PLACE: An Open-Source Python Package for Laboratory Automation, Control, and Experimentation

Johnson, Jami L.; Wörden, Henrik tom; Wijk, Kasper van

doi:10.1177/2211068214553022

Cited by 16 publications

(5 citation statements)

References 22 publications

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“…The source and receiver lasers are aligned on opposite sides of the sample (Figure 2), and waves are detected using a laser vibrometer, which measures the displacement at the sample surface due to a wave perturbation (for details, see Blum et al, 2013;Simpson et al, 2019). The sample is rotated at 1°increments on a computerized stage, and data acquisition is automatized (Johnson et al, 2014). Figure 2 shows that there are two rings close to the sample edges (red and green arrows) where waveforms are acquired as a function of angle to foliation (waveform data are presented in supporting information Figure S1).…”

Section: Samples and Methodsmentioning

confidence: 99%

Seismic Anisotropy and Its Impact on Imaging the Shallow Alpine Fault: An Experimental and Modeling Perspective

et al. 2020

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The transpressional Alpine Fault in New Zealand has created a thick shear zone with associated highly anisotropic rocks. Low seismic velocity zones and high seismic reflectivity are recorded in the Alpine Fault Zone, but no study has explored the underlying physical rock parameters of the shallow crust that control these observations. Protomylonites are the volumetrically dominant lithology of the fault zone. Here we combine experimental measurements of P‐wave speeds with numerical models of elastic wave anisotropy of protomylonite samples to explore how the fault zone can be seismically imaged. Numerical models that account for the porosity‐free real samples' fabric elastic tensors from electron backscatter diffraction (EBSD) are calculated by MTEX and a finite element model (FEM), while microfractures are modeled with differential effective medium (DEM) theory. At effective pressures representative of the Alpine Fault brittle zone, experimental wave speeds are lower than those predicted by MTEX/FEM. A possible DEM model suggests that a combination of random and aligned microfractures with aspect ratios increasing with pressure can explain the experimental wave speeds for pressures <70 MPa. Such microporosity in the form of foliation‐ and mica basal plane‐parallel microfractures and grain boundaries is validated with synchrotron X‐ray microtomography and transmission electron microscopy (TEM) images. Finally, by modeling anisotropy of seismic reflection coefficients with angle of incidence, we demonstrate that the high reflectivity and low‐velocity zone (LVZ) observed at the Alpine Fault can only be explained if this microporosity is accounted for throughout the brittle fault zone, even at depths of 7–10 km.

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Section: Samples and Methodsmentioning

confidence: 99%

Seismic Anisotropy and Its Impact on Imaging the Shallow Alpine Fault: An Experimental and Modeling Perspective

et al. 2020

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“…The past two decades have seen rapid advancement in automation in the field of laboratory medicine, from high output automatic analyzers to the use of machine learning algorithms of artificial intelligence. [8][9][10][11][12][13][14][15][16][17] The scarcity of trained manpower, need for rapid turnaround time, and minimal subjective bias are some of the factors that have driven manufacturers to bring new equipment for automated analysis. This study included three types of BFs similar to studies by Rastogi et al 18 and Cho et al 19 for performance evaluation of automated BF analyzer using HF-BF parameters as discriminators, whereas others like Wu et al, 20 Wrong et al, 21 and Zimmerman et al 22 used single type of BF for analysis (►Figs.…”

Section: Discussionmentioning

confidence: 99%

High Fluorescent Cells on Automated Body Fluid Analysis as Discriminator for Malignant Cell Detection

Saini,

Sareen,

Gupta

2023

South Asian J Cancer

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The automated examination of body fluids (BF) serves as a valuable screening tool for the presence of malignant cells in such samples. Malignant cells are identified as high fluorescence cells (HFC) when analyzed using the Sysmex XN-1000 automated analyzer. This study aimed to assess the correlation between HFC cell counts generated by the automated analyzer and manual cytological examination for detecting malignant cells. Additionally, it sought to establish reliable cutoff values for malignant cells since there is a lack of literature on this subject. Conducted at the department of pathology hematology and cytology laboratory in a tertiary care hospital in India from January 2019 to May 2020, this hospital-based comparative study analyzed 120 BF samples, each subjected to cytological evaluation. The mean age of the study population was 52 years, with 70 male and 50 female patients (male-to-female ratio of 1.4:1). The samples consisted of 53 ascitic fluids (44.17%), 46 pleural fluids (38.33%), and 21 cerebrospinal fluids (CSF; 17.50%). Cytopathological examination revealed malignant cells in 50 (41.67%) of the BF samples, with 70 (58.33%) samples classified as nonmalignant. Specifically, among the ascitic fluids, 24 (48%) were malignant, while 29 (41.43%) were nonmalignant. For pleural fluids, 24 (48%) were malignant, and 22 (31.43%) were nonmalignant. In CSF, 2 (4%) samples were malignant, and 19 (27.14%) were nonmalignant. The total white blood cell counts provided by automated hematology analyzers were significantly higher in malignant samples, ranging from a minimum of 100 cells to a maximum of 60,000, with a median count of 800. Nonmalignant samples had white blood cell counts ranging from 2 to 12,000, with a median count of 100. Subgroup analysis for ascitic, pleural, and CSF samples revealed significantly higher median HFC counts in malignant samples. Receiver operating characteristic curve analysis indicated that the HF-BF parameter could effectively distinguish between benign and malignant fluids. For HF#, the area under the curve (AUC) was 0.844, with a sensitivity of 82% and specificity of 81%, while HF% had an AUC of 0.706, with sensitivity and specificity values of 72% and 72.9%, respectively. This study highlights that the HFC count in the BF mode of Sysmex XN-1000 can be a valuable tool for predicting the presence of malignant cells in serous fluids and for selecting samples for further microscopic examination. Based on this study, cutoff values of 15.70/µL for absolute HFC count and 5.05% for relative HFC count can be applied to screen BF samples for malignancy, offering good sensitivity and specificity.

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“…Signals were recorded by an Alazar Tech digital oscilloscope card at 1 MS/s and 16-bits dynamic range. The entire data acquisition system was controlled by the open-source software PLACE [33]. We have previously reported the approach described above to study apples [9], with the exception of the detection sensor head which was placed on a long rotating arm.…”

Section: Methodsmentioning

confidence: 99%

Non-contact acoustic method to measure depth-dependent elastic properties of a kiwifruit

Cobus¹,

Wijk²

2022

Preprint

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We present non-contact acoustic measurements of the mechanical properties of a golden kiwifruit. With a laser source in emission, we measure the transmitted acoustic waves using a rotating laser ultrasound detector with a fibre head. Two main propagating waves are observed; a low-frequency Rayleigh wave, and a high frequency wave which propagates through the outer flesh of the fruit. Theoretical modeling of this second wave enables estimates of the wave velocity in the two outer layers of the fruit flesh, and of several viscoelastic parameters. In particular, we find that both acoustic wave velocities, Young's modulus E, and bulk modulus K evolve with fruit age, while the high-frequency wave velocity and K also differ between outer and inner fruit layers.

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PLACE: An Open-Source Python Package for Laboratory Automation, Control, and Experimentation

Cited by 16 publications

References 22 publications

Seismic Anisotropy and Its Impact on Imaging the Shallow Alpine Fault: An Experimental and Modeling Perspective

Seismic Anisotropy and Its Impact on Imaging the Shallow Alpine Fault: An Experimental and Modeling Perspective

High Fluorescent Cells on Automated Body Fluid Analysis as Discriminator for Malignant Cell Detection

Non-contact acoustic method to measure depth-dependent elastic properties of a kiwifruit

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