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We investigated the material properties of Cremonese soundboards using a wide range of spectroscopic, microscopic, and chemical techniques. We found similar types of spruce in Cremonese soundboards as in modern instruments, but Cremonese spruces exhibit unnatural elemental compositions and oxidation patterns that suggest artificial manipulation. Combining analytical data and historical information, we may deduce the minerals being added and their potential functions—borax and metal sulfates for fungal suppression, table salt for moisture control, alum for molecular crosslinking, and potash or quicklime for alkaline treatment. The overall purpose may have been wood preservation or acoustic tuning. Hemicellulose fragmentation and altered cellulose nanostructures are observed in heavily treated Stradivari specimens, which show diminished second‐harmonic generation signals. Guarneri's practice of crosslinking wood fibers via aluminum coordination may also affect mechanical and acoustic properties. Our data suggest that old masters undertook materials engineering experiments to produce soundboards with unique properties., ICP‐MS is used to identify the unique mineral recipe used by Antonio Stradivari to treat his spruce wood, the material for the violin top plate. Chemical manipulation led to hemicellulose fragmentation and cellulose rearrangement. Incorporating engineered wood may contribute to the unique tonal qualities of these acclaimed instruments.

Powerful detectors at modern experimental facilities routinely collect data at multiple GB/s. Online analysis methods are needed to enable the collection of only interesting subsets of such massive data streams, such as by explicitly discarding some data elements or by directing instruments to relevant areas of experimental space. Thus, methods are required for configuring and running distributed computing pipelines-what we call flows-that link instruments, computers (e.g., for analysis, simulation, artificial intelligence [AI] model training), edge computing (e.g., for analysis), data stores, metadata catalogs, and high-speed networks. We review common patterns associated with such flows and describe methods for instantiating these patterns. We present experiences with the application of these methods to the processing of data from five different scientific instruments, each of which engages powerful computers for data inversion,model training, or other purposes. We also discuss implications of such methods for operators and users of scientific facilities.

The growth of the distance education modality allows researchers to present varied studies related to the theme. Together with these studies emerge concepts such as Learning Analytics (LA). The LA is an area that will analyze, measure, collect and relate student data in their contexts. However, the use of this data brings with it a new concern regarding the protection, privacy and correct use of the data. The European Union already finds personal data protection legislation with a broad General Data Protection Regulation (GDPR). In Brazil, the legislation available today is available in the law called - Lei Geral de Proteção de Dados Pessoais (LGPDP) and its effect begins in August 2020. This article aims to present the result of a Systematic Literature Review (RSL) that search to identify academic research related to Learning Analytics (LA) and General Data Protection Regulation (GDPR). After applying the inclusion and exclusion requirements of the selected articles ten articles were selected for analysis. With the analysis it is possible to identify an alignment between the LA and GDPR showing that LA should follow the guidelines of GDPR. El rápido crecimiento de la modalidad de educación a distancia ha permitido a los investigadores presentar diversos estudios relacionados con el tema. Junto con estos estudios, surgen conceptos como Learning Analytics (LA), que es un área que tiene como objetivo medir, recopilar, analizar e informar datos de los estudiantes en sus contextos. Sin embargo, el uso de estos datos de los estudiantes plantea una nueva preocupación relacionada con la protección, la privacidad y el uso correcto de los datos. La Unión Europea ya está legislando sobre la protección de datos personales con un amplio General Data Protection Regulation (GDPR). En Brasil, la legislación disponible hoy es la Ley denominada Lei Geral de Proteção de Dados Pessoais (LGPDP) y su efecto comienza en agosto de 2020. Este artículo tiene como objetivo presentar el resultado de una Revisión sistemática de literatura (RSL) que buscó Identificar investigaciones académicas relacionadas con Learning Analytics (LA) y el General Data Protection Regulation (GDPR). Después de aplicar los criterios de inclusión y exclusión en los artículos obtenidos, se seleccionaron diez artículos para su análisis. Con el análisis fue posible concluir que existe una alineación entre los conceptos de LA y GDPR y que LA debe seguir las pautas de GDPR. O rápido crescimento da modalidade de educação a distância propiciou que pesquisadores apresentassem estudos variados relacionados ao tema. Junto a estes estudos emergem conceitos tais como o de Learning Analytics (LA), que se constitui em uma área que se propõe a medir, coletar, analisar e relatar dados de discentes em seus contextos. Todavia, o uso destes dados de discentes traz à tona nova preocupação relacionada a proteção, privacidade e o correto uso dos dados. A União Europeia já se encontra legislando sobre a proteção de dados pessoais com um amplo Regulamento Geral sobre a Proteção de Dados (GDPR). No Brasil, a legislação hoje disponível constitui-se na Lei Geral de Proteção de Dados Pessoais (LGPDP) e seu efeito se inicia em agosto de 2020. Este artigo tem por objetivo apresentar o resultado de uma Revisão Sistemática da Literatura (RSL) que buscou identificar pesquisas acadêmicas relacionadas a temática Learning Analytics (LA) e General Data Protection Regulation (GDPR). Após a aplicação dos critérios de Inclusão e exclusão sobre os artigos obtidos, foram selecionados dez artigos para análise. Com a análise foi possível concluir que existe um alinhamento entre os conceitos de LA e GDPR e que o LA deve seguir as orientações do GDPR. &nbsp

We present a fully modular and scalable software pipeline for processing electron microscope (EM) images of brain slices into 3D visualization of individual neurons and demonstrate an end-to-end segmentation of a large EM volume using a supercomputer. Our pipeline scales multiple packages used by the EM community with minimal changes to the original source codes. We tested each step of the pipeline individually, on a workstation, a cluster, and a supercomputer. Furthermore, we can compose workflows from these operations using a Balsam database that can be triggered during the data acquisition or with the use of different front ends and control the granularity of the pipeline execution. We describe the implementation of our pipeline and modifications required to integrate and scale up existing codes. The modular nature of our environment enables diverse research groups to contribute to the pipeline without disrupting the workflow, i.e. new individual codes can be easily integrated for each step on the pipeline.

Neural microarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures from the mouse brain . In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure., Measurement(s) brain measurement Technology Type(s) micro-computed tomography Factor Type(s) brain region Sample Characteristic - Organism Mus musculus Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13005527

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

Mapping all the neurons in the brain requires automatic reconstruction of entire cells from volume electron microscopy data. The flood-filling network (FFN) architecture has demonstrated leading performance for segmenting structures from this data. However, the training of the network is computationally expensive. In order to reduce the training time, we implemented synchronous and data-parallel distributed training using the Horovod library, which is different from the asynchronous training scheme used in the published FFN code. We demonstrated that our distributed training scaled well up to 2048 Intel Knights Landing (KNL) nodes on the Theta supercomputer. Our trained models achieved similar level of inference performance, but took less training time compared to previous methods. Our study on the effects of different batch sizes on FFN training suggests ways to further improve training efficiency. Our findings on optimal learning rate and batch sizes agree with previous works., Comment: 9 pages, 10 figures

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline's reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

Exponential increases in data volumes and velocities are overwhelming finite human capabilities. Continued progress in science and engineering demands that we automate a broad spectrum of currently manual research data manipulation tasks, from data transfer and sharing to acquisition, publication, and analysis. These needs are particularly evident in large-scale experimental science, in which researchers are typically granted short periods of instrument time and must maximize experiment efficiency as well as output data quality and accuracy. To address the need for automation, which is pervasive across science and engineering, we present our experiences using Trigger-Action-Programming to automate a real-world scientific workflow. We evaluate our methods by applying them to a neuroanatomy application in which a synchrotron is used to image cm-scale mouse brains with sub-micrometer resolution. In this use case, data is acquired in real-time at the synchrotron and are automatically passed through a complex automation flow that involves reconstruction using HPC resources, human-in-the-loop coordination, and finally data publication and visualization. We describe the lessons learned from these experiences and outline the design for a new research automation platform.

The penetration power of x-rays allows one to image large objects. For example, centimeter-sized specimens can be imaged with micron-level resolution using synchrotron sources. In this case, however, the limited beam diameter and detector size preclude the acquisition of the full sample in a single take, necessitating strategies for combining data from multiple regions. Object stitching involves the combination of local tomography data from overlapping regions, while projection stitching involves the collection of projections at multiple offset positions from the rotation axis followed by data merging and reconstruction. We compare these two approaches in terms of radiation dose applied to the specimen, and reconstructed image quality. Object stitching involves an easier data alignment problem, and immediate viewing of subregions before the entire dataset has been acquired. Projection stitching is more dose-efficient, and avoids certain artifacts of local tomography; however, it also involves a more difficult data assembly and alignment procedure, in that it is more sensitive to accumulative registration error., Under review

X-rays offer high penetration with the potential for tomography of centimetre-sized specimens, but synchrotron beamlines often provide illumination that is only millimetres wide. Here an approach is demonstrated termedTomosaicfor tomographic imaging of large samples that extend beyond the illumination field of view of an X-ray imaging system. This includes software modules for image stitching and calibration, while making use of existing modules available in other packages for alignment and reconstruction. The approach is compatible with conventional beamline hardware, while providing a dose-efficient method of data acquisition. By using parallelization on a distributed computing system, it provides a solution for handling teravoxel-sized or larger datasets that cannot be processed on a single workstation in a reasonable time. Using experimental data, the package is shown to provide good quality three-dimensional reconstruction for centimetre-sized samples with sub-micrometre pixel size.

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically constructing more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. We address these shortcomings with a new fast, flexible, learning-free method for sparse segmentation and reconstruction of neural volumes. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions, which are of sufficient quality for preliminary biological study. Compared to existing supervised learning methods, it is both significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.

We introduce an automated and scalable data collection, transfer, and processing pipeline for tomographic measurement of centimeter-sized mouse brains with sub-micrometer resolution. Parallelized computation is implemented in data processing to allow fast handling of tera-voxel sized datasets.

In this article, we propose a new mathematical approach for the computation of electromagnetic wave amplitudes in grazing incidence X-ray fluorescence ( gixrf )—an analytical method for surface and near-surface layer analysis. The new contribution comes from an applied point of view, in order to have stable and fast algorithms to simulate the fluorescence intensity from a stacking of thin layer films. The calculation of transmitted/reflected amplitudes is an important part of the direct and/or inverse problem. An analysis of the amplitude versus layer thickness is also given.

We present a simple apparatus for femtosecond laser induced generation of X-rays. The apparatus consists of a vacuum chamber containing an off-axis parabolic focusing mirror, a reel system, a debris protection setup, a quartz window for the incoming laser beam, and an X-ray window. Before entering the vacuum chamber, the femtosecond laser is expanded with an all reflective telescope design to minimize laser intensity losses and pulse broadening while allowing for focusing as well as peak intensity optimization. The laser pulse duration was characterized by second-harmonic generation frequency resolved optical gating. A high spatial resolution knife-edge technique was implemented to characterize the beam size at the focus of the X-ray generation apparatus. We have characterized x-ray spectra obtained with three different samples: titanium, iron:chromium alloy, and copper. In all three cases, the femtosecond laser generated X-rays give spectral lines consistent with literature reports. We present a rms amplitude analysis of the generated X-ray pulses, and provide an upper bound for the duration of the X-ray pulses., Comment: 5 pages, 4 figures

A new Transmission X-ray Microscope (TXM), optimized for in-situ nano-tomography experiments, has been designed and built at the Advanced Photon Source (APS). The instrument has been in operation for the last two years and is supporting users over large fields of Science, from energy storage and material science to natural sciences. The flexibility of our X-ray microscope design permits evolutionary geometries and can accommodate relatively heavy, up to 5 kg, and bulky in-situ cells while ensuring high spatial resolution, which is expected to improve steadily thanks to the support of the RD program led by the APS-Upgrade project on Fresnel zone plates (FZP). The robust sample stack, designed with minimum degrees of freedom shows a stability better than 4 nm rms at the sample location. The TXM operates with optics fabricated in-house. A spatial resolution of 30 nm per voxel has been demonstrated when the microscope operates with a 60 nm outermost zone width FZP with a measured efficiency of 18% at 8 keV. 20 nm FZP are also currently available and should be in routine use within the next few months once a new matching condenser is produced. In parallel, efficiency is being improved with opto-mechanical engineering (FZP stacking system) and software developments (more efficient reconstruction algorithms combined with different data acquisition schemes), enabling 3D dynamic studies when sample evolution occurs within a couple of tens of seconds.

A hybrid method of stitching X-ray computed tomography (CT) datasets is proposed and the feasibility to apply the scheme in a synchrotron tomography beamline with micrometre resolution is shown. The proposed method enables the field of view of the system to be extended while spatial resolution and experimental setup remain unchanged. The approach relies on taking full tomographic datasets at different positions in a mosaic array and registering the frames using Fourier phase correlation and a residue-based correlation. To ensure correlation correctness, the limits for the shifts are determined from the experimental motor position readouts. The masked correlation image is then minimized to obtain the correct shift. The partial datasets are blended in the sinogram space to be compatible with common CT reconstructors. The feasibility to use the algorithm to blend the partial datasets in projection space is also shown, creating a new single dataset, and standard reconstruction algorithms are used to restore high-resolution slices even with a small number of projections.

An x-ray microtomography station implemented at the X-ray Applied Crystallography Laboratory of the State University of Campinas is described. The station is based on a propagation based phase contrast imaging setup with a microfocus source and digital x-ray area detectors. Due to its simplicity, this setup is ideal for fast, high resolution imaging and microtomography of small biological specimens and materials research samples. It can also be coupled to gratings to use and develop new techniques as the harmonic spatial coherent imaging, which allow scattering contrast imaging. Details of the experimental setup, equipment, and software integration are described. Test microtomography for setup commissioning and characterization is shown. We conclude that phase contrast enhanced x-ray imaging and microtomography with resolution below 5 μm voxel size are possible and data sets as wide as 2000 × 2000 × 2000 voxels are obtained with this instrumentation.

In this work we have determined the magnetic structure of ${R}_{2}$CoGa${}_{8}$ ($R=$ Gd, Tb, and Dy) intermetallic compounds using x-ray resonant magnetic scattering in order to study the evolution of the anisotropic magnetic properties along the series for $R=$ Gd-Tm. The three compounds have a commensurate antiferromagnetic spin structure with a magnetic propagation vector $\stackrel{P\vec}{\ensuremath{\tau}}=$ $(\frac{1}{2},\frac{1}{2},\frac{1}{2})$ and a N\'eel temperature of approximately 20, 28.5, and 15.2 K for $R=$ Gd, Tb, and Dy, respectively. The critical exponent $\ensuremath{\beta}$ obtained from the temperature dependence of the magnetic peaks suggest a three-dimensional universality class for the three compounds. Comparing the simulated and integrated intensities we conclude that the magnetic moment direction is in the $ab$ plane for the Gd${}_{2}$CoGa${}_{8}$ compound and parallel to the $c$ axis for the Tb${}_{2}$CoGa${}_{8}$ and Dy${}_{2}$CoGa${}_{8}$ compounds. The evolution of the magnetic properties of the ${R}_{2}$CoGa${}_{8}$ series for $R=$ Gd-Tm is discussed taking into account the indirect Ruderman-Kittel-Kasuya-Yoshida interaction and crystalline-electric field effects. The comparison between the reported magnetic properties of the Ga-based compounds with those for the In-based isostructural family reveals differences in their exchange couplings that contribute to the understanding of the role of the $f$-electron magnetism in these classes of materials.

Num mercado altamente globalizado, o com?rcio internacional ? reconhecido como uma alternativa para o ?xito e a sobreviv?ncia das organiza??es. Para a realiza??o do servi?o log?stico internacional, surgiu o agente de carga. Seu objetivo ? o de atender ?s organiza??es que exportam ou importam produtos, cuidando de todos os detalhes existentes no processo log?stico. Desta forma, conhecer as necessidades das organiza??es ? fator primordial para o agente de carga. Na primeira etapa, de car?ter qualitativa, foram identificados atributos considerados e as vantagens quando da contrata??o de um agente de carga. Numa segunda etapa, de car?ter quantitativa, foram entrevistados 150 (cento e cinq?enta) profissionais. O objetivo dessas entrevistas foi o de quantificar a import?ncia dos 12 (doze) atributos identificados na primeira etapa. Os atributos pre?o, qualidade no servi?o, confiabilidade e agilidade apresentaram os maiores graus de import?ncia. Como vantagens na utiliza??o do agente de carga, foram identificados os pre?os praticados, o canal de comunica??o, o foco direcionado, a centraliza??o, o conhecimento t?cnico, o atendimento, o contato ?nico e a seguran?a. Com a identifica??o dos atributos relacionados pelos entrevistados, a mensura??o e a ordena??o dos atributos importantes e, por fim, com a identifica??o das vantagens na contrata??o do agente de carga a pesquisa se prop?e a auxiliar o agente de carga nas suas tomadas de decis?es operacionais.