Acquisition Setup Research Articles

Design and fabrication of a dual laser Raman spectrometer with a single one-dimensional CCD detector.

The combination of two spectrometers in dual-laser Raman devices without the need for moving parts represents a significant advancement. This study focuses on design and fabrication of a dual spectrometer with no moving components, allowing data to be gathered using a single detector. This instrument consists of two Czerny-Turner optical arrangements which is symmetrically merged. Dual spectrometer single detector has two light inputs, each of them, concentrating the light separately on a one linear charge-coupled device detector through two independent optical paths. In this innovative spectrometer design, no optical moving parts are used, and therefore, the wavelength displacement error in repeating the spectroscopic experiment is zero. The independent nature of the optical paths enables the optimization of each spectrometer arrangement without affecting the other. The final spectrometer has a spectral resolution of 4.6 and 6.11cm- 1 for Full Width at Half Maximum across the wavelength ranges of 532 to 708nm and 784.65 to 1100nm, respectively. Switching between the two different acquisition setups can be done seamlessly and quickly, with the ability to record approximately 2000 spectra per second. Standard neon and mercury-argon lamps' atomic radiation spectra, along with Raman scattering data from a cyclohexane standard sample, were successfully recorded using laser wavelengths of 532nm and 784.65nm.

Unsupervised clustering analysis-based characterization of spatial profiles of inaccuracy in apparent diffusion coefficient values with varying acquisition plan orientation and diffusion weighting gradient direction - A large multicenter phantom study.

This large multicenter study of 37 magnetic resonance imaging scanners aimed at characterizing, for the first time, spatial profiles of inaccuracy (namely, Δ-profiles) in apparent diffusion coefficient (ADC) values with varying acquisition plan orientation and diffusion weighting gradient direction, using a statistical approach exploiting unsupervised clustering analysis.&#xD;A diffusion-weighted imaging (DWI) protocol (b-value: 0-200-400-600-800-1000 s/mm2) with different combinations of acquisition plan orientation (axial/sagittal/coronal) and diffusion weighting gradient direction (anterior-posterior/left-right/feet-head) was acquired on a standard water phantom. For each acquisition setup, Δ-profiles along the 3 main orthogonal directions were characterized by fitting data with a second order polynomial function (ar2+br+c) . Moreover, for each Δ-profile, the maximum minus minimum of the fitting function (δmax) was calculated. &#xD;The parametersa,b,c, andδmaxshowed some significant variations between scanner systems by different manufacturers or with different static magnetic field strengths, as well as between different acquisition/estimation setups. Unsupervised clustering analysis showed two evident clusters with significantly different values of parameter a (p< 0.0001), which can be grouped by acquisition protocol/Δ-profile direction but not scanner system.&#xD;The results of ∆-profiles confirm an appreciable inter-scanner variability in ADC measurement and corroborate the importance of guarantying the reliability of ADC estimations in clinical or research studies, considering for each scanner system the specific acquisition sequence in terms of acquisition plan orientation and diffusion weighting gradient direction.

SYMPATHIQUE: image-based tracking of symptoms and monitoring of pathogenesis to decompose quantitative disease resistance in the field

BackgroundQuantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions.ResultsWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR.ConclusionsThe proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.

Nondestructive intelligent detection of total viable count in pork based on miniaturized spectral sensor

Changes in the freshness of pork due to microbial action during complex transportation and storage indicate an urgent need for in-situ, real-time monitoring techniques for chemical spoilage of meat. This study reported the use of a portable detection device based on a miniaturized visible/near-infrared spectrometer, combined with data noise reduction and machine learning methods, to predict the total viable count (TVC) in pork samples. A rapid detection device for pork TVC was designed based on the miniaturized spectrometer; a spectral preprocessing method based on the resolution of the spectrometer was proposed; the effects of different preprocessing methods on the full-wavelength modeling effect were compared; and four different feature wavelength extraction algorithms were utilized for the feature wavelengths of pork TVC. The modeling effects of different simplified models were compared. The results showed that resolution interval correction combined with standard normal variation was the most effective in full-wavelength modeling, with correlation coefficients of prediction set (RP), root mean square errors in prediction set (RMSEP), and relative percent deviation (RPD) of 0.918, 0.464 (lg CFU/g), and 2.508, respectively; interval random frog − partial least squares regression (iRF-PLSR) had the best predictive ability among all simplified models, the number of wavelengths used in the simplified model was reduced by 85.45% compared with the full-wavelength model. In contrast, the model performance was improved with RP, RMSEP, and RPD of 0.948, 0.392 (lg CFU/g) and 2.970, respectively. The combination of a rational spectral acquisition setup and a data processing methodology, the miniaturized spectrometer showed competitive results with the complex detection system in predicting meat TVC.

Comparative Analysis of Data Acquisition Setups for Fast Timing in ToF-PET Applications

Comparative Analysis of Data Acquisition Setups for Fast Timing in ToF-PET Applications

Characterization of Gamma Spectroscopy Setup, Efficiency Calibration of HPGe detector used for Radiological substances

Background: Contamination of radiological substances in environment is one of the most important factors and harmful for living beings if the activity is high enough with long half-life.‘Gamma Spectrometry Setup’ is comparatively better for detection and analysis of gamma radiations in any type of specimens. Initiating the data acquisition setup, it’s essential to calibrate the entire setup for accuracy measurement. Materials and Methods:The main purpose of the research work is to characterize the data acquisition setup using137Cs and 60Co point sources. 137Cs emits γ-rays of 661.66 keV and 60Coemits two different γ-rays; 1173.2 KeV &amp; 1332.5 KeV. Setup standardization is an essential part of any research works. Results: The HPGe detector used with the setup is also good enough for γ-energy detection. Detector efficiency calibration is another major task needed for activity calculation, using gamma energy lines detected in a sample detector efficiency can be calculated. Conclusion: Initiating every research experiment, the setup calibration should be checked for accurate results

Integrated Amphibious Distributed Acoustic Sensing for Seismic Monitoring in the Xinfengjiang Reservoir

Abstract The rapidly advancing technology of distributed acoustic sensing (DAS) has profoundly impacted the field of underwater geophysics. Our study investigates the effectiveness of DAS in underwater geological stability monitoring, with a particular focus on microseismic monitoring in the Xinfengjiang reservoir. The 6.2 km long acquisition setup, covering both land and reservoir bottom, was verified using temporary shore-based short-period seismometers to ensure reliable data acquisition in various environments. Higher background noise was observed on the land section compared with the lakebed section during the day, whereas both sections exhibited similar noise levels at night. We confirmed that the DAS system was capable of detecting distant microseismic events, some of which were previously unreported. These detections exhibited temporal and phase consistency with neighboring seismometers. Comparison of signal-to-noise ratios indicates that the lakebed section demonstrates higher sensitivity. This system delivers cost-effective performance through natural settling, negating the requirement for costly embedding methods. Moreover, the DAS system identified “comet-like” small-scale signals on the lakebed that had eluded shore-based seismometers. This exemplifies the exceptional high-density and high-resolution capabilities of DAS technology in both aquatic and terrestrial environments. This study underscores the pivotal role of the DAS technology in conducting underwater microseismic monitoring, real-time seismic monitoring, seismic mechanism research, and earthquake hazard assessment.

Rapid detection of salmon louse larvae in seawater based on machine learning

The proliferation of salmon lice is one of the most challenging problems in salmon farming, seriously affecting the welfare of both farmed and wild salmonids. The detection of salmon lice in seawater, especially in the (pre-) infectious stage, supports the accurate management of salmon lice proliferation. Methods for detecting salmon lice in seawater to date involve traditional light and fluorescence microscopy and Polymerase Chain Reaction (PCR) methods. However, these methods require sample pre-processing and are laborious and time-consuming, rendering them difficult to apply for the task of continuous, rapid salmon lice detection in large water volumes. This study aims to develop a rapid and easy-to-use method for the detection of salmon lice in seawater. A novel approach was introduced for the detection of salmon lice at different larval stages in seawater by leveraging machine learning. A setup for the rapid acquisition of images of salmon lice in seawater was developed, and a dataset comprising 8316 images encompassing salmon lice “Nauplius Live,” “Nauplius Shell” and “Copepodite” was created. Then 16 object detection models including one- and two-stage Convolutional Neural Network (CNN-) based models and Transformer-based models were trained, tested, and compared on this comprehensive dataset. RT-DETR showed the best performance among all models tested with a precision of 0.972 and a recall of 0.978. The YOLO series models, except for YOLOv3, showcased remarkable inference speeds (ranging from 152 to 416 frames per second) while maintaining high precision (ranging from 0.968 to 0.977) and recall (ranging from 0.964 to 0.970). To our knowledge, this is the first time machine learning has been applied to the task of rapidly detecting salmon louse larvae at different stages in seawater samples. The results demonstrate that the machine learning approaches can achieve rapid easy-to-use and cost-effective detection of salmon lice in seawater. In the future, this study has the potential for application in measuring salmon louse larvae numbers in aquatic environments, supporting the enhancement of the welfare of both farmed and wild salmon.

Forest 3D Radar Reflectivity Reconstruction at X-Band Using a Lidar Derived Polarimetric Coherence Tomography Basis

Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis.

Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks

Introduction. The positioning of γ ray interactions in positron emission tomography (PET) detectors is commonly made through the evaluation of the Anger logic flood histograms. machine learning techniques, leveraging features extracted from signal waveform, have demonstrated successful applications in addressing various challenges in PET instrumentation. Aim. This paper evaluates the use of artificial neural networks (NN) for γ ray interaction positioning in pixelated scintillators coupled to a multiplexed array of silicon photomultipliers (SiPM). Methods. An array of 16 Cerium doped Lutetium-based (LYSO) crystal pixels (cross-section 2 × 2 mm2) coupled to 16 SiPM (S13360-1350) were used for the experimental setup. Data from each of the 16 LYSO pixels was recorded, a total of 160000 events. The detectors were irradiated by 511 keV annihilation γ rays from a Sodium-22 (22Na) source. Another LYSO crystal was used for electronic collimation. Features extracted from the signal waveform were used to train the model. Two models were tested: i) single multiple-class neural network (mcNN), with 16 possible outputs followed by a softmax and ii) 16 binary classification neural networks (bNN), each one specialized in identifying events occurred in each position. Results. Both NN models showed a mean positioning accuracy above 85% on the evaluation dataset, although the mcNN is faster to train. Discussion The method’s accuracy is affected by the introduction of misclassified events that interacted in the neighbour’s crystals and were misclassified during the dataset acquisition. Electronic collimation reduces this effect, however results could be improved using a more complex acquisition setup, such as a light-sharing configuration. Conclusions The methods comparison showed that mcNN and bNN can surpass the Anger logic, showing the feasibility of using these models in positioning procedures of future multiplexed detector systems in a linear configuration.

Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs.

The long-term, continuous analysis of electroencephalography (EEG) signals on wearable devices to automatically detect seizures in epileptic patients is a high-potential application field for deep neural networks, and specifically for transformers, which are highly suited for end-to-end time series processing without handcrafted feature extraction. In this work, we propose a small-scale transformer detector, the EEGformer, compatible with unobtrusive acquisition setups that use only the temporal channels. EEGformer is the result of a hardware-oriented design exploration, aiming for efficient execution on tiny low-power micro-controller units (MCUs) and low latency and false alarm rate to increase patient and caregiver acceptance.Tests conducted on the CHB-MIT dataset show a 20% reduction of the onset detection latency with respect to the state-of-the-art model for temporal acquisition, with a competitive 73% seizure detection probability and 0.15 false-positive-per-hour (FP/h). Further investigations on a novel and challenging scalp EEG dataset result in the successful detection of 88% of the annotated seizure events, with 0.45 FP/h.We evaluate the deployment of the EEGformer on three commercial low-power computing platforms: the single-core Apollo4 MCU and the GAP8 and GAP9 parallel MCUs. The most efficient implementation (on GAP9) results in as low as 13.7 ms and 0.31 mJ per inference, demonstrating the feasibility of deploying the EEGformer on wearable seizure detection systems with reduced channel count and multi-day battery duration.

Combined onshore and offshore wide-scale seismic data acquisition and imaging for carbon capture and storage exploration in Havnsø, Denmark

Strong global actions for climate change include carbon capture and storage (CCS) as a feasible solution to reach carbon neutrality and raise opportunities for detailed subsurface investigations. An acquisition set-up designed for onshore-offshore zones was maximized for wide-scale high-resolution structural imaging and implemented to cover a domal structure of interest for CCS utilization close to the town of Havnsø (Denmark). The challenges of the combined acquisition and processing of land and marine multisensor data along a 42 km seismic profile are analyzed, the suggested solutions are applied, and the limitations are discussed. On the onshore side, a nodal array and a seismic landstreamer system were simultaneously used, whereas along the transition zone, a marine seismic streamer and ocean-bottom seismometers were added to record the seismic response generated by two seismic vibrator sources. The adopted sensing domains (velocity, acceleration, and pressure) were studied, and different processing steps were evaluated to enable their processing and subsequent data set merging. Results suggest, as the best approach, a separate prestack processing of the different data sets and the computation of new geometries and new surface-consistent residual static correction after their merging. The data acquired in the transition zone illuminate, for the first time, the subsurface geology of the region, delineating an expected domal closure. The final seismic section shows high continuity of the reflections with good resolution along the entire profile, identifying the main reservoir structure and the surrounding areas, which are important to ensure reservoir integrity and safe exploitation over longer time scales. Shallow and deep reflections are consistent with the stratigraphic column from a well log near the profile. The presented study shows a comprehensive workflow for processing such a multisensor data set in onshore and transition zone settings.

Vein segmentation and visualization of upper and lower extremities using convolution neural network.

The study focused on developing a reliable real-time venous localization, identification, and visualization framework based upon deep learning (DL) self-parametrized Convolution Neural Network (CNN) algorithm for segmentation of the venous map for both lower and upper limb dataset acquired under unconstrained conditions using near-infrared (NIR) imaging setup, specifically to assist vascular surgeons during venipuncture, vascular surgeries, or Chronic Venous Disease (CVD) treatments. A portable image acquisition setup has been designed to collect venous data (upper and lower extremities) from 72 subjects. A manually annotated image dataset was used to train and compare the performance of existing well-known CNN-based architectures such as ResNet and VGGNet with self-parameterized U-Net, improving automated vein segmentation and visualization. Experimental results indicated that self-parameterized U-Net performs better at segmenting the unconstrained dataset in comparison with conventional CNN feature-based learning models, with a Dice score of 0.58 and displaying 96.7 % accuracy for real-time vein visualization, making it appropriate to locate veins in real-time under unconstrained conditions. Self-parameterized U-Net for vein segmentation and visualization has the potential to reduce risks associated with traditional venipuncture or CVD treatments by outperforming conventional CNN architectures, providing vascular assistance, and improving patient care and treatment outcomes.

Deep automatic soil roughness estimation from digital images

ABSTRACT Soil roughness, defined as the irregularities of the soil surface, yields significant information about soil water storage, infiltration and overland flow and, thus, is a key factor in characterizing the quality of the terrain; it is often used as input in many synthetic general agricultural models and in particular in soil moisture estimation models. In this paper, we propose a framework that combines a specific setup for data acquisition with deep convolutional networks for actual estimation. The former relies on projecting a line red laser beam on the analysed soil surface followed by digital color image acquisition. The later, involves two convolutional models that are trained in a supervised manner to predict the soil roughness. The data set was produced in the laboratory both on synthetic and real soil samples. The labels used in the training process are the soil roughness values measured by using a pinboard. The detailed evaluation showed that the error of the automatic precision lies in the range of ground truth deviation, thus validating the proposed procedure.

Optimized signal of calcifications in wide-angle digital breast tomosynthesis: a virtual imaging trial.

Evaluate microcalcification detectability in digital breast tomosynthesis (DBT) and synthetic 2D mammography (SM) for different acquisition setups using a virtual imaging trial (VIT) approach. Medio-lateral oblique (MLO) DBT acquisitions on eight patients were performed at twice the automatic exposure controlled (AEC) dose. The noise was added to the projections to simulate a given dose trajectory. Virtual microcalcification models were added to a given projection set using an in-house VIT framework. Three setups were evaluated: (1) standard acquisition with 25 projections at AEC dose, (2) 25 projections with a convex dose distribution, and (3) sparse setup with 13 projections, every second one over the angular range. The total scan dose and angular range remained constant. DBT volume reconstruction and synthetic mammography image generation were performed using a Siemens prototype algorithm. Lesion detectability was assessed through a Jackknife-alternative free-response receiver operating characteristic (JAFROC) study with six observers. For DBT, the area under the curve (AUC) was 0.97 ± 0.01 for the standard, 0.95 ± 0.02 for the convex, and 0.89 ± 0.03 for the sparse setup. There was no significant difference between standard and convex dose distributions (p = 0.309). Sparse projections significantly reduced detectability (p = 0.001). Synthetic images had a higher AUC with the convex setup, though not significantly (p = 0.435). DBT required four times more reading time than synthetic mammography. A convex setup did not significantly improve detectability in DBT compared to the standard setup. Synthetic images exhibited a non-significant increase in detectability with the convex setup. Sparse setup significantly reduced detectability in both DBT and synthetic mammography. This virtual imaging trial study allowed the design and efficient testing of different dose distribution trajectories with real mammography images, using a dose-neutral protocol. • In DBT, a convex dose distribution did not increase the detectability of microcalcifications compared to the current standard setup but increased detectability for the SM images. • A sparse setup decreased microcalcification detectability in both DBT and SM images compared to the convex and current clinical setups. • Optimal microcalcification cluster detection in the system studied was achieved using either the standard or convex dose setting, with the default number of projections.

See Elegans: Simple-to-use, accurate, and automatic 3D detection of neural activity from densely packed neurons.

In the emerging field of whole-brain imaging at single-cell resolution, which represents one of the new frontiers to investigate the link between brain activity and behavior, the nematode Caenorhabditis elegans offers one of the most characterized models for systems neuroscience. Whole-brain recordings consist of 3D time series of volumes that need to be processed to obtain neuronal traces. Current solutions for this task are either computationally demanding or limited to specific acquisition setups. Here, we propose See Elegans, a direct programming algorithm that combines different techniques for automatic neuron segmentation and tracking without the need for the RFP channel, and we compare it with other available algorithms. While outperforming them in most cases, our solution offers a novel method to guide the identification of a subset of head neurons based on position and activity. The built-in interface allows the user to follow and manually curate each of the processing steps. See Elegans is thus a simple-to-use interface aimed at speeding up the post-processing of volumetric calcium imaging recordings while maintaining a high level of accuracy and low computational demands. (Contact: enrico.lanza@iit.it).

Influence of Sampling Rate on Reproducibility and Accuracy of Miniature Kolsky Bar Experiments

Abstract The purpose of this work is to develop and verify a method for quantitatively analyzing data collected from Kolsky bar experiments and to confirm its validity by comparing it to a finite element (FE) model. This study also aims to demonstrate the need for higher sample rate capture in miniature Kolsky bars, 3.16 mm diameter used in this work, by comparing results from two different data acquisition setups on identically sized experimental setups. We identified that the sample capture rate needed to accurately depict experimental results on small-scale systems is at least 400 kHz, which is far greater than what is typically assumed for lager systems. Finally, a statistical method for evaluating results is presented and expanded upon, which removes the dependence on the knowledge and experience of the experimentalist to interpret the data. Using this analysis technique on the two different systems examined in this study, we find upwards of 3.5 times better loading condition reproducibility and up to a 20 MPa reduction in the standard deviation of the sample stress profile, confirming the need for higher quality frequency capture rates.

Learned Regularizations for Multi‐Parameter Elastic Full Waveform Inversion Using Diffusion Models

AbstractElastic full waveform inversion (EFWI) promises to account for the Earth's elastic nature and corresponding reflectivity, which is often disregarded in the commonly used acoustic FWI. However, EFWI usually requires a more sophisticated recording apparatus (beyond the usual single‐component data). Even in the presence of multicomponent recordings, an empirical formulation that relates the elastic parameters is usually employed. Such approximations, thus, render the inverted elastic parameters (and their relationship) hostage to our assumptions. To overcome these limitations, we introduce learned regularization using diffusion models. Specifically, we first train the (unsupervised) diffusion model to understand the coupling relationship of the distribution of the elastic parameters and use the trained model in the inversion process with a negligible additional computational cost. To fully realize the effect of our regularization and to mimic a realistic scenario, the vertical component of the particle velocity is used to invert the elastic parameters. Unlike other learned (deep) regularizers, diffusion models offer a unique conditional capability that suits the nature of an FWI process (going from low to high frequency) while maintaining the problem‐agnostic feature of such regularizers. Numerical experiments, ranging from synthetic to land field data, show that our framework solves the illumination effects from an imperfect acquisition setup and provides more realistic elastic parameter ratios than the conventional EFWI. We also empirically demonstrate that, unlike traditional regularization schemes, our framework converges to better model estimates that fit the observed data better.

Induction of Paraquat-Mediated Parkinsonian Phenotype in Zebrafish.

Paraquat (PQ) is a well-known neurotoxin closely associated with neurodegenerative Parkinson's disease (PD). Zebrafish are utilized as a model for PD research because of their well-defined neuropathology and locomotor behavior. Here, we highlight protocols for inducing PD using PQ and analyzing locomotor activity in adult zebrafish. Basic Protocol 1 details the treatment of adult male zebrafish with 60 mg/kg PQ via intraperitoneal injection to induce a PD-like phenotype, followed by the steps to perform a locomotor assay. Basic Protocol 2 provides step-by-step guidance for processing the acquired videos in ToxTrac software to understand the locomotor parameters of 0.9% saline- and 60 mg/kg PQ-injected adult zebrafish. The simplicity of the treatment strategy, low-cost video acquisition setup, and free video processing make these protocols accessible without prior experience. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Development of Parkinson's disease features in adult zebrafish Basic Protocol 2: ToxTrac analysis for locomotor assay.

Fast-tracked aging assessment of polymer insulators in coastal environments exposed to industrial air pollutants

ABSTRACT The demand of electrical energy has been increasing worldwide since the start of the 21st century. The focus on energy demand, efficiency increase, and loss control is one of the major concerns. Following the approach to control the power loss specifically in underdeveloped countries in the power distribution network where polymer insulators are installed. This research aims to study the impact of industrial pollutants by adopting accelerated aging techniques on polymer insulators installed in coastal regions. An experimental chamber is designed to model the artificial effects of the environment on polymer insulators. The polymer insulator was kept in an experimental chamber for 45 days to apply the environmental effects on it. Different environmental effects are applied in the experimental chamber including temperature, humidity, pollutants, and ultraviolet light. These environmental effects are controlled through different designated sensors that are managed by a data acquisition setup. After the accelerated aging process of polymer insulators in the experimental chamber the leakage current has been measured between healthy and contaminated polymer insulators. The healthy insulator shows 52e−9 amp leakage current while the contaminated insulator shows 149e−9 amp of leakage current after the aging process. The experimental results also concluded that the environmental impact on polymer insulators in the coastal regions is significant. Therefore, to avoid power loss due to leakage current it is recommended to have periodic cleaning of the polymer insulators specifically in the coastal regions that are installed in the power distribution network. This will help to reduce the power loss due to contamination in environment.