bluuebunny/crossref_metadata_2025_split huggingface dataset:
Climate and vector potential of medically important North American ticks.
Authors: Eisen Lars , Eisen Rebecca 
Published: December 2021

Abstract

This expert opinion focuses on climate-related factors and how these interact with landscape and tick host/pathogen reservoir factors to impact the vector potential for the three most notable North American human-biting tick species: Ixodes scapularis, Amblyomma americanum and the American dog tick, Dermacentor variabilis.
Human behaviour trumps entomological risk.
Authors: Telford Sam 
Published: December 2021

Abstract

This expert opinion argues that human aspects of risk are poorly studied and need to be considered in any discussion of the role of global climate change on risk for vector-borne infections, particularly for those transmitted by ticks. It also covers the many models that are used predict the future distribution of Lyme disease and other tick-borne infections in the USA and elsewhere, based on anticipated changes in weather.
It's all in the timing: effect of tick phenology on pathogen transmission dynamics.
Authors: Diuk-Wasser Maria 
Published: December 2021

Abstract

This expert opinion discusses how the prevalence and persistence of common Ixodes spp.-borne pathogens in the northern hemisphere depends on a delicate balance (determined by the interactions between tick phenology, the duration of infection or infectivity in the host and the life expectancy of hosts); how alternative pathways can enhance persistence; and how these climate-sensitive processes may drive tick and pathogen geographic distribution.
Climate change and tick-borne encephalitis in the Greater Alpine region.
Authors: Rubel Franz 
Published: December 2021

Abstract

Tick-borne encephalitis (TBE) is a viral tick-borne disease. The distribution of human TBE cases ranges from the French departments bordering Germany through Central and Eastern Europe, the Caucasus and Kazakhstan to the Far East of Russia and China. In this expert opinion, TBE is described in the greater area of the European Alps, denoted as the Greater Alpine Region (GAR). It also includes reported tick-borne encephalitis cases and evidence for climate change impacts on tick density and distribution as well as the prevalence and intensity of TBE.
Epidemiology of severe fever with thrombocytopaenia syndrome in China.
Authors: Liu Wei , Fang Li-Qun , Li Hao 
Published: December 2021

Abstract

Severe fever with thrombocytopaenia syndrome (SFTS) is a novel emerging virus infection that was first reported in the People's Republic of China in 2010. This expert opinion describes the epidemiological characters of SFTS (geographical distribution, demographic characteristics, seasonal distribution) and epidemiological risks of SFTS infection in China.
The emergence of tick-borne diseases in domestic animals in Australia.
Authors: Oskam Charlotte , Ronai Isobel , Irwin Peter 
Published: December 2021

Abstract

In this expert opinion, two case studies of tick-borne diseases of domestic animals in Australia have been selected to illustrate how climate change can potentially influence the spread of introduced tick species and the pathogens they transmit. The first reports an incursion of canine monocytic ehrlichiosis (CME) into northern regions of Australia in April/June 2020 and the second describes the geographical expansion of bovine anaemia due to Theileria orientalis group (BATOG) across Australia since 2006. The introduction of novel pathogens into ecosystems exposed to climate change provides a unique opportunity to study disease dynamics in real time.
The emergence of ticks and tick-borne diseases in the USA.
Authors: Hart Charles , Thangamani Saravanan 
Published: December 2021

Abstract

Ticks are the primary source of vector-borne pathogens in the USA. The native tick species of the USA are adapted for a number of distinct ecosystems and mammalian hosts. As such, several overlapping sylvatic cycles exist within the tick populations. These occur in nature without human involvement. The incursion of humans into these environments can result in sylvatic spillover of these pathogens to humans, causing clinical disease. As tick populations shift, the risk of exposure to emerging, newly introduced and re-emerging pathogens increases, posing a direct threat to human and animal health. Several genera are the source of most tick-borne pathogens in the USA. These include Dermacentor, Amblyomma and Ixodes, with a lesser role for Ornithodoros soft ticks. This expert opinion focuses on the influence of the changing landscape of ticks in the USA and its impact on the emergence of tick-borne pathogens that occurs as a result.
Role of climate and other factors in determining the dynamics of tick and tick-transmitted pathogen populations and distribution in western, central and eastern Africa.
Authors: Githaka Naftaly , Kanduma Esther , Bishop Richard 
Published: December 2021

Abstract

This expert opinion discusses the potential impact of climatic change on vector abundance, survival and transmission of tick-borne pathogens in western, central and eastern Africa. It also discusses the following cases: (1) Rhipicephalus microplus with a focus in West and Central Africa and (2) northern expansion of Rhipicephalus appendiculatus into South Sudan through anthropogenic cattle movement.
Agreement between isokinetic eccentric hamstring strength, Nordic hamstring strength and Nordic break‐point angle in a sample of trained and healthy individuals
Authors: Miralles‐Iborra A. , Elvira J. L. L. , Urban T. , Calado A. , Del Coso J. , Moreno‐Pérez V. 
Published: December 2021

ABSTRACTDifferent eccentric strength assessments are used to identifying the risk of hamstring injury in athletes. However, there is scarce information to determine the association between Nordic‐based measurements and the gold standard measurement of eccentric hamstring strength in an isokinetic dynamometer. To investigate the relationship of different measures of eccentric hamstring strength (break‐point angle and eccentric strength during Nordic exercise) with eccentric hamstring peak torque measured with an isokinetic dynamometer. Forty‐six participants volunteered to participate in this study. Eccentric peak force in the Smart‐Nordic System and break‐point angle measured as trunk lowering at >20°/s and >150°/s2 during the Nordic exercise via high‐frequency video‐analysis were compared to eccentric hamstring peak torque in an isokinetic dynamometer set to produce knee extension at 30°/s. There was a moderate association between peak eccentric strength with the Smart‐Nordic and isokinetic eccentric hamstring peak torque (r = −0.65, p < 0.001, 95%CI = [−0.76‐(−0.50)]). The association between Nordic break‐point angle measured at 20°/s (r = 0.49, p < 0.001, 95%CI = [0.30–0.64]) and at 150°/s2 (r = 0.52, p < 0.001, 95%CI = [0.33–0.66]) presented statistically significant but lower associations with isokinetic eccentric hamstring peak torque. All Nordic‐based measurements presented acceptable associations with the gold standard isokinetic eccentric hamstring peak torque. However, peak eccentric force measured on the Smart‐Nordic device had a better agreement with eccentric peak torque measured with an isokinetic dynamometer than the video‐based measurements.Highlights
Peak eccentric force measured on the isometric strain‐gauges device had a better agreement with eccentric hamstring peak torque measured with an isokinetic dynamometer than the break‐point angle.
The Nordic‐based measurements are an alternative cost‐effective method to assess eccentric muscle strength and weakness when the access to isokinetic dynamometry is not feasible.
Caution should be taken with certain variables such as participants body mass, hip position and movement speed when interpreting the results of Nordic‐based measurements.
Complex structures for Klein–Gordon theory on globally hyperbolic spacetimes
Authors: Much Albert , Oeckl Robert 
Published: December 2021

Abstract
We develop a rigorous method to parametrize complex structures for Klein–Gordon theory in globally hyperbolic spacetimes that satisfy a completeness condition. The complex structures are conserved under time-evolution and implement unitary quantizations. They can be interpreted as corresponding to global choices of vacuum. The main ingredient in our construction is a system of operator differential equations. We provide a number of theorems ensuring that all ingredients and steps in the construction are well-defined. We apply the method to exhibit natural quantizations for certain classes of globally hyperbolic spacetimes. In particular, we consider static, expanding and Friedmann–Robertson–Walker spacetimes. Moreover, for a huge class of spacetimes we prove that the differential equation for the complex structure is given by the Gelfand–Dikki equation.
Exploration and categorisation of pre-service physics teachers’ understanding of superconductivity and nanotechnology
Authors: Kriek Jeanne , Lemmer Miriam , Ojal John 
Published: December 2021

Abstract
Nanoscience and technology is a major field of scientific research and technological innovation but understanding concepts such as superconductivity and nanotechnology are not straightforward. To develop focused teaching strategies, it is necessary to categorise students’ prior understanding as different categories of prior ideas require different teaching strategies. An exploratory case study research design was followed to explore and categorise 21 pre-service physics teachers’ (PSPTs’) understanding of basic concepts of superconductivity and nanotechnology at a University in Oman. Self-compiled open ended questionnaires were used to elicit PSPT understanding of superconductivity and nanotechnology. Their understanding was categorised in epistemological and ontological categories. These six categories were: lateral alternative conceptions; ontological conceptions; naïve physics; phenomenological primitives; mixed conceptions; and loose ideas. Findings from this pre-instructional study indicated that naïve physics ideas and lateral alternative conceptions were dominant and that the PSPTs’ conceptions were diversified and inconsistent. These results could lead to the development of evidence-based pedagogy, which is fundamental to the advancement of an effective physics education curriculum for these two contemporary topics.
Simultaneous determination of mass parameter and radial marker in Schwarzschild geometry
Authors: Varela Victor , Leal Lorenzo 
Published: December 2021

Abstract
We show that mass parameter and radial marker values can be indirectly measured in thought experiments performed in Schwarzschild spacetime, without using the Newtonian limit of general relativity or approximations based on Euclidean geometry. Our approach involves different proper time quantifications as well as solutions to systems of algebraic equations, and aims to strengthen the conceptual independence of general relativity from Newtonian gravity.
Leidenfrost drop dynamics: a forgotten past and modern day rediscoveries
Authors: Stewart Seán 
Published: December 2021

Abstract
When drops of liquid are placed onto highly heated substrates at temperatures well above their boiling point, the drops float on a thin layer of vapour formed between the liquid and the hot surface. In a 290 year old phenomenon referred to as the Leidenfrost effect, drops freed from contact with the surface below can undergo a range of surprising and unexpected dynamical behaviour. In this paper we trace various early developments associated with the dynamics of Leidenfrost drops. By showing how many of the more recent discoveries found in the dynamic behaviour of Leidenfrost drops were either anticipated or antedated, we hope to draw attention to the long, rich, and largely overlooked history of the Leidenfrost effect and show there is much one can learn from its forgotten past.
New transform to project axisymmetric deflection fields along arbitrary rays
Authors: Sipkens T A , Grauer S J , Steinberg A M , Rogak S N , Kirchen P 
Published: December 2021

Abstract
Axisymmetric tomography is used to extract quantitative information from line-of-sight measurements of gas flow and combustion fields. For instance, background-oriented schlieren (BOS) measurements are typically inverted by tomographic reconstruction to estimate the density field of a high-speed or high-temperature flow. Conventional reconstruction algorithms are based on the inverse Abel transform, which assumes that rays are parallel throughout the target object. However, camera rays are not parallel, and this discrepancy can result in significant errors in many practical imaging scenarios. We present a generalization of the Abel transform for use in tomographic reconstruction of light-ray deflections through an axisymmetric target. The new transform models the exact path of camera rays instead of assuming parallel paths, thereby improving the accuracy of estimates. We demonstrate our approach with a simulated BOS scenario in which we reconstruct noisy synthetic deflection data across a range of camera positions. Results are compared to state-of-the-art Abel-based algorithms. Reconstructions computed using the new transform are consistently more stable and accurate than conventional reconstructions.
Alignment subdomain-based deep convolutional transfer learning for machinery fault diagnosis under different working conditions
Authors: Li Yibing , Wan Hu , Jiang Li 
Published: December 2021

Abstract
In recent years, transfer learning (TL) methods have been extensively used in machinery fault diagnosis under different working conditions. However, most of these TL methods perform poorly in the actual industrial applications, due to the fact that they mainly focus on the global distribution of different domains without considering the distribution of subdomains belonging to the same category in different domains. Therefore, we propose an alignment subdomain-based deep convolutional transfer learning (AS-DCTL) network for machinery fault diagnosis. First, continuous wavelet transform is used to transform the original vibration signal into a 2D time-frequency image. Then, AS-DCTL uses a convolutional neural network as the feature extractor to extract the features of the source and target domain samples and introduces maximum mean difference (MMD) to align the global distribution of the extracted features. Simultaneously, we use local MMD as a metric criterion to align the distribution of related subdomains, by adding weights to similar samples in the source domain and target domain. The experimental results of the two case studies show that the proposed AS-DCTL network can achieve higher recognition accuracy and classification effect, in comparison with the current mainstream TL methods.
Microstructure-dependent rate theory model of defect segregation and phase stability in irradiated polycrystalline LiAlO2
Authors: Hu Shenyang , Li Yulan , Shang Shunli , Liu Zi-Kui , Burkes Douglas , Senor David J 
Published: December 2021

Abstract
Gamma lithium aluminate (LiAlO2) is a breeder material for tritium and is one of key components in a tritium-producing burnable absorber rod (TPBAR). Dissolution and precipitation of second phases such as LiAl5O8 and voids are observed in irradiated LiAlO2. Such microstructure changes cause the degradation of thermomechanical properties of LiAlO2 and affect tritium retention and release kinetics, and hence, the TPBAR performance. In this work, a microstructure-dependent model of radiation-induced segregation (RIS) has been developed for investigating the accumulation of species and phase stability in polycrystalline LiAlO2 structures under irradiation. Three sublattices (i.e. [Li, Al, V]I [O, Vo]II [Lii, Ali, Oi, Vi]III), and concentrations of six diffusive species (i.e. Li; vacancy of Li or Al at [Li, Al, V]I sublattice, O vacancy at [O, Vo]II sublattice, and Li, Al and O interstitials at [Lii, Ali, Oi, Vi]III interstitial sublattices; are used to describe spatial and temporal distributions of defects and chemistry. Microstructure-dependent thermodynamic and kinetic properties including the generation, reaction, and chemical potentials of defects and defect mobility are taken into account in the model. The parametric studies demonstrated the capability of the developed RIS model to assess the effect of thermodynamic and kinetic properties of defects on the segregation and depletion of species in polycrystalline structures and to explain the phase stability observed in irradiated LiAlO2 samples. The developed RIS model will be extended to study the precipitation of LiAl5O8 and voids and tritium retention by integrating the phase-field method.
A generalizable machine learning potential of Ag–Au nanoalloys and its application to surface reconstruction, segregation and diffusion
Authors: Wang YiNan , Zhang LinFeng , Xu Ben , Wang XiaoYang , Wang Han 
Published: December 2021

Abstract
Owing to the excellent catalytic properties of Ag–Au binary nanoalloys, nanostructured Ag–Au, such as Ag–Au nanoparticles and nanopillars, has been under intense investigation. To achieve high accuracy in molecular simulations of Ag–Au nanoalloys, the surface properties must be modeled with first-principles precision. In this work, we constructed a generalizable machine learning interatomic potential for Ag–Au nanoalloys based on deep neural networks trained from a database constructed with first-principles calculations. This potential is highlighted by the accurate prediction of Au (111) surface reconstruction and the segregation of Au toward the Ag–Au nanoalloy surface, where the empirical force field (EFF) failed in both cases. Moreover, regarding the adsorption and diffusion of adatoms on surfaces, the overall performance of our potential is better than the EFFs. We stress that the reported surface properties are blind to the potential modeling in the sense that none of the surface configurations is explicitly included in the training database; therefore, the reported potential is expected to have a strong generalization ability to a wide range of properties and to play a key role in investigating nanostructured Ag–Au evolution, where accurate descriptions of free surfaces are necessary.
Temperature measurement of intracranial cerebrospinal fluid using second-order motion compensation diffusion tensor imaging
Authors: Shibukawa Shuhei , Niwa Tetsu , Miyati Tosiaki , Ogino Tetsuo , Yoshimaru Daisuke , Kuroda Kagayaki 
Published: December 2021

AbstractTo reduce the determination errors of CSF pulsation in diffusion-weighted image (DWI) thermometry, we investigated whether applying second-order motion compensation diffusion tensor imaging (2nd-MC DTI) and fractional anisotropy (FA) processing improves the measurement of intracranial cerebrospinal fluid (CSF) temperature. In a phantom study, we investigated the relationship between temperature and FA in artificial CSF (ACSF) to determine the threshold for FA processing. The calculated temperatures of ACSF were compared with those of water. In a human study, 18 healthy volunteers were scanned using conventional DTI (c-DTI) and 2nd-MC DTI on a 3.0 T magnetic resonance imaging (MRI) system. A temperature map was created using diffusion coefficients from each DWI with/without FA processing. The temperatures of intracranial CSF were compared between each DTI image using Welch’s analysis of variance and Games–Howell’s multiple comparisons. In the phantom study, FA did not exceed 0.1 at any temperature. Consequently, pixels exceeding the threshold of 0.1 were removed from the temperature map. Intracranial CSF temperatures significantly differed between the four methods (p < 0.0001). The lowest temperature was 2nd-MC DTI with FA processing (mean, 35.62 °C), followed in order by c-DTI with FA processing (mean, 36.16 °C), 2nd-MC DTI (mean, 37.08 °C), and c-DTI (mean, 39.08 °C;p < 0.01 for each). Because the calculated temperature of ACSF was estimated to be lower than that of water, the temperature of 2nd-DTI with FA processing was considered reasonable. The method of 2nd-MC DTI with FA processing enabled determining intracranial CSF temperature with a reduction in CSF pulsation.
Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT
Authors: Iyer Aditi , Thor Maria , Onochie Ifeanyirochukwu , Hesse Jennifer , Zakeri Kaveh , LoCastro Eve , Jiang Jue , Veeraraghavan Harini , Elguindi Sharif , Lee Nancy Y , Deasy Joseph O , Apte Aditya P 
Published: December 2021

Abstract

Objective. Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process. Approach. CT scans of 242 head and neck (H&N) cancer patients acquired from 2004 to 2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded framework was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021. Main results. Medians and inter-quartile ranges of Dice similarity coefficients (DSC) computed on the retrospective testing set (N = 24) were 0.87 (0.85–0.89) for the masseters, 0.80 (0.79–0.81) for the medial pterygoids, 0.81 (0.79–0.84) for the larynx, and 0.69 (0.67–0.71) for the constrictor. Auto-segmentations, when compared to two sets of manual segmentations in 10 randomly selected scans, showed better agreement (DSC) with each observer than inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request via https://github.com/cerr/CERR/wiki/Auto-Segmentation-models. Significance. We developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.
Assessing cardiac stiffness using ultrasound shear wave elastography
Authors: Caenen Annette , Pernot Mathieu , Nightingale Kathryn R , Voigt Jens-Uwe , Vos Hendrik J , Segers Patrick , D’hooge Jan 
Published: December 2021

Abstract
Shear wave elastography offers a new dimension to echocardiography: it measures myocardial stiffness. Therefore, it could provide additional insights into the pathophysiology of cardiac diseases affecting myocardial stiffness and potentially improve diagnosis or guide patient treatment. The technique detects fast mechanical waves on the heart wall with high frame rate echography, and converts their propagation speed into a stiffness value. A proper interpretation of shear wave data is required as the shear wave interacts with the intrinsic, yet dynamically changing geometrical and material characteristics of the heart under pressure. This dramatically alters the wave physics of the propagating wave, demanding adapted processing methods compared to other shear wave elastography applications as breast tumor and liver stiffness staging. Furthermore, several advanced analysis methods have been proposed to extract supplementary material features such as viscosity and anisotropy, potentially offering additional diagnostic value. This review explains the general mechanical concepts underlying cardiac shear wave elastography and provides an overview of the preclinical and clinical studies within the field. We also identify the mechanical and technical challenges ahead to make shear wave elastography a valuable tool for clinical practice.