Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System.

An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.

Introducing the Pi-CON Methodology to Overcome Usability Deficits during Remote Patient Monitoring.

The adoption of telehealth has soared, and with that the acceptance of Remote Patient Monitoring (RPM) and virtual care. A review of the literature illustrates, however, that poor device usability can impact the generated data when using Patient-Generated Health Data (PGHD) devices, such as wearables or home use medical devices, when used outside a health facility. The Pi-CON methodology is introduced to overcome these challenges and guide the definition of user-friendly and intuitive devices in the future. Pi-CON stands for passive, continuous, and non-contact, and describes the ability to acquire health data, such as vital signs, continuously and passively with limited user interaction and without attaching any sensors to the patient. The paper highlights the advantages of Pi-CON by leveraging various sensors and techniques, such as radar, remote photoplethysmography, and infrared. It illustrates potential concerns and discusses future applications Pi-CON could be used for, including gait and fall monitoring by installing an omnipresent sensor based on the Pi-CON methodology. This would allow automatic data collection once a person is recognized, and could be extended with an integrated gateway so multiple cameras could be installed to enable data feeds to a cloud-based interface, allowing clinicians and family members to monitor patient health status remotely at any time.

Systematic RADaR analysis of responses to the open-ended question in the Culture of Care Barometer survey of a Dutch hospital.

OBJECTIVES: Systematically measuring the work environment of healthcare employees is key to continuously improving the quality of care and addressing staff shortages. In this study, we systematically analyse the responses to the one open-ended question posed in the Dutch version of the Culture of Care Barometer (CoCB-NL) to examine (1) if the responses offered new insights into healthcare employees' perceptions of their work environment and (2) if the original CoCB had any themes missing. DESIGN: Retrospective text analysis using Rigorous and Accelerated Data Reduction technique. SETTING: University hospital in the Netherlands using the CoCB-NL as part of the annual employee survey. PARTICIPANTS: All hospital employees were invited to participate in the study (N=14 671). In total, 2287 employees responded to the open-ended question. RESULTS: 2287 comments were analysed. Comments that contained more than one topic were split according to topic, adding to the total (n=2915). Of this total, 372 comments were excluded because they lacked content or respondents indicated they had nothing to add. Subsequently, 2543 comments were allocated to 33 themes. Most comments (n=2113) addressed the 24 themes related to the close-ended questions in the CoCB-NL. The themes most commented on concerned questions on 'organisational support'. The remaining 430 comments covered nine additional themes that addressed concerns about work environment factors (team connectedness, team effectiveness, corporate vision, administrative burden and performance pressure) and themes (diversity and inclusion, legal frameworks and collective bargaining, resilience and work-life balance, and personal matters). CONCLUSIONS: Analysing responses to the open-ended question in the CoCB-NL led to new insights into relevant elements of the work environment and missing themes in the COCB-NL. Moreover, the analysis revealed important themes that not only require attention from healthcare organisations to ensure adequate improvements in their employees' work environment but should also be considered to further develop the CoCB-NL.

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar.

Through-wall radar human body pose recognition technology has broad applications in both military and civilian sectors. Identifying the current pose of targets behind walls and predicting subsequent pose changes are significant challenges. Conventional methods typically utilize radar information along with machine learning algorithms such as SVM and random forests to aid in recognition. However, these approaches have limitations, particularly in complex scenarios. In response to this challenge, this paper proposes a cross-modal supervised through-wall radar human body pose recognition method. By integrating information from both cameras and radar, a cross-modal dataset was constructed, and a corresponding deep learning network architecture was designed. During training, the network effectively learned the pose features of targets obscured by walls, enabling accurate pose recognition (e.g., standing, crouching) in scenarios with unknown wall obstructions. The experimental results demonstrated the superiority of the proposed method over traditional approaches, offering an effective and innovative solution for practical through-wall radar applications. The contribution of this study lies in the integration of deep learning with cross-modal supervision, providing new perspectives for enhancing the robustness and accuracy of target pose recognition.

Highly efficient photonic radar by incorporating MDM-WDM and machine learning classifiers under adverse weather conditions.

Photonic radar, a cornerstone in the innovative applications of microwave photonics, emerges as a pivotal technology for future Intelligent Transportation Systems (ITS). Offering enhanced accuracy and reliability, it stands at the forefront of target detection and recognition across varying weather conditions. Recent advancements have concentrated on augmenting radar performance through high-speed, wide-band signal processing-a direct benefit of modern photonics' attributes such as EMI immunity, minimal transmission loss, and wide bandwidth. Our work introduces a cutting-edge photonic radar system that employs Frequency Modulated Continuous Wave (FMCW) signals, synergized with Mode Division and Wavelength Division Multiplexing (MDM-WDM). This fusion not only enhances target detection and recognition capabilities across diverse weather scenarios, including various intensities of fog and solar scintillations, but also demonstrates substantial resilience against solar noise. Furthermore, we have integrated machine learning techniques, including Decision Tree, Extremely Randomized Trees (ERT), and Random Forest classifiers, to substantially enhance target recognition accuracy. The results are telling: an accuracy of 91.51%, high sensitivity (91.47%), specificity (97.17%), and an F1 Score of 91.46%. These metrics underscore the efficacy of our approach in refining ITS radar systems, illustrating how advancements in microwave photonics can revolutionize traditional methodologies and systems.

Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications.

Millimeter-wave (mmWave) radars attain high resolution without compromising privacy while being unaffected by environmental factors such as rain, dust, and fog. This study explores the challenges of using mmWave radars for the simultaneous detection of people and small animals, a critical concern in applications like indoor wireless energy transfer systems. This work proposes innovative methodologies for enhancing detection accuracy and overcoming the inherent difficulties posed by differences in target size and volume. In particular, we explore two distinct positioning scenarios that involve up to four mmWave radars in an indoor environment to detect and track both humans and small animals. We compare the outcomes achieved through the implementation of three distinct data-fusion methods. It was shown that using a single radar without the application of a tracking algorithm resulted in a sensitivity of 46.1%. However, this sensitivity significantly increased to 97.10% upon utilizing four radars using with the optimal fusion method and tracking. This improvement highlights the effectiveness of employing multiple radars together with data fusion techniques, significantly enhancing sensitivity and reliability in target detection.

Analysis and prediction of ground deformation in Yinxi Industrial Park based on time-series InSAR technology.

Monitoring ground deformation in industrial parks is of great importance for the economic development of urban areas. However, limited research has been conducted on the deformation mechanism in industrial parks, and there is a lack of integrated monitoring and prediction models. Therefore, this study proposes a comprehensive monitoring and prediction model for industrial parks, utilizing time-series Interferometry Synthetic Aperture Radar (InSAR) technology and the Whale Optimization Algorithm-Back Propagation (WOA-BP) neural network algorithm. Taking Yinxi Industrial Park in Baiyin District as a case study, we used 68 scenes of Sentinel-1A ascending and descending orbit data from June 2018 to April 2021. The Stanford Method for Persistent Scatterers-Permanent Scatterers (StaMPS-PS) and the Small Baseline Subsets-Interferometry Synthetic Aperture Radar (SBAS-InSAR) technologies were employed to obtain the surface deformation information of the park. The deformation information obtained by the two technologies was cross-validated in terms of temporal and spatial distribution, and the vertical and east-west deformation of the park was obtained by combining the ascending and descending orbit data. The results show that the deformation feature points in the line of sight (LOS) direction obtained by the two technologies have a high consistency in spatial distribution, using the ascending orbit data as an example. Additionally, the SBAS-InSAR technology was used to obtain the east-west and vertical deformation results of the park after merging the ascending and descending orbit data for the same period. It was found that the park is mainly affected by vertical deformation, with a maximum subsidence rate of 14.67 mm/yr. The subsidence areas correspond to the deformation positions observed in field survey photos. Based on the ascending orbit deformation data, the two technologies were validated with 585 points of the same latitude and longitude, and the coefficient of determination R2 was found to be 0.82, with a root mean square error (RMSE) of 2.20 mm/a. The deformation rates were also highly consistent. Due to the 47% increase in the number of sampling points provided by the StaMPS-PS technique compared to the SBAS-InSAR technique, the former was found to be more applicable in the industrial park. Based on the ground deformation mechanism in the park, we combined the StaMPS-PS technique with the WOA-BP neural network to construct a deformation zone prediction model. We conducted predictive studies on the deformation zones of buildings and roads within the park, and the results showed that the WOA-optimized BP neural network achieved higher accuracy and lower overall error compared to the unoptimized network. Finally, we analyzed and discussed the geological conditions and inducing factors of ground deformation in the park, providing a reference for a better understanding of the deformation mechanism and early warning of disasters in the industrial park.

Ground penetrating radar and electrical resistivity tomography surveys with a subsequent intrusive investigation in search for the missing Beaumont children in Adelaide, South Australia.

Jane, Arnna, and Grant Beaumont went missing from Glenelg Beach in Adelaide, South Australia on 26 January (Australia Day) 1966. Despite multiple land and sea searches over nearly 60 years, the children have not been found. New credible eyewitness testimony led to a site of interest at the now disused New Castalloy factory in North Plympton, Adelaide. This site has a complex stratigraphy of anthropogenic fill, which made ground penetrating radar (GPR) investigations unpromising. Electrical resistivity tomography (ERT), while not commonly used in a forensic capacity, provided an alternative approach that allowed suitable depth penetration to resolve a feature of interest, which was subsequently excavated by the South Australia Police. This feature did contain organic, and animal remains but, sadly, not the grave of Jane, Arnna, and Grant Beaumont. However, this investigation highlights the potential to use ERT in a forensic capacity, as well as the limitations of using geophysical techniques for covert burial detection.

Effects of rare kidney diseases on kidney failure: a longitudinal analysis of the UK National Registry of Rare Kidney Diseases (RaDaR) cohort.

BACKGROUND: Individuals with rare kidney diseases account for 5-10% of people with chronic kidney disease, but constitute more than 25% of patients receiving kidney replacement therapy. The National Registry of Rare Kidney Diseases (RaDaR) gathers longitudinal data from patients with these conditions, which we used to study disease progression and outcomes of death and kidney failure. METHODS: People aged 0-96 years living with 28 types of rare kidney diseases were recruited from 108 UK renal care facilities. The primary outcomes were cumulative incidence of mortality and kidney failure in individuals with rare kidney diseases, which were calculated and compared with that of unselected patients with chronic kidney disease. Cumulative incidence and Kaplan-Meier survival estimates were calculated for the following outcomes: median age at kidney failure; median age at death; time from start of dialysis to death; and time from diagnosis to estimated glomerular filtration rate (eGFR) thresholds, allowing calculation of time from last eGFR of 75 mL/min per 1·73 m2 or more to first eGFR of less than 30 mL/min per 1·73 m2 (the therapeutic trial window). FINDINGS: Between Jan 18, 2010, and July 25, 2022, 27 285 participants were recruited to RaDaR. Median follow-up time from diagnosis was 9·6 years (IQR 5·9-16·7). RaDaR participants had significantly higher 5-year cumulative incidence of kidney failure than 2·81 million UK patients with all-cause chronic kidney disease (28% vs 1%; p<0·0001), but better survival rates (standardised mortality ratio 0·42 [95% CI 0·32-0·52]; p<0·0001). Median age at kidney failure, median age at death, time from start of dialysis to death, time from diagnosis to eGFR thresholds, and therapeutic trial window all varied substantially between rare diseases. INTERPRETATION: Patients with rare kidney diseases differ from the general population of individuals with chronic kidney disease: they have higher 5-year rates of kidney failure but higher survival than other patients with chronic kidney disease stages 3-5, and so are over-represented in the cohort of patients requiring kidney replacement therapy. Addressing unmet therapeutic need for patients with rare kidney diseases could have a large beneficial effect on long-term kidney replacement therapy demand. FUNDING: RaDaR is funded by the Medical Research Council, Kidney Research UK, Kidney Care UK, and the Polycystic Kidney Disease Charity.

Radar-Guided Localization and Resection for Metastatic Nodal and Soft Tissue Melanoma: A Single-Institution Retrospective Study.

BACKGROUND: Radar-guided localization (RGL) offers a wire-free, nonradioactive surgical guidance method consisting of a small percutaneously-placed radar reflector and handheld probe. This study investigates the feasibility, timing, and outcomes of RGL for melanoma metastasectomy. METHODS: We retrospectively identified patients at our cancer center who underwent RGL resection of metastatic melanoma between December 2020-June 2023. Data pertaining to patients' melanoma history, management, reflector placement and retrieval, and follow-up was extracted from patient charts and analyzed using descriptive statistics. RESULTS: Twenty-three RGL cases were performed in patients with stage III-IV locoregional or oligometastatic disease, 10 of whom had reflectors placed prior to neoadjuvant therapy. Procedures included soft tissue nodule removals (8), index lymph node removals (13), and therapeutic lymph node dissections (2). Reflectors were located and retrieved intraoperatively in 96% of cases from a range of 2 to 282 days after placement; the last reflector was not able to be located during surgery via probe or intraoperative ultrasound. One retrieved reflector had migrated from the index lesion, thus overall success rate of reflector and associated index lesion removal was 21 of 23 (91%). All RGL-localized and retrieved index lesions that contained viable tumor (10) had microscopically negative margins. There were no complications attributable to reflector insertion and no unexpected complications of RGL surgery. CONCLUSION: In our practice, RGL is a safe and effective surgical localization method for soft tissue and nodal melanoma metastases. The inert nature of the reflector enables implantation prior to neoadjuvant therapy with utility in index lymph node removal.

There are a variety of tools available to localize melanoma that had spread to deep layers of the skin or lymph nodes that can guide surgeons to the cancer when the tumor cannot be felt. We evaluated a marker that reflects radar signals that has been studied in breast surgery but not in melanoma. The marker was placed in the tumor before surgery and was located during surgery using a handheld probe, guiding the surgeon to the correct location. An advantage of the radar-reflecting marker we studied is that since it is safe to stay in the body, it can be placed ahead of the use of cancer medications and can keep track of the tumor as it responds to treatment. In a review of 23 surgeries in which the radar-reflecting marker was used, there was one case where the marker migrated away from the tumor and one case where the marker was not able to be located. Monitoring or alternative definitive treatment was provided in each of these cases. Overall, we found the marker to be an effective tumor localization tool for surgeons and safe for patients. Other marker options available are unable or less suitable to be placed a long time in advance of surgery due to either technical or safety reasons, so the radar-reflecting marker is especially useful when it is placed in a tumor ahead of medical treatment leading up to planned surgical treatment.

Remote Emotion Recognition Using Continuous-Wave Bio-Radar System.

The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions.

Evaluating the potential of bioacoustics in avian migration research by citizen science and weather radar observations.

The study of nocturnal bird migration brings observational challenges because of reduced visibility and observability of birds at night. Remote sensing tools, especially radars, have long been the preferred choice of scientists to study nocturnal migrations. A major downside of these remote sensing tools is the lack of species-level information. With technological advances in recent decades and with improved accessibility and affordability of acoustic tools, sound recordings have steeply increased in popularity. In Europe, there is no exhaustive qualitative and quantitative evaluation of the content of such acoustic databases and therefore the value for migration science and migration-related applications, such as bird collision hazard assessments, is mostly unknown. In the present work we compared migration schedules estimated from citizen science data with quantitative temporal occurrence of species in four years of acoustic recordings. Furthermore, we contrasted acoustic recordings with citizen science observations and weather radar data from one spring and one autumn season to assess the qualitative and quantitative yield of acoustic recordings for migration-related research and applications. Migration intensity estimated from weather radar data correlated best at low levels with acoustic records including all species in spring while in autumn passerine species showed stronger correlation than the entire species composition. Our findings identify a minor number of species whose call records may be eligible for applications derived from acoustics. Especially the highly vocal species Song thrush and Redwing showed relatively good correlations with radar and citizen science migration schedules. Most long-distance passerine migrants and many other migrants were not captured by acoustics and an estimated seasonal average of about 50% of nocturnally migrating passerine populations remained undetected. Overall, the ability of acoustic records to act as a proxy of overall migration dynamics is highly dependent on the migration period and species involved.

Influence of Growth, Maturation, and Sex on Maximal Power, Force, and Velocity During Overground Sprinting.

ABSTRACT: Sudlow, A, Galantine, P, Del Sordo, G, Raymond, J-J, Dalleau, G, Peyrot, N, and Duché, P. Influence of growth, maturation, and sex on maximal power, force, and velocity during overground sprinting. J Strength Cond Res 38(3): 491-500, 2024-In pediatric populations maximal anaerobic power, force, and velocity capabilities are influenced by changes in body dimensions and muscle function. The aim of this study was to investigate the influences of growth, maturation, and sex on short-term anaerobic performance. One hundred forty children pre-, mid-, and postpeak height velocity performed two 30-m sprints concurrently measured using a radar device. Maximal power (Pmax), force (F0), and velocity (v0) were calculated from sprint velocity-time data and normalized using sex-specific, multiplicative, allometric models containing body mass, fat-free mass (FFM), or height, and chronological age. Absolute values for Pmax, F0, and v0 were higher with increasing maturity (p < 0.01; d ≥ 0.96), and boys had greater outputs than girls (p < 0.01; d ≥ 1.19). When Pmax and v0 were scaled all maturity-related and sex-related differences were removed. When F0 was scaled using models excluding age, all maturity-related differences were removed except for the least mature group (p < 0.05; d ≥ 0.88) and boys maintained higher values than girls (p < 0.05; d ≥ 0.92). All maturity-related and sex-related differences were removed when F0 was scaled using models including age. Maturity-related and sex-related variance in Pmax and v0 can be entirely explained when FFM, height, and chronological age are accounted for. Regarding F0, there seems to be a threshold after which the inclusion of age is no longer necessary to account for maturity-related differences. In young prepubertal children, the inclusion of age likely accounts for deficits in neuromuscular capacities and motor skills, which body dimensions cannot account for. Practitioners should focus on eliciting neural adaptations and enhancing motor coordination in prepubertal children to improve anaerobic performance during overground sprinting.

Lightweight high-precision SAR ship detection method based on YOLOv7-LDS.

The current challenges in Synthetic Aperture Radar (SAR) ship detection tasks revolve around handling significant variations in target sizes and managing high computational expenses, which hinder practical deployment on satellite or mobile airborne platforms. In response to these challenges, this research presents YOLOv7-LDS, a lightweight yet highly accurate SAR ship detection model built upon the YOLOv7 framework. In the core of YOLOv7-LDS's architecture, we introduce a streamlined feature extraction network that strikes a delicate balance between detection precision and computational efficiency. This network is founded on Shufflenetv2 and incorporates Squeeze-and-Excitation (SE) attention mechanisms as its key elements. Additionally, in the Neck section, we introduce the Weighted Efficient Aggregation Network (DCW-ELAN), a fundamental feature extraction module that leverages Coordinate Attention (CA) and Depthwise Convolution (DWConv). This module efficiently aggregates features while preserving the ability to identify small-scale variations, ensuring top-quality feature extraction. Furthermore, we introduce a lightweight Spatial Pyramid Dilated Convolution Cross-Stage Partial Channel (LSPHDCCSPC) module. LSPHDCCSPC is a condensed version of the Spatial Pyramid Pooling Cross-Stage Partial Channel (SPPCSPC) module, incorporating Dilated Convolution (DConv) as a central component for extracting multi-scale information. The experimental results show that YOLOv7-LDS achieves a remarkable Mean Average Precision (mAP) of 99.1% and 95.8% on the SAR Ship Detection Dataset (SSDD) and the NWPU VHR-10 dataset with a parameter count (Params) of 3.4 million, a Giga Floating Point Operations Per Second (GFLOPs) of 6.1 and an Inference Time (IT) of 4.8 milliseconds. YOLOv7-LDS effectively strikes a fine balance between computational cost and detection performance, surpassing many of the current state-of-the-art object detection models. As a result, it offers a more resilient solution for maritime ship monitoring.

Potential of APSIS-InSAR for measuring surface oscillations of tropical peatlands.

Tropical peatland across Southeast Asia is drained extensively for production of pulpwood, palm oil and other food crops. Associated increases in peat decomposition have led to widespread subsidence, deterioration of peat condition and CO2 emissions. However, quantification of subsidence and peat condition from these processes is challenging due to the scale and inaccessibility of dense tropical peat swamp forests. The development of satellite interferometric synthetic aperture radar (InSAR) has the potential to solve this problem. The Advanced Pixel System using Intermittent Baseline Subset (APSIS, formerly ISBAS) modelling technique provides improved coverage across almost all land surfaces irrespective of ground cover, enabling derivation of a time series of tropical peatland surface oscillations across whole catchments. This study aimed to establish the extent to which APSIS-InSAR can monitor seasonal patterns of tropical peat surface oscillations at North Selangor Peat Swamp Forest, Peninsular Malaysia. Results showed that C-band SAR could penetrate the forest canopy over tropical peat swamp forests intermittently and was applicable to a range of land covers. Therefore the APSIS technique has the potential for monitoring peat surface oscillations under tropical forest canopy using regularly acquired C-band Sentinel-1 InSAR data, enabling continuous monitoring of tropical peatland surface motion at a spatial resolution of 20 m.

A simple method for estimating the coarse lateral root biomass of shrubs using ground-penetrating radar: Validation by Caragana microphylla Lam. in Inner Mongolia.

The potential increases in carbon stocks in arid regions due to recent shrub encroachment have attracted extensive interest among both ecologists and carbon policy analysts. Quantifying the shrub root biomass amount in these ecosystems is essential to understanding the ecological changes occurring. In this paper, we proposed a simple nondestructive method for estimating the coarse lateral root biomass of shrubs based on the root counts obtained from ground-penetrating radar (GPR) radargrams. Root data were gathered via field experiments using GPR with antenna center frequencies of 900 MHz and 400 MHz. Five Caragana microphylla Lam. shrubs of different sizes were selected for measuring objects, and a total of 40 GPR survey lines were established for GPR data acquisition. The soil profile wall excavation method was used to obtain the total root biomass from each radargram. A model for estimating the root biomass was built by establishing the relationship between the root biomass in each profile and the root counts interpreted from the radargrams. According to the mathematical relationship between the root diameter and root biomass, the proxy root radius was derived, which could explain the rationality of the proposed model from the biological mechanism. The established model provided high confidence in estimating the root dry biomass using the GPR data obtained at the two antenna frequencies (R2= 0.73 for 900 MHz and R2= 0.71 for 400 MHz). The leave-one-out cross-validation results showed that the model exhibits satisfactory performance. This study expands the application of geophysical methods in root research and offers a new simplified method for estimating the root biomass from GPR data under field conditions.

Metformin-Associated Lactic Acidosis-Is This on Your Radar?

BACKGROUND: Metformin is a biguanide hyperglycemic agent used to manage non-insulin-dependent diabetes mellitus. Adverse reactions include mainly mild gastrointestinal adverse effects, but severe complications, such as metformin-associated lactic acidosis (MALA) can occur. Metformin is excreted renally and, therefore, not recommended in patients with renal impairment. The reported incidence of MALA is 3 cases per 100,000 patient-years. CASE REPORT: A 79-year-old woman with a complex medical history, including end-stage renal disease on dialysis and type 2 diabetes, presented to the emergency department (ED) for altered mental status. Prior to arrival, she was found to be hypoglycemic. Her laboratory results were significant for creatinine of 6.56 mg/dL and an anion gap of 52 mmol/L. The venous blood gas revealed a venous pH of 6.857 [reference range (7.32-7.43)], pCO2 of 15.9 mm Hg (40.6-60 mm Hg), HCO3 of 2.7 mmol/L (21-30 mmol/L), lactate of 27 mmol/L (0.5-2 mmol/L), and ammonia of 233 µmol/L. The patient was dialyzed emergently in the ED; repeat laboratory test results showed blood urea nitrogen of 10 mg/dL, creatinine of 1.65 mg/dL, carbon dioxide of 26 mmol/L, and anion gap of 13 mmol/L. The repeat ammonia was 16 µmol/L. The patient's metabolic encephalopathy resolved, and she was discharged home on hospital day 3. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: MALA has a high mortality rate (36%). Laboratory markers have not been found to be a reliable predictor of mortality. Sodium bicarbonate is controversial, but a pH < 7.15 indicates consideration of its use. A pH < 7.1 and a lactate level > 20 mmol/L indicate the need for emergent hemodialysis. Prompt recognition and management in the ED with early hemodialysis can result in good patient outcomes, with a return to their baseline function despite severe laboratory findings.

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology.

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.