Sampling Frequency Research Articles

Immunogenicity of Atezolizumab: Influence of Testing Method and Sampling Frequency on Reported Anti-drug Antibody Incidence Rates.

Measurement of anti-drug antibodies (ADA) to assess the incidence of ADA in a clinical trial is a critical step in immunogenicity assessment during the development of a protein therapeutic. We developed novel graphical approaches to illustrate clinical trial ADA data for the PD-L1 inhibitor atezolizumab (Tecentriq) that included a systematic analysis of the impact of the timing of ADA sampling and ADA assay drug tolerance on reported ADA incidence. We found that approaches used across the industry for ADA incidence analysis provide a limited view of immunogenicity in oncology studies, where ADA detection may be confounded by both drug dosage and patient attrition. Moreover, these approaches can miss important temporal information about the immune response. Our results demonstrated that the methodology of ADA assessment for the atezolizumab program was specifically designed to capture most ADA responses to ensure accurate reporting of ADA incidence. We further showed that the use of sparse sampling and/or ADA test methods with insufficient drug tolerance may result in a significant underreporting of ADA incidence. We conclude that the comparison of ADA incidence between different drugs can be highly misleading and that a test method with appropriate sensitivity in the presence of the drug and a clinical sampling scheme that is aligned with ADA responses to a drug is required to accurately report ADA incidence.

Tidal and residual currents in the inner shelf of East China Sea detected from underway ADCP observations

Characterizing the spatial variability of net flow transport in a tide-dominated area like the East China Sea (ECS) is key to understanding the material and heat budget as well as their impacts on local ecosystem. However, the residual circulation in the ECS was much less confirmed by observations as it can only be derived from very sparse mooring data. Here for the first time we obtain the spatial patterns of tidal and residual currents in the inner ECS shelf from ADCP underway measurements conducted in 12 cruises using a spatial least squares (SLS) fitting technique. The SLS method is validated with the traditional harmonic analysis using numerical simulation results and then implemented with the observational data. The tidal current is dominated by two semidiurnal components M2 and S2 in this area, with the major axis of tidal ellipse along northwest-southeast direction. The ADCP-derived tide parameters are in good agreement with those from the mooring data and barotropic tide model. The annual spatial pattern of residual circulation is characterized by a southward coastal current and a northward shelf current, which are identified as the East China Sea Coastal Current (ECSCC) from Changjiang River runoff and the Taiwan Warm Current (TWC) originated from Taiwan Strait and Kuroshio. Their magnitudes are comparable but of one order smaller than tides. The vertical structure of TWC shows a subsurface-intensified feature in line with previous mooring observations. The north limit of TWC is approximately at 31.5°N around the submerged valley, beyond which the northward TWC becomes almost invisible. By a series of virtual cruise experiments, we show that the observation data length is the most crucial factor controlling the SLS fitting accuracy, while the role of other factors like sampling frequency is secondary. With a fixed-length dataset, the degree of polynomial function used for SLS fitting must be properly determined to minimize the under-fitting and over-fitting error, and there is also a trade-off in the fitting accuracy between tidal and residual currents depending on the fitting strategy.

Adapting COVID-19 research infrastructure to capture influenza and respiratory syncytial virus alongside SARS-CoV-2 in UK healthcare workers winter 2022/23 and beyond: protocol for a pragmatic sub-study

Introduction During the COVID-19 pandemic, extensive research was conducted on SARS-CoV-2; however, important questions about other respiratory pathogens remain unanswered. A severe influenza season in 2022–2023 with simultaneous circulation of SARS-CoV2 and respiratory syncytial virus is anticipated. This sub-study aims to determine the incidence and impact of these respiratory viruses on healthcare workers, the symptoms they experienced, the effectiveness of both COVID-19 and influenza vaccination and the burden of these infections on the National Health Service (NHS) workforce. Methods and analysis This is a longitudinal prospective cohort sub-study, utilising the population and infrastructure of the SARS-CoV-2 Immunity & Reinfection Evaluation (SIREN) study, which focuses on hospital staff in the UK. Participants undergo fortnightly nucleic acid amplification testing on a multiplex assay including SARS-CoV-2, influenza A and B and RSV, regardless of symptoms. Questionnaires are completed every two weeks, capturing symptoms, sick days, exposures, and vaccination records. Serum samples are collected monthly or quarterly from participants associated with a SIREN site. This sub-study commenced on 28/11/22 to align with the predicted influenza season and participants’ influenza vaccine status. The SIREN Participant Involvement Panel shaped the aims and methods for the study, highlighting its acceptability. UK devolved administrations were supported to develop local protocols. Analysis plans include incidence of asymptomatic and symptomatic infection, comparisons of vaccination coverage, assessment of sick day burden, and effectiveness of seasonal influenza against infection and time off work. Data are also integrated into UKHSA nosocomial modelling. Ethics and dissemination The protocol was approved by the Berkshire Research Ethics Committee (IRAS ID 284460, REC Reference 20SC0230) on 14/11/2022. Participants were informed in advance. As the frequency and method of sampling remained the same, implied consent processes were approved by the committee. Participants returning to the study give informed consent. Regular reports to advisory groups and peer-reviewed publications are planned to disseminate findings and inform decision making. Clinical trial registration number: ISRCTN11041050; registration date: 12 January 2021. Sub study included in protocol version: v8.0, and amended in v9.0

Prevalence of HPV16 L1 protein in oral biopsies: A diagnostic study from Ecuador

This study was designed to investigate the expression of HPV16 L1-protein in biopsies of oral mucosa samples. The expression of HPV16 L1 protein was investigated in biopsies taken from oral mucosa from patients who required pathological diagnosis of oral lesions. Seventy-two samples were incubated with anti-L1 protein monoclonal antibodies and protein detection was revealed with diaminobenzidine. Expression of L1 protein was performed by a pathologist blinded for tissue diagnosis under light microscopy. Most of the lesions of oral mucosa were present in lining mucosa (75 %) and the most frequent lesion were mucocele (n = 17, 23.6 %), epithelial hyperplasia (n = 6, 8.33 %), fibroma (n = 5, 6.9 %) and inflammatory hyperplasia (n = 5, 6.9 %). L1 protein expression was observed only in five (6.9 %) samples (two squamous cell carcinomas, two epithelial hyperplasia, and one gingival hyperplasia). We concluded that L1 expression in oral biopsies presented a low frequency in oral mucosal biopsies samples.

Assessing The Potential Of PIV Data To Resolve Hidden Frequency Scales

The present work investigates the performance of an advection-based flow reconstruction model to increase the temporal resolution of PIV measurements recorded in a turbulent, planar jet. Using a semi-Lagrangian technique in combination with Rapid Distortion Theory, a modified trajectory tracking procedure is implemented. The method introduces a specification for a two-dimensional convective velocity based on the least squares minimization of the linearized advection equation, in contrast to the previously introduced one-dimensional mean velocity profile outlined in Vocke et al. (2023). The new implementation is based on local flow measurements, making it well-suited towards streamwise heterogeneity and spatially developing flows. With this, the flow at some unknown time can be estimated from the know flow measurements at the forward and backward time. Spectral analysis illustrates the model's proficiency in recovering spatiotemporal information far exceeding the Nyquist frequency, with spectral reconstruction errors of less than 5% for the most extreme case. It is demonstrated that the spectral content can be estimated at least two orders of magnitude beyond the sampling frequency of the original recording. Improved performance compared to alternative methods is demonstrated with only minor impact on computational time. This indicates the approach may be used as a tool for experimental researchers a) having no access to a high-speed PIV or b) with high-speed PIV to increase the spectral resolution even further.

Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern México

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of wind energy estimations in a desert city in Northwestern Mexico. Three time series of wind speed data corresponding to 2017, 2018, and 2019 were used for statistical modeling. These series were recorded with a sonic anemometer at a sampling frequency of 10 Hz. Analyses based on these data were performed at different stationarity periods (5, 30, 60, and 600 s). The estimation of the parameters characterizing the probability density functions (PDFs) was carried out using different methods; the statistical models were evaluated by the coefficient of determination and Nash–Sutcliffe efficiency coefficient, and their accuracy was estimated by the measured quadratic error, mean square error, mean absolute error, and mean absolute percentage error. Weibull, using the energy pattern factor method, and Gamma, using the Method of Moments, were the probability density functions that best described the statistical behavior of wind speed and were better at estimating the generated energy. We conclude that the proposed methodology will increase the confidence of both wind speed estimation and the energy supplied by small-scale wind installations.

Research on the identification method based on energy distribution of adaptive WPD for railway tunnel lining void

ABSTRACT As the main disease of railway tunnels, void disease affects the safe operation of railways. The traditional detection technology recognition void relies on the intuitive characteristics of the signal, there are still problems such as poor void edge recognition capability and low intelligence level. To solve this problem, this study used Comsol software to establish a sound-solid coupling finite element model of the tunnel, extracted the acoustic signals of voids under various conditions, analysed the acoustic signal characteristics under different conditions and proposed the acoustic sensitive frequency band of tunnel lining void. The test model of Partial tunnel lining is established by the method of layering pouring. The wavelet packet algorithm is improved by the maximum cross correlation coefficient, the optimal wavelet basis decomposition algorithm is proposed. The layer number of the wavelet packet decomposition (WPD) algorithm was calculated based on the sensitive frequency band and the energy distribution of the sub-signal is obtained. Then, the energy distribution of the sensitive frequency band is used to estimate the void boundary. The experimental results show that the acoustic frequency band of the tunnel void mainly distributes in the range of 0–10,000 Hz and the sensitive frequency band of the void concentrated around 0–3000 Hz. According to the sampling frequency and the sensitive frequency band of the void, the sub-signal energy distribution of each layer is obtained. The energy distribution of sub-signal 1 has strong void boundary recognition ability. The research results will be beneficial to the damage detection and state assessment of tunnel lining.

Machine learning analysis of pressure fluctuations in a gas-solid fluidized bed

Pressure fluctuations in a gas-solid fluidized bed involve much information of the dynamic system. To uncover the value and significance of the pressure fluctuations time series, two meaningful tasks, i.e., the fluidization regime classification and the future state prediction, are investigated using Machine Learning (ML) algorithms, including Back Propagation neural network (BP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), Radial Basis Function network (RBF), Random Forest (RF) and Support Vector Machine (SVM). Findings indicate that the six ML methods rank their performance for the classification task from high to low as: BP ∼ RF > SVM > CNN ∼ LSTM ∼ RBF. For the one-step-ahead future state prediction, the accuracy goes from high to low as: BP ∼ RBF > CNN ∼ LSTM > SVM ∼ RF, whilst for the multistep-ahead prediction the LSTM approach shows the best performance. Additionally, the sampling frequency of pressure time series has significant effects on both tasks. Overall, this study demonstrates the capability of machine learning methods for the value analysis of nonlinear time series and the better operation of gas-solid fluidization systems.

Airborne magnetic anomaly detection based on Bi-stable stochastic resonance system

In order to address the problem with low signal-to-noise ratio (SNR) detection for airborne magnetic anomaly detection (MAD), an adaptive detection method based on bi-stable stochastic resonance (SR) system is proposed. The method adopts a series structure, S-SR, effectively improving detection speed. Allowing for the difficulty in parameter selection for the SR system, this method can achieve the adaptive selection of system parameters. On this basis, the effect of different sampling frequencies in the Runge–Kutta method on the numerical model of the SR system is investigated. Finally, the simulation and experiment analysis of MAD methods are performed to draw a comparison between the S-SR, orthogonal basis functions (OBF) decomposition, and high-order crossing (HOC) methods. According to the simulation and experiment results, the S-SR method outperforms the OBF and HOC methods in the outcome and range of detection under the same conditions.

Numerical Simulation of Rock Vibration Response under Ultrasonic High-Frequency Vibration with High Confining Pressure

As deep oil and gas resources and Carbon Capture and Storage (CCS) are developed, enhancing drilling efficiency in hard rock formations has emerged as a critical technology in oil and gas extraction. The advancement of ultrasonic, high-frequency vibration rock-breaking technology significantly facilitates efficient rock crushing. When subjected to ultrasonic high-frequency vibrations, the rock’s response is a crucial issue in implementing ultrasonic vibration rock crushing technology. This study employed numerical simulation and theoretical deduction methods, utilizing a multi-physics approach that couples solid mechanics with pressure acoustics. It integrated information on common influencing parameters of ultrasonic generators and reservoir rock properties to establish model parameters, analyze simulation results, and perform theoretical deductions. The research investigated the response patterns of different-sized rock samples under high-frequency ultrasound vibration excitation across various frequencies, amplitudes, and confining pressure conditions. Through the development of a three-dimensional model and the application of principles from solid mechanics and elastoplasticity, the study derived equations that describe the resonance frequencies of rock blocks under confining pressure as functions of relevant rock parameters. The findings indicate that ultrasonic vibrations can effectively induce rock displacement. Under excitation frequency sources, the rock exhibits a natural frequency correlated with the rock sample size. When the excitation frequency approximates the natural frequency, the rock resonates. At this point, the rock’s surface displacement is maximal. The rock undergoes tensile stress, leading to stress concentration that facilitates rock damage and fragmentation. Increasing the excitation amplitude enhances rock crushing, as it amplifies the maximum surface displacement under the same frequency excitation. Confining pressure exerts an inhibitory effect on the rock’s vibration response, but it does not alter the resonance frequency of the rock sample, a fact verified by both numerical simulation and theoretical results. Based on the research findings in this paper, it can help to optimize the parameters of ultrasonic vibration rock breaking in field application to achieve the best rock-breaking effect.

Detectability of hemodynamic oscillations in cerebral cortex through functional near-infrared spectroscopy: a simulation study.

We explore the feasibility of using time-domain (TD) and continuous-wave (CW) functional near-infrared spectroscopy (fNIRS) to monitor brain hemodynamic oscillations during resting-state activity in humans, a phenomenon that is of increasing interest in the scientific and medical community and appears to be crucial to advancing the understanding of both healthy and pathological brain functioning. Our general object is to maximize fNIRS sensitivity to brain resting-state oscillations. More specifically, we aim to define comprehensive guidelines for optimizing main operational parameters in fNIRS measurements [average photon count rate, measurement length, sampling frequency, and source-detector distance (SSD)]. In addition, we compare TD and CW fNIRS performance for the detection and localization of oscillations. A series of synthetic TD and CW fNIRS signals were generated by exploiting the solution of the diffusion equation for two different geometries of the probed medium: a homogeneous medium and a bilayer medium. Known and periodical perturbations of the concentrations of oxy- and deoxy-hemoglobin were imposed in the medium, determining changes in its optical properties. The homogeneous slab model was used to determine the effect of multiple measurement parameters on fNIRS sensitivity to oscillatory phenomena, and the bilayer model was used to evaluate and compare the abilities of TD and CW fNIRS in detecting and isolating oscillations occurring at different depths. For TD fNIRS, two approaches to enhance depth-selectivity were evaluated: first, a time-windowing of the photon distribution of time-of-flight was performed, and then, the time-dependent mean partial pathlength (TMPP) method was used to retrieve the hemoglobin concentrations in the medium. In the homogeneous medium case, the sensitivity of TD and CW fNIRS to periodical perturbations of the optical properties increases proportionally with the average photon count rate, the measurement length, and the sampling frequency and approximatively with the square of the SSD. In the bilayer medium case, the time-windowing method can detect and correctly localize the presence of oscillatory components in the TD fNIRS signal, even in the presence of very low photon count rates. The TMPP method demonstrates how to correctly retrieve the periodical variation of hemoglobin at different depths from the TD fNIRS signal acquired at a single SSD. For CW fNIRS, measurements taken at typical SSDs used for short-separation channel regression show notable sensitivity to oscillations occurring in the deep layer, challenging the assumptions underlying this correction method when the focus is on analyzing oscillatory phenomena. We demonstrated that the TD fNIRS technique allows for the detection and depth-localization of periodical fluctuations of the hemoglobin concentrations within the probed medium using an acquisition at a single SSD, offering an alternative to multi-distance CW fNIRS setups. Moreover, we offered some valuable guidelines that can assist researchers in defining optimal experimental protocols for fNIRS studies.

Capturing Flow Experiences in Everyday Life: A Comparison of Recall and Momentary Measurement

In a real-life study using time-based ambulatory assessment, we investigated how to unobtrusively capture within-subject and between-subject variations in flow in everyday life. We compared two observation approaches, momentary states, and coverage, which differed in the reference of flow reports and sampling frequency. Depending on condition, participants (N = 38) answered either ten or five queries per day that referred to the current state or the last two hours (n = 1442 observations in total). We found no effect of either approach on compliance, flow intensity, or flow reports over time. However, the approaches differed with respect to flow probability reports, within-subject variability in flow reports, and perceived burden. In addition, we introduced a reduced three-item version of the Flow Short Scale. Our results indicate acceptable to good reliability as well as concurrent, convergent and discriminant validity of this scale. Based on our findings, we recommend that the choice of observation approach for capturing everyday flow depends on the outcome of interest, the targeted comparison within- or between-subjects, and the expected task variability of the participants. Limitations regarding our sampling procedure and the retrospective assessment of flow experiences are discussed.

Comparison of two isotopic hydrograph separation methods in the Hydrological Open Air Laboratory, Austria

AbstractExploring the contributions of new and old water to runoff during precipitation events in agricultural catchments is essential for understanding runoff generation, solute transport, and soil erosion. The aim of this study was to investigate the variability in the isotopic composition of precipitation and runoff in the 66 ha agricultural catchment in Austria, in the Hydrological Open Air Laboratory (HOAL), in order to compare two isotope hydrograph separation methods. The classical two‐component (IHS) and the ensemble hydrograph separation (EHS) were applied to multiple large events in May–October of 2013–2018 using δ18O and δ2H. The peak flow new water contributions obtained by IHS were compared with the average new water fraction from EHS. The average new water fraction calculated with EHS based on regular weekly sampling was close to zero, which can be explained by the large diffuse groundwater discharge into the stream between the events. When only investigating events with high temporal resolution sampling, the results suggest that EHS provided average new water fractions during peak flows (0.46 ± 0.04 for δ18O, 0.47 ± 0.03 for δ2H) that were close to the averages obtained by IHS (0.47 for δ18O, 0.50 for δ2H). New water fractions tended to be higher for larger rainfall intensities. High peak flow new water fractions could be explained by the agricultural land use and soils with low permeability promoting overland flow generation and by some of the tile drainage systems contributing to the delivery of water. In conclusion, a weekly sampling frequency was not sufficient in the HOAL but instead high‐resolution sampling during events was necessary to estimate the average new water contributions during events. While EHS may be a more robust approach compared to IHS, as it relaxes some of the assumptions of IHS, IHS can provide information on the variability of new water contributions of individual events.

РОЗРОБКА ТА ДОСЛІДЖЕННЯ АЛГОРИТМІВ ВИМІРЮВАННЯ ПАРАМЕТРІВ ЕЛЕКТРОЕНЕРГІЇ У РАЗІ ВИКОРИСТАННЯ ФІКСОВАНОЇ ЧАСТОТИ ДИСКРЕТИЗАЦІЇ СИГНАЛІВ

A study of the possibilities of measuring current values, phase shift angles and other parameters of electrical networks using these data, when sampling with a frequency not multiple of the network frequency, was carried out. In the proposed algorithm, an intermediate linear approximation is used to calculate the instantaneous values of interpolated signals at equidistant moments of time during the period of the main frequency of the network. The cases when such sampling is performed on one device with sequential or simultaneous coding of input signals and the case when sampling frequencies do not match in each measurement channel are considered. A study of the use of these methods was conducted using the developed computer model and layout of the distributed measurement system. References 32, figures 19.

Association of HLA-DQ4/5 genotype polymorphisms with celiac disease in a group of children in Southwest Iran: A case-control study.

Celiac disease (CD) has proinflammatory and pathogenic immune responses to gluten in intestinal tissue, leading to structural changes in the mucosa of the small intestine. The association of human leukocyte antigen (HLA)-DQ2 and DQ8 genotypes with CD has been previously reported. This test has a negative predictive value close to 100%, so its main purpose is to rule out the detection of CD completely or almost completely. There is limited information regarding HLA-DQ4/5 in CD. This study was conducted to determine the HLA-DQ4/5 genotypes in a group of Southwestern Iranian children with CD. We conducted a case-control study in Southwest Iran involving 100 participants, employing a nonprobabilistic sampling method. Samples were taken from participants' oral buccal mucosa at Imam Sajjad Hospital of Yasuj, Iran. Then DNA was extracted from these samples and used to determine the frequency of HLA-DQ4/5 genotypes through Sequence-Specific Primer-Polymerase Chain Reaction assay. SPSS 20 was utilized for statistical analyses. Fifty diagnosed patients with CD (high anti-tissue transglutaminase [tTG]-IgA level [upper limit of normal] with pathological findings of Marsh III) and 50 non-CD individuals (normal anti-tTG-IgA level and normal total IgA level) were enrolled in the study from August 5, 2022 to October 15, 2023. Findings showed that the DQ4a*4b allele has the highest frequency in the CD samples (78%, p < 0.01) followed by the DQ5a*5b allele (12%, p < 0.01). Additionally, there was a higher prevalence of DQ4/DQ5 in patients with CD compared to controls (odds ratio = 6.5, confidence interval = 0.84 to 69.46, p < 0.01). Furthermore, a significant association was found among HLA DQ4/5 genotype, age (>9.5) (p < 0.01), and gender (female) (p < 0.05). The observed significant differences among HLA-DQ4 and HLA-DQ5 in Iranian CD samples against controls and the high value of the relative risks showed the significant function of the studied alleles in the prevalence of CD in Iranian patients.

Research on Bioimpedance Technology Based on Real Axis Equidistant Method

Bioimpedance technology (BMI) is a non-invasive detection technology that widely applied in volume measurement, human tissue structure analysis and human body composition analysis. It uses the electrical characteristics, such as impedance, admittance and dielectric constant, and changes of biological tissues and organs to extract biomedical information about human physiological and pathological conditions. This paper analyzes the influence of the distribution of frequency points on the characteristic parameters in the measurement of bio-impedance spectrum. A new method of sampling frequency points, namely the real axis equidistant method, is proposed, and this algorithm is used in the actual test. Without changing the total number of measurement frequency points, this method can effectively improve the accuracy of feature parameter α and fc calculation by increasing the number of intermediate sampling frequency points, and improve the accuracy of human body composition estimation. At last, the impedance of human body section is measured, and the characteristic parameters of human body section bio-impedance spectrum are analyzed. The continuous bioelectrical impedance changes caused by water and sugar were observed. It was initially found that drinking water led to an increase in impedance and drinking sugar led to a decrease in impedance. It lays a theoretical foundation for further research on human body data.

Non-invasive pulse arrival time is associated with cardiac index in pediatric heart transplant patients with normal ejection fraction

Objective. Cardiac Index (CI) is a key physiologic parameter to ensure end organ perfusion in the pediatric intensive care unit (PICU). Determination of CI requires invasive cardiac measurements and is not routinely done at the PICU bedside. To date, there is no gold standard non-invasive means to determine CI. This study aims to use a novel non-invasive methodology, based on routine continuous physiologic data, called Pulse Arrival Time (PAT) as a surrogate for CI in patients with normal Ejection Fraction (EF). Approach. Electrocardiogram (ECG) and photoplethysmogram (PPG) signals were collected from beside monitors at a sampling frequency of 250 samples per second. Continuous PAT, derived from the ECG and PPG waveforms was averaged per patient. Pearson’s correlation coefficient was calculated between PAT and CI, PAT and heart rate (HR), and PAT and EF. Main Results. Twenty patients underwent right heart cardiac catheterization. The mean age of patients was 11.7 ± 5.4 years old, ranging from 11 months old to 19 years old, the median age was 13.4 years old. HR in this cohort was 93.8 ± 17.0 beats per minute. The average EF was 54.4 ± 9.6%. The average CI was 3.51 ± 0.72 l min−1 m−2, with ranging from 2.6 to 4.77 l min−1 m−2. The average PAT was 0.31 ± 0.12 s. Pearson correlation analysis showed a positive correlation between PAT and CI (0.57, p < 0.01). Pearson correlation between HR and CI, and correlation between EF and CI was 0.22 (p = 0.35) and 0.03 (p = 0.23) respectively. The correlation between PAT, when indexed by HR (i.e. PAT × HR), and CI minimally improved to 0.58 (p < 0.01). Significance. This pilot study demonstrates that PAT may serve as a valuable surrogate marker for CI at the bedside, as a non-invasive and continuous modality in the PICU. The use of PAT in clinical practice remains to be thoroughly investigated.

A Digital Backend with Pulse Detection for Radar Astronomy

In radar astronomy, the digital backend and data recording system process and store echo signals in real-time, facilitating the monitoring of near-earth objects such as space debris, satellites, and asteroids. In this paper, a radar astronomy digital backend (RADB), designed for radar astronomy observation and data recording, is proposed. With a sampling rate of 1.12 GSps, the RADB accommodates various analog intermediate frequency (IF) front-ends. In addition to continuously recording echo signals, the RADB has three pulse storage modes and employs an architecture that combines a two-stage decimation (TSD) unit and a decimated pulse detection (DPD) unit. The TSD unit reduces the sampling frequency based on the bandwidth of the signal, after shifting the IF signal to the baseband. Compared to a single-stage decimation, the proposed TSD structure effectively reduces FIR resource consumption without compromising performance. Meanwhile, the DPD unit identifies pulse echo signals and selectively enables the backend to store data only when pulses are detected. This process further reduces the burden on data transmission and storage. Furthermore, the matched filtering pulse detection method in the DPD unit enhances triggering performance, particularly under weak signal conditions. Preliminary performance evaluations in a laboratory demonstrate that the TSD unit reduces data volume by 56 times, while the DPD unit achieves a further reduction of 20 times. Concurrently, a Moon reflection experiment is also conducted at the Yunnan Kunming Electromagnetic Environment Observation and Research Station by using a 29 m antenna. Analysis and processing of stored data validate the effectiveness of the proposed design.

Observation of meteors from space with the Mini-EUSO detector on board the International Space Station

Context. Observations of meteors in the Earth’s atmosphere offer a unique tool for determining the flux of meteoroids that are too small to be detected by direct telescopic observations. Although these objects are routinely observed from ground-based facilities, such as meteor and fireball networks, space-based instruments come with notable advantages and have the potential to achieve a broad and uniform exposure. Aims. In this paper, we describe the first observations of meteor events with Mini-EUSO, a very wide field-of-view telescope launched in August 2019 from the Baikonur cosmodrome and installed on board the Russian Zvezda module of the International Space Station. Mini-EUSO can map the night-time Earth in the near-UV range (290-130 nm) with a field of view equal to 44° × 44° and a spatial resolution of about 4.7 km at an altitude of 100 km from the ground. The detector saves triggered transient phenomena with a sampling frequency of 2.5 µs and 320 µs, as well as a continuous acquisition at 40.96 ms scale that is suitable for meteor observations. Methods. We designed two dedicated and complementary trigger methods, together with an analysis pipeline able to estimate the main physical parameters of the observed population of meteors, such as the duration, horizontal speed, azimuth, and absolute magnitude. To compute the absolute flux of meteors from Mini-EUSO observations, we implemented a simulation framework able to estimate the detection efficiency as a function of the meteor magnitude and the background illumination conditions. Results. The instrument detected 24 thousand meteors within the first 40 data-taking sessions from November 2019 to August 2021, for a total observation time of approximately 6 days with a limiting absolute magnitude of +6. Our estimation of the absolute flux density of meteoroids in the range of mass between 10−5 kg to 10−1 kg was found to be comparable to other results available in the literature. Conclusions. The results of this work prove the potential for space-based observations to increase the statistics of meteor observations achievable with instruments operating on the ground. The slope of the mass distribution of meteoroids sampled with Mini-EUSO suggests a mass index of either s = 2.09 ± 0.02 or s = 2.31 ± 0.03, according to two different methodologies for the computation of the pre-atmospheric mass starting from the luminosity of each event.

Advancing hospital infection surveillance: Automated detection of hospital-onset bacteremia in a large university hospital

Background: Traditional manual surveillance of healthcare-associated infections often neglects certain hospital areas. This study investigated the potential of automated hospital-onset bacteremia (HOB) detection to enhance surveillance comprehensiveness. Our focus was on assessing baseline HOB levels across a large university hospital and demonstrating the algorithm’s ability to discern infection patterns in different hospital areas. Method: Conducted at one of the three sites of Charité university hospital in Berlin, Germany, our study employed an automated HOB detection algorithm, collaboratively developed with the PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network. HOB was defined as a positive blood culture (BC) with a recognized pathogen or two positive BC with common commensals, occurring two days post-admission or later. A retrospective data analysis spanned 2018-2022, with data extraction in January 2024. HOB rates were calculated per 1,000 patient-days and correlated with BC sampling frequency. Trends over time were examined. A comparison was made between intensive care units (ICU) and non-ICU. Result: Data from 58 wards (10 ICU, 48 non-ICU) with 262,058 in-patient admissions and 1,634,793 patient-days were included. Baseline characteristics of the hospital site are summarized in Table 1. Aggregated HOB rates over a 5-year period (Table 2) were substantially higher in ICU compared to non-ICU, applicable to all HOB, HOB with common commensals, and HOB with pathogens. A scatterplot (Figure 1) illustrates a strong correlation between BC sampling frequency and HOB rates in ICU, while Figure 2 displays HOB rates and BC sampling frequency in ICU over time. Notably, HOB rates were higher in the years 2020 and 2021, followed by a decrease in 2022, despite a continually high BC sampling rate. Discussion: The algorithm consistently detected HOB, providing insights into distinctions between ICU and non-ICU. While fewer HOB occurred in non-ICU, continuous monitoring remains crucial, given that the total number of HOB in non-ICU was higher than in ICU, and HOB in non-ICU might currently be overlooked by established surveillance activities. The automated system also demonstrated its effectiveness in identifying trends over time. Although speculative, it appears likely that the higher HOB rates in 2020 and 2021 were at least partially attributable to the COVID-19 pandemic and accompanying factors. There was a notable correlation between HOB and BC sampling frequency. However, the fact that HOB incidence in ICU decreased in 2022 despite persistently high BC sampling frequency rates, indicates the influence of other factors on HOB rates.