Use of Rapid Antigen Triple Test Nasal Swabs (COVID-VIRO ALL-IN TRIPLEX: Severe Acute Respiratory Syndrome Coronavirus 2, Respiratory Syncytial Virus, and Influenza) in Children With Respiratory Symptoms: A Real-life Prospective Study.

Background: In autumn 2022, the epidemics due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), respiratory syncytial virus (RSV), and influenza overlapped, and these diseases can present with the same symptomatology. The use of a triple antigen test (SARS-CoV-2 + influenza A/B + RSV) seems crucial for accurate viral diagnosis in the context of implementing long-acting monoclonal antibody vaccination against RSV in the upcoming RSV season. Methods: We assessed the usefulness of the triple test in real life in this prospective study performed from October 2022 to May 2023 and involving 116 pediatricians (2 emergency department pediatricians and 114 ambulatory pediatricians). Children <15 years old with flu-like illness (with fever), bronchiolitis (dyspnea ± wheezing), otitis, and croup were enrolled and sampled with a nasal triple test. Results: For 8329 children with flu-like illness (65.3%), bronchiolitis (17.9%), otitis (8.8%), and croup (6.3%), the use of the triple test led to a viral diagnosis in 47.9% of cases. The highest RSV positivity occurred in children with bronchiolitis (32.9%). The highest influenza A and B positivity (24.6% and 19.6%) occurred in children with flu-like illness. A succession of 3 epidemics (RSV and influenza A and B) occurred over time with several overlap periods. Conclusions: The triple test allowed for a viral diagnosis in half of our cases. The upcoming introduction of RSV prevention will emphasize the need for active surveillance with viral results both in ambulatory settings and hospitals. Clinical Trials Registration. NCT0441231.

An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning.

Several Diptera species are known to transmit pathogens of medical and veterinary interest. However, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. A computer vision-based system that uses Wing interferential patterns (WIPs) to identify these insects could solve this problem. This study introduces a dataset for training and evaluating a recognition system for dipteran insects of medical and veterinary importance using WIPs. The dataset includes pictures of Culicidae, Calliphoridae, Muscidae, Tabanidae, Ceratopogonidae, and Psychodidae. The dataset is complemented by previously published datasets of Glossinidae and some Culicidae members. The new dataset contains 2,399 pictures of 18 genera, with each genus documented by a variable number of species and annotated as a class. The dataset covers species variation, with some genera having up to 300 samples.

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies.

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

Species identification of phlebotomine sandflies using deep learning and wing interferential pattern (WIP).

Sandflies (Diptera; Psychodidae) are medical and veterinary vectors that transmit diverse parasitic, viral, and bacterial pathogens. Their identification has always been challenging, particularly at the specific and sub-specific levels, because it relies on examining minute and mostly internal structures. Here, to circumvent such limitations, we have evaluated the accuracy and reliability of Wing Interferential Patterns (WIPs) generated on the surface of sandfly wings in conjunction with deep learning (DL) procedures to assign specimens at various taxonomic levels. Our dataset proves that the method can accurately identify sandflies over other dipteran insects at the family, genus, subgenus, and species level with an accuracy higher than 77.0%, regardless of the taxonomic level challenged. This approach does not require inspection of internal organs to address identification, does not rely on identification keys, and can be implemented under field or near-field conditions, showing promise for sandfly pro-active and passive entomological surveys in an era of scarcity in medical entomologists.

Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest.

Hematophagous insects belonging to the Aedes genus are proven vectors of viral and filarial pathogens of medical interest. Aedes albopictus is an increasingly important vector because of its rapid worldwide expansion. In the context of global climate change and the emergence of zoonotic infectious diseases, identification tools with field application are required to strengthen efforts in the entomological survey of arthropods with medical interest. Large scales and proactive entomological surveys of Aedes mosquitoes need skilled technicians and/or costly technical equipment, further puzzled by the vast amount of named species. In this study, we developed an automatic classification system of Aedes species by taking advantage of the species-specific marker displayed by Wing Interferential Patterns. A database holding 494 photomicrographs of 24 Aedes spp. from which those documented with more than ten pictures have undergone a deep learning methodology to train a convolutional neural network and test its accuracy to classify samples at the genus, subgenus, and species taxonomic levels. We recorded an accuracy of 95% at the genus level and > 85% for two (Ochlerotatus and Stegomyia) out of three subgenera tested. Lastly, eight were accurately classified among the 10 Aedes sp. that have undergone a training process with an overall accuracy of > 70%. Altogether, these results demonstrate the potential of this methodology for Aedes species identification and will represent a tool for the future implementation of large-scale entomological surveys.

Neonatal bacterial infections: Diagnosis, bacterial epidemiology and antibiotic treatment.

Severe bacterial infections have a higher incidence in the neonatal period than at any other pediatric age. Incidence is even higher in premature babies than in term newborns, and severity is increased in the absence of early diagnosis and treatment. By contrast, clinical signs are nonspecific and sometimes trivial, and biomarkers perform poorly during the first 24 hours of infection. For decades, this has led to having too many children treated for extended periods with broad-spectrum antibiotics. Today, the challenge is to prescribe antibiotics in a targeted way, by identifying truly infected newborns. Over the last ten years, major paradigm shifts have occurred and should be taken into account, as a result of growing awareness of the ecological impact of early antibiotic therapy, notably antibiotic resistance, by choosing the narrowest spectrum antibiotic and stopping antibiotic therapy as soon as the diagnosis of infection has been reasonably ruled out. Among the biological tests, the most important are blood cultures. At least one blood culture, taken under aseptic conditions, of sufficient volume (1 to 2 mL), and using pediatric bottles must be taken as soon as the decision to treat has been made, before starting any antibiotic therapy. The bacteria responsible for early-onset bacterial neonatal infections (EBNI) have not changed significantly over recent years and remain dominated by Group B Streptococcus and Escherichia coli, which are the main targets of treatment. GBS is largely predominant in full-term infants, but the proportion of infections due to E. coli increases with prematurity.

Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species.

We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can identify Anopheles spp. with an accuracy of up to 100% for ten out of 20 species. Although, this accuracy was moderate (> 65%) or weak (50%) for three and seven species. The accuracy of the process to discriminate cryptic or sibling species is also assessed on three species belonging to the Gambiae complex. Strikingly, An. gambiae, An. arabiensis and An. coluzzii, morphologically indistinguishable species belonging to the Gambiae complex, were distinguished with 100%, 100%, and 88% accuracy respectively. Therefore, this tool would help entomological surveys of malaria vectors and vector control implementation. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.

Implementation of a Bio-Inspired Neural Architecture for Autonomous Vehicles on a Multi-FPGA Platform.

Autonomous vehicles require efficient self-localisation mechanisms and cameras are the most common sensors due to their low cost and rich input. However, the computational intensity of visual localisation varies depending on the environment and requires real-time processing and energy-efficient decision-making. FPGAs provide a solution for prototyping and estimating such energy savings. We propose a distributed solution for implementing a large bio-inspired visual localisation model. The workflow includes (1) an image processing IP that provides pixel information for each visual landmark detected in each captured image, (2) an implementation of N-LOC, a bio-inspired neural architecture, on an FPGA board and (3) a distributed version of N-LOC with evaluation on a single FPGA and a design for use on a multi-FPGA platform. Comparisons with a pure software solution demonstrate that our hardware-based IP implementation yields up to 9× lower latency and 7× higher throughput (frames/second) while maintaining energy efficiency. Our system has a power footprint as low as 2.741 W for the whole system, which is up to 5.5-6× less than what Nvidia Jetson TX2 consumes on average. Our proposed solution offers a promising approach for implementing energy-efficient visual localisation models on FPGA platforms.

The Human Activity Radar Challenge: benchmarking based on the 'Radar signatures of human activities' dataset from Glasgow University.

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.

Radar-based human activity recognition with adaptive thresholding towards resource constrained platforms.

Radar systems are increasingly being employed in healthcare applications for human activity recognition due to their advantages in terms of privacy, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are however often complex, focusing on a single domain of radar, and requiring significant computational resources that prevent their deployment in embedded platforms which often have limited memory and computational resources. To address this issue, we present an adaptive magnitude thresholding approach for highlighting the region of interest in the multi-domain micro-Doppler signatures. The region of interest is beneficial to extract salient features, meanwhile it ensures the simplicity of calculations with less computational cost. The results for the proposed approach show an accuracy of up to 93.1% for six activities, outperforming state-of-the-art deep learning methods on the same dataset with an over tenfold reduction in both training time and memory footprint, and a twofold reduction in inference time compared to a series of deep learning implementations. These results can help bridge the gap toward embedded platform deployment.

BIMMS: A versatile and portable system for biological tissue and electrode-tissue interface electrical characterization.

The presented design is a low-cost, compact, and open-source USB-controlled platform for biological tissue and electrode-tissue interface electrical measurements, capable of potentiostatic and galvanostatic electrical impedance spectroscopy up to 10 MHz and cyclic voltammetry with voltage compliance of +-8 V and up to 2.4 mA while ensuring tissue-safety conditions. The data acquisition and generation are based on an Analog Discovery 2 platform (Digilent, USA). We provide accuracy analysis and comparisons with a commercially available calibrated impedance analyzer. Impedance measurements are demonstrated on implanted electrodes for neural stimulation and on an isolated ex-vivo calf brain as an example use case of the presented design.

Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification.

A simple method for accurately identifying Glossina spp in the field is a challenge to sustain the future elimination of Human African Trypanosomiasis (HAT) as a public health scourge, as well as for the sustainable management of African Animal Trypanosomiasis (AAT). Current methods for Glossina species identification heavily rely on a few well-trained experts. Methodologies that rely on molecular methodologies like DNA barcoding or mass spectrometry protein profiling (MALDI TOFF) haven't been thoroughly investigated for Glossina sp. Nevertheless, because they are destructive, costly, time-consuming, and expensive in infrastructure and materials, they might not be well adapted for the survey of arthropod vectors involved in the transmission of pathogens responsible for Neglected Tropical Diseases, like HAT. This study demonstrates a new type of methodology to classify Glossina species. In conjunction with a deep learning architecture, a database of Wing Interference Patterns (WIPs) representative of the Glossina species involved in the transmission of HAT and AAT was used. This database has 1766 pictures representing 23 Glossina species. This cost-effective methodology, which requires mounting wings on slides and using a commercially available microscope, demonstrates that WIPs are an excellent medium to automatically recognize Glossina species with very high accuracy.

Using Novel Multi-Frequency Analysis Methods to Retrieve Material and Temperature Information in Tactile Sensing Areas.

This article presents a novel artificial skin technology based on the Electric Impedance Tomography (EIT) that employs multi-frequency currents for detecting the material and the temperature of objects in contact with piezoresistive sheets. To date, few artificial skins in the literature are capable of detecting an object's material, e.g., wood, skin, leather, or plastic. EIT-based artificial skins have been employed mostly to detect the position of the contact but not its characteristics. Thanks to multi-frequency currents, our EIT-based artificial skin is capable of characterising the spectral profile of objects in contact and identifying an object's material at ambient temperature. Moreover, our model is capable of detecting several levels of temperature (from -10 up to 60 °C) and can also maintain a certain accuracy for material identification. In addition to the known capabilities of EIT-based artificial skins concerning detecting pressure and location of objects, as well as being low cost, these two novel modalities demonstrate the potential of EIT-based artificial skins to achieve global tactile sensing.

Diagnostic accuracy of SARS-CoV-2 rapid antigen test from self-collected anterior nasal swabs in children compared to rapid antigen test and RT-PCR from nasopharyngeal swabs collected by healthcare workers: A multicentric prospective study.

Testing for SARS-CoV-2 is central to COVID-19 management. Rapid antigen test from self-collected anterior nasal swabs (SCANS-RAT) are often used in children but their performance have not been assessed in real-life. We aimed to compare this testing method to the two methods usually used: reverse transcription polymerase chain reaction from nasopharyngeal swabs collected by healthcare workers (HCW-PCR) and rapid antigen test from nasopharyngeal swabs collected by healthcare workers (HCW-RAT), estimating the accuracy and acceptance, in a pediatric real-life study. From September 2021 to January 2022, we performed a manufacturer-independent cross-sectional, prospective, multicenter study involving 74 pediatric ambulatory centers and 5 emergency units throughout France. Children ≥6 months to 15 years old with suggestive symptoms of COVID-19 or children in contact with a COVID-19-positive patient were prospectively enrolled. We included 836 children (median 4 years), 774 (92.6%) were symptomatic. The comparators were HCW-PCR for 267 children, and HCW-RAT for 593 children. The sensitivity of the SCANS-RAT test compared to HCW-RAT was 91.3% (95%CI 82.8; 96.4). Sensitivity was 70.4% (95%CI 59.2; 80.0) compared to all HCW-PCR and 84.6% (95%CI 71.9; 93.1) when considering cycle threshold <33. The specificity was always >97%. Among children aged ≥6 years, 90.9% of SCANS-RAT were self-collected without adult intervention. On appreciation rating (from 1, very pleasant, to 10, very unpleasant), 77.9% of children chose a score ≤3. SCANS-RAT have good sensitivity and specificity and are well accepted by children. A repeated screening strategy using these tests can play a major role in controlling the pandemic.

A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics.

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.

Deep learning for biosignal control: insights from basic to real-time methods with recommendations.

Objective.Biosignal control is an interaction modality that allows users to interact with electronic devices by decoding the biological signals emanating from the movements or thoughts of the user. This manner of interaction with devices can enhance the sense of agency for users and enable persons suffering from a paralyzing condition to interact with everyday devices that would otherwise be challenging for them to use. It can also improve control of prosthetic devices and exoskeletons by making the interaction feel more natural and intuitive. However, with the current state of the art, several issues still need to be addressed to reliably decode user intent from biosignals and provide an improved user experience over other interaction modalities. One solution is to leverage advances in deep learning (DL) methods to provide more reliable decoding at the expense of added computational complexity. This scoping review introduces the basic concepts of DL and assists readers in deploying DL methods to a real-time control system that should operate under real-world conditions.Approach.The scope of this review covers any electronic device, but with an emphasis on robotic devices, as this is the most active area of research in biosignal control. We review the literature pertaining to the implementation and evaluation of control systems that incorporate DL to identify the main gaps and issues in the field, and formulate suggestions on how to mitigate them.Main results.The results highlight the main challenges in biosignal control with DL methods. Additionally, we were able to formulate guidelines on the best approach to designing, implementing and evaluating research prototypes that use DL in their biosignal control systems.Significance.This review should assist researchers that are new to the fields of biosignal control and DL in successfully deploying a full biosignal control system. Experts in their respective fields can use this article to identify possible avenues of research that would further advance the development of biosignal control with DL methods.

LPMP: A Bio-Inspired Model for Visual Localization in Challenging Environments.

Autonomous vehicles require precise and reliable self-localization to cope with dynamic environments. The field of visual place recognition (VPR) aims to solve this challenge by relying on the visual modality to recognize a place despite changes in the appearance of the perceived visual scene. In this paper, we propose to tackle the VPR problem following a neuro-cybernetic approach. To this end, the Log-Polar Max-Pi (LPMP) model is introduced. This bio-inspired neural network allows building a neural representation of the environment via an unsupervised one-shot learning. Inspired by the spatial cognition of mammals, visual information in the LPMP model are processed through two distinct pathways: a "what" pathway that extracts and learns the local visual signatures (landmarks) of a visual scene and a "where" pathway that computes their azimuth. These two pieces of information are then merged to build a visuospatial code that is characteristic of the place where the visual scene was perceived. Three main contributions are presented in this article: 1) the LPMP model is studied and compared with NetVLAD and CoHog, two state-of-the-art VPR models; 2) a test benchmark for the evaluation of VPR models according to the type of environment traveled is proposed based on the Oxford car dataset; and 3) the impact of the use of a novel detector leading to an uneven paving of an environment is evaluated in terms of the localization performance and compared to a regular paving. Our experiments show that the LPMP model can achieve comparable or better localization performance than NetVLAD and CoHog.

CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy.

Background and study aims Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. With a mean number of 50,000 SB frames per video, SBCE reading is time-consuming and tedious (30 to 60 minutes per video). We describe a large, multicenter database named CAD-CAP (Computer-Assisted Diagnosis for CAPsule Endoscopy, CAD-CAP). This database aims to serve the development of CAD tools for CE reading. Materials and methods Twelve French endoscopy centers were involved. All available third-generation SB-CE videos (Pillcam, Medtronic) were retrospectively selected from these centers and deidentified. Any pathological frame was extracted and included in the database. Manual segmentation of findings within these frames was performed by two pre-med students trained and supervised by an expert reader. All frames were then classified by type and clinical relevance by a panel of three expert readers. An automated extraction process was also developed to create a dataset of normal, proofread, control images from normal, complete, SB-CE videos. Results Four-thousand-one-hundred-and-seventy-four SB-CE were included. Of them, 1,480 videos (35 %) containing at least one pathological finding were selected. Findings from 5,184 frames (with their short video sequences) were extracted and delimited: 718 frames with fresh blood, 3,097 frames with vascular lesions, and 1,369 frames with inflammatory and ulcerative lesions. Twenty-thousand normal frames were extracted from 206 SB-CE normal videos. CAD-CAP has already been used for development of automated tools for angiectasia detection and also for two international challenges on medical computerized analysis.

Osteoarticular Infections of the Chest Wall Due to Kingella Kingae: A Series of 8 Cases.

Osteoarticular infections of the chest wall are relatively uncommon in pediatric patients and affect primarily infants and toddlers. Clinical presentation is often vague and nonspecific. Laboratory findings may be unremarkable in osteoarticular chest wall infections and not suggestive of an osteoarticular infection. Causative microbes are frequently identified if specific nucleic acid amplification assays are carried out. In the young pediatric population, there is evidence that Kingella kingae is 1 of the main the main causative pathogens of osteoarticular infections of the chest wall.