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.

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.

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.

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.

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.

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.

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.

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.

The feasibility of an augment reality system to study the psychophysiological correlates of fear-related responses.

BACKGROUND: Previous studies have successfully used augmented reality (AR) as an aid to exposure-based treatments for anxiety disorders. However, to the best of our knowledge, none of these studies have measured the physiological correlates of the fear response, relying solely on self-reports and behavioral avoidance tests. METHODS: As the physiological defensive reactivity pattern impacts on the treatment effectiveness, we tested the feasibility of an AR system integrated in a mobile and wearable device for assessing the psychophysiological mechanisms (heart rate) involved in fear responses in real-life contexts. Specific phobia was used as a model given its prototypical defensive hyperreactivity toward the feared stimulus (spiders to spider phobics, in the current study). RESULTS: The results showed that the stimuli presented using AR were able to induce physiological alterations in the participants, which were specific depending on the stimulus type (fear or neutral) and on the participants' level of spider fear (phobic and control group). These physiological correlates of the fear response were reflected both in the intensity of heart rate (in relation to the baseline) and in the time needed to react and recover after the stimulus exposure. Finally, we tested a theoretical model that showed that the physiological responses of phobic individuals when facing their phobic stimulus only explained its own data. CONCLUSIONS: We argue in favor of the system's feasibility at capturing and quantifying the physiological dimension of fear-related responses, which may be of great value for diagnostic and treatment purposes in anxiety disorders, namely specific phobia.

Biometric and Emotion Identification: An ECG Compression Based Method.

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.

Comparative analysis between biofilm formation and gene expression in Staphylococcus epidermidis isolates.

AIM: To understand the relationship between ica, aap and bhp gene expression and the implications in biofilm formation in selected clinical and commensal Staphylococcus epidermidis isolates. MATERIAL & METHODS: Isolates were analyzed regarding their biofilm-forming capacity, biochemical matrix composition, biofilm spatial organization and expression of biofilm-related genes. RESULTS: On polysaccharide intercellular adhesin-dependent biofilms, aap and bhp contributions for the biofilm growth were negligible, despite very high levels of expression. In contrast, smaller increases in icaA expression contributed significantly to biofilm growth. Interestingly, no biological differences were observed between clinical and commensal strains. CONCLUSION: These results reinforce the concept that S. epidermidis is an 'accidental pathogen,' and that the ica operon is the main mechanism of biofilm formation in clinical and commensal isolates.

A fast-growing cold skin abscess revealing disseminated Mycobacterium intracellulare infection in an HIV-infected patient.

A 66-year-old woman with HIV-1 infection recently commenced on antiretroviral therapy (CD4+ 25 cells/mm3 was referred to the Dermatology Clinic the following month due to a well-demarcated nodule in the extensor surface of the left arm with evident fluctuation but only slight pain on palpation, with no increase in temperature. Surgical drainage was performed with aspiration of yellowish-green exudate, with no characteristic smell. In culture of cutaneous exudate, Mycobacterium intracellulare was isolated. Upon careful review of the laboratory tests that were in progress at discharge, the same agent was isolated in one of the bronchoalveolar lavage cultures. The diagnosis of cutaneous abscess caused by M. intracellulare from hematogenous dissemination of lung infection was made. The patient was treated with clarithromycin, ethambutol and rifabutin for 24 months. M. intracellulare species and Mycobacterium avium constitute the Mycobacterium avium-intracellulare complex (MAC), responsible for the majority of human infections by atypical mycobacteria. They are ubiquitous bacteria and MAC infection mainly affect immunocompromised patients, with M. intracellulare being isolated in <5% of HIV patients with MAC infection. Cutaneous infection is rare and may present clinically with erythematous plaques, chronic ulcers or abscesses. When present, skin involvement is usually secondary to pulmonary infection.

Rapidly involuting congenital hemangioma

Abstract: Rapidly involuting congenital hemangioma is a rare vascular tumor that generally has a good prognosis. The authors describe a case of a newborn girl with a left cervical vascular lesion. Image exams were performed, and the lesion slowly decreased, leaving redundant skin. Considering all of the findings, a final diagnosis of a rapidly involuting congenital hemangiomas was suspected.

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.