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.

Analysis of Physiological Responses during Pain Induction.

Pain is a complex phenomenon that arises from the interaction of multiple neuroanatomic and neurochemical systems with several cognitive and affective processes. Nowadays, the assessment of pain intensity still relies on the use of self-reports. However, recent research has shown a connection between the perception of pain and exacerbated stress response in the Autonomic Nervous System. As a result, there has been an increasing analysis of the use of autonomic reactivity with the objective to assess pain. In the present study, the methods include pre-processing, feature extraction, and feature analysis. For the purpose of understanding and characterizing physiological responses of pain, different physiological signals were, simultaneously, recorded while a pain-inducing protocol was performed. The obtained results, for the electrocardiogram (ECG), showed a statistically significant increase in the heart rate, during the painful period compared to non-painful periods. Additionally, heart rate variability features demonstrated a decrease in the Parasympathetic Nervous System influence. The features from the electromyogram (EMG) showed an increase in power and contraction force of the muscle during the pain induction task. Lastly, the electrodermal activity (EDA) showed an adjustment of the sudomotor activity, implying an increase in the Sympathetic Nervous System activity during the experience of pain.

On the Impact of the Data Acquisition Protocol on ECG Biometric Identification.

Electrocardiographic (ECG) signals have been used for clinical purposes for a long time. Notwithstanding, they may also be used as the input for a biometric identification system. Several studies, as well as some prototypes, are already based on this principle. One of the methods already used for biometric identification relies on a measure of similarity based on the Kolmogorov Complexity, called the Normalized Relative Compression (NRC)-this approach evaluates the similarity between two ECG segments without the need to delineate the signal wave. This methodology is the basis of the present work. We have collected a dataset of ECG signals from twenty participants on two different sessions, making use of three different kits simultaneously-one of them using dry electrodes, placed on their fingers; the other two using wet sensors placed on their wrists and chests. The aim of this work was to study the influence of the ECG protocol collection, regarding the biometric identification system's performance. Several variables in the data acquisition are not controllable, so some of them will be inspected to understand their influence in the system. Movement, data collection point, time interval between train and test datasets and ECG segment duration are examples of variables that may affect the system, and they are studied in this paper. Through this study, it was concluded that this biometric identification system needs at least 10 s of data to guarantee that the system learns the essential information. It was also observed that "off-the-person" data acquisition led to a better performance over time, when compared to "on-the-person" places.

Candida auris: A Quick Review on Identification, Current Treatments, and Challenges.

Candida auris is a novel and major fungal pathogen that has triggered several outbreaks in the last decade. The few drugs available to treat fungal diseases, the fact that this yeast has a high rate of multidrug resistance and the occurrence of misleading identifications, and the ability of forming biofilms (naturally more resistant to drugs) has made treatments of C. auris infections highly difficult. This review intends to quickly illustrate the main issues in C. auris identification, available treatments and the associated mechanisms of resistance, and the novel and alternative treatment and drugs (natural and synthetic) that have been recently reported.

Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification.

Emotional responses are associated with distinct body alterations and are crucial to foster adaptive responses, well-being, and survival. Emotion identification may improve peoples' emotion regulation strategies and interaction with multiple life contexts. Several studies have investigated emotion classification systems, but most of them are based on the analysis of only one, a few, or isolated physiological signals. Understanding how informative the individual signals are and how their combination works would allow to develop more cost-effective, informative, and objective systems for emotion detection, processing, and interpretation. In the present work, electrocardiogram, electromyogram, and electrodermal activity were processed in order to find a physiological model of emotions. Both a unimodal and a multimodal approach were used to analyze what signal, or combination of signals, may better describe an emotional response, using a sample of 55 healthy subjects. The method was divided in: (1) signal preprocessing; (2) feature extraction; (3) classification using random forest and neural networks. Results suggest that the electrocardiogram (ECG) signal is the most effective for emotion classification. Yet, the combination of all signals provides the best emotion identification performance, with all signals providing crucial information for the system. This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification, which can critically drive effective evaluation, monitoring and intervention, regarding emotional processing and regulation, considering multiple contexts.

Secondary Skin Plasmacytomas.

A 78-year old man was diagnosed in 2006 with IgAκ multiple myeloma (MM) (stage III-A). The patient was referred to our dermatology department in 2012 for evaluation of erythematous skin nodules on the anterior right aspect of the thorax; the skin lesions were noted during hospitalization for multiple bone fractures. He was on fourth-line chemotherapy (with vincristine/adriamycin/dexamethasone) because of constant disease progression. The patient was unaware of the skin lesions' evolution over time and did not recall when they had first appeared. He had no pain, itching, or spontaneous bleeding.

BeMonitored: Monitoring psychophysiology and behavior using Android in phobias.

It is of the utmost importance that researchers can recreate, as accurately as possible, real-life conditions in psychological studies. However, that is not always possible. Given that phobias are rather context-specific, their study is the ideal candidate to assess the feasibility of using a mobile and wearable device for obtaining physiological and behavioral data. In this article, we propose BeMonitored, a smartphone-based solution to support more ecologically valid monitoring of psychological experiments. BeMonitored delivers customizable, specific context-dependent audiovisual stimuli and uses external resources connected via Bluetooth or a smartphone's own resources, while capturing the participant's behavior, physiology, and environment. We used BeMonitored in a spider phobia case study and showed that spider phobics differed from control participants in face motion, captured by the smartphone camera. Moreover, our results also revealed heart rate differences between spider and neutral stimuli in phobic participants. The presented results emphasize the usefulness of smartphones for phobia monitoring. Considering their intrinsic characteristics, smartphones may constitute the natural evolution from the lab to more realistic contexts.

Transient symptomatic zinc deficiency in a preterm exclusively breast-fed infant.

A 5-month-old female infant, preterm, exclusively breast-fed, presented with a 2-month history of erythematous, erosive, and crusted patches and plaques in a peri-oral, scalp, genital, and peri-anal distribution. A clinical diagnosis of zinc deficiency was confirmed by a low serum zinc level in the infant and decreased maternal breast milk zinc. Complete resolution occurred within two weeks of oral zinc supplementation. Acquired zinc deficiency is a rare nutritional disorder of infants. Early diagnosis and adequate treatment will prevent associated morbidity and complications.

Time series big data: a survey on data stream frameworks, analysis and algorithms.

Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data's biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, and infrastructures. The potential of big data increases when we apply it in real-time by providing real-time analysis, predictions, and forecasts, among many other applications. Our goal with this article is to provide a viewpoint on how to build a system capable of processing big data in real-time, performing analysis, and applying algorithms. A system should be designed to handle vast amounts of data and provide valuable knowledge through analysis and algorithms. This article explores the current approaches and how they can be used for the real-time operations and predictions.

Host Soluble Factors Cause Changes in Staphylococcus epidermidis Antibiotic Susceptibility and Biofilm Formation Ability.

Staphylococcus epidermidis is a major nosocomial pathogen with a remarkable ability to adhere to the surfaces of indwelling medical devices and form biofilms. Unlike other nosocomial pathogens, the interaction of S. epidermidis with host factors has not been the focus of substantial research. This study aimed to assess the alterations in the antibiotic susceptibility and biofilm formation ability of S. epidermidis in the presence of host serum factors. S. epidermidis strain RP62A was cultured in a laboratory culture medium with or without human serum/plasma, and changes in antibiotic susceptibility, biofilm formation, and gene expression were evaluated. The data obtained revealed that exposure to host serum factors increased the susceptibility of S. epidermidis to glycopeptide antibiotics and was also detrimental to biofilm formation. Gene expression analysis revealed downregulation of both dltA and fmtC genes shortly after human serum/plasma exposure. The importance of transferrin-mediated iron sequestration as a host anti-biofilm strategy against S. epidermidis was also emphasized. We have demonstrated that serum factors play a pivotal role as part of the host's anti-infective strategy against S. epidermidis infections, highlighting the importance of incorporating such factors during in vitro studies with this pathogen.

Correlation between clinical signs of depth of anaesthesia and cerebral state index responses in dogs with different target-controlled infusions of propofol.

OBJECTIVE: To evaluate if the cerebral state index (CSI), measured by a Cerebral State Monitor (CSM), can predict depth of anaesthesia as assessed clinically or by estimated propofol plasma concentrations. STUDY DESIGN: Prospective clinical study. ANIMALS: Fourteen mixed breed dogs, weighing 24.5 ± 4.7 kg, scheduled to undergo neutering procedures. METHODS: Dogs were premedicated with 0.05 mg kg(-1) acepromazine intramuscularly. The CSM and cardiovascular monitoring equipment were attached. Anaesthesia was induced with propofol using a target controlled infusion (TCI) to varying plasma propofol targets (PropCp). Following endotracheal intubation the dogs were ventilated with oxygen. Anaesthetic maintenance was with propofol by TCI. A PropCp of 3 µg dL(-1) was set initially, then PropCps were increased in 1 µg dL(-1) steps to 7, 9 and then 11 µg dL(-1). Each PropCp was held constant for a 5 minute period, at the end of which depth of anaesthesia was classified using a previously evaluated scale of 'planes' based on palpebral and corneal reflexes and eye position. Cerebral state index (CSI), burst suppression (BSR) and electromyogram were measured at these time points. The prediction probability (PK) of these variables, or of the PropCp in predicting depth of anaesthesia was calculated. RESULTS: The PKs for predicting anaesthetic planes were 0.74, 0.91, 0.76 and 0.78 for CSI, BSR, EMG and PropCp, respectively. The PKs for PropCp to predict CSI, BSR and EMG were 0.65, 0.71 and 0.65 respectively. CONCLUSION AND CLINICAL RELEVANCE: The Cerebral State Monitor was able to detect very deep planes of anaesthesia when BSR occurs, but was not able to distinguish between the intermediate anaesthetic planes likely to be used in clinical anaesthesia.

Transcriptome Mining to Identify Molecular Markers for the Diagnosis of Staphylococcus epidermidis Bloodstream Infections.

Bloodstream infections caused by Staphylococcus epidermidis are often misdiagnosed since no diagnostic marker found so far can unequivocally discriminate "true" infection from sample contamination. While attempts have been made to find genomic and/or phenotypic differences between invasive and commensal isolates, possible changes in the transcriptome of these isolates under in vivo-mimicking conditions have not been investigated. Herein, we characterized the transcriptome, by RNA sequencing, of three clinical and three commensal isolates after 2 h of exposure to whole human blood. Bioinformatics analysis was used to rank the genes with the highest potential to distinguish invasive from commensal isolates and among the ten genes identified as candidates, the gene SERP2441 showed the highest potential. A collection of 56 clinical and commensal isolates was then used to validate, by quantitative PCR, the discriminative power of the selected genes. A significant variation was observed among isolates, and the discriminative power of the selected genes was lost, undermining their potential use as markers. Nevertheless, future studies should include an RNA sequencing characterization of a larger collection of isolates, as well as a wider range of conditions to increase the chances of finding further candidate markers for the diagnosis of bloodstream infections caused by S. epidermidis.

Multidimensional assessment of anxiety through the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA): From dimensionality to response prediction across emotional contexts.

The assessment of mal-adaptive anxiety is crucial, considering the associated personal, economic, and societal burden. The State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA) is a self-report instrument developed to provide multidimensional anxiety assessment in four dimensions: trait-cognitive, trait-somatic, state-cognitive and state-somatic. This research aimed to extend STICSA's psychometric studies through the assessment of its dimensionality, reliability, measurement invariance and nomological validity in the Portuguese population. Additionally, the predictive validity of STICSA-Trait was also evaluated, through the analysis of the relationship between self-reported trait anxiety and both the subjective and the psychophysiological response across distinct emotional situations. Similarly to previous studies, results supported both a four-factor and two separated bi-factor structures. Measurement invariance across sex groups was also supported, and good nomological validity was observed. Moreover, STICSA trait-cognitive dimension was associated with differences in self-reported arousal between groups of high/low anxiety, whereas STICSA trait-somatic dimension was related to differences in both the subjective and psychophysiological response. Together, these results support STICSA as a useful instrument for a broader anxiety assessment, crucial for an informed diagnosis and practice.

Treatment of Akata EBV-positive cells with doxorubicin causes more EBV reactivation than treatment with etoposide.

BACKGROUND/AIMS: EBV has been associated with Burkitt lymphoma (BL). It establishes a latent infection but its reactivation has been observed in patients receiving long-term chemotherapy. The effect of doxorubicin on virus reactivation has been described previously, but the effect of etoposide or cytarabine on EBV reactivation has not been reported in the literature. The aim of this work was to carry out such a study. METHODS: Akata EBV-positive cell lines were treated with etoposide, doxorubicin or cytarabine. Viable cells were analyzed by trypan blue, programmed cell death by TUNEL assay, mRNA levels by RT-PCR and cellular or viral proteins by Western blot. Viruses were visualized by electron microscopy. RESULTS: All of the studied drugs caused cell death by apoptosis. Comparing the effect of etoposide and doxorubicin (at their IC(50)) in the EBV-positive cells, etoposide caused less EBV reactivation than doxorubicin. Cytarabine apparently did not reactivate EBV. CONCLUSION: When treating Akata EBV-positive cells with the respective IC(50) of the following drugs, etoposide induced less EBV reactivation than doxorubicin, and cytarabine apparently did not induce EBV reactivation.

Optimizing a reliable ex vivo human blood model to analyze expression of Staphylococcus epidermidis genes.

Human blood is often used as an ex vivo model to mimic the environment encountered by pathogens inside the host. A significant variety of experimental conditions has been reported. However, optimization strategies are often not described. This study aimed to evaluate key parameters that are expected to influence Staphylococcus epidermidis gene expression when using human blood ex vivo models. Our data confirmed that blood antimicrobial activity was dependent on initial bacterial concentration. Furthermore, blood degradation over time resulted in lower antimicrobial activity, with a 2% loss of leukocytes viability correlating with a 5-fold loss of antimicrobial activity against S. epidermidis. We further demonstrated that the volume of human blood could be reduced to as little as 0.18 mL without affecting the stability of gene expression of the tested genes. Overall, the data described herein highlight experimental parameters that should be considered when using a human blood ex vivo model for S. epidermidis gene expression analysis.