bluuebunny/crossref_metadata_2025_split huggingface dataset:
Seasonal patterns in stable isotope and fatty acid profiles of southern stingrays (Hypanus americana) at Stingray City Sandbar, Grand Cayman
Authors: Hoopes Lisa A. , Clauss Tonya M. , Browning Nicole E. , Delaune Alexa J. , Wetherbee Bradley M. , Shivji Mahmood , Harvey Jessica C. , Harvey Guy C. M. 
Published: November 2020

AbstractEcotourism opportunities in the marine environment often rely heavily on provisioning to ensure the viewing of cryptic species by the public. However, intentional feeding of wildlife can impact numerous aspects of an animals’ behavior and ecology. Southern stingrays (Hypanus americana) provisioned at Stingray City Sandbar (SCS) in Grand Cayman have altered diel activity patterns and decreased measures of health. This study looked at seasonal changes in stable isotope (SI) and fatty acid (FA) profiles of provisioned stingrays at SCS. Plasma δ15N was higher in male stingrays (11.86 ± 1.71‰) compared to females (10.70 ± 1.71‰). Lower values for δ15N in males and females were measured in October during low tourist season, suggesting stingrays may be forced to rely on native prey items to supplement the decreased amount of provisioned squid available during this time. Plasma FA profiles were significantly different between sexes and across sampling time points, with FAs 22:6n3, 16:0, 20:5n3, 18:1n3C, 18:0 and 18:1n9T contributing to dissimilarity scores between groups. Dietary FAs primarily contributed to differences between males and females lending further evidence to differences in foraging patterns at SCS, likely due to intraspecific competition. Further, canonical analysis of principal coordinates (CAP) analysis of FA profiles suggest similar diets during peak tourist season and differences in diet between males and females during the low season. This study demonstrates alterations in feeding ecology in stingrays at SCS which is of critical importance for effective management of the SCS aggregation.
Fungal–bacterial interaction selects for quorum sensing mutants with increased production of natural antifungal compounds
Authors: Albarracín Orio Andrea G. , Petras Daniel , Tobares Romina A. , Aksenov Alexander A. , Wang Mingxun , Juncosa Florencia , Sayago Pamela , Moyano Alejandro J. , Dorrestein Pieter C. , Smania Andrea M. 
Published: November 2020

AbstractSoil microorganisms coexist and interact showing antagonistic or mutualistic behaviors. Here, we show that an environmental strain of Bacillus subtilis undergoes heritable phenotypic variation upon interaction with the soil fungal pathogen Setophoma terrestris (ST). Metabolomics analysis revealed differential profiles in B. subtilis before (pre-ST) and after (post-ST) interacting with the fungus, which paradoxically involved the absence of lipopeptides surfactin and plipastatin and yet acquisition of antifungal activity in post-ST variants. The profile of volatile compounds showed that 2-heptanone and 2-octanone were the most discriminating metabolites present at higher concentrations in post-ST during the interaction process. Both ketones showed strong antifungal activity, which was lost with the addition of exogenous surfactin. Whole-genome analyses indicate that mutations in ComQPXA quorum-sensing system, constituted the genetic bases of post-ST conversion, which rewired B. subtilis metabolism towards the depletion of surfactins and the production of antifungal compounds during its antagonistic interaction with S. terrestris.
Adaptation by copy number variation increases insecticide resistance in the fall armyworm
Authors: Gimenez Sylvie , Abdelgaffar Heba , Goff Gaelle Le , Hilliou Frédérique , Blanco Carlos A. , Hänniger Sabine , Bretaudeau Anthony , Legeai Fabrice , Nègre Nicolas , Jurat-Fuentes Juan Luis , d’Alençon Emmanuelle , Nam Kiwoong 
Published: November 2020

AbstractUnderstanding the genetic basis of insecticide resistance is a key topic in agricultural ecology. The adaptive evolution of multi-copy detoxification genes has been interpreted as a cause of insecticide resistance, yet the same pattern can also be generated by the adaptation to host-plant defense toxins. In this study, we tested in the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), if adaptation by copy number variation caused insecticide resistance in two geographically distinct populations with different levels of resistance and the two host-plant strains. We observed a significant allelic differentiation of genomic copy number variations between the two geographic populations, but not between host-plant strains. A locus with positively selected copy number variation included a CYP gene cluster. Toxicological tests supported a central role for CYP enzymes in deltamethrin resistance. Our results indicate that copy number variation of detoxification genes might be responsible for insecticide resistance in fall armyworm and that evolutionary forces causing insecticide resistance could be independent of host-plant adaptation.
Ethical Considerations on Pediatric Genetic Testing Results in Electronic Health Records
Authors: Kanungo Shibani , Barr Jayne , Crutchfield Parker , Fealko Casey , Soares Neelkamal 
Published: November 2020

AbstractBackground Advances in technology and access to expanded genetic testing have resulted in more children and adolescents receiving genetic testing for diagnostic and prognostic purposes. With increased adoption of the electronic health record (EHR), genetic testing is increasingly resulted in the EHR. However, this leads to challenges in both storage and disclosure of genetic results, particularly when parental results are combined with child genetic results.Privacy and Ethical Considerations Accidental disclosure and erroneous documentation of genetic results can occur due to the nature of their presentation in the EHR and documentation processes by clinicians. Genetic information is both sensitive and identifying, and requires a considered approach to both timing and extent of disclosure to families and access to clinicians.Methods This article uses an interdisciplinary approach to explore ethical issues surrounding privacy, confidentiality of genetic data, and access to genetic results by health care providers and family members, and provides suggestions in a stakeholder format for best practices on this topic for clinicians and informaticians. Suggestions are made for clinicians on documenting and accessing genetic information in the EHR, and on collaborating with genetics specialists and disclosure of genetic results to families. Additional considerations for families including ethics around results of adolescents and special scenarios for blended families and foster minors are also provided. Finally, administrators and informaticians are provided best practices on both institutional processes and EHR architecture, including security and access control, with emphasis on the minimum necessary paradigm and parent/patient engagement and control of the use and disclosure of data.Conclusion The authors hope that these best practices energize specialty societies to craft practice guidelines on genetic information management in the EHR with interdisciplinary input that addresses all stakeholder needs.
Game theoretic analysis of persons, the pharmaceutical industry, and donors in disease contraction and recovery
Authors: Hausken Kjell , Ncube Mthuli 
Published: November 2020

AbstractDisease contraction and recovery depend on complex interaction between persons potentially contracting and recovering from the disease, the pharmaceutical industry potentially developing drugs, and donors potentially subsidizing drug development and drug purchases. Instead of analyzing each of these three kinds of players separately, assuming the behavior of the other two kinds of players to be given, this article analyzes the three kinds of players holistically and how they mutually interact and react to each other. A five-period game between N persons and a pharmaceutical company is developed. Each person chooses safe or risky behavior, and whether or not to buy a drug. The objectives are to determine which strategies the N persons and the pharmaceutical company choose depending on the model parameters. The pharmaceutical company develops the drug if sufficiently many persons contract the disease and buy the drug. A donor chooses parametrically whether to subsidize drug development and drug purchases. Nature chooses probabilistically disease contraction, and recovery versus death with and without applying the drug. The methodological procedure is to solve the game with backward induction specifying the conditions for each of five outcomes ranging from safe behavior to risky behavior and buying the drug. The results in the form of five outcomes for a person are safe behavior, risky behavior and no disease contraction, disease contraction without drug availability, disease contraction with drug availability but without buying the drug, and disease contraction and buying the drug. These five outcomes are spread across two outcomes for the pharmaceutical company which are not to develop versus to develop the drug. The utility for the donor is specified for these two outcomes. A procedure for estimating the parameters is presented based on HIV/AIDS data. The results are discussed in terms of how various parameter combinations cause the five outcomes. An example illustrates the players’ strategic choices.
Decoding transcreation in corporate website localization into Arabic
Authors: Kassawat Madiha 
Published: November 2020

Abstract
As adaptation is often associated with localization, and with the promotion of transcreation as an extra service
in the industry, there is a need to analyze how and to what extent transcreation is used in localization as a type of
target-culture-focused translation. This paper looks into adaptation and analyzes transcreation as a translation strategy. It
investigates online promotional texts localized into Arabic (for Saudi Arabia) as an under-researched language in website
localization. The analysis follows a functionalist approach and examines 15 international corporate websites. The results
illustrate components of transcreated texts and shed light on the use of different procedures to achieve the creative persuasive
purpose.
French-language COVID-19 terminology
Authors: Bowker Lynne 
Published: November 2020

Abstract
The COVID-19 pandemic situation developed very quickly, driving an urgent and global need to communicate public health information that left relatively little time for traditional and formal language planning activities. This article investigates and compares French-language COVID-19-related terms appearing in linguistic resources developed in Canada and Europe to determine whether this terminology appears to be international or localized. Findings reveal that regional variation exists and that one contributing factor is that de-terminologization is being accelerated by the popular media. Another key factor leading to linguistic differences is the language situation (i.e., majority vs minority situation). Overall, while there is considerable overlap in the terminology used in the two resources, there are enough differences to warrant underlining the importance of localizing terminological content in a situation such as a pandemic in order to ensure that communication of critical information is as effective as possible.
Gradability across grammatical domains
Authors: Bowler Margit , Gluckman John 
Published: November 2020

AbstractThis paper argues that ordered abstract scales are applicable to analyses of a range of grammatical domains. We argue this based on data from a number of unrelated languages, primarily Logoori (Bantu, JE 41; Kenya). The Logoori verbkudokacan be translated into English as ‘to arrive’/‘to reach,’ ‘to be enough,’ and ‘must,’ depending on its linguistic context. We propose that these meanings arise from a single semantic denotation that is sensitive to a shared gradable component in the semantics of linguistic expressions referring to spatial paths, gradable predicates, measures of plural count nouns/mass nouns, and modals. The central theoretical issue addressed in this paper is the application of ordered, abstract scales in a model of grammar. This data and proposal is an important contribution to the literature arguing for a gradable model of modality (Lassiter 2014,2017b, among others).
Solidification dynamics in metal additive manufacturing: analysis of model assumptions
*
Authors: Stump B , Plotkowski A , Coleman J 
Published: November 2020

Abstract
Solidification characteristics in the meltpool drive the process-microstructure relationship which helps dictate the material properties of as-built parts in additive manufacturing; therefore, being able to accurately and quickly simulate the size, shape, and solidification characteristics in the melt pool is of great interest to the field. This study investigates various important physical phenomena (dynamic material properties, fluid-flow, radiation and vaporization) which can either be included or neglected in a continuum finite volume model (FVM) and their effect on the solidification conditions. Additionally, since the simplest form of such a model (conduction only) has an analytic solution which is much faster, its viability is also considered. Since the inclusion of some of these physical phenomena will inherently change the net energy input as well as the amount of energy needed to achieve melting of a control volume, each set of included phenomena had an effective absorption efficiency which was calibrated to closely match the dimensions of the melt pool to that of the ground truth data. The ground truth data for this study was defined to be the output of the FVM which included all the physical phenomena (OF). This study then goes on to compare the effects on solidification conditions each of these calibrated models has. It was found that most of the change in solidification conditions comes from the inclusion of latent heat. A posterior correlation factor (PCF) is then introduced to enable an analytic model to predict similar solidification conditions to OF model.
Phase separated bi-metallic PtNi nanoparticles formed by pulsed laser dewetting
Authors: Ahn Kangwoo , Lee Su Yong , Cho In Hwa , Kim Yoonhee , Kang Hyon Chol , Noh Do Young 
Published: November 2020

Abstract
We present morphological and compositional analysis of phase-separated Pt–Ni alloy nanoparticles (NPs) formed by ns pulsed laser dewetting. The PtNi NPs obtained by the pulsed laser dewetting consist of phase-separated multiple domains including Pt3Ni, PtNi and PtNi3 phases with various crystal orientations as revealed by transmission electron microscopy, which is in contrast to thermal dewetting resulting NPs of a uniform composition. A three-dimensional (3D) electron density map of a dewetted PtNi NP obtained using the coherent x-ray diffraction microscopy elucidates the 3D morphology of Pt- and Ni-rich regions together with a nano-cavity formed during the pulsed laser irradiation.
Synergy between alkali activation and a salt template in superactive carbon production from lignin
Authors: Ponomarev Nikolai P , Kallioinen Mari 
Published: November 2020

Abstract
Due to growing demand, the performance of traditional active carbon is insufficient. An innovative solution is superactive carbon with an ultra-high surface area as high as 3000 m2 g−1. However, this material is very costly due to the considerable amount of alkali used in its manufacturing. To obtain superactive carbon from lignin, KOH and KCl were used simultaneously. The method was thoroughly studied to describe the mechanism of pore origin and control the pore size. Because of synergy between KOH and KCl, superactive carbon with an ultra-high surface area (2938 ± 42 m2 g−1) was obtained at essentially diminished KOH consumption (1 g g−1) in contrast to previously reported methods. The process was optimised using the response surface method. The pore size can be tuned by varying the amount of KOH and temperature. Observed synergy enabled reduced alkali consumption, overcoming the barrier to widespread implementation of superactive carbon.
Recursion and Hamiltonian operators for integrable nonabelian difference equations
Authors: Casati Matteo , Wang Jing Ping 
Published: November 2020

Abstract
In this paper, we carry out the algebraic study of integrable differential-difference equations whose field variables take values in an associative (but not commutative) algebra. We adapt the Hamiltonian formalism to nonabelian difference Laurent polynomials and describe how to obtain a recursion operator from the Lax representation of an integrable nonabelian differential-difference system. As an application, we study a family of integrable equations: the nonabelian Narita–Itoh–Bogoyavlensky lattice, for which we construct their recursion operators and Hamiltonian operators and prove the locality of infinitely many commuting symmetries generated from their highly nonlocal recursion operators. Finally, we discuss the nonabelian version of several integrable difference systems, including the relativistic Toda chain and Ablowitz–Ladik lattice.
DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy
Authors: Neph Ryan , Lyu Qihui , Huang Yangsibo , Yang You Ming , Sheng Ke 
Published: November 2020

Abstract
Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.
Helium radiography with a digital tracking calorimeter—a Monte Carlo study for secondary track rejection
Authors: Pettersen Helge Egil Seime , Volz Lennart , Sølie Jarle Rambo , Alme Johan , Barnaföldi Gergely Gábor , Barthel Rene , van den Brink Anthony , Borshchov Vyacheslav , Chaar Mamdouh , Eikeland Viljar , Genov Georgi , Grøttvik Ola , Helstrup Håvard , Keidel Ralf , Kobdaj Chinorat , van der Kolk Naomi , Mehendale Shruti , Meric Ilker , Harald Odland Odd , Papp Gábor , Peitzmann Thomas , Piersimoni Pierluigi , Protsenko Maksym , Ur Rehman Attiq , Richter Matthias , Tefre Samnøy Andreas , Seco Joao , Shafiee Hesam , Songmoolnak Arnon , Tambave Ganesh , Tymchuk Ihor , Ullaland Kjetil , Varga-Kofarago Monika , Wagner Boris , Xiao RenZheng , Yang Shiming , Yokoyama Hiroki , Röhrich Dieter 
Published: November 2020

Abstract
Radiation therapy using protons and heavier ions is a fast-growing therapeutic option for cancer patients. A clinical system for particle imaging in particle therapy would enable online patient position verification, estimation of the dose deposition through range monitoring and a reduction of uncertainties in the calculation of the relative stopping power of the patient.
Several prototype imaging modalities offer radiography and computed tomography using protons and heavy ions. A Digital Tracking Calorimeter (DTC), currently under development, has been proposed as one such detector. In the DTC 43 longitudinal layers of laterally stacked ALPIDE CMOS monolithic active pixel sensor chips are able to reconstruct a large number of simultaneously recorded proton tracks.
In this study, we explored the capability of the DTC for helium imaging which offers favorable spatial resolution over proton imaging. Helium ions exhibit a larger cross section for inelastic nuclear interactions, increasing the number of produced secondaries in the imaged object and in the detector itself. To that end, a filtering process able to remove a large fraction of the secondaries was identified, and the track reconstruction process was adapted for helium ions.
By filtering on the energy loss along the tracks, on the incoming angle and on the particle ranges, 97.5% of the secondaries were removed. After passing through 16 cm water, 50.0% of the primary helium ions survived; after the proposed filtering 42.4% of the primaries remained; finally after subsequent image reconstruction 31% of the primaries remained. Helium track reconstruction leads to more track matching errors compared to protons due to the increased available focus strength of the helium beam. In a head phantom radiograph, the Water Equivalent Path Length error envelope was 1.0 mm for helium and 1.1 mm for protons. This accuracy is expected to be sufficient for helium imaging for pre-treatment verification purposes.
Data-driven dose calculation algorithm based on deep U-Net
Authors: Fan Jiawei , Xing Lei , Dong Peng , Wang Jiazhou , Hu Weigang , Yang Yong 
Published: November 2020

Abstract
Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation algorithms commonly used in current clinical practice have to compromise between calculation accuracy and efficiency, which may result in unsatisfactory dose accuracy or highly intensive computation time in many clinical situations. The purpose of this work is to develop a novel dose calculation algorithm based on the deep learning method for radiation therapy. In this study we performed a feasibility investigation on implementing a fast and accurate dose calculation based on a deep learning technique. A two-dimensional (2D) fluence map was first converted into a three-dimensional (3D) volume using ray traversal algorithm. 3D U-Net like deep residual network was then established to learn a mapping between this converted 3D volume, CT and 3D dose distribution. Therefore an indirect relationship was built between a fluence map and its corresponding 3D dose distribution without using significantly complex neural networks. Two hundred patients, including nasopharyngeal, lung, rectum and breast cancer cases, were collected and applied to train the proposed network. Additional 47 patients were randomly selected to evaluate the accuracy of the proposed method through comparing dose distributions, dose volume histograms and clinical indices with the results from a treatment planning system (TPS), which was used as the ground truth in this study. The proposed deep learning based dose calculation algorithm achieved good predictive performance. For 47 tested patients, the average per-voxel bias of the deep learning calculated value and standard deviation (normalized to the prescription), relative to the TPS calculation, is 0.17%±2.28%. The average deep learning calculated values and standard deviations for relevant clinical indices were compared with the TPS calculated results and the t-test p-values demonstrated the consistency between them. In this study we developed a new deep learning based dose calculation method. This approach was evaluated by the clinical cases with different sites. Our results demonstrated its feasibility and reliability and indicated its great potential to improve the efficiency and accuracy of radiation dose calculation for different treatment modalities.