Template Creation Research Articles

A lightweight BRLWE-based post-quantum cryptosystem with side-channel resilience for IoT security

The rapid advancement of quantum computing poses a significant threat to conventional cryptographic systems, particularly in the context of Internet of Things (IoT) security. This paper introduces PQ-IoTCrypt, a lightweight post-quantum cryptosystem for resource-constrained IoT devices. PQ-IoTCrypt builds upon the binary ring learning with errors problem, incorporating optimizations for efficient implementation on 8-bit microcontrollers commonly found in IoT environments. We introduce a symmetric discrete uniform distribution and streamlined polynomial arithmetic to reduce computational overhead while maintaining a high-security level. Additionally, we present a comprehensive power side-channel analysis framework for lattice-based post-quantum cryptography, demonstrating PQ-IoTCrypt's resilience against various side-channel attacks, including advanced ciphertext selection criteria, IoT-optimized template creation, and a hierarchical chosen-ciphertext attack methodology tailored for IoT deployments. Experimental results show that PQ-IoTCrypt achieves a 9.9% reduction in total encryption time compared to the next best baseline at the 256-bit security level while requiring significantly fewer ciphertexts for successful attacks. PQ-IoTCrypt demonstrates superior performance in key generation, encryption, and decryption processes, with times reduced by 12.7 %, 9.1 %, and 9.2 %, respectively, compared to the closest competitor. This work contributes to the standardization efforts of post-quantum IoT security and offers valuable insights for real-world deployment of quantum-resistant cryptography in resource-limited settings.

ТЕХНОЛОГІЯ РОЗРОБЛЕННЯ ОСВІТНІХ ПРОГРАМ ІНШОМОВНОЇ ПІДГОТОВКИ МАГІСТРІВ-ФІЛОЛОГІВ

The article highlights the ideas of a technological approach to development of master’s training provision - teachers / lecturers of foreign languages in the context of modernization of the regulatory and legal foundation of higher education in Ukraine. The positive aspects of technological construction are the structuring of educational programs and the specification of permanent elements of their key parts. It is emphasized that the focus of educational programs on a student determines the modernization of the content and learning results, which are materialized in credits and the list of competencies that correspond to a certain educational and qualification level of a specific specialty. The technological approach to the creation of educational programs is characterized as an important factor in the organization and conduct of educational activities, as well as the quality and productivity of foreign language training of philology masters. The author explains that a certain unification of educational programs makes it possible to clarify and approve the basic format of these documents, and taking into account many factors that affect the profile and composition of each program and is a safeguard against their template creation. It is about different categories of programs (professional, scientific, career), different levels of higher education in Ukraine, specific professional modifications of each educational program, etc. The development of educational programs is implemented in the conditions of a systemic interconnection between learning outcomes and set competencies and in the context of standardization of higher education in Ukraine. The article is intended for teachers and students who research the history of pedagogy and the methodology of developing educational programs in the field of higher education.

Denoising of the gradient artifact present in simultaneous studies of muscle blood oxygen level dependent (BOLD) signal and electromyography (EMG)

The MR-induced gradient artifact affects EMG recordings during simultaneous muscle BOLD/EMG acquisitions. However, no dedicated hardware can remove the gradient artifact easily, and alternative methods are expensive and time-consuming. This study aimed to develop three denoising methods requiring different processing levels and MR-compatible hardware.At two time points, surface EMG was recorded from the lower leg of 6 participants (50:50 sex ratio, age = 26.24.6 yrs., height = 173.59.2 cm, weight = 71.511.4 kg) using a plantar flexion-based block design consisting of 30s of rest followed by 30s of flexion for 5 min, under three conditions: inside the MRI bore, with and without a BOLD sequence (3 T, BOLD sequence, GRE EPI, 10 slices, 64×64 matrix, 2 mm thickness, and TE/TR/flip = 35/3000 ms/70), and outside the MRI environment. Simultaneous BOLD/EMG recordings were denoised using average artifact subtraction with three methods of artifact template creation, each having varying timing and hardware requirements. Method M1 builds the artifact template by recording the scanner triggers coming from the MRI; M2 creates the artifact template with a constant artifact period computed as TR/[number of slices]; M3 estimates the artifact template by looking at the periodicity of the gradient artifact located in the EMG recordings. Following postprocessing, SNR analysis was performed, comparing rest-to-flexion periods, to assess the efficacy of denoising methods and to compare differences between conditions.Linear mixed-effects models showed no significant differences in the mean SNR between denoising methods (p = 0.656). Furthermore, EMG SNR measurements were significantly affected by the magnetic environment (p < 0.05) but not by muscle fatigue over time (p = 0.975).EMG recordings contaminated with gradient artifacts during simultaneous BOLD/EMG can be efficiently denoised using all proposed methods, with two methods requiring no extra hardware. With minimal post-processing, EMG can easily be performed during muscle BOLD MRI studies.

Educators’ Academic Insights on Artificial Intelligence: Challenges and Opportunities

The study on " Educators’ Academic Insights on Artificial Intelligence – Challenges and Opportunities" was conducted to gain a deeper understanding of the rapidly evolving phenomenon of AI in education. This research serves multiple objectives. Firstly, it aims to foster awareness regarding the integration of AI into teaching and learning practices by providing clear definitions of AI and explaining key AI-related terms. It also seeks to illustrate AI's diverse applications within a broader context, with a special focus on AI-supported research and learning platforms. Additionally, the study delves into the current discourse surrounding chatbots, contributing to address the central research question. Lastly, this initiative aims to provide valuable recommendations for effectively harnessing AI in education, enhancing the teaching and learning experience. The researchers conducted a review of literature concerning artificial intelligence. They adopted a qualitative method, using open-ended questions to collect feedback from educators globally, including those from the University of Technology and Applied Sciences, Al Musannah, and participants in the online discussion forum at Oxford English Learning Exchange.com. The qualitative data was analysed, leading to the identification of key themes and subthemes derived from the responses of research participants. The study's findings incorporated a wide range of concerns expressed by educators, comprising ten key subthemes. These concerns ranged from doubts about AI's ability to replace human educators and fears of its potential to hinder student development to worries about its hyped popularity and its perceived futuristic nature. Educators stressed the importance of effective AI training while emphasizing the need to prioritize human expertise over excessive reliance on AI. They were also acutely aware of both the advantages and disadvantages of AI, viewing it as both a potential boon and a looming threat. Furthermore, educators recognized the potential for enjoyable experiences with AI and acknowledged the pivotal role of users in determining the extent of AI adoption. Content analysis revealed additional apprehensions, such as concerns about job displacement, AI's impact on critical thinking, teacher frustration in assessing AI-assisted student writing, the use of AI-generated content for assessments, potential erosion of human services, stifling of user and learner creativity by AI, the risk of errors in AI-generated information, opportunities for cheating in exams, and concerns about the overreliance on and overrating of AI platforms. Positively, the findings included an array of opportunities that AI platforms offer. Study participants highlighted various aspects of these opportunities that surpassed their concerns and associated risks. The opportunities are categorized into twenty subthemes: enhancing learner motivation, facilitating template creation, utilizing AI as an educational aid, promoting proper training and fostering positive AI usage, harnessing AI for teaching challenging subjects, enabling personalized learning experiences, offering an interactive tutoring experience, supporting remote learning, facilitating self-study, providing comprehensive educational content overviews, giving instantaneous feedback and evaluation, functioning as search engines and chatbots, enabling content validation, efficiency in terms of cost and time, streamlining material preparation, facilitating skill and language enhancement, promoting familiarity with topics and vocabulary, enabling text-to-speech and speech-to-text conversions, editing multimedia elements, and facilitating content generation.

Research and Implementation of Container Based Application Orchestration Service Technology

With the rapid development of cloud computing technology, Kubernetes(K8S), as the main orchestration tool for cloud native applications, has become the preferred choice for enterprises and developers. This article is based on container based application orchestration service technology. Through a set of templates containing cloud resource descriptions, it quickly completes functions such as application creation and configuration, application batch cloning, and application multi environment deployment. It simplifies and automates the lifecycle management capabilities required for cloud applications, such as resource planning, application design, deployment, status monitoring, and scaling. Users can more conveniently complete infrastructure management and operation and maintenance work, In order to focus more on innovation and research and development, and improve work efficiency. The actual application effect of the technology used in this article depends to a certain extent on the ability level of basic service resources, and manual template creation is required for the first use. In production use, a certain professional ability is required to create a good application layout template, adjust and optimize resources according to the production environment, in order to significantly improve the effectiveness and efficiency of practical applications.

A Workflow for Minimizing Errors in Template‐Based Automated Item‐Generation Development

AbstractThe template‐based automated item‐generation (TAIG) approach that involves template creation, item generation, item selection, field‐testing, and evaluation has more steps than the traditional item development method. Consequentially, there is more margin for error in this process, and any template errors can be cascaded to the generated items. Therefore, it is essential to eliminate the source of errors and ensure the quality of the template so items can be problem‐free. The article introduces a process to reduce template errors at the early stage of template development, minimize the impact of template errors on generated items, and increase the survival rates of generated items. The article also discusses a statistical method to establish confidence in the quality of the template by systematically examining the quality of the generated items. The proposed method can reduce the review process for some items generated from a template.

Born-fair Data Projects Using Cookiecutter Templates

Implementing research data management best practices and FAIR principles (Wilkinson et al., 2016) is vital for transparent, reproducible research, as well as efficient, sustainable science that avoids duplication of effort. Scientists can also benefit directly from incorporating data management into their data collection and analysis workflows. However, there is an initial cost to adoption that poses a burden and substantial barrier to entry even to well-intentioned researchers. In our experience in statistical and RDM-focused consulting, this cost increases as a project progresses, with a late-stage conversion being the most costly in terms of resources and energy because a working analysis needs to be adapted in one step. Therefore, we believe that it is useful to adopt best practices for data stewardship early-on in a project, and ideally from the get-go.&#x0D; In this contribution, we present a tool for creating and instantiating project templates that conform to good practices with regard to the data management and analysis projects more generally. In the same vein as "born-open data" (Rouder, 2016) where data is published immediately upon collection, our goal is to establish born-FAIR datasets that implement proven methods for data stewardship as early on in the research data lifecycle as is feasible. Our aim is to encourage researchers and analysts to incorporate best practices into their workflows from the onset by providing data and analysis templates that implement desirable properties. By adopting these templates, researchers immediately gain access to a number of tools that simplify their work and make it more efficient, while also providing a foundation for increased reproducibility, data documentation through codebooks, as well as metadata for long-term archival.Because a broad-strokes approach may not work in practice due to idiosyncrasies of individual research projects, one size may not fit all. For this reason, the templates contain customisation options that researchers can use to tailor the templates to their requirements.&#x0D; The templates build on the well-established cookiecutter library for the Python programming language (Greenfeld et al., 2022), which we additionally extend to R, a programming language somewhat more common among statisticians and social scientists, thereby creating a cross-platform infrastructure. Both libraries create a project skeleton with a pre-specified directory structure, and include configuration for commonly used tools. Upon template creation, a wizard guides users through a customisation step, allowing them to adapt templates to their needs and catering to the demands of a project at hand.&#x0D; Owing to the open-source nature of the project and the firmly established and well-documented standard, researchers can easily adapt templates and create their own, to accommodate their specific needs and domain requirements. We hope to foster a community of researchers who share and improve their workflows, and anticipate further uses of the templates for teaching and other purposes.&#x0D; At CoRDI, we hope to introduce our project to the wider NFDI community and propose it as a lightweight, interoperable and interdisciplinary standard, benefiting all researchers across domains. By streamlining advanced users' workflows, and making reproducible practices more accessible, we aim to enable and facilitate the uptake of RDM across the communities represented there, and build integrations to interoperate with the multitude of services currently under development and in use.&#x0D; To summarise, we introduce templates for data analysis and archival that researchers can apply themselves, to render possible and encourage better practices during analysis, and prepare data for long-term storage and later re-use. Our hope is to encourage more researchers to adopt RDM best practices more frequently and earlier in projects, demonstrating the value of a more structured workflow and facilitating a shift to FAIR principles more generally.

Optimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applications.

Face recognition has become an integral part of modern security processes. This paper introduces an optimization approach for the quantile interval method (QIM), a promising classifier learning technique used in face recognition to create face templates and improve recognition accuracy. Our research offers a three-fold contribution to the field. Firstly, (i) we strengthened the evidence that QIM outperforms other contemporary template creation approaches. For this reason, we investigate seven template creation methods, which include four cluster description-based methods and three estimation-based methods. Further, (ii) we extended testing; we use a nearly four times larger database compared to the previous study, which includes a new set, and we report the recognition performance on this extended database. Additionally, we distinguish between open- and closed-set identification. Thirdly, (iii) we perform an evaluation of the cluster estimation-based method (specifically QIM) with an in-depth analysis of its parameter setup in order to make its implementation feasible. We provide instructions and recommendations for the correct parameter setup. Our research confirms that QIM's application in template creation improves recognition performance. In the case of automatic application and optimization of QIM parameters, improvement recognition is about 4-10% depending on the dataset. In the case of a too general dataset, QIM also provides an improvement, but the incorporation of QIM into an automated algorithm is not possible, since QIM, in this case, requires manual setting of optimal parameters. This research contributes to the advancement of secure and accurate face recognition systems, paving the way for its adoption in various security applications.

Leveraging GPT-4 for Post Hoc Transformation of Free-text Radiology Reports into Structured Reporting: A Multilingual Feasibility Study.

Leveraging GPT-4 for Post Hoc Transformation of Free-text Radiology Reports into Structured Reporting: A Multilingual Feasibility Study.

Comparison of performance between an -FDG PET normal brain template and a commercial template using the MIMneuro software.

The aim of this study was to create and validate a normal brain template of -fluorodeoxyglucose ( ) uptake using the MIMneuro software to improve clinical practice. One hundred and nine volunteers underwent an positron emission tomography/computed tomography scan. Sixty-three participants with normal Alzheimer's disease (AD) biomarkers were used to create a template. A group of 23 participants with abnormal AD biomarkers and an additional group of 23 participants with normal AD biomarkers were used to validate the performance of the generated template. The MIMneuro software was used for the analysis and template creation. The performance of our newly created template was compared with that of the MIMneuro software template in the validation groups. Results were confirmed by visual analysis by nuclear medicine physicians. Our created template provided higher sensitivity, specificity, positive predictive value, and negative predictive value (NPV; 90%, 97.83%, 100%, and 100%, respectively) than did the MIMneuro template when using the positive validation group. Similarly, slightly higher performance was observed for our template than for the MIMneuro template in the negative validation group (the highest specificity and NPV were 100% and 100%, respectively). Our normal brain template for was shown to be clinically useful because it enabled more accurate discrimination between aging brain and patients with AD. Thus, the template may improve the accuracy of AD diagnoses.

Battery screen print defect detection based on stationary velocity fields neural network matching and optical flow rectification.

In this study, an automatic defect detection method is proposed for screen printing in battery manufacturing. It is based on stationary velocity field (SVF) neural network template matching and the Lucas-Kanade (L-K) optical flow algorithm. The new method can recognize and classify different defects, such as lacking, skew, and blur, under the condition of irregular shape distortion. Three critical processing stages are performed during detection: (1) Image preprocessing was performed to acquire the printed region of interest and then image blocking was carried out for template creation. (2) The SVF network for image registration was constructed and the corresponding dataset was built based on oriented fast and rotated brief feature matching. (3) Irregular print distortion was rectified and defects were extracted using L-K optical flow and image subtraction. Software and hardware systems have been developed to support this method in industrial applications. To improve environment adaptation, we proposed a dynamic template updating mechanism to optimize the detection template. From the experiments, it can be concluded that the method has desirable performance in terms of accuracy (97%), time efficiency (485ms), and resolution (0.039mm). The proposed method possesses the advantages of image registration, defect extraction, and industrial efficiency compared to conventional methods. Although they suffer from irregular print distortions in batteries, the proposed method still ensures a higher detection accuracy.

Individual navigation templates for pedicle screw placement in spine surgery: a systematic review

The literature review presents data from world studies on the use of individual navigation templates in spine surgery and analyzes the published results of the application of individual navigation templates on different parts of the spine. The accuracy and safety of fixed transpedicular screws were evaluated using the templates. The main stages of template creation were highlighted, including the visualization methods for 3D modeling, 3D printing methods, available materials, as well as the time and costs associated with 3D printing were determined. For information analysis 23 publications were selected, 8 of them relate to the cervical spine, 10 — to the thoracic, and 5 — to the lumbar spine. The data of 460 patients in whom 3,294 screws had been fixed using the templates were analyzed, and the analysis has revealed that out of 3,294 screws, 3,059 (92.8%) have the highest level of safe fixation.

Individual navigation templates for pedicle screw placement in spine surgery: a systematic review

The literature review presents data from world studies on the use of individual navigation templates in spine surgery and analyzes the published results of the application of individual navigation templates on different parts of the spine. The accuracy and safety of fixed transpedicular screws were evaluated using the templates. The main stages of template creation were highlighted, including the visualization methods for 3D modeling, 3D printing methods, available materials, as well as the time and costs associated with 3D printing were determined. For information analysis 23 publications were selected, 8 of them relate to the cervical spine, 10 — to the thoracic, and 5 — to the lumbar spine. The data of 460 patients in whom 3,294 screws had been fixed using the templates were analyzed, and the analysis has revealed that out of 3,294 screws, 3,059 (92.8%) have the highest level of safe fixation.

Effective minutiae extraction and template creation in fingerprints

Despite years of research in using the fingerprint as a biometric entity, there remains a niche to improve the underlying accuracy, specifically on enhancement, feature extraction and classification consideration. Attempted in this paper is a ridge compensation filtering technique for enhancement, followed by minutiae extraction and false minutiae removal and eventually a procedure for template creation. The extracted features are evaluated using metrics like accuracy, error rate and goodness index. These values were found to be better when compared with the results of some state-of-art algorithms, which endorse the effectiveness of the first two stages. Mahalanobis distance (MD) is used to recognise the fingerprints through the generated features that resulted in accuracy above 92%. Finally, convolution neural network (CNN) is used to cross-examine the recognition capacity. Though CNN also able to give a similar accuracy, the proposed algorithm is simple and robust and hence easily embedded in hand-held devices.

A groupwise registration and tractography framework for cardiac myofiber architecture description by diffusion MRI: An application to the ventricular junctions.

BackgroundKnowledge of the normal myocardial–myocyte orientation could theoretically allow the definition of relevant quantitative biomarkers in clinical routine to diagnose heart pathologies. A whole heart diffusion tensor template representative of the global myofiber organization over species is therefore crucial for comparisons across populations. In this study, we developed a groupwise registration and tractography framework to resolve the global myofiber arrangement of large mammalian sheep hearts. To demonstrate the potential application of the proposed method, a novel description of sub-regions in the intraventricular septum is presented.MethodsThree explanted sheep (ovine) hearts (size ~12×8×6 cm3, heart weight ~ 150 g) were perfused with contrast agent and fixative and imaged in a 9.4T magnet. A group-wise registration of high-resolution anatomical and diffusion-weighted images were performed to generate anatomical and diffusion tensor templates. Diffusion tensor metrics (eigenvalues, eigenvectors, fractional anisotropy …) were computed to provide a quantitative and spatially-resolved analysis of cardiac microstructure. Then tractography was performed using deterministic and probabilistic algorithms and used for different purposes: i) Visualization of myofiber architecture, ii) Segmentation of sub-area depicting the same fiber organization, iii) Seeding and Tract Editing. Finally, dissection was performed to confirm the existence of macroscopic structures identified in the diffusion tensor template.ResultsThe template creation takes advantage of high-resolution anatomical and diffusion-weighted images obtained at an isotropic resolution of 150 μm and 600 μm respectively, covering ventricles and atria and providing information on the normal myocardial architecture. The diffusion metric distributions from the template were found close to the one of the individual samples validating the registration procedure. Small new sub-regions exhibiting spatially sharp variations in fiber orientation close to the junctions of the septum and ventricles were identified. Each substructure was defined and represented using streamlines. The existence of a fiber-bundles in the posterior junction was validated by anatomical dissection. A complex structural organization of the anterior junction in comparison to the posterior junction was evidenced by the high-resolution acquisition.ConclusionsA new framework combining cardiac template generation and tractography was applied on the whole sheep heart. The framework can be used for anatomical investigation, characterization of microstructure and visualization of myofiber orientation across samples. Finally, a novel description of the ventricular junction in large mammalian sheep hearts was proposed.

Assessing the Effectiveness of Generalized Likelihood Ratio Test Detector Schemes in Seismic Event Detection and the Avoidance of Nontarget Signals

ABSTRACT Cross-correlation techniques have played a long-standing and pivotal role in seismic event monitoring. However, the performance of correlation-based detectors is challenged by nuisance seismicity, or nontarget signals. Such detections are a problem when the mission is to automatically map events to the correct source region. Using aftershocks of the 2014 Mw 6.0 South Napa, California, earthquake, we demonstrate the effectiveness of utilizing a dynamic correlation processor framework in a generalized likelihood ratio test (GLRT) detector configuration to minimize nontarget detections. A GLRT maximizes a detection statistic with respect to one or more unknown parameters. In this case, the detection statistic is a template signal match against the waveform in a window sliding over a data stream, and the unknown parameter is an index variable indicating group membership of the template event or events. Detected events are assigned to the event group that yields the largest detection statistic. Our results show that a GLRT detector will outperform a suite of independently operating correlation and subspace detectors in terms of having a lower nontarget detection rate at a given missed detection rate. We also show that a GLRT detector composed of a few high-rank subspace detectors has a slightly higher nontarget detection rate, but a significantly lower missed detection rate, than a GLRT detector composed of many low-rank subspace detectors. The high-rank GLRT configuration produced impressive results even with marginal data (single channel, single station, and very low time bandwidth product), which bodes well for the utility of building efficient aftershock classification systems and global monitoring systems at larger scales. However, future work is required to assess performance at the regional scale and to assess the performance of the system at detecting target events not used in the detector template creation.

Automatic, fast, hierarchical, and non-overlapping gating of flow cytometric data with flowEMMi v2

Flow cytometry has become a powerful technology for studying microbial community dynamics and ecology. These dynamics are tracked over long periods of time based on two-parameter community fingerprints consisting of subsets of cell distributions with similar cell properties. These subsets are highlighted by cytometric gates which are assembled into a gate template. Gate templates then are used to compare samples over time or between sites. The template is usually created manually by the operator which is time consuming, prone to human error and dependent on human expertise. Manual gating thus lacks reproducibility, which in turn might impact ecological downstream analyses such as various diversity parameters, turnover and nestedness or stability measures. We present a new version of our flowEMMi algorithm – originally designed for an automated construction of a gate template, which now (i) generates non-overlapping elliptical gates within a few minutes. Gate templates (ii) can be created for both single measurements and time-series measurements, allowing immediate downstream data analyses and on-line evaluation. Furthermore, it is possible to (iii) adjust gate sizes to Gaussian distribution confidence levels. This automatic approach (iv) makes the gate template creation objective and reproducible. Moreover, it can (v) generate hierarchies of gates. flowEMMi v2 is essential not only for exploratory studies, but also for routine monitoring and control of biotechnological processes. Therefore, flowEMMi v2 bridges a crucial bottleneck between automated cell sample collection and processing, and automated flow cytometric measurement on the one hand as well as automated downstream statistical analysis on the other hand.

Secure Cancelable Template Based on Double Random Phase Encoding and Entropy Segmentation

In this paper, a proposed cancellable biometric scheme is based on multiple biometric image identifiers, Arnold’s cat map and double random phase encoding (DRPE) to obtain cancellable biometric templates. The proposed segmentation scheme that is used to select the region of interest for generating cancelable templates is based on chaos entropy low correlation statistical metrics. The objective of segmentation is to reduce the computational cost and reliability of template creation. The left and right biometric (iris, fingerprint, palm print and face) are divided into non-overlapping blocks of the same dimensions. To define the region of interest (ROI), we select the block with the highest entropy. To shorten the registration process time and achieve a high level of security, we select 25% of the image volume of the biometric data. In addition, the low-cost security requirement lies in the use of selective encryption (SE) technology. The step of selecting the maximum entropy is executed on all biometric blocks. The maximum right and left multi-biometric blocks are arranged in descending order from the entropy perspective and select 50% of each biometric couple and store the single matrix. The obtained matrix is scrambled with a certain number of iterations using Arnold’s Cat Map (ACM).The obtained scrambled matrix is encrypted with the DRPE to generate the cancellable biometric templates, which are further concatenated. The simulation results display better performance of the suggested cancellable biometric system in noise scenarios using the area under the receiver operating characteristic (AROC). The strength of the suggested technique is examined with correlation, irregular deviation, maximum difference and maximum deviation. The recommended proposed approach shows that the ability to distinguish the authentic and imposter biometrics of user seven in different levels of the noise environment.

Population-Average Brain Templates and Application to Automated Voxel-Wise Analysis Pipelines for Cynomolgus Macaque.

The growing number of neuroimaging studies of cynomolgus macaques require extending existing templates to facilitate species-specific application of voxel-wise neuroimaging methodologies. This study aimed to create population-averaged structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) templates for the cynomolgus macaques and apply the templates in fully automated voxel-wise analyses. We presented the development of symmetric and asymmetric MRI and DTI templates from a sample of 63 young male cynomolgus monkeys with the use of optimized template creation approaches. We also generated the associated average tissue probability maps and Diffeomorphic Anatomical Registration using Exponentiated Lie Algebra templates for use with the Statistical Parametric Mapping (SPM), as well as the average fractional anisotropy/skeleton targets for incorporation into tract-based spatial statistics (TBSS) framework. Both asymmetric and symmetric templates in a standardized coordinate space demonstrated low bias and high contrast. Fully automated processing using SPM was accomplished for all native MRI datasets and demonstrated outstanding performance regarding skull-stripping, segmentation, and normalization when using the MRI templates. Automated normalization to the DTI template was excellently achieved for all native DTI images using the TBSS pipeline. The cynomolgus MRI and DTI templates are anticipated to provide a common platform for precise single-subject data analysis and facilitate comparison of neuroimaging findings in cynomolgus monkeys across studies and sites. It is also hoped that the procedures of template creation and fully-automated voxel-wise frameworks will provide a straightforward avenue for investigating brain function, development, and neuro-psychopathological disorders in non-human primate models.

Data Shepherding in Nanotechnology. The Exposure Field Campaign Template.

In this paper, we demonstrate the realization process of a pragmatic approach on developing a template for capturing field monitoring data in nanomanufacturing processes. The template serves the fundamental principles which make data scientifically Findable, Accessible, Interoperable and Reusable (FAIR principles), as well as encouraging individuals to reuse it. In our case, the data shepherds’ (the guider of data) template creation workflow consists of the following steps: (1) Identify relevant stakeholders, (2) Distribute questionnaires to capture a general description of the data to be generated, (3) Understand the needs and requirements of each stakeholder, (4) Interactive simple communication with the stakeholders for variables/descriptors selection, and (5) Design of the template and annotation of descriptors. We provide an annotated template for capturing exposure field campaign monitoring data, and increase their interoperability, while comparing it with existing templates. This paper enables the data creators of exposure field campaign data to store data in a FAIR way and helps the scientific community, such as data shepherds, by avoiding extensive steps for template creation and by utilizing the pragmatic structure and/or the template proposed herein, in the case of a nanotechnology project (Anticipating Safety Issues at the Design of Nano Product Development, ASINA).