Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group.

Importance: The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, comprehensive criteria establishing development and performance evaluation standards are required to ensure product fairness, reliability, and safety.

Objective: To consolidate limited existing literature with expert opinion to guide developers and reviewers of dermatology AI.

A machine learning approach for single cell interphase cell cycle staging.

The cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in nuclear morphology are also hallmarks of many diseases including cancer. Regrettably, automated reliable tools for cell cycle staging at single cell level using in situ images are still limited.

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.

Background: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice.

Objective: The objective of the study was to systematically analyse the current state of research on reader studies involving melanoma and to assess their potential clinical relevance by evaluating three main aspects: test set characteristics (holdout/out-of-distribution data set, composition), test setting (experimental/clinical, inclusion of metadata) and representativeness of participating clinicians.

Methods: PubMed, Medline and ScienceDirect were screened for peer-reviewed studies published between 2017 and 2021 and dealing with AI-based skin cancer classification involving melanoma.

Clinical Effects of Immersive Multimodal BCI-VR Training after Bilateral Neuromodulation with rTMS on Upper Limb Motor Recovery after Stroke. A Study Protocol for a Randomized Controlled Trial.

The motor sequelae after a stroke are frequently persistent and cause a high degree of disability. Cortical ischemic or hemorrhagic strokes affecting the cortico-spinal pathways are known to cause a reduction of cortical excitability in the lesioned area not only for the local connectivity impairment but also due to a contralateral hemisphere inhibitory action. Non-invasive brain stimulation using high frequency repetitive magnetic transcranial stimulation (rTMS) over the lesioned hemisphere and contralateral cortical inhibition using low-frequency rTMS have been shown to increase the excitability of the lesioned hemisphere.

A Soft Tactile Sensor Based on Magnetics and Hybrid Flexible-Rigid Electronics.

Tactile sensing is crucial for robots to manipulate objects successfully. However, integrating tactile sensors into robotic hands is still challenging, mainly due to the need to cover small multi-curved surfaces with several components that must be miniaturized. In this paper, we report the design of a novel magnetic-based tactile sensor to be integrated into the robotic hand of the humanoid robot Vizzy.

Characterization of Degenerate Configurations in Attitude Determination of Three-Vehicle Heterogeneous Formations.

The existence of multiple solutions to an attitude determination problem impacts the design of estimation schemes, potentially increasing the errors by a significant value. It is therefore essential to identify such cases in any attitude problem. In this paper, the cases where multiple attitudes satisfy all constraints of a three-vehicle heterogeneous formation are identified.

Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units.

Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results than those that use only visible spectral band images. In this work, a novel architecture for facial recognition that uses multiple deep convolutional neural networks and multispectral images is proposed.

Improving parametric estimation in the brain from multispin-echo sequences using a fusion bootstrap moves solver.

Purpose: To simultaneously estimate the field (along with the T ) in the brain with multispin-echo (MSE) sequences and dictionary matching.

Methods: T mapping provides clinically relevant information such as in the assessment of brain degenerative diseases. It is commonly obtained with MSE sequences, and accuracy can be further improved by matching the MSE signal to a precomputed dictionary of echo-modulation curves.

Modelling 3D saccade generation by feedforward optimal control.

An interesting problem for the human saccadic eye-movement system is how to deal with the degrees-of-freedom problem: the six extra-ocular muscles provide three rotational degrees of freedom, while only two are needed to point gaze at any direction. Measurements show that 3D eye orientations during head-fixed saccades in far-viewing conditions lie in Listing's plane (LP), in which the eye's cyclotorsion is zero (Listing's law, LL). Moreover, while saccades are executed as single-axis rotations around a stable eye-angular velocity axis, they follow straight trajectories in LP.

Radiological Scouting, Monitoring and Inspection Using Drones.

Human populations and natural ecosystems are bound to be exposed to ionizing radiation from the deposition of artificial radionuclides resulting from nuclear accidents, nuclear devices or radiological dispersive devices ("dirty bombs"). On the other hand, Naturally Occurring Radioactive Material industries such as phosphate production or uranium mining, contribute to the on site storage of residuals with enhanced concentrations of natural radionuclides. Therefore, in the context of the European agreements concerning nuclear energy, namely the European Atomic Energy Community Treaty, monitoring is an essential feature of the environmental radiological surveillance.

EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos.

Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not support effective quantitative benchmarking. In this paper, we introduce a comprehensive endoscopic SLAM dataset consisting of 3D point cloud data for six porcine organs, capsule and standard endoscopy recordings, synthetically generated data as well as clinically in use conventional endoscope recording of the phantom colon with computed tomography(CT) scan ground truth.

Disposal Behavior of Used Masks during the COVID-19 Pandemic in the Moroccan Community: Potential Environmental Impact.

The spread of coronavirus disease-2019 (COVID-19) levied on the Moroccan authorities to increase their mask production capacity, which reached up to 12 million facemask units produced per day. This increase in personal protective equipment (PPE) production and consumption is an efficient tool to address the spread of COVID-19. However, this results in more plastic and microplastic debris being added into the land and marine environments, which will harm the ecosystem, wildlife, and public health.

Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks.

Liquid crystal (LC)-based materials are promising platforms to develop rapid, miniaturised and low-cost gas sensor devices. In hybrid gel films containing LC droplets, characteristic optical texture variations are observed due to orientational transitions of LC molecules in the presence of distinct volatile organic compounds (VOC). Here, we investigate the use of deep convolutional neural networks (CNN) as pattern recognition systems to analyse optical textures dynamics in LC droplets exposed to a set of different VOCs.

A single stiffened nucleus alters cell dynamics and coherence in a monolayer.

Force transmission throughout a monolayer is the result of complex interactions between cells. Monolayer adaptation to force imbalances such as singular stiffened cells provides insight into the initiation of disease and fibrosis. Here, NRK-52E cells transfected with ∆50LA, which significantly stiffens the nucleus.

An Exploratory Study of Training Intensity in EEG Neurofeedback.

Neurofeedback training has shown benefits in clinical treatment and behavioral performance enhancement. Despite the wide range of applications, no consensus has been reached about the optimal training schedule. In this work, an EEG neurofeedback practical experiment was conducted aimed at investigating the effects of training intensity on the enhancement of the amplitude in the individual upper alpha band.

New Approaches Based on Non-Invasive Brain Stimulation and Mental Representation Techniques Targeting Pain in Parkinson's Disease Patients: Two Study Protocols for Two Randomized Controlled Trials.

Pain is an under-reported but prevalent symptom in Parkinson's Disease (PD), impacting patients' quality of life. Both pain and PD conditions cause cortical excitability reduction and non-invasive brain stimulation. Mental representation techniques are thought to be able to counteract it, also resulting effectively in chronic pain conditions.

Cerebrovascular Reactivity Mapping Without Gas Challenges: A Methodological Guide.

Cerebrovascular reactivity (CVR) is defined as the ability of vessels to alter their caliber in response to vasoactive factors, by means of dilating or constricting, in order to increase or decrease regional cerebral blood flow (CBF). Importantly, CVR may provide a sensitive biomarker for pathologies where vasculature is compromised. Furthermore, the spatiotemporal dynamics of CVR observed in healthy subjects, reflecting regional differences in cerebral vascular tone and response, may also be important in functional MRI studies based on neurovascular coupling mechanisms.

Analysis Protocols for MRI Mapping of the Blood Oxygenation-Sensitive Parameters T* and T in the Kidney.

Renal hypoxia is generally accepted as a key pathophysiologic event in acute kidney injury of various origins and has also been suggested to play a role in the development of chronic kidney disease. Here we describe step-by-step data analysis protocols for MRI monitoring of renal oxygenation in rodents via the deoxyhemoglobin concentration sensitive MR parameters T* and T-a contrast mechanism known as the blood oxygenation level dependent (BOLD) effect.This chapter describes how to use the analysis tools provided by vendors of animal and clinical MR systems, as well as how to develop an analysis software.

Break the Ice: a Survey on Socially Aware Engagement for Human-Robot First Encounters.

Society is starting to come up with exciting applications for social robots like butlers, coaches, and waiters. However, these robots face a challenging task: to meet people during a first encounter. This survey explores the literature that contributes to this task.

GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma.

Purpose: There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment decisions.

Observability Analysis and Observer Design for a Nonlinear Three-Tank System: Theory and Experiments.

This paper addresses the observability analysis and observer design for a nonlinear interacting three-tank system. The plant configuration is first described using the process and instrumentation diagram (P&ID) and a state-space realization is derived; some insights about the behavior of the nonlinear system, considering equilibrium points and the phase portrait are provided. Then, observability in the Hermann-Krener sense is analyzed.

A distinct neuromelanin magnetic resonance imaging pattern in parkinsonian multiple system atrophy.

Background: Parkinsonian variant of multiple system atrophy is a neurodegenerative disorder frequently misdiagnosed as Parkinson's disease. No early imaging biomarkers currently differentiate these disorders.

Methods: Simple visual imaging analysis of the substantia nigra and locus coeruleus in neuromelanin-sensitive magnetic resonance imaging and nigrosome 1 in susceptibility-weighted sequences was performed in thirty patients with parkinsonian variant of multiple system atrophy fulfilling possible/probable second consensus diagnostic criteria.

User-Experience with Haptic Feedback Technologies and Text Input in Interactive Multimedia Devices.

Remote control devices are commonly used for interaction with multimedia equipment and applications (e.g., smart TVs, gaming, etc.