including 70–80 km of WNW–ESE divergence since 17–16 Ma, slow to no motion from 26 to 17 Ma, and 109 ± 38 km of WNW–ESE divergence from 40 to ≈26 Ma absent any deformation within eastern Antarctica before 26 Ma.
Exploring the factors in information seeking behavior: a perspective from multinational COPD online forums
Authors: Boyce LeAnn , Harun Ahasan , Prybutok Gayle , Prybutok Victor R
Published: March 2021

This research establishes a theoretical framework for evaluating antecedents of the information seeking behaviors of online forum participants with chronic obstructive pulmonary disease (COPD). We evaluated the proposed framework using partial least squares structural equation modeling (PLS-SEM) after gathering data using a cross-sectional survey. We subsequently assessed the framework using importance−performance map analysis (IPMA). Findings suggest that perceived ease of use does not singularly influence COPD forum users’ information seeking behavior. IPMA analysis reveals that the opportunity to interact with other forum members creates the greatest impact on COPD forum users’ mindsets, among all the indicators. For healthcare professionals, the results of this study provide a blueprint in terms of utilizing COPD online forums to foster recurrent associations among forum administrators and users and by creating a strong social and informational resource for COPD information seekers.
Salvage NAD+ biosynthetic pathway enzymes moonlight as molecular chaperones to protect against proteotoxicity
Authors: Pinkerton Meredith , Ruetenik Andrea , Bazylianska Viktoriia , Nyvltova Eva , Barrientos Antoni
Published: March 2021

Human neurodegenerative proteinopathies are disorders associated with abnormal protein depositions in brain neurons. They include polyglutamine (polyQ) conditions such as Huntington’s disease (HD) and α-synucleinopathies such as Parkinson’s disease (PD). Overexpression of NMNAT/Nma1, an enzyme in the NAD+ biosynthetic salvage pathway, acts as an efficient suppressor of proteotoxicities in yeast, fly and mouse models. Screens in yeast models of HD and PD allowed us to identify three additional enzymes of the same pathway that achieve similar protection against proteotoxic stress: Npt1, Pnc1 and Qns1. The mechanism by which these proteins maintain proteostasis has not been identified. Here, we report that their ability to maintain proteostasis in yeast models of HD and PD is independent of their catalytic activity and does not require cellular protein quality control systems such as the proteasome or autophagy. Furthermore, we show that, under proteotoxic stress, the four proteins are recruited as molecular chaperones with holdase and foldase activities. The NAD+ salvage proteins act by preventing misfolding and, together with the Hsp90 chaperone, promoting the refolding of extended polyQ domains and α-synuclein (α-Syn). Our results illustrate the existence of an evolutionarily conserved strategy of repurposing or moonlighting housekeeping enzymes under stress conditions to maintain proteostasis. We conclude that the entire salvage NAD+ biosynthetic pathway links NAD+ metabolism and proteostasis and emerges as a target for therapeutics to combat age-associated neurodegenerative proteotoxicities.
Application and discussion on entransy analysis of ammonia/salt absorption heat pump systems
Authors: Yang Lei , Huang Sihao , Lu Zhenneng , Gong Yulie , Li Huashan
Published: March 2021

This study investigates the application of the entransy analysis in ammonia/salt absorption heat pump (AHP) systems. The results of the entransy analysis are compared with those of energy analysis and exergy analysis under typical and various operating conditions. Entransy dissipation and exergy loss in each component, as well as coefficient of performance (COP), exergy efficiency and entransy efficiency of systems, are discussed. The changing trends of entransy dissipation in each component are similar under various operating conditions. However, the entransy analysis performs better than the exergy analysis in evaluating the irreversible loss of each component. Moreover, the differences between the exergy analysis and entransy analysis are mainly in absorber, generator and SHE. Especially in the NH3/NaSCN system, the proportion of entransy dissipation in generator is 60.8%, which is almost twice of the proportion of exergy loss (34.9%). In addition, under various operating conditions, entransy efficiency and COP have roughly the same changing trend. Meanwhile, entransy efficiency is more reasonable than exergy efficiency in evaluating the performance of systems under the absorption temperature, which varies from 30°C to 50°C. These comparison results demonstrate that the entransy analysis is appropriate to evaluate the performance of ammonia/salt AHP systems and suitable for analyzing the irreversibility of each component.
Antimicrobial resistance surveillance of Clostridioides difficile in Australia, 2015–18
Authors: Putsathit Papanin , Hong Stacey , George Narelle , Hemphill Christine , Huntington Peter G , Korman Tony M , Kotsanas Despina , Lahra Monica , McDougall Rodney , McGlinchey Andrew , Moore Casey V , Nimmo Graeme R , Prendergast Louise , Robson Jennifer , Waring Lynette , Wehrhahn Michael C , Weldhagen Gerhard F , Wilson Richard M , Riley Thomas V , Knight Daniel R
Published: March 2021

Background

Clostridioides difficile was listed as an urgent antimicrobial resistance (AMR) threat in a report by the CDC in 2019. AMR drives the evolution of C. difficile and facilitates its emergence and spread. The C. difficile Antimicrobial Resistance Surveillance (CDARS) study is nationwide longitudinal surveillance of C. difficile infection (CDI) in Australia.

Objectives

To determine the antimicrobial susceptibility of C. difficile isolated in Australia between 2015 and 2018.

Methods

A total of 1091 strains of C. difficile were collected over a 3 year period by a network of 10 diagnostic microbiology laboratories in five Australian states. These strains were tested for their susceptibility to nine antimicrobials using the CLSI agar incorporation method.

Results

All strains were susceptible to metronidazole, fidaxomicin, rifaximin and amoxicillin/clavulanate and low numbers of resistant strains were observed for meropenem (0.1%; 1/1091), moxifloxacin (3.5%; 38/1091) and vancomycin (5.7%; 62/1091). Resistance to clindamycin was common (85.2%; 929/1091), followed by resistance to ceftriaxone (18.8%; 205/1091). The in vitro activity of fidaxomicin [geometric mean MIC (GM) = 0.101 mg/L] was superior to that of vancomycin (1.700 mg/L) and metronidazole (0.229 mg/L). The prevalence of MDR C. difficile, as defined by resistance to ≥3 antimicrobial classes, was low (1.7%; 19/1091).

Conclusions

The majority of C. difficile isolated in Australia did not show reduced susceptibility to antimicrobials recommended for treatment of CDI (vancomycin, metronidazole and fidaxomicin). Resistance to carbapenems and fluoroquinolones was low and MDR was uncommon; however, clindamycin resistance was frequent. One fluoroquinolone-resistant ribotype 027 strain was detected.
The Bentley Battle: Family firm triumphs over luxury car manufacturer in trade mark dispute
Authors: Strath Janet
Published: March 2021

Bentley Motors Ltd v Bentley 1962 Ltd [2020] EWCA Civ 1726, 16 December 2020

The Court of Appeal of England and Wales has upheld the decision of the High Court that the luxury car manufacturer Bentley Motors infringed registered trade marks belonging to small family-owned company Bentley Clothing, by using the identical trade mark on clothing and headgear.