Tracking team mental workload by multimodal measurements in the operating room

Mental workload and its effects on surgical performance are underexplored topics, despite their importance for operating room (OR) efficiency and patient safety. We developed a multimodal platform that can simultaneously collect data from EEG, heart rate and breathing rate, tool handle pressure, and eye tracker from mobile subjects. We performed experiments using the Fundamentals of Laparoscopic Surgery model, with 22 subjects of varying skill levels ranging from nonsurgeon to expert. The results indicated significant modulations of the measurements depending on pupil size, heart rate variability, P300 response, tool pressure, task difficulty, time-on-task, and skill level. These provide evidence that physiology based metrics can be used in automated classification of fine gradations of skill, the assessment and certification of surgery trainees, developing real-time flags and warnings for the OR, and validating new OR technology

Ontology-Driven Food Category Classification in Images

The self-management of chronic diseases related to dietary habits includes the necessity of tracking what people eat. Most of the approaches proposed in the literature classify food pictures by labels describing the whole recipe. The main drawback of this kind of strategy is that a wrong prediction of the recipe leads to a wrong prediction of any ingredient of such a recipe. In this paper we present a multi-label food classification approach, exploiting deep neural networks, where each food picture is classified with labels describing the food categories of the ingredients in each recipe. The aim of our approach is to support the detection of food categories in order to detect which one might be dangerous for a user affected by chronic disease. Our approach relies on background knowledge where recipes, food categories, and their relatedness with chronic diseases are modeled within a state-of-the-art ontology. Experiments conducted on a new publicly released dataset demonstrated the effectiveness of the proposed approach with respect to state-of-the-art classification strategies

Momentary lapse of control: A cognitive continuum approach to understanding and mitigating perseveration in human error

Everyday complex and stressful real-life situations can overwhelm the human brain to an extent that the person is no longer able to accurately evaluate the situation and persists in irrational actions or strategies. Safety analyses reveal that such perseverative behavior is exhibited by operators in many critical domains, which can lead to potentially fatal incidents. There are neuroimaging evidences of changes in healthy brain functioning when engaged in non-adaptive behaviors that are akin to executive deficits such as perseveration shown in patients with brain lesion. In this respect, we suggest a cognitive continuum whereby stressors can render the healthy brain temporarily impaired. We show that the dorsolateral prefrontal cortex is a key structure for executive and attentional control whereby any transient (stressors, neurostimulation) or permanent (lesion) impairment compromises adaptive behavior. Using this neuropsychological insight, we discuss solutions involving training, neurostimulation, and the design of cognitive countermeasures for mitigating perseveration

Rare Primary Central Nervous System Tumors in Adults: An Overview

Overall, tumors of primary central nervous system (CNS) are quite common in adults with an incidence rate close to 30 new cases/100,000 inhabitants per year. Significant clinical and biological advances have been accomplished in the most common adult primary CNS tumors (i.e., diffuse gliomas). However, most CNS tumor subtypes are rare with an incidence rate below the threshold defining rare disease of 6.0 new cases/100,000 inhabitants per year. Close to 150 entities of primary CNS tumors have now been identified by the novel integrated histomolecular classification published by the World Health Organization (WHO) and its updates by the c-IMPACT NOW consortium (the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy). While these entities can be better classified into smaller groups either by their histomolecular features and/or by their location, assessing their treatment by clinical trials and improving the survival of patients remain challenging. Despite these tumors are rare, research, and advances remain slower compared to diffuse gliomas for instance. In some cases (i.e., ependymoma, medulloblastoma) the understanding is high because single or few driver mutations have been defined. The European Union has launched European Reference Networks (ERNs) dedicated to support advances on the clinical side of rare diseases including rare cancers. The ERN for rare solid adult tumors is termed EURACAN. Within EURACAN, Domain 10 brings together the European patient advocacy groups (ePAGs) and physicians dedicated to improving outcomes in rare primary CNS tumors and also aims at supporting research, care and teaching in the field. In this review, we discuss the relevant biological and clinical characteristics, clinical management of patients, and research directions for the following types of rare primary CNS tumors: medulloblastoma, pineal region tumors, glioneuronal and rare glial tumors, ependymal tumors, grade III meningioma and mesenchymal tumors, primary central nervous system lymphoma, germ cell tumors, spinal cord tumors and rare pituitary tumors

An End-to-End Semantic Platform for Nutritional Diseases Management

The self-management of nutritional diseases requires a system that combines food tracking with the potential risks of food categories on people’s health based on their personal health records (PHRs). The challenges range from the design of an effective food image classification strategy to the development of a full-fledged knowledge-based system. This maps the results of the classification strategy into semantic information that can be exploited for reasoning. However, current works mainly address the single challenges separately without their integration into a whole pipeline. In this paper, we propose a new end-to-end semantic platform where: (i) the classification strategy aims to extract food categories from food pictures; (ii) an ontology is used for detecting the risk factors of food categories for specific diseases; (iii) the Linked Open Data (LOD) Cloud is queried for extracting information concerning related diseases and comorbidities; and, (iv) information from the users’ PHRs are exploited for generating proper personal feedback. Experiments are conducted on a new publicly released dataset. Quantitative and qualitative evaluations, from two living labs, demonstrate the effectiveness and the suitability of the proposed approach

Cumulative incidence and risk factors for radiation induced leukoencephalopathy in high grade glioma long term survivors

The incidence and risk factors associated with radiation-induced leukoencephalopathy (RIL) in long-term survivors of high-grade glioma (HGG) are still poorly investigated. We performed a retrospective research in our institutional database for patients with supratentorial HGG treated with focal radiotherapy, having a progression-free overall survival > 30 months and available germline DNA. We reviewed MRI scans for signs of leukoencephalopathy on T2/FLAIR sequences, and medical records for information on cerebrovascular risk factors and neurological symptoms. We investigated a panel of candidate single nucleotide polymorphisms (SNPs) to assess genetic risk. Eighty-one HGG patients (18 grade IV and 63 grade III, 50M/31F) were included in the study. The median age at the time of radiotherapy was 48 years old (range 18–69). The median follow-up after the completion of radiotherapy was 79 months. A total of 44 patients (44/81, 54.3%) developed RIL during follow-up. Twenty-nine of the 44 patients developed consistent symptoms such as subcortical dementia (n = 28), gait disturbances (n = 12), and urinary incontinence (n = 9). The cumulative incidence of RIL was 21% at 12 months, 42% at 36 months, and 48% at 60 months. Age > 60 years, smoking, and the germline SNP rs2120825 (PPARg locus) were associated with an increased risk of RIL. Our study identified potential risk factors for the development of RIL (age, smoking, and the germline SNP rs2120825) and established the rationale for testing PPARg agonists in the prevention and management of late-delayed radiation-induced neurotoxicity

Traces of Fallback Breccia on the Rim of Barringer Meteorite Crater (a.k.a. Meteor Crater), Arizona

Barringer Meteorite Crater (a.k.a. Meteor Crater), Arizona, is one of the youngest and best preserved impact craters on Earth. For that rea-son, it provides a baseline for similar craters formed in the geologic past, formed elsewhere in the Solar Sys-tem, and illuminates the astronomical and geological processes that produce them. The crater has not, how-ever, escaped erosion completely. While Shoemaker [1] mapped a breccia with fallback components inside the crater, he did not locate it beyond the crater rim. He only found remnants of that type of debris in re-worked alluvium [1; see also 2]. Fallback breccia and any base-surge deposits have, thus, been missing components in studies of material ejected beyond the transient crater rim

Disruption in neural phase synchrony is related to identification of inattentional deafness in real-world setting

Individuals often have reduced ability to hear alarms in real world situations (e.g., anesthesia monitoring, flying airplanes) when attention is focused on another task, sometimes with devastating consequences. This phenomenon is called inattentional deafness and usually occurs under critical high workload conditions. It is difficult to simulate the critical nature of these tasks in the laboratory. In this study, dry electroencephalography is used to investigate inattentional deafness in real flight while piloting an airplane. The pilots participating in the experiment responded to audio alarms while experiencing critical high workload situations. It was found that missed relative to detected alarms were marked by reduced stimulus evoked phase synchrony in theta and alpha frequencies (6–14 Hz) from 120 to 230 ms poststimulus onset. Correlation of alarm detection performance with intertrial coherence measures of neural phase synchrony showed different frequency and time ranges for detected and missed alarms. These results are consistent with selective attentional processes actively disrupting oscillatory coherence in sensory networks not involved with the primary task (piloting in this case) under critical high load conditions. This hypothesis is corroborated by analyses of flight parameters showing greater maneuvering associated with difficult phases of flight occurring during missed alarms. Our results suggest modulation of neural oscillation is a general mechanism of attention utilizing enhancement of phase synchrony to sharpen alarm perception during successful divided attention, and disruption of phase synchrony in brain networks when attentional demands of the primary task are great, such as in the case of inattentional deafness

Can dissonance engineering improve risk analysis of human–machine systems?

The paper discusses dissonance engineering and its application to risk analysis of human–machine systems. Dissonance engineering relates to sciences and technologies relevant to dissonances, defined as conflicts between knowledge. The richness of the concept of dissonance is illustrated by a taxonomy that covers a variety of cognitive and organisational dissonances based on different conflict modes and baselines of their analysis. Knowledge control is discussed and related to strategies for accepting or rejecting dissonances. This acceptability process can be justified by a risk analysis of dissonances which takes into account their positive and negative impacts and several assessment criteria. A risk analysis method is presented and discussed along with practical examples of application. The paper then provides key points to motivate the development of risk analysis methods dedicated to dissonances in order to identify the balance between the positive and negative impacts and to improve the design and use of future human–machine system by reinforcing knowledge