Journal Articles

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Articles. Typically the realization of research papers reporting original research findings published in a journal issue. (URI: http://purl.org/coar/resource_type/c_6501) Item types include:

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A Course or a Pathway? Addressing French as a Second Language Teacher Recruitment and Retention in Canadian BEd Programs
(Canadian Society for the Study of Education, 2023-06-27) Smith, Cameron W.; Masson, Mimi; Spiliotopoulos, Valia; Kristmanson, Paula
Institutions strive to offer programs that address both the needs of the educational system and incorporate current pedagogical research. Creating a program that is relevant, inspiring, and accessible to aspiring French as a Second Language (FSL) teachers, while also equipping them with the skills and knowledge deemed necessary by the education system, is a delicate balancing act. This study reviewed 44 FSL teacher education programs that lead to professional certification across Canada. Environmental scans drew information from the program websites related to admission requirements, program structure and content, practicum, and graduation criteria. Follow-up interviews with program stakeholders were conducted to verify or clarify the data. The results highlight the inconsistencies that exist among programs for developing FSL educators. We position the ways in which Canadian faculties of education might provide a more holistic “pathway” approach to recruiting, preparing, and retaining emerging FSL teachers.
A Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control
(Institute of Electrical and Electronics Engineers, 2020) Ameri, Ali; Akhaee, Mohammad Ali; Scheme, Erik; Englehart, Kevin
An important barrier to commercialization of pattern recognition myoelectric control of prostheses is the lack of robustness to confounding factors such as electrode shift, skin impedance variations, and learning effects. To overcome this challenge, a novel supervised adaptation approach based on transfer learning (TL) with convolutional neural networks (CNNs) is proposed which requires only a short training session (a few seconds for each class) to recalibrate the system. TL is proposed as a solution to the problem of insufficient calibration data due to short training times for both classification and regression-based control schemes. This approach was validated for electrode shift of roughly 2.5cm with 13 able-bodied subjects to estimate individual and combined wrist motions. With this method, the original CNN (trained before the shift) was fine-tuned with the calibration data from after shifting. The results show that the proposed technique outperforms training a CNN from scratch (random initialization of weights) or a support vector machine (SVM) using the minimal calibration data. Moreover, it demonstrates superior performance than previous LDA and QDA-based adaptation approaches. As the outcomes confirm, the proposed CNN TL method provides a practical solution for adaptation to external factors, improving the robustness of electromyogram (EMG) pattern recognition systems.
A descriptive study found low prevalence of presumed predatory publications in a subset of Cochrane reviews
(Elsevier, 2022-12) Boulos, Leah; Rothfus, Melissa; Goudreau, Alex; Manley, Alison
Objective: To examine the prevalence of presumed predatory publications in Cochrane reviews, which are considered the gold standard. Study Design and Setting: We selected two Cochrane Networks with broad scope: the Musculoskeletal, Oral, Skin and Sensory (MOSS) Network and the Public Health and Health Systems Network. From reviews produced by all Review Groups in those Networks in 2018 and 2019, we extracted included study citations published after 2000. For each citation, we assessed the journal and publisher using an algorithmic process based on characteristics known to be common among predatory publishers. Knowing that predatory status can be fluid and subjective, we scored citations on a spectrum from "reputable" to "presumed predatory" based on publication characteristics available at the time of assessment. Results: We extracted 6965 citations from 321 reviews. Of these citations, 5734 were published by entities widely accepted as reputable, leaving 1591 for further assessment. We flagged 75 citations as concerning. Discussion: Cochrane reviews across diverse topic areas included studies from flagged publishers, although this number is small. Because of this, there is potential for studies from predatory journals to influence the conclusions of systematic reviews. Researchers should stay aware of this potential threat to the quality of reviews.
A DNA barcode examination of the Laminariaceae (Phaeophyceae) in Canada reveals novel biogeographical and evolutionary insights
(Taylor and Francis, 2010) McDevit, Daniel, C.; Saunders, Gary, W.
DNA barcoding is becoming a widely applied tool for the rapid and accurate identification of eukaryotic species. In this study we used the DNA barcode for large-scale screening of the brown algal family Laminariaceae in Canada. With the examination of 194 COI-5P (5′ end of cytochrome c oxidase 1) sequences (150 newly determined) from representatives of this family, we confirmed the presence of 12 species in Canadian waters (Cymathaere triplicata, Laminaria digitata, L. ephemera, L. setchellii, L. solidungula, L. yezoensis, Macrocystis integrifolia, Nereocystis leutkeana, Postelsia palmaeformis, Saccharina groenlandica, S. latissima and S. sessilis). Saccharina groenlandica, a species with a history of taxonomic confusion, was found in the Pacific, Hudson Bay (subarctic) and Atlantic Canada extending greatly our comprehension of the biogeography of this species. Additionally, COI-5P data from S. latissima, combined with ITS results, provided insights into historical distributional patterns and uncovered a hybridization zone between incipient species in this complex. These discoveries highlight how the growth of a worldwide barcode database for the assignment of individuals to genetic species will uncover new perspectives on biogeography and species diversity on a global scale.
A DNA barcode examination of the red algal family Dumontiaceae in Canadian waters reveals substantial cryptic species diversity. 1. The foliose Dilsea–Neodilsea complex and Weeksia
(Canadian Science Publishing, 2008) Saunders, Gary, W.
The field of DNA barcoding is working towards generating a genetic system for the quick and accurate identification of eukaryotic species. For the more systematic minded, however, DNA barcoding offers a new approach towards screening and uniting large numbers of biological specimens in genetic groups as a first step towards assigning them to species and genera in an approach best termed “molecular-assisted alpha taxonomy”. This approach is particularly amenable in organisms with simple morphologies, a propensity for convergence, extensive phenotypic plasticity, and life histories with an alternation of heteromorphic generations. It is hard to imagine a group of organisms better defined by all of these traits than the marine macroalgae. In an effort to assess the utility of the DNA barcode (COI-5′) for testing the current concepts of biodiversity of marine macroalgae in Canada, a study to assess species diversity in the red algal family, Dumontiaceae, was initiated. Through this work I confirm the presence in Canadian waters of Dilsea californica (J. Agardh) Kuntze, Dilsea integra (Kjellman) Rosenvinge, and Neodilsea borealis (I.A. Abbott) Lindstrom of the Dilsea–Neodilsea complex, and Weeksia coccinea (Harvey) Lindstrom for the genus Weeksia. However, our work has uncovered two additional species of the former complex, Dilsea lindstromiae Saunders sp. nov. and Dilsea pygmaea (Setchell) Setchell, and an additional species of the latter, Weeksia reticulata Setchell, effectively doubling representation of these foliose dumontiacean genera in Canadian waters.
A folded model for compositional data analysis
(Wiley, 2020) Tsagris, Michail; Stewart, Connie
A folded type model is developed for analysing compositional data. The proposed model involves an extension of the α-transformation for compositional data and provides a new and flexible class of distributions for modelling data defined on the simplex sample space. Despite its rather seemingly complex structure, employment of the EM algorithm guarantees efficient parameter estimation. The model is validated through simulation studies and examples which illustrate that the proposed model performs better in terms of capturing the data structure, when compared to the popular logistic normal distribution, and can be advantageous over a similar model without folding.
A linearly extendible multi-artifact removal approach for improved upper extremity EEG-based motor imagery decoding
(IOP Publishing Ltd, 2021) Asogbon, Mojisola Grace; Samuel, Oluwarotimi Williams; Li, Xiangxin; Nsugbe, Ejay; Scheme, Erik; Li, Guanglin
Background and Objective: Non-invasive multichannel Electroencephalography (EEG) recordings provide an alternative source of neural information from which motor imagery (MI) patterns associated with limb movement intent can be decoded for use as control inputs for rehabilitation robots. The presence of multiple inherent dynamic artifacts in EEG signals, however, poses processing challenges for brain-computer interface (BCI) systems. A large proportion of the existing EEG signal preprocessing methods focus on isolating single artifact per time from an ensemble of EEG trials and require calibration and/or reference electrodes, resulting in increased complexity of their application to MI-EEG controlled rehabilitation devices in practical settings. Also, a few existing multi-artifacts removal methods though explored in other domains, they have rarely been investigated in the space of MI-EEG signals for multiple artifacts cancellation in a simultaneous manner. Approach: Building on the premise of previous works, this study propose a semi-automatic EEG preprocessing method that combines Generalized Eigenvalue Decomposition driven by low-rank approximation and a Multi-channel Wiener Filter (GEVD-MWF) that employs a learning technique for simultaneous elimination of multiple artifacts from MI-EEG signals. The proposed method is applied to remove multiple artifacts from 64-channel EEG signals recorded from transhumeral amputees while they performed distinct classes of upper limb MI tasks before decoding their movement intent using a selection of features and machine learning algorithms. Main Results: Experimental results show that the proposed GEVD-MWF method yields significant improvements in MI decoding accuracies, in the range of 13.23%-41.21% compared to four existing popular artifact removal algorithms. Further investigation revealed that the GEVD-MWF approach enabled accuracies in the range of 90.44% - 99.67% using "single trial" EEG recordings, which could eliminate the need to record and process large ensembles of EEG trials as commonly required in some existing approaches. Additionally, using a variant of the sequential forward floating selection algorithm, a subset of 9 channels was used to obtain a decoding accuracy of 93.73%±1.58%. Significance: Given its improved performance, reduced data requirements, and feasibility with few channels, the proposed GEVD-MWF could potentially spur the development of effective real-time control strategies for multi-degree of freedom EEG-based miniaturized rehabilitation robotic interfaces.
A meta-analysis of sex differences in human navigation skills
(Springer, 2019) Nazareth, Alina; Huang, Xing; Voyer, Daniel; Newcombe, Nora
There are inconsistent reports regarding behavioral sex differences in the human navigation literature. This meta-analysis quantifies the overall magnitude of sex differences in large-scale navigation skills in a variety of paradigms and populations, and examines potential moderators, using 694 effect sizes from 266 studies and a multilevel analytic approach. Overall, male participants outperform female participants, with a small to medium effect size (d = 0.34 to 0.38). The type of task, the type of dependent variable and the testing environment significantly contribute to variability in effect sizes, although there are only a few situations in which differences are either nonexistent or very large. Pointing and recall tasks (and the deviation scores associated with them) show larger sex differences than distance estimation tasks or learning to criterion. Studies with children younger than 13 years showed much smaller effect sizes (d = .15) than older age groups. We discuss the implications of these findings for understanding sex differences in human spatial navigation and identify avenues for future navigation research.
A molecular assessment of species diversity and generic boundaries in the red algal tribes Polysiphonieae and Streblocladieae (Rhodomelaceae, Rhodophyta) in Canada
(Taylor and Francis, 2018) Savoie, Amanda, M.; Saunders, Gary, W.
Sequence data generated during a Canadian barcode survey (COI-5P) of the tribes Polysiphonieae and Streblocladieae, a large and taxonomically challenging group of red algae, revealed significant taxonomic confusion and hidden species diversity. Polysiphonia pacifica Hollenberg, P. paniculata Montagne, P. stricta (Dillwyn) Greville and Vertebrata fucoides (Hudson) Kuntze were all complexes of two or more genetically distinct yet overlooked species. One variety of P. pacifica was elevated to the rank of species as P. determinata (Hollenberg) Savoie & Saunders, stat. nov. Several new additions to the Canadian flora were recorded including P. kapraunii Stuercke & Freshwater and P. morrowii Harvey. Subsequent multi-gene (COI-5P, LSU and rbcL) phylogenetic analyses confirmed that the genus Polysiphonia Greville was polyphyletic, and currently assigned species resolved with many other genera. Polysiphonia sensu stricto was restricted to a group of species that formed a monophyletic lineage with the type, Polysiphonia stricta. Carradoriella P.C.Silva was resurrected based on the South African species Carradoriella virgata (C.Agardh) P.C.Silva. Species previously attributed to Polysiphonia were transferred to Carradoriella, Leptosiphonia and Vertebrata as well as to three new genera described here: Acanthosiphonia gen. nov., based on A. echinata (Harvey) comb. nov.; Eutrichosiphonia gen. nov. for E. confusa (Hollenberg) comb. nov. and E. sabulosia (B.Kim & M.S.Kim) comb. nov.; and Kapraunia gen. nov., which includes K. schneideri (Stuercke & Freshwater) comb. nov. and three additional species.
A molecular phylogenetic and DNA barcode assessment of the tribe Pterosiphonieae (Ceramiales, Rhodophyta) emphasizing the Northeast Pacific
(Canadian Science Publishing, 2016) Savoie, Amanda, M.; Saunders, Gary, W.
Sequence data (COI-5P and rbcL) for North American members of the tribe Pterosiphonieae were compared with collections from around the world. Phylogenetic analyses resolved Pterosiphonia as polyphyletic and many species required transfer to other genera. In our analyses Pterosiphonia sensu stricto included only the type species P. cloiophylla (C. Agardh) Falkenberg and P. complanata (Clemente) Falkenberg, as well as the South African species P. stegengae sp. nov. A new genus, Xiphosiphonia gen. nov., was described for X. ardreana (Maggs & Hommersand) comb. nov., X. pennata (C. Agardh) comb. nov., and X. pinnulata (Kützing) comb. nov. Some Asian, European and North American species previously attributed to Pterosiphonia were transferred to Symphyocladia including S. baileyi (Harvey) comb. nov., S. dendroidea (Montagne) comb. nov., S. plumosa nom. nov. (for P. gracilis Kylin), and S. tanakae (S. Uwai & M. Masuda) comb. nov. We also described two new North American species, Symphyocladia brevicaulis sp. nov. and S. rosea sp. nov. Other species formed a well-supported clade for which the genus name Polyostea Ruprecht was resurrected. Included in Polyostea were P. arctica (J. Agardh) comb. nov., P. bipinnata (Postels & Ruprecht) Ruprecht, P. hamata (E.S. Sinova) comb. nov., and P. robusta (N.L. Gardner) comb. nov.
A Multi-Feature Fusion Using Deep Transfer Learning for Earthquake Building Damage Detection
(Taylor and Francis, 2021) Abdi, Ghasem; Jabari, Shabnam
With the recent tremendous improvements in the spatial, spectral, and temporal resolutions of remote sensing imaging systems, there has been a dramatic increase in the applications of remote sensing images. Amongst different applications of very high-resolution remote sensing images, damage detection for rapid emergency response is one of the most challenging ones. Recently, deep learning frameworks have enhanced the performance of earthquake damage detection by automatic extraction of strong deep features. However, most of the existing studies in this area focus on using nadir satellite images or orthophotos which limits the available data sources. This limitation decreases the temporal resolution of the practical images, which is a serious issue considering the emergency nature of damage detection applications. The objective of this study is to present a multimodal integrated structure to combine orthophoto and off-nadir images for earthquake building damage detection. In this context, a multi-feature fusion method based on deep transfer learning is presented, which contains four different steps, namely pre-processing, deep feature extraction, deep feature fusion, and transfer learning. To validate the presented framework, two comparative experiments are conducted on the 2010 Haiti earthquake using pre- and post-event off-nadir satellite images, which were collected by WorldView-2 (WV-2) satellite platform as well as a post-event airborne orthophoto. The results demonstrate considerable advantages in identifying damaged and non-damaged buildings with over 83% for the overall accuracy.
A Multi-Variate Approach to Predicting Myoelectric Control Usability
(Institute of Electrical and Electronics Engineers, 2021) Nawfel, Jena L.; Englehart, Kevin B.; Scheme, Erik J.
Pattern recognition techniques leveraging the use of electromyography signals have become a popular approach to provide intuitive control of myoelectric devices. Performance of these control interfaces is commonly quantified using offline classification accuracy, despite studies having shown that this metric is a poor indicator of usability. Researchers have identified alternative offline metrics that better correlate with online performance; however, the relationship has yet to be fully defined in the literature. This has necessitated the continued trial-and-error-style online testing of algorithms developed using offline approaches. To bridge this information divide, we conducted an exploratory study where thirty-two different metrics from the offline training data were extracted. A correlation analysis and an ordinary least squares regression were implemented to investigate the relationship between the offline metrics and six aspects online use. The results indicate that the current offline standard, classification accuracy, is a poor indicator of usability and that other metrics may hold predictive power. The metrics identified in this work also may constitute more representative evaluation criteria when designing and reporting new control schemes. Furthermore, linear combinations of offline training metrics generate substantially more accurate predictions than using individual metrics. We found that the offline metric feature efficiency generated the best predictions for the usability metric throughput. A combination of two offline metrics (mean semi-principal axes and mean absolute value) significantly outperformed feature efficiency alone, with a 166% increase in the predicted R 2 value (i.e., VEcv). These findings suggest that combinations of metrics could provide a more robust framework for predicting usability.
A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN
(MDPI, 2020) Waris, Asim; ur Rehman, Muhammad Zia; Niazi, Imran Khan; Jochumsen, Mads; Englehart, Kevin; Jensen, Winnie; Haavik, Heidi; Kamavuako, Ernest Nlandu
Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed to quantify the robustness of the real-time performance parameters. Intramuscular wires were used to record EMG signals, which were kept inside the muscles for five consecutive days. Tests were performed on multiple days using Fitts’ law. Throughput, completion rate, path efficiency and overshoot were evaluated as performance metrics using three train/test strategies. Each train/test scheme was categorized on the basis of data quantity and the time difference between training and testing data. An artificial neural network (ANN) classifier was trained and tested on (i) data from the same day (WDT), (ii) data collected from the previous day and tested on present-day (BDT) and (iii) trained on all previous days including the present day and tested on present-day (CDT). It was found that the completion rate (91.6 ± 3.6%) of CDT was significantly better (p < 0.01) than BDT (74.02 ± 5.8%) and WDT (88.16 ± 3.6%). For BDT, on average, the first session of each day was significantly better (p < 0.01) than the second and third sessions for completion rate (77.9 ± 14.0%) and path efficiency (88.9 ± 16.9%). Subjects demonstrated the ability to achieve targets successfully with wire electrodes. Results also suggest that time variations in the iEMG signal can be catered by concatenating the data over several days. This scheme can be helpful in attaining stable and robust performance.
A proportional control scheme for high density force myography
(IOP Publishing, 2018-08) Belyea, Alexander T.; Englehart, Kevin B.; Scheme, Erik J.
Objective. Force myography (FMG) has been shown to be a potentially higher accuracy alternative to electromyography for pattern recognition based prosthetic control. Classification accuracy, however, is just one factor that affects the usability of a control system. Others, like the ability to start and stop, to coordinate dynamic movements, and to control the velocity of the device through some proportional control scheme can be of equal importance. To impart effective fine control using FMG-based pattern recognition, it is important that a method of controlling the velocity of each motion be developed. Methods. In this work force myography data were collected from 14 able bodied participants and one amputee participant as they performed a set of wrist and hand motions. The offline proportional control performance of a standard mean signal amplitude approach and a proposed regression-based alternative was compared. The impact of providing feedback during training, as well as the use of constrained or unconstrained hand and wrist contractions, were also evaluated. Results. It is shown that the commonly used mean of rectified channel amplitudes approach commonly employed with electromyography does not translate to force myography. The proposed class-based regression proportional control approach is shown significantly outperform this standard approach (ρ < 0.001), yielding a R2 correlation coefficients of 0.837 and 0.830 for constrained and unconstrained forearm contractions, respectively for able bodied participants. No significant difference (ρ = 0.693) was found in R2 performance when feedback was provided during training or not. The amputee subject achieved a classification accuracy of 83.4% ± 3.47% demonstrating the ability to distinguish contractions well with FMG. In proportional control the amputee participant achieved an R2 of of 0.375 for regression based proportional control during unconstrained contractions. This is lower than the unconstrained case for able-bodied subjects for this particular amputee, possibly due to difficultly in visualizing contraction level modulation without feedback. This may be remedied in the use of a prosthetic limb that would provide real-time feedback in the form of device speed. Conclusion. A novel class-specific regression-based approach is proposed for multi-class control is described and shown to provide an effective means of providing FMG-based proportional control.
A revision of the genus Cryptonemia (Halymeniaceae, Rhodophyta) in Bermuda, western Atlantic Ocean, including five new species and C. bermudensis (Collins & M. Howe) comb. nov.
(Taylor and Francis, 2018) Schneider, Craig, W.; Lane, Christopher, E.; Saunders, Gary, W.
Cryptonemia specimens collected in Bermuda over the past two decades were analysed using gene sequences encoding the large subunit of the nuclear ribosomal DNA and the large subunit of RuBisCO as genetic markers to elucidate their phylogenetic positions. They were additionally subjected to morphological assessment and compared with historical collections from the islands. Six species are presently found in the flora including C. bermudensis comb. nov., based on Halymenia bermudensis, and the following five new species: C. abyssalis, C. antricola, C. atrocostalis, C. lacunicola and C. perparva. Of the eight species known in the western Atlantic flora prior to this study, none is found in Bermuda. Specimens reported in the islands in the 1900s attributed to C. crenulata and C. luxurians are representative of the new species, C. antricola and C. atrocostalis, respectively.
A survey on neuromarketing using EEG signals
(IEEE, 2021-03-12) Khurana, Vaishali; Gahalawat, Monika; Kumar, Pradeep; Roy, Partha Pratim; Dogra, Debi Prosad; Scheme, Erik; Soleymani, Mohammad
Neuromarketing is the application of neuroscience to the understanding of consumer preferences toward products and services. As such, it studies the neural activity associated with preference and purchase intent. Neuromarketing is considered an emerging area of research, driven in part by the approximately 400 billion dollars spent annually on advertisement and promotion. Given the size of this market, even a slight improvement in performance can have an immense impact. Traditional approaches to marketing consider a posteriori user feedback in the form of questionnaires, product ratings, or review comments, but these approaches do not fully capture or explain the real-time decision-making process of consumers. Various physiological measurement techniques have been proposed to facilitate the recording of this crucial aspect of the decision-making process, including brain imaging techniques [functional magnetic resonance imaging (fMRI), electroencephalography (EEG), steady state topography (SST)], and various biometric sensors. The use of EEG in neuromarketing is especially promising. EEG detects the sequential changes of brain activity, without appreciable time delay, needed to assess both the unconscious reaction and sensory reaction of the customer. Several types of EEG devices are now available in the market, each with its own advantages and disadvantages. Researchers have conducted experiments using many of these devices, across different age groups and different categories of products. Because of the deep insights that can be gained, the field of neuromarketing research is carefully monitored by consumer and research protection groups to ensure that subjects are properly protected. This article surveys a range of considerations for EEG-based neuromarketing strategies, including the types of information that can be gathered, how marketing stimuli are presented to consumers, how such strategies may affect the consumer in terms of appeal and memory, machine learning techniques applied in the field, and the variety of challenges faced, including ethics, in this emerging field.
A symmetrical component feature extraction method for fault detection in induction machines
(IEEE, 2019-09) St-Onge, Xavier F.; Cameron, James; Saleh, Saleh; Scheme, Erik J.
Induction motors (IMs) are among the fully developed electromechanical technologies that are still in use today. Over the course of the last century, their structure, control, and operation have been undergone through several stages of development. Among stages of development, the automated control and continuous monitoring of IMs has benefited from the emergence of modern artificial intelligence (AI) methods. IM automation schemes have demonstrated the ability to provide machine fault detection and diagnosis (FDD) function. AI-based FDD methods in IMs have employed frequency-domain, time-frequency, and time-domain analyses as the basis of their feature extraction schemes. A promising feature extraction scheme is one that uses symmetrical components (SCs) in time-domain FDD systems. Current SC-based approaches, however, are limited in their generalizability to different fault classes, may require detailed machine models, and can suffer from computational limitations. In this paper, an improved feature extraction method that uses SCs for a pattern recognition based FDD scheme for three-phase (3φ) IMs will be presented. This novel feature extraction method will be implemented and verified experimentally to demonstrate high classification performance, increased generalizability, and low computational cost.
A theory-based primary health care intervention for women who have left abusive partners.
(2011) Ford-Gilboe, Marilyn; Merritt-Gray, Marilyn; Varcoe, Colleen; Wuest, Judith
Although intimate partner violence is a significant global health problem, few tested interventions have been designed to improve women's health and quality of life, particularly beyond the crisis of leaving. The Intervention for Health Enhancement After Leaving is a comprehensive, trauma informed, primary health care intervention, which builds on the grounded theory Strengthening Capacity to Limit Intrusion and other research findings. Delivered by a nurse and a domestic violence advocate working collaboratively with women through 6 components (safeguarding, managing basics, managing symptoms, cautious connecting, renewing self, and regenerating family), this promising intervention is in the early phases of testing.
A uranium atlas, from 365 to 505 nm
(Elsevier, 2020-03-02) Ross, Amanda J.; Crozet, Patricia; Adam, Allan G.; Tokaryk, Dennis W.
A Fourier-transform spectrum of the emission from a commercial uranium hollow-cathode lamp 19,800–27,400 cm−1 is proposed, in ascii format, as a possible aid to calibration of laser excitation spectra.
Activity and Distribution of Paxillin, Focal Adhesion Kinase, and Cadherin Indicate Cooperative Roles during Zebrafish Morphogenesis
(2003) Crawford, Bryan, D.; Henry, Clarissa, A.; Clason, Todd, A.; Becker, Amanda, L.; Hille, Merrill, B.
We investigated the focal adhesion proteins paxillin and Fak, and the cell-cell adhesion protein cadherin in developing zebrafish (Danio rerio) embryos. Cadherins are expressed in presomitic mesoderm where they delineate cells. The initiation of somite formation coincides with an increase in the phosphorylation of Fak, and the accumulation of Fak, phosphorylated Fak, paxillin, and fibronectin at nascent somite boundaries. In the notochord, cadherins are expressed on cells during intercalation, and phosphorylated Fak accumulates in circumferential rings where the notochord cells contact laminin in the perichordal sheath. Subsequently, changes in the orienta- tions of collagen fibers in the sheath suggest that Fak-mediated adhesion allows longitudinal expansion of the notochord, but not lateral expansion, resulting in notochord elongation. Novel observations showed that focal adhesion kinase and paxillin concentrate at sites of cell-cell adhesion in the epithelial enveloping layer and may associate with actin cytoskeleton at epithelial junctions containing cadherins. Fak is phosphorylated at these epithelial junctions but is not phosphorylated on Tyr397, implicating a noncanonical mechanism of regulation. These data suggest that Fak and paxillin may function in the integration of cadherin-based and integrin-based cell adhesion during the morphogenesis of the early zebrafish embryo.

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