Low-pass Research Articles

Familiarity analysis and time-advance experimental study of LP-NGD RC-circuit intended to operate with Unity-normalized gain

Purpose The purpose of this paper is to develop a familiarity analysis of resistive-capacitive (RC) network active circuit operating with unfamiliar low-pass (LP) type negative group delay (NGD) behavior. The design method of NGD circuit is validated by simulation with commercial tool and experimental measurement. Design/methodology/approach The present research work methodology is structured in three main parts. The familiarity theory of RC-network LP-NGD circuit is developed. The LP-NGD circuit parameters are expressed in function of the targeted time-advance. Then, the feasibility study is based on the theory, simulation and measurement result comparisons. Findings The RC-network based LP-NGD proof of concept is validated with −1 and −0.5 ms targeted time-advances after design, simulation, test and characterized. The LP-NGD circuit unity gain prototype presents NGD cut-off frequencies of about 269 and 569 Hz for the targeted time-advances, −1 and −0.5 ms, respectively. Bi-exponential and arbitrary waveform signals were tested to verify the targeted time-advance. Research limitations/implications The performance of the unfamiliar LP-NGD topology developed in the present study is limited by the parasitic elements of constituting lumped components. Practical implications The NGD circuit enables to naturally reduce the undesired delay effect from the electronic and communication systems. The NGD circuit can be exploited to reduce the delay induced by electronic devices and system. Social implications As social impacts of the NGD circuit application, the NGD function is one of prominent solutions to improve the technology performances of future electronic device in term of communication aspect and the transportation system. Originality/value The originality of the paper concerns the theoretical approach of the RC-network parameters in function of the targeted time-advance and the input signal bandwidth. In addition, the experimental results are also particularly original.

A 1.87 µW Capacitively Coupled Chopper Instrumentation Amplifier with a 0.36 mV Output Ripple and a 1.8 GΩ Input Impedance for Biomedical Recording

Chopper and capacitively coupled techniques are employed in instrumentation amplifiers to create capacitively coupled chopper instrumentation amplifiers (CCIAs) that obtain a high noise power efficiency. However, the CCIA has some disadvantages due to the chopper technique, namely chopper ripple and a low input impedance. The amplifier can easily saturate due to the chopper ripple of the CCIA, especially in extremely low noise problems. Therefore, ripple attenuation is required when designing CCIAs. To record biomedical information, a CCIA with a low power consumption and a low noise, low output ripple, and high input impedance (Zin) is presented in this paper. By introducing a ripple attenuation loop (RAL) including the chopping offset amplifier and a low pass filter, the chopping ripple can be reduced to 0.36 mV. To increase the Zin of the CCIA up to 1.8 GΩ, an impedance boost loop (IBL) is added. By using 180 nm CMOS technology, the 0.123 mm2 CCIA consumes 1.87 µW at a supply voltage of 1 V. According to the simulation results using Cadance, the proposed CCIA architecture achieves a noise floor of 136 nV/√Hz, an input-referred noise (IRN) of 2.16 µVrms, a closed-loop gain of 40 dB, a power supply rejection ratio (PSRR) of 108.6 dB, and a common-mode rejection ratio (CMRR) of 118.7 dB. The proposed CCIA is a helpful method for monitoring neural potentials.

The Detectability of Post-Seismic Ground Displacement Using DInSAR and SBAS in Longwall Coal Mining: A Case Study in the Upper Silesian Coal Basin, Poland

The Upper Silesian coal basin (USCB) in Poland faces significant ground deformation issues resulting from mining activities conducted without backfill, which can persist for years. These activities can cause damage to surface structures and phenomena such as induced seismicity. Ground deformations can be monitored using differential synthetic aperture radar interferometry (DInSAR). However, various DInSAR approaches have their own advantages and limitations, particularly regarding accuracy and atmospheric filtering. This is especially important for high-frequency displacement signals associated with seismic activity, which can be filtered out. Therefore, this study aims to assess the detectability of mining-induced seismic events using interferometric techniques, focusing on the USCB area. In this experiment, we tested two InSAR approaches: conventional DInSAR without atmospheric filtering and the small baseline subset (SBAS) approach, where the atmospheric phase screen was estimated and removed using high-pass and low-pass filtering. The results indicate that, in most cases, post-seismic ground displacement is not detectable using both methods. This suggests that mining-related seismic events typically do not cause significant post-seismic ground displacement. Out of the 17 selected seismic events, only two were clearly visible in the DInSAR estimated deformation, while for four other events, some displacement signals could neither be definitively confirmed nor negated. Conversely, only one seismic event was clearly detectable in the SBAS displacement time series, with no evidence of induced tremors found for the other events. DInSAR proved to be more effective in capturing displacement signals compared to SBAS. This could be attributed to the small magnitude of the tremors and, consequently, the small size of the seismic sources. Throughout the investigated period, all registered events had magnitudes less than 4.0. This highlights the challenge of identifying any significant influence of low-magnitude tremors on ground deformation, necessitating further investigations. Moreover, SBAS techniques tend to underestimate mining displacement rates, leading to smoothed deformation estimates, which may render post-seismic effects invisible for events with low magnitudes. However, after an in-depth analysis of the 17 seismic events in the USCB, DInSAR was found to be more effective in capturing displacement signals compared to SBAS. This indicates the need for significant caution when applying atmospheric filtering to high-frequency displacement signals.

A 0.98ppm/°C, high PSR, low noise curvature-compensated CMOS bandgap reference with resistor-less low-pass filter

A 0.98ppm/°C, high PSR, low noise curvature-compensated CMOS bandgap reference with resistor-less low-pass filter

Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET

BackgroundResidual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) — a locally adaptive image filter — allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering.MethodsAltogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ([18F]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[^{18}\ ext {F}]$$\\end{document}FDG, [18F]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[^{18}\ ext {F}]$$\\end{document}L-DOPA, [68Ga]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[^{68}\ ext {Ga}]$$\\end{document}DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the “optimal” filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs.ResultsThe automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUVmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {SUV}_\ ext {max}$$\\end{document} values of the unfiltered images with a low mean ± SD difference of δSUVmaxCNN,STD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {SUV}_\ ext {max}^{\ ext {CNN},\ ext {STD}}$$\\end{document} = (−3.9 ± 5.2)% and δSUVmaxBF,STD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {SUV}_\ ext {max}^{\ ext {BF},\ ext {STD}}$$\\end{document} = (−4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUVmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {SUV}_\ ext {max}$$\\end{document} values in the vast majority of cases with an overall average difference of δSUVmaxCNN,BF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {SUV}_\ ext {max}^{\ ext {CNN},\ ext {BF}}$$\\end{document} = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δNoiseCNN,BF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta \ ext {Noise}^{\ ext {CNN},\ ext {BF}}$$\\end{document} = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed.ConclusionsOur results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

Extension Of Spatial Filtering Technique Using Kalman Filters For Real-Time Pipe Flow Profile Measurements

Spatial filtering is used for real-time velocity measurement due to its computational efficiency. However, this method can be noisy in real measurement environments, requiring the averaging of a substantial number of measurements to achieve reliable results, which compromises temporal resolution. This study proposes enhancing the spatial filtering algorithm by implementing Kalman filtering for pipe flow profile measurements. Kalman filtering is expected to mitigate the noise issue more effectively than traditional methods like low-pass filtering while maintaining or improving the temporal resolution of measurements.

Grounded and floating memristor emulator employing ICCTA: decremental or incremental operation

ABSTRACT This article describes floating and grounded memristor emulator built with an Inverting Current Conveyor Transconductance Amplifier (ICCTA). One ICCTA, one capacitor, and three resistors are used to implement the charge controlled floating memristor emulator. In contrast, a flux controlled grounded memristor emulator consists of one ICCTA, one resistor, and one capacitor. Both presented circuits do not incorporate complex circuits such as analog multiplier circuits, passive inductors, and analog to digital converter circuits in their implementation, which is advantageous in terms of circuit realisation. The designed circuits may be operated in incremental as well as decremental configurations by appropriate setting of MOS switches. In addition, PVT (process, voltage, and temperature) analysis of the proposed memristor is also included. Furthermore, the non-ideal explanation of the proposed circuits is mathematically examined. The proposed emulators are simulated in SPICE simulator using 180 nm technology. Meminductor circuit and memristor-based low pass filter are used to validate the effectiveness of the designed circuits.

Grounded and floating memristor emulator employing ICCTA: decremental or incremental operation

ABSTRACT This article describes floating and grounded memristor emulator built with an Inverting Current Conveyor Transconductance Amplifier (ICCTA). One ICCTA, one capacitor, and three resistors are used to implement the charge controlled floating memristor emulator. In contrast, a flux controlled grounded memristor emulator consists of one ICCTA, one resistor, and one capacitor. Both presented circuits do not incorporate complex circuits such as analog multiplier circuits, passive inductors, and analog to digital converter circuits in their implementation, which is advantageous in terms of circuit realisation. The designed circuits may be operated in incremental as well as decremental configurations by appropriate setting of MOS switches. In addition, PVT (process, voltage, and temperature) analysis of the proposed memristor is also included. Furthermore, the non-ideal explanation of the proposed circuits is mathematically examined. The proposed emulators are simulated in SPICE simulator using 180 nm technology. Meminductor circuit and memristor-based low pass filter are used to validate the effectiveness of the designed circuits.

Contributions of brain regions to machine learning-based classifications of attention deficit hyperactivity disorder (ADHD) utilizing EEG signals

Objective The study presented focuses on the creation of a machine learning (ML) model that uses electrophysiological (EEG) data to identify kids with attention deficit hyperactivity disorder (ADHD) from healthy controls. The EEG signals are acquired during cognitive tasks to distinguish children with ADHD from their counterparts. Methodology The EEG data recorded in cognitive exercises was filtered using low pass Bessel filter and notch filters to remove artifacts, by the data set owners. To identify unique EEG patterns, we used many well-known classifiers, including Naïve Bayes (NB), Random Forest, Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost and Linear Discriminant Analysis (LDA), to identify distinct EEG patterns. Input features comprised EEG data from nineteen channels, individually and in combination. Findings Study indicates that EEG-based categorization can differentiate between individuals with ADHD and healthy individuals with accuracy of 84%. The RF classifier achieved a maximum accuracy of 0.84 when particular region combinations were used. Evaluation of classification performance utilizing hemisphere-specific EEG data yielded promising outcomes, particularly in the right hemisphere channels. Novelty The study goes beyond traditional methodologies by investigating the effect of regional data on categorization results. The contributions of various brain regions to these classifications are being extensively researched. Understanding the role of different brain regions in ADHD can lead to better diagnosis and treatment options for individuals with ADHD. The study of categorization ability, utilizing EEG data specific to each hemisphere, particularly channels in the right hemisphere region, provides further granularity to the findings.

Contributions of brain regions to machine learning-based classifications of attention deficit hyperactivity disorder (ADHD) utilizing EEG signals

Objective The study presented focuses on the creation of a machine learning (ML) model that uses electrophysiological (EEG) data to identify kids with attention deficit hyperactivity disorder (ADHD) from healthy controls. The EEG signals are acquired during cognitive tasks to distinguish children with ADHD from their counterparts. Methodology The EEG data recorded in cognitive exercises was filtered using low pass Bessel filter and notch filters to remove artifacts, by the data set owners. To identify unique EEG patterns, we used many well-known classifiers, including Naïve Bayes (NB), Random Forest, Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost and Linear Discriminant Analysis (LDA), to identify distinct EEG patterns. Input features comprised EEG data from nineteen channels, individually and in combination. Findings Study indicates that EEG-based categorization can differentiate between individuals with ADHD and healthy individuals with accuracy of 84%. The RF classifier achieved a maximum accuracy of 0.84 when particular region combinations were used. Evaluation of classification performance utilizing hemisphere-specific EEG data yielded promising outcomes, particularly in the right hemisphere channels. Novelty The study goes beyond traditional methodologies by investigating the effect of regional data on categorization results. The contributions of various brain regions to these classifications are being extensively researched. Understanding the role of different brain regions in ADHD can lead to better diagnosis and treatment options for individuals with ADHD. The study of categorization ability, utilizing EEG data specific to each hemisphere, particularly channels in the right hemisphere region, provides further granularity to the findings.

Lean Blowout Proximity Sensing in a Low Emission Liquid-Fueled Combustor

ABSTRACT Advanced aircraft engine combustor designs, intended for achieving low NOx emissions, necessitate lean combustion with small operating margins above the LBO boundary, posing a high risk of exhibiting lean blowout (LBO) problems. Employing active LBO controllers receiving input from LBO proximity sensors can allow for narrower margins, thereby yielding multiple performance gains. Although several previous studies investigated LBO proximity sensing, they predominantly focused on premixed gas-fueled combustors. The application of proximity sensing approaches to liquid-fueled combustor designs, relevant to aircraft engines have not received adequate attention. Therefore, this study investigates LBO proximity sensing in a low emission lean direct injection (LDI) liquid-fueled combustor design developed by NASA. The combustor operates at atmospheric pressure with preheated air. The proximity sensing is examined by analyzing both OH* and CH* chemiluminescence signals, with a focus on assessing their relative performances. The signal analysis approaches include spectral, partial extinction event (LBO precursor) detection, signal RMS, and the CH*/OH* ratio, with an emphasis on the first two methods. Both steady-state and transient operating conditions have been examined. The combustor did not exhibit large-scale flame partial extinction and re-ignition events near LBO, as commonly observed in gas-fueled combustors. However, an increase in flame unsteadiness and partial extinction events with very subtle signatures in optical signals, have been observed. Low-pass filtering has been observed to considerably enhance the precursor signatures enabling their improved detection. With a reduction in LBO margin, an increase in signal low-frequency power, RMS, and precursor event occurrence rate have been observed, which were utilized for providing LBO proximity measures. A key finding from this study is that CH* signals provide significantly superior performance compared to OH* for the proximity sensing in all the approaches, and the potential reasons are discussed.

Probe-guided endoscopic system for 5-aminolevulinic acid-based photodynamic diagnosis in cholangiocarcinoma

Background and AimThe diagnostic accuracy for cholangiocarcinoma (CCA) is inadequate, necessitating the exploration of novel diagnostic approaches. Protoporphyrin IX (Pp IX), a metabolic product of 5-aminolevulinic acid (5-ALA), emits red fluorescence upon blue light exposure. Because it accumulates selectively in cancer cells, photodynamic diagnosis using 5-ALA (5-ALA-PDD) has been integrated into clinical practice for diverse cancer types. Nevertheless, there is currently no device capable of capturing Pp IX-derived fluorescence for real-time 5-ALA-PDD within the biliary tract, largely due to challenges in device miniaturization. MethodsTo investigate the feasibility of real-time 5ALA-PDD in CCA, we developed two essential components of the cholangioscopy system: a small-diameter flexible camera and a light guide for emitting blue light. We evaluated the detectability of Pp IX fluorescence using these devices in experimental gels and animal models. ResultsOur camera and light guide were smoothly inserted into the lumen of existing cholangioscopes. Incorporating a long-pass filter at the camera tip enabled efficient detection of red fluorescence without significantly impacting white-light observation. The integration of these devices facilitated clear visualization of red fluorescence from gels containing Pp IX at concentrations of 5 μM or higher. Additionally, when observing subcutaneous human CCA tumor models in nude mice treated with 5-ALA, we successfully demonstrated distinct red fluorescence from Pp IX accumulation in tumors compared to peritumoral subcutaneous areas. ConclusionThe integration of our device combination holds promise for real-time 5-ALA-PDD in human CCA, potentially enhancing the diagnostic accuracy for this complex condition.

Garden of forking paths in ERP research - Effects of varying pre-processing and analysis steps in an N400 experiment.

This study tackles the Garden of Forking Paths, as a challenge for replicability and reproducibility of ERP studies. Here, we applied a multiverse analysis to a sample ERP N400 dataset, donated by an independent research team. We analyzed this dataset using 14 pipelines selected to showcase the full range of methodological variability found in the N400 literature using systematic review approach. The selected pipelines were compared in depth by looking into statistical test outcomes, descriptive statistics, effect size, data quality, and statistical power. In this way we provide a worked example of how analytic flexibility can impact results in research fields with high dimensionality such as ERP, when analyzed using standard null-hypothesis significance testing. Out of the methodological decisions that were varied, high-pass filter cut-off, artifact removal method, baseline duration, reference, measurement latency and locations, and amplitude measure (peak vs. mean) were all shown to affect at least some of the study outcome measures. Low-pass filtering was the only step which did not notably influence any of these measures. This study shows that even some of the seemingly minor procedural deviations can influence the conclusions of an ERP study. We demonstrate the power of multiverse analysis in both identifying the most reliable effects in a given study, and for providing insights into consequences of methodological decisions.

P-272 Cannabis negatively impacts female fertility: an in vitro and clinical investigation

Abstract Study question How does tetrahydrocannabinol (THC) impacts human oocyte maturation and female fertility? Summary answer THC alters the oocyte’s RNA profile and affects meiotic spindle formation resulting in an increased rate of aneuploidy in the oocyte and in the blastocyst. What is known already In the past decades, cannabis consumption and legalization has been increasing globally. It is the third most used substance by reproductive aged people and our group demonstrated that tetrahydrocannabinol (THC) reaches the ovarian follicle. THC is known to impact male fertility, but due to the challenges associated with investigating the female gamete in humans, there is no study about the human oocyte. Animal studies have shown that THC impacts oocyte maturation in a concentration dependent manner, but between models, the results are conflicting. Study design, size, duration For the in vitro exposure assessment, GV oocytes naïve to THC were collected from consenting patients undergoing IVF-ICSI from July 2022 to June 2024 (IRB: Veritas #16518). Per patient, oocytes were split into three groups: Control, THC1 (physiological concentration of THC, based on previous human studies) and THC2 (supra-physiological concentration, based on animal studies), and cultured for 24h in our standard clinical in vitro maturation media. Participants/materials, setting, methods Single-cell RNAseq and pathway analysis were conducted to identify key pathways altered by THC treatments (n = 86 from 24 patients). Spindle morphology was assessed using confocal microscopy. Polar body biopsy was performed and ploidy assessed using our clinical low pass whole genome sequencing pipeline. Also, we conducted a multi-year retrospective study (n = 1059 patients) by measuring the concentration of THC in their follicular fluid by mass spectrometry and collecting and comparing IVF outcomes between cases and controls. Main results and the role of chance In vitro, THC treatment marginally increased the maturation rate (46% for Ctrl (n = 103), 51% for THC1 (n = 103) and 58% for THC2 (n = 102)). Using time-lapse imaging, no difference was observed between key developmental stages of oocyte maturation including GVBD and polar body extrusion. Single-cell RNAseq revealed significant alterations to the transcriptome profile of THC treated oocytes. When treated with THC1 (physiological concentration), 89 upregulated and 227 downregulated genes were identified, this was exaggerated when treated with THC2 (supraphysiologic concentration) 402 upregulated and 62 downregulated genes were identified. Pathway analysis showed that differentially expressed genes in THC1 group were associated with immune system and synaptic transmission, while THC2 group showed genes enriched in meiotic spindle formation and apoptosis. Moreover, preliminary results demonstrated abnormal spindle configuration and misaligned chromosomes in THC-treated groups and a concurrent decrease in the number of euploid oocytes. Furthermore, retrospective analysis of clinical IVF outcomes revealed that patients whose FF was positive for THC (62 patients, 6%) also had a slight increased maturation rate (77% vs 73%, p=ns), but a significant reduction of their embryo euploid rate when compared to control patients (59% vs 70%, p < 0.01). Limitations, reasons for caution The in vitro exposure to THC utilized GV oocytes that did not mature after ovarian stimulation, so they might have a reduced capacity of becoming MII. The THC measurement was assessed only on the day of retrieval and we do not know the route, dose, or duration of THC consumption. Wider implications of the findings This is the first study investigating the impact of cannabis on human oocytes. Our data suggests that cannabis consumption may reduce the number of euploid embryos available for transfer. With cannabis legalization increasing worldwide, further investigation into its consequences is critical for clinical consultation and legalization guidelines. Trial registration number Not applicable

P-451 Improvement and validation of a cost effective PGT-A test for the detection of aneuploidy, triploidy and mosaicism

Abstract Study question This study aims to validate a PGT-A platform to simultaneously sequence and genotype embryos to detect triploidy as well as high and low frequency mosaicism. Summary answer Previda Plus platform validation establishes the capability to concurrently identify aneuploidy, triploidy and mosaicism with high and low frequencies at a significantly reduced cost. What is known already Low pass sequencing platforms can identify with great accuracy monosomy and trisomy karyotypes from embryo biopsies by comparing DNA quantities across chromosomes, however, these platforms exhibit limitations in identifying triploidy since all chromosomes exhibit uniform concentration. Triploidy detection necessitates genotyping involving the detection of highly variable SNPs to distinguish different haplotypes. Intermediate copy number may represent putative mosaicism. Recent publications show that embryos with low-level mosaicism demonstrate comparable implantation rates to euploid embryos, therefore validating cut offs to differentiate between low-level and high-level mosaics from euploid and aneuploid embryos is necessary. Study design, size, duration To validate triploidy detection, we measured allele frequencies (AF) using a high frequency SNP panel. The SNP frequency profile of a diploid exhibits a mix of AF of 100% and 50%, while a triploid exhibits AF of 100%, 66%, and 33%. To validate mosaicism, cell lines that were diploid, trisomic, or monosomic for a specific chromosome were mixed in pairs in increments of 10% (From 0%:100% line-1:line-2, to 100%:0%) with multiple iterations of each combination. Participants/materials, setting, methods Fifteen cell lines (triploid or diploid) underwent amplification using a 384 highly variable SNP panel. Pooled SNP and PGT-A products underwent processing in a single sequencing workflow. Ploidy Scores (PS) were derived from the total number of SNPs, 66% and 33%, and cutoff values were established to discriminate between diploid and triploid. For mosaicism validation, thirteen different trisomic cell lines were mixed in 10% ratio increments with 15 different monosomic cell lines. Main results and the role of chance Amplified samples exhibited distinct separation in ploidy scores between diploidy (PS of 0.91 to 1.85), and triploidy samples (PS of 0.28 to 0.61) with no overlap of boxplots. Specificity, sensitivity, and accuracy were 100%. For mosaicism validation, mixing different ratios of cell lines featuring monosomy and cell lines for a trisomy for different chromosomes resulted in ratios of monosomy: disomy for one chromosome and trisomy: disomy for the other. We assessed 32 combinations of cell lines, each one in 10% increments of each cell line from 0:100% to 100%:0%. Mixes with 40% trisomy mosaicism exhibited a 2.35 median ploidy, while 50%, 60%, 70% and 80% mixes show 2.45, 2.50, 2.60, and 2.70, respectively. Mixes with 40% monosomy mosaicism exhibited a median ploidy of 1.6, while 50%, 60%, 70% and 80% mixes showed 1.50, 1.45, 1.35, and 1.25, respectively. The literature recommends considering <40% mosaicism as euploid and 80-100% as aneuploid, therefore, ploidy scores below 2.35 are deemed euploid, ploidy scores at or above 2.35, but below 2.6 (40%-60%), are classified as low level mosaic, and ploidy scores equal to or above 2.6, but below 2.7, are classified as high mosaic. Embryos equal to or above 2.8 (80%+) are considered aneuploid. Limitations, reasons for caution This test is not designed to screen for tetraploidy (92 XXYY) and has not undergone validation for it nor haploidy. Validation procedures solely utilized cell lines. Reanalysis of the remainder of embryos deemed triploid or high-rate mosaic using this validation results are currently underway. Wider implications of the findings This Previda Plus platform combined with proprietary chemistry provides a rapid, cost effective, and a streamlined workflow for aneuploidy, triploidy, and mosaicism for use in PGT-A of blastocysts biopsies. Trial registration number not applicable

ADAPTATION OF THE FEEDBACK TRANSFER FUNCTION FOR OSICILATION BASED TESTING OF SECOND-ORDER ACTIVE RC FILTERS

This paper, explores possible solutions for the feedback transfer functions applied in active RC filters for oscillation-based testing (OBT). Three types of filter transfer functions, namely low-pass (LP), high-pass (HP) and band-pass (BP) are examined. Active filters are realized with the realistic operational amplifier model targeting the 180nm CMOS technology. To confirm the theoretical insights, three realizations of LP, HP, BP second-order filterers with proposed feedbacks are designed and simulated. One of the solutions, namely the BP filter, is further examined by inserting defects in the circuit. Based on the time domain simulations, the key parameters are extracted from the oscillating filter’s output signal. The fault dictionary has been created and appropriate classification of the defects is performed.

High-frequency and low-frequency dual-channel graph attention network

Most existing graph convolution layers use learnable or fixed weights to sum up neighbor features to aggregate neighbor information. Since the attention values are always positive, these graph convolution layers perform as low-pass filters, which may result in their poor performance on heterophilic graphs. In this paper, two graph convolutional layers are proposed, HLGAT and NGAT. NGAT is a convolution network using only negative attention values, which only make the aggregation of high-frequency information of neighbor nodes. HLGAT makes the aggregation of low-frequency and high-frequency information by two channels, respectively, and fuses two outputs by using a learnable way. On node-classification task, both NGAT and HLGAT offer significant performance improvement compared to existing methods. The results clearly show that: (1) High-frequency information of neighborhoods plays a decisive role in heterophilic graphs. (2) The aggregation of low-frequency and high-frequency information of neighbor nodes can significantly improve the performance on heterophilic graphs.

FLK: A filter with learned kinematics for real-time 3D human pose estimation

There is a growing interest in adopting 3D human pose estimation in safety-critical systems, from healthcare to Industry 5.0. Nevertheless, when applied in such settings, these neural networks may suffer from estimation inaccuracy. Besides imprecise or inconsistent annotations in the training dataset, the inaccuracy is caused by poor image quality, rare poses, dropped frames, or heavy occlusions in the scene. In addition, these scenarios often require the software results to have temporal constraints, such as real-time and zero- or low-latency, which make many of the filtering solutions proposed in the literature inapplicable. This paper proposes FLK, a Filter with Learned Kinematics, to refine 3D human motion data in real-time and at zero/low latency. The temporal core combines a Kalman filter and a low-pass filter, which learns the motion model through a recurrent neural network. The spatial core takes advantage of the biomechanical constraints of the human body to provide spatial coherency between keypoints. The combination of the cores allows the filter to adequately address different types of noise, from jittering to dropped frames. We test the filter on motion data from multiple datasets and seven 3D human pose estimation backbones, improving accuracy up to 140 mm with non-Gaussian noise and 53 mm with missing information.

A novel filtering patch antenna with high selectivity and wide out‐of‐band suppression

AbstractIn this study, a novel filtering patch antenna with high selectivity and wide out‐of‐band suppression is presented. The antenna consists of a square patch, a metal ground and a feeding line, and four radiation nulls are introduced out of the passband. By designing the stepped feeding line with metal vias, two radiation nulls are introduced out of the passband. A U‐shaped slot is etched on the ground, and a third radiation null is produced. A lowpass filter, integrated into the feeding line, results in the fourth radiation null and the harmonic suppression response. By introducing these structures, the antenna achieves satisfactory frequency selectivity and bilateral wide harmonic suppressions. The proposed design is verified by the simulation and measurement results. Measured impedance bandwidth is 15% (3.33–3.87 GHz), which can perfectly cover the 5G‐N78 band, and measured peak gain can reach 9.0 dBi. The measured edge roll‐off rates in the lower and upper sidebands are 232 and 139 dB/GHz, respectively.

Impedance-matched coplanar-waveguide metal-powder low-pass filters for cryogenic applications.

We developed impedance-matched metal-powder low-pass filters based on coplanar waveguide design and characterized them at room temperature and 77.4K. The coplanar waveguide metal-powder (CPW-MP) filters have a return loss better than 9.8 dB at frequencies up to 10GHz at 77.4K. We find that the filter attenuation per length scales linearly with frequency from 29 to 220 dB/m within the 1-5GHz frequency range at 77.4K, achieving the total attenuation above 100 dB for the 1 m-long CPW-MP filter at frequencies above 2GHz. The CPW-MP filter integrated with the multipole LC filter demonstrates the low cut-off frequency, the attenuation above 100 dB in the stop band, and the high roll-off of 590 dB per decade. The proposed CPW-MP filter is easy to fabricate, has very good thermal-shock resistance, is flexible for integration with other types of low-pass filters, delivers the same performance as classical cryogenic filters, is more compact compared to 50Ω coax cables with the same type of lossy dielectric, and provides ample opportunities for further miniaturization.