Data Acquisition Period Research Articles

Early detection of students' mental health issues from a traditional daily health observation scheme in Japanese schools and its digitalization.

The implementation of school-based mental health screening offers promise for early detection of mental health issues in children; however, various barriers hinder its widespread adoption. This study aimed to investigate the predictive value of digital data obtained from an established daily health observation scheme in Japanese schools to identify later mental health issues in children. Data for the analysis were obtained from 2,433 students enrolled in five public schools. The data acquisition period spanned 76 school days, from September 1, 2022, to December 23, 2022, and student absences were recorded during this period. Depressive and anxiety symptoms were assessed in January 2023. The students' daily physical and emotional health status was recorded as "daily health issue" scores and group-based trajectory modeling was employed to classify the long-term trends in these scores. Additionally, rolling z-scores were utilized to capture variability in daily health issue scores, with z-scores above +1 considered unusual responses. After 4 months of daily health observations, students' response trends were classified into five trajectory groups. The group experiencing the highest number of daily health issues (Group 5; 5.4% of the sample) exhibited more subsequent depressive and anxiety symptoms compared to the group with fewer issues (Group 1; 47.5%) (incident rate ratio [IRR] = 5.17; 95% confidence interval [CI]: 3.82, 6.99). Group 5 also demonstrated significantly more days of absence than Group 1 (IRR = 2.14, 95% CI: 1.19, 3.85). The average daily health issue scores for the entire period were associated with both depressive/anxiety symptoms and the number of days absent from school (IRR = 1.59, 95% CI: 1.45, 1.73; IRR = 1.18, 95% CI: 1.04, 1.35, respectively). Furthermore, a higher number of unusual responses during the entire period was also associated with more depressive/anxiety symptoms (IRR = 1.10, 95% CI: 1.07, 1.12). The current study is the first to demonstrate the predictive capability of a traditional daily health observation scheme to identify mental health issues in children. This study highlights the scheme's potential to screen and safeguard children's mental health, emphasizing the importance of digitalization and collaboration with various stakeholders.

Characterization of accurate 3D collimator-detector response function for single- and multi-lofthole collimated SPECT cameras.

Collimator-detector response function (CDRF) of a SPECT scanner refers to the image generated from a point source of activity. This research aims to characterize the CDRF of a breast-dedicated SPECT imager equipped with a lofthole collimator using GATE Monte Carlo simulation. To do so, a cylindrical multi-lofthole collimation system with lofthole apertures dedicated to breast imaging was modeled using the GATE Monte Carlo simulator. The dependency of the CDRF on the source-to-collimator distance of a single-lofthole as well as 8-lofthole collimations was assessed and then compared. In addition, the 3D-sensitivity map of the 8-lofthole collimation was derived. Finally, fair comparisons were conducted between the response of the 8-lofthole collimator and that of an 8-pinhole and also existing analytical derivations. In all cases, a data acquisition period of 5.0 min with an in-air 99mTc point source was considered. For the single-lofthole collimator, 4.5 times increasing the magnification factor leads to a 16- and twofold improvement in the sensitivity and spatial resolution, respectively. In the single-lofthole collimator, the resolution and sensitivity are degraded as the source-to-aperture distance increases. For the cylindrical 8-lofthole collimator, the findings confirm that CDRF strongly depends on source-to-aperture distance and angle of photon incidence. For a 30 mm in-plane offset point, a 25% increase in sensitivity is observed compared to that of the center of the FOV. Increasing the angle from 0 to 34 results in a 50% reduction in sensitivity. Furthermore, the findings illustrate that spatial resolution follows a quadratic function as where d is an offset along the x-, y-, and z-axis, and R0 is the spatial resolution at the center of the FOV. In conclusion, both spatial resolution and sensitivity of the lofthole collimation are considerably angle- and offset-dependent within the FOV of single- and multi-lofthole collimated SPECT imagers.

Assessing water position through distributed temperature sensing using Rayleigh-based optical frequency-domain reflectometry: a laboratory feasibility study

This study demonstrates the ability of the Rayleigh-based phase-noise compensated optical frequency-domain reflectometry (PNC-OFDR) sensing method to monitor the distributed temperature field with an ultra-short data acquisition period of 2 ms, a spatial resolution of 2 cm, and a temperature resolution of 0.1 °C. A heating cable (H-cable) was embedded within a cylindrical concrete mortar specimen and subjected to various heating powers. A sensing optical cable (T-cable) was placed adjacent to the heating cable to monitor the temperature distribution continuously. Two water-holding boxes were installed along the specimen at two positions to retain water. The results of the study indicated that the PNC-OFDR technique demonstrated a high sensitivity to even small temperature changes, enabling it to accurately pinpoint the locations of water at two distinct points. The research determined the minimal heating power required for successfully locating the water positions. The magnitude of the heating power exerted a significant impact on the temperature change. Three distinct phases of temperature increment were observed for a given heating period: rapid, fast, and gentle increase. The insights gained from this study have the potential to be applied in natural fields, allowing for the detection of groundwater and seepage phenomena in vulnerable slopes.

GPUPRSI: GPU implementation of seismic interferometry for retrieving reflection responses from passive source seismic recordings

Passive seismic exploration is an environmentally friendly, economical, and highly accessible exploration method that is widely used in different-scale subsurface imaging. Retrieving reflections from ambient noise is a newly developed passive seismic method that can be used in many fields such as mineral exploration and near-surface imaging, but the interferometry calculation is time-consuming because it requires a longer period of data acquisition to improve the signal-to-noise ratio of the retrieved reflections. In this study, we introduced a graphical processing unit (GPU)- based implementation of seismic interferometry for retrieving reflection responses from passive source seismic recordings. Because all traces are involved in the computation process, but the size of passive source data often exceeds available memory, repeated disk reads lead to a decrease in computational efficiency. We design a strategy of grouping computations followed by stacking to minimize disk input and output, simultaneously keeping the memory requirements low. Passive source data is read and written only once, and there is no requirement for the memory size to be greater than the data size. Additionally, acceleration technologies such as asynchronous execution, asynchronous memory transfer, and GPU-accelerated libraries are used. We test the efficiency using short data where the number of sampling points per trace is on the order of 30,000, and long data, where the number of sampling points per trace is on the order of 30,000,000. For short data, the average speedup is 4 compared with a multi-core central processing unit (CPU); for long data, the speedup can reach 24. The GPU-based implementation of interferometry greatly reduces the calculation time for retrieving reflections from passive source seismic recordings, providing a solution to the problem of large calculation in three-dimensional (3D) passive source reflection exploration.

Development and Application of an IoT-Based System for Soil Water Status Monitoring in a Soil Profile.

Soil water content (θ), matric potential (h) and hydraulic conductivity (K) are key parameters for hydrological and environmental processes. Several sensors have been developed for measuring soil θ-h-K relationships. The cost of such commercially available sensors may vary over several orders of magnitude. In recent years, some sensors have been designed in the framework of Internet of Things (i.e., IoT) systems to make remote real-time soil data acquisition more straightforward, enabling low-cost field-scale monitoring at high spatio-temporal scales. In this paper, we introduce a new multi-parameter sensor designed for the simultaneous estimation of θ and h at different soil depths and, due to the sensor's specific layout, the soil hydraulic conductivity function via the instantaneous profile method (IPM). Our findings indicate that a second-order polynomial function is the most suitable model (R2 = 0.99) for capturing the behavior of the capacitive-based sensor in estimating θ in the examined soil, which has a silty-loam texture. The effectiveness of low-cost capacitive sensors, coupled with the IPM method, was confirmed as a viable alternative to time domain reflectometry (TDR) probes. Notably, the layout of the sensor makes the IPM method less labor-intensive to implement. The proposed monitoring system consistently demonstrated robust performance throughout extended periods of data acquisition and is highly suitable for ongoing monitoring of soil water status.

Mental health stigma and internship nursing students’ attitudes toward seeking professional psychological help: a cross-sectional study

BackgroundSelf-stigma and negative attitudes toward mental illness have been identified as significant barriers that hinder individuals from seeking psychological assistance, leading to adverse consequences in their lives.AimThis study aimed to assess mental health stigma and internship nursing students’ attitudes toward seeking professional help.MethodsA cross-sectional design was conducted in the current study including a purposive sample of 228 participants of internship nursing students using a Socio-demographic data sheet, Self-Stigma of Seeking Help scale (SSOSH), and Attitude toward seeking professional psychological help scale (ATSPPH). The data acquisition period spanned from August to November 2022.ResultsThis study revealed significant insights into the attitudes of internship nursing students toward seeking professional psychological help. Gender emerged as a notable factor, with males exhibiting higher levels of self-stigma compared to females (mean = 2.872). Attitudes towards seeking professional help varied significantly based on age, gender, residence, and marital status. Specifically, participants aged 23 years, females, rural residents, and married individuals demonstrated the highest Mean scores reflecting attitudes that lean more positively towards seeking help. Furthermore, a significant negative correlation (r = -0.372, p < 0.01) was observed between self-stigma and attitudes toward seeking professional help.ConclusionThis study concluded that more than three-fifths of internship nursing students hadn’t stigma towards seeking professional psychological help while the vast majority had a positive attitude towards seeking professional psychological help. However, the majority of students reported that financial barriers, specifically the high cost of therapy, prevent them from seeking psychological help. This highlights the need for more affordable mental health services for this population.

Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction.

The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual's walking habit and thus increase the accuracy in diagnosis. It is observed that the TE value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling a significant reduction in the data acquisition period. The extracted feature set is then inputted into fundamental classification algorithms, with support vector machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step in a larger project aiming to identify the specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.

Removing temperature drift and temporal variation in thermal infrared images of a UAV uncooled thermal infrared imager

A thermal infrared (TIR) imager mounted on an unmanned aerial vehicle (UAV) has been widely used to obtain land surface temperature (LST) at very high-spatial resolutions. The TIR images are valuable for various applications, including mapping fine-scale surface evapotranspiration and monitoring crop water stress. However, UAV-borne uncooled TIR imagers generally suffer from temperature drift and temporal variation impact due to a long data acquisition period, significantly decreasing the initially acquired brightness temperature (BT) reliability and hindering subsequent applications. Here, a so-called DRAT (Digital number probability density function fitting and RAdiative Transfer simulation-based) method is proposed for simultaneously removing such temperature drift and temperature variation on the temporal scale. The DRAT is post-processing-based and only needs very few ground BT observations for calibration, thus significantly reducing dependence on auxiliary data. Test results show that the visual effect of the normalized TIR mosaics has been significantly improved, displaying more reasonable temperature distribution and good consistency with the land cover. The corrected BT has a root mean square error of 1.51 K and a mean bias error of 0.19 K by in situ data. The DRAT is valuable for obtaining high-accuracy LST and is feasible for other UAV uncooled TIR imagers with similar sensor structures and working principles. Therefore, it can significantly enhance the applicability of UAV TIR remote sensing.

Comparing Focused-Tracked and Plane Wave-Tracked ARFI Log(VoA) In Silico and in Application to Human Carotid Atherosclerotic Plaque, Ex Vivo.

A significant risk factor for ischemic stroke is carotid atherosclerotic plaque that is susceptible to rupture, with rupture potential conveyed by plaque morphology. Human carotid plaque composition and structure have been delineated noninvasively and in vivo by evaluating log(VoA), a parameter derived as the decadic log of the second time derivative of displacement induced by an acoustic radiation force impulse (ARFI). In prior work, ARFI-induced displacement was measured using conventional focused tracking; however, this requires a long data acquisition period, thereby reducing framerate. We herein evaluate if ARFI log(VoA) framerate can be increased without a reduction in plaque imaging performance using plane wave tracking instead. In silico, both focused- and plane wave-tracked log(VoA) decreased with increasing echobrightness, quantified as signal-to-noise ratio (SNR), but did not vary with material elasticity for SNRs below 40 dB. For SNRs of 40-60 dB, both focused- and plane wave-tracked log(VoA) varied with SNR and material elasticity. Above 60 dB SNR, both focused- and plane wave-tracked log(VoA) varied with material elasticity alone. This suggests that log(VoA) discriminates features according to a combination of their echobrightness and mechanical property. Further, while both focused- and plane-wave tracked log(VoA) values were artifactually inflated by mechanical reflections at inclusion boundaries, plane wave-tracked log(VoA) was more strongly impacted by off-axis scattering. Applied to three excised human cadaveric carotid plaques with spatially aligned histological validation, both log(VoA) methods detected regions of lipid, collagen, and calcium (CAL) deposits. These findings support that plane wave tracking performs comparably to focused tracking for log(VoA) imaging and that plane wave-tracked log(VoA) is a viable approach to discriminating clinically relevant atherosclerotic plaque features at a 30-fold higher framerate than by focused tracking.

Remote Sensing Analysis of the Surface Urban Heat Island Effect in Vitoria-Gasteiz, 1985 to 2021

Vitoria-Gasteiz has taken several urban greening actions such as the introduction of a ring of parks that connect the city’s surroundings, a sustainable mobility plan, and urban green structure strategies. Previous studies establish a connection to the importance of greening to mitigate the surface urban heat island (SUHI) and evaluate the effectiveness of these measures on urban climate. In this study, land surface temperature (LST), a remote sensing (RS) parameter, recorded by Landsat satellites (5, 7, and 8) was used to evaluate the effect of SUHI in Vitoria-Gasteiz between 1985–2021. The aim was to evaluate whether the urban greening actions influenced the local thermal conditions and, consequently, helped minimize the SUHI. Thirty sampling locations were identified, corresponding to different local climate zones (LCZ), at which LST data were extracted. A total of 218 images were processed and separated into summer and winter. Four of the 30 locations had, since 2003, on-site meteorological stations with regular air temperature (Tair) measurements which were used to validate the LST data. The results showed that Spearman’s correlation between Tair and LST was higher than 0.88 in all locations. An amount of 21 points maintained the same LCZ classification throughout the analysed period and nine underwent a LCZ transformation. The highest average temperature was identified in the city centre (urbanized area), and the lowest average was in a forest on the outskirts of the city. SUHI was more intense during the summer. A significant increase in SUHI intensity was identified in areas transformed from natural to urban LCZs. However, SUHI during satellite data acquisition periods has shown a minimal change in areas where sustainable practices have been implemented. RS was valuable for analysing the thermal behaviour of the LCZs, despite the limitation inherent in the satellite’s time of passage, in which the SUHI effect is not as evident.

Ultrafast single-molecule imaging reveals focal adhesion nano-architecture and molecular dynamics.

Using our newly developed ultrafast camera described in the companion paper, we reduced the data acquisition periods required for photoactivation/photoconversion localization microscopy (PALM, using mEos3.2) and direct stochastic reconstruction microscopy (dSTORM, using HMSiR) by a factor of ≈30 compared with standard methods, for much greater view-fields, with localization precisions of 29 and 19 nm, respectively, thus opening up previously inaccessible spatiotemporal scales to cell biology research. Simultaneous two-color PALM-dSTORM and PALM-ultrafast (10 kHz) single fluorescent-molecule imaging-tracking has been realized. They revealed the dynamic nanoorganization of the focal adhesion (FA), leading to the compartmentalized archipelago FA model, consisting of FA-protein islands with broad diversities in size (13-100 nm; mean island diameter ≈30 nm), protein copy numbers, compositions, and stoichiometries, which dot the partitioned fluid membrane (74-nm compartments in the FA vs. 109-nm compartments outside the FA). Integrins are recruited to these islands by hop diffusion. The FA-protein islands form loose ≈320 nm clusters and function as units for recruiting FA proteins.

Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses

Proteomics research has been transformed due to high-throughput liquid chromatography (LC-MS/MS) tandem mass spectrometry instruments combined with highly sophisticated automated sample preparation and multiplexing workflows. However, scaling proteomics experiments to large sample cohorts (hundreds to thousands) requires thoughtful quality control (QC) protocols. Robust QC protocols can help with reproducibility, quantitative accuracy, and provide opportunities for more decisive troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of N = 335 patient samples using tandem mass tag (TMTpro) 16-plex batches. Over the course of a 10-month data acquisition period for this cohort we collected 271 pooled QC LC-MS/MS result files obtained from MS/MS analysis of a patient-derived pooled plasma sample, representative of the entire cohort population. This sample was tagged with TMTzero or TMTpro reagents and used to inform the daily performance of the LC-MS/MS instruments and to allow within and across sample batch normalization. Analytical variability of a number of instrumental and data analysis metrics including protein and peptide identifications, peptide spectral matches (PSMs), number of obtained MS/MS spectra, average peptide abundance, percent of peptides with a Δ m/z between ±0.003 Da, percent of MS/MS spectra obtained at the maximum injection time, and the retention time of selected tracking peptides were evaluated to help inform the design of a robust LC-MS/MS QC workflow for use in future cohort studies. This study also led to general tips for using selected metrics to inform real-time troubleshooting of LC-MS/MS performance issues with daily QC checks.

Spatial resolution enhancement of the land surface thermal field imagery based on multiple regression models on multispectral data from various space systems

The methodology has been developed for enhancing the spatial resolution of the land surface thermal field satellite imagery based on the following steps: coupling images in the visible, thermal, and radar ranges into the single multispectral data product; constructing regression models of the images’ relationship; performing the linear regression of the pseudo-thermal product with enhanced spatial resolution from the visible and radar ranges data. The methodology is implemented on the Google Earth Engine open cloud platform using the Earth Engine API and the software scripts created in the JavaScript language with the processing of multispectral image collections of various space systems at specified time intervals. The possibility of practical synthesis of the pseudo-thermal image with an enhanced spatial resolution of 10 m based on the thermal image with the resolution of 100 m and the multispectral composite with the layers’ resolution of 10 m and 30 m is shown. The technology has been developed for synthesis and calibration of the land surface temperature product with enhanced spatial resolution and daily data providing rate based on the brightness temperature product in the B10 band of Landsat 8 and linear regression on the MODIS, ASTER, and Sentinel 1 products with daily to moderate data providing rates. The software in JavaScript has been developed, and technology has been implemented in the interactive web service form with open access on the Google Earth Engine Apps cloud platform. The final data product provides the satisfactory relative root mean square error of the brightness temperature recovery of not more than 6 % according to the reference cross-calibration data of the B10 Landsat 8 band in the moderate thermal field (up to 100° C). The relative root mean square errors of the synthesized data according to the reference data on high-temperature sites (fire, hot lava) up to 28 % are due to the fact that the synthesized product contains information from high-temperature spectral bands (B07-B09 from ASTER), while the reference product (B10 from Landsat 8) does not contain such information. Technology implementation examples show that cross-calibration of the synthesized product can be performed during the year from March to October according to reference thermal images of natural or artificial objects. Objects selected for calibration must have stable thermal characteristics at the time of the satellite flight during the data acquisition period. Keywords: land surface temperature, brightness temperature, space resolution of imagery, multiply linear regression, heterogeneous multispectral data coupling, data providing rate, product cross-calibration, Google Earth Engine.

A deep learning model to discern indoor from outdoor environments based on data recorded by a tri-axial digital magnetic sensor

The increased use of wearable devices (WDs) for monitoring daily-life activities has led to the development of different location-driven applications. The first fundamental distinction is between indoor and outdoor environments. The most intuitive approach is the analysis of GPS coordinates or Wi-Fi signals [1] but both solutions are power consuming. In this study, we proposed and tested the use of deep learning techniques for indoor/outdoor discrimination based on local magnetic field properties of the specific environment during free-living activities. Eight participants were recruited in four different centres (Turin, Italy; Sheffield, UK; Newcastle upon Tyne, UK; Tel Aviv, Israel) and were equipped with the INDIP system [2] (including four magneto-inertial units attached to each foot, lower back, and non-dominant wrist), a smartphone (running the Aeqora mobile application) and were monitored during 2.5-hours of daily free-living activities. Magnetometer data was used to train a deep learning model, while indoor/outdoor probability based on GPS coordinates was provided by the Aeqora app and used as a reference. For each WD, the following features were extracted: x, y, z components and norm of the magnetometer and the 10-sample moving average (0.1 s window) of the latter features as a “contextual” rating. A bi-layer long short-term memory structure with a linear layer as a tail and with a gaussian error linear unit as activation unit has been implemented [3]. To achieve a lower-bias training and a more robust model, the network has been validated by exploiting a leave one subject out validation approach. In addition, the classification is based on two different observation timeframes: windows of length equal to the magnetometer data acquisition period (0.01 s) and 1 s windows. The average accuracy of the model, across participants, in the classification of indoor/outdoor environments while using as input one WD at a time and all WDs together is reported in Table 1. Based on this preliminary analysis, the model seems suitable for discerning indoor from outdoor environments with an average accuracy score higher than 88.3% in participants spread through three countries (different morphology of the territory, culture, lifestyle, etc.). With this respect, considering a longer observation time (1 s vs. 0.01 s) has resulted in increasing of the accuracy for all conditions. Overall, the best performances were obtained by using the whole INDIP system, with a 94.1% score. However, very similar performances were obtained when only one WD is considered. The effect of the experimental setup (e.g., the number and position of the WDs) and the input of the model (e.g., the length of the time-observation window) on the performance metrics require further investigations.

SARS-CoV-2 Infection During Pregnancy.

Using data from the German CRONOS registry, we assessed the risk of a complicated course of COVID-19 in women with a SARS-CoV-2-infection during pregnancy, with particular consideration of gestational age, vaccination status, and pandemic dynamics. Data acquired in two separate periods (March 2020 to August 2021; January to June 2022) for CRONOS, a prospective, hospital-based observational study (DRKS00021208), were studied with logistic regression models. Odds ratios comparing 32 with 22 weeks of gestation were calculated for relevant COVID-19-specific events occurring within 4 weeks of a positive test result. Data from 3481 women were evaluated. The risk of all of the defined COVID-19-specific events was low among women who became ill with COVID-19 during the first trimester and rose with increasing gestational age into the early third trimester. For example, the odds ratio for hospitalization because of a COVID-19 infection, comparing 32 versus 22 weeks of gestation, was 1.4 (95% confidence interval [1.2; 1.7]). This risk was lower in the second period of data acquisition than in the first (OR 0.66; 95% CI [0.50; 0.88]), and it was even lower if the pregnant patient had been vaccinated against COVID-19 (OR 0.27; 95% CI [0.18; 0.41]). These findings can serve as a basis for counseling about prophylactic or therapeutic measures, such as the administration of monoclonal antibodies. They underscore the efficacy of vaccination for pregnant women even during the omicron phase of the pandemic.

A wireless signal-based sensing framework for robotics

In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots’ mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots’ local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of (i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and (ii) a Cramer–Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. We show that allowing robots to use their full mobility in 3D space while performing SAR results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the robots’ motion, a computable quantity for which we derive a closed form. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with a total AOA error of less than 10◦ for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 m2 environment with occlusions.

Pan-cancer proteomic map of 949 human cell lines.

SummaryThe proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk.

Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis.

Sensors with a higher sampling rate produce higher-quality data. However, for more extended periods of data acquisition, as in the continuous monitoring of patients, the handling of the generated big data becomes increasingly complicated. This study aimed to determine the validity and reliability of low-sampling-frequency accelerometer (SENS) measurements in patients with knee osteoarthritis. Data were collected simultaneously using SENS and a previously validated sensor (Xsens) during two repetitions of overground walking. The processed acceleration signals were compared with respect to different coordinate axes to determine the test–retest reliability and the agreement between the two systems in the time and frequency domains. In total, 44 participants were included. With respect to different axes, the interclass correlation coefficient for the repeatability of SENS measurements was [0.93–0.96]. The concordance correlation coefficients for the two systems’ agreement were [0.81–0.91] in the time domain and [0.43–0.99] in the frequency domain. The absolute biases estimated by the Bland–Altman method were [0.0005–0.008] in the time domain and [0–0.008] in the frequency domain. Low-sampling-frequency accelerometers can provide relatively valid data for measuring the gait accelerations in patients with knee osteoarthritis and can be used in the future for remote patient monitoring.

Multiple cancer type classification by small RNA expression profiles with plasma samples from multiple facilities.

Liquid biopsy is expected to be a promising cancer screening method because of its low invasiveness and the possibility of detecting multiple types in a single test. In the last decade, many studies on cancer detection using small RNAs in blood have been reported. To put small RNA tests into practical use as a multiple cancer type screening test, it is necessary to develop a method that can be applied to multiple facilities. We collected samples of eight cancer types and healthy controls from 20 facilities to evaluate the performance of cancer type classification. A total of 2,475 cancer samples and 496 healthy control samples were collected using a standardized protocol. After obtaining a small RNA expression profile, we constructed a classification model and evaluated its performance. First, we investigated the classification performance using samples from five single facilities. Each model showed areas under the receiver curve (AUC) ranging from 0.67 to 0.89. Second, we performed principal component analysis (PCA) to examine the characteristics of the facilities. The degree of hemolysis and the data acquisition period affected the expression profiles. Finally, we constructed the classification model by reducing the influence of these factors, and its performance had an AUC of 0.76. The results reveal that small RNA can be used for the classification of cancer types in samples from a single facility. However, interfacility biases will affect the classification of samples from multiple facilities. These findings will provide important insights to improve the performance of multiple cancer type classifications using small RNA expression profiles acquired from multiple facilities.

Efficient MAC Adaptive Protocol on Wireless Sensor Network

Wireless Sensor Networks (WSNs) have attracted a lot of attention from the research community and industry in recent years. WSNs maintenance associated with battery replacement can increase system operating costs, especially for wireless sensor networks located in hard-to-reach and dangerous places. In this study, an adaptive Medium Access Control (MAC) is proposed that can regulate the period of data acquisition and transmission. In contrast to conventional MAC, the applied adaptive MAC regulates the data transmission period based on the estimated energy use in the previous cycle. This study focuses on comparing energy efficiency between conventional and adaptive MAC. Energy usage information is retrieved directly on the sensor node. In star topology, the proposed MAC can increase the lifetime of the sensor network up to 6.67% in a star topology. In the hierarchical topology, the proposed MAC can increase network energy efficiency up to 9.17%. The resulting increase in network throughput is 17.73% for the Star network and 33.81% for the Hierarchy network. The star topology without implementing adaptive MAC has the lowest throughput of 0.188 kb/s. The highest throughput is achieved by a hierarchical topology that applies MAC with a throughput of 2.157 kb/s.