Improved method for stress detection using bio-sensor technology and machine learning algorithms

Maintaining an optimal stress level is vital in our lives, yet many individuals struggle to identify the sources of their stress. As emotional stability and mental awareness become increasingly important, wearable medical technology has gained popularity in recent years. This technology enables real-time monitoring, providing medical professionals with crucial physiological data to enhance patient care. Current stress-detection methods, such as ECG, BVP, and body movement analysis, are limited by their rigidity and susceptibility to noise interference. To overcome these limitations, we introduce STRESS-CARE, a versatile stress detection sensor employing a hybrid approach. This innovative system utilizes a sweat sensor, cutting-edge context identification methods, and machine learning algorithms. STRESS-CARE processes sensor data and models environmental fluctuations using an XG Boost classifier. By combining these advanced techniques, we aim to revolutionize stress detection, offering a more adaptive and robust solution for improved stress management and overall well-being.•In the proposed method, we introduce a state-of-the-art stress detection device with Galvanic Skin Response (GSR) sweat sensors, outperforming traditional Electrocardiogram (ECG) methods while remaining non-invasive•Integrating machine learning, particularly XG-Boost algorithms, enhances detection accuracy and reliability.•This study sheds light on noise context comprehension for various wearable devices, offering crucial guidance for optimizing stress detection in multiple contexts and applications.

Gunshot residue (GSR): Frequency of residue types encountered in case work and background levels on control samples

The evaluation of criminal cases involving the discharge of a firearm requires reliable and up to date information regarding the transfer and persistence of gunshot residue (GSR). Similarly, knowledge of background levels of GSR on relevant populations and awareness of the potential for contamination/secondary transfer is essential. In this paper we build on previous work published by this laboratory and provide an update on the frequency of gunshot residue types in discharged cartridge casings (DCC) encountered in casework within the Republic of Ireland. In conjunction, an examination of the types of firearms encountered in casework and the associated residue types is undertaken. Finally, a review of levels of GSR particles detected on control samples taken from members of An Garda Síochána, the Irish police is detailed. Control samples are taken before a police officer samples a detainee suspected of involvement in an incident where a firearm was discharged and/or subsequently handled.

New layers of regulation of the general stress response sigma factor RpoS

The general stress response (GSR) sigma factor RpoS from Escherichia coli has emerged as one of the key paradigms for study of how numerous signal inputs are accepted at multiple levels into a single pathway for regulation of gene expression output. While many studies have elucidated the key pathways controlling the production and activity of this sigma factor, recent discoveries have uncovered still more regulatory mechanisms which feed into the network. Moreover, while the regulon of this sigma factor comprises a large proportion of the E. coli genome, the downstream expression levels of all the RpoS target genes are not identically affected by RpoS upregulation but respond heterogeneously, both within and between cells. This minireview highlights the most recent developments in our understanding of RpoS regulation and expression, in particular those which influence the regulatory network at different levels from previously well-studied pathways.

Plasma oxidative stress marker levels related to functional brain abnormalities in first-episode drug-naive major depressive disorder

Major Depressive Disorder (MDD) is marked by abnormal brain function and elevated plasma oxidative stress markers. The specific relationship between these factors in MDD remains unclear. In this study, we conducted resting-state fMRI scans on fifty-seven first-episode, drug-naive MDD patients and sixty healthy controls. Plasma levels of oxidative stress markers (superoxide dismutase (SOD) and glutathione reductase (GSR)) were assessed using ELISA. Our results revealed a positive correlation between plasma SOD and GSR levels in MDD patients and the amplitude of low-frequency fluctuation (ALFF) values in key brain regions-thalamus, anterior cingulate gyrus, and superior frontal gyrus. Further analysis indicated positive correlations between plasma SOD and GSR levels and specific ALFF values in MDD patients without suicidal ideation, with these correlations not significant in MDD patients with suicidal ideation. Additionally, seed-based whole-brain functional connectivity analysis demonstrated a negative correlation between plasma GSR levels and connectivity between the thalamus and insula, while plasma SOD levels showed a positive correlation with connectivity between the thalamus and precuneus. These findings contribute to our understanding of MDD's pathophysiology and heterogeneity, highlighting the association between plasma oxidative stress markers and functional abnormalities in diverse brain regions.

A preliminary study

Since the second half of the XX century, primer mixtures based on mercury fulminate have become a rare occurrence on small ammunition markets in Western Europe and North America. As a consequence, Hg-containing gunshot residue (GSR) particles have not been as deeply investigated as residues from lead-based primer mixtures. As a matter of fact, no mention of GSR particles from mercuric primers is made by the current ASTM standard procedure for gunshot residue analysis. However, those laboratories dealing with ammunition and firearms produced in Eastern Europe or Asia still have a forensic interest in Hg-containing GSR. In this paper, a brief description of chemical composition and inner morphology of GSR particles from three different mercuric primers is reported. Regarding composition, arguments are given to promote SbSnHg residues to Characteristic of GSR particles when mercuric primers are discharged. From a morphological point of view, presence of inner nodules and other inhomogeneities were shown in GSR particles milled in a FIB/SEM. Moreover, mercury vaporization under the electron beam was observed for a particle reduced to a lamella. Mercury evanescence in GSR was interpreted in terms of mercury segregation during particle formation and higher mobility of Hg atoms in presence of defects (vacancies) in a strained lattice.

Cognition-Emotion Interaction during L2 Sentence Comprehension: The Correlation of ERP and GSR Responses to Sense Combinations

This study mainly examined the role of the combination of three senses (i.e., auditory, visual, and tactile) and five senses (i.e., auditory, visual, tactile, olfactory, and gustatory) in the correlation between electrophysiological and electrodermal responses underlying second language (L2) sentence comprehension. Forty subjects did two acceptability judgment tasks, encompassing congruent and semantically/pragmatically incongruent sentences. The event-related potential (ERP) and galvanic skin response (GSR) data for both the target and final words of the sentences were collected and analyzed. The results revealed that there is an interaction between cognitive and emotional responses in both semantically and pragmatically incongruent sentences, yet the timing of the interaction is longer in sentences with pragmatic incongruity due to their complexity. Based on the ERP and GSR correlation results, it was further found that the five-sense combination approach improves L2 sentence comprehension and interest in learning materials yet reduces the level of excitement or arousal. While this approach might be beneficial for some learners, it might be detrimental for those in favor of stimulating learning environments.

Uncertainty model for automated gunshot residue particle length measurements obtained from electron microscopy images

Currently, the use of algorithms and computer vision systems for metrological purposes has increased in different areas of knowledge to reduce human error and process deviations, consequently increasing reliability and reducing measurement uncertainties. This study presents a model for estimating the uncertainty of Feret's diameter (DF ) measurements of scanning electron microscopy (SEM) images from regular and irregular gunshot residue (GSR) particles at different magnifications. The data were extracted using the automatic measurement algorithm developed by the Brazilian Institute of Metrology, Quality and Technology (Inmetro). The proposed uncertainty model was based on the recommendations of the guide to the expression of uncertainty in measurement (GUM). The gold standard technique to identify and detect GSR particles is the SEM coupled to energy dispersive X-ray spectroscopy (SEM/EDS), which was used in the study. The low uncertainty values obtained in this study are justified by the refinement of the measurements performed at each stage of digital image procedures. The proposed uncertainty model contributes in an innovative way to the metrological evaluation of regular and irregular GSR particles at different images magnifications. The correct morphometry definition of these particles allows to study their distinction from other possible sources of GSR and, above all, their correlation with the type of ammunition used when firing the firearm. These measurement uncertainty calculations can be applied to any object images acquired by SEM, which provides more confidence in the results of measurements of the object of interest.

Uninterrupted real-time cerebral stress level monitoring using wearable biosensors: A review

Stress is the major unseen bug for the health of humans with the increasing workaholic era. Long periods of avoidance are the main precursor for chronic disorders that are quite tough to treat. As precaution is better than cure, stress detection and monitoring are vital. Although there are ways to measure stress clinically, there is still a constant need and demand for methods that measure stress personally and in an ex vitro manner for the convenience of the user. The concept of continuous stress monitoring has been introduced to tackle the issue of unseen stress accumulating in the body simultaneously with being user-friendly and reliable. Stress biosensors nowadays provide real-time, noninvasive, and continuous monitoring of stress. These biosensors are innovative anthropogenic creations that are a combination of biomarkers and indicators like heart rate variation, electrodermal activity, skin temperature, galvanic skin response, and electroencephalograph of stress in the body along with machine learning algorithms and techniques. The collaboration of biological markers, artificial intelligence techniques, and data science tools makes stress biosensors a hot topic for research. These attributes have made continuous stress detection a possibility with ease. The advancement in stress biosensing technologies has made a great impact on the lives of human beings so far. This article focuses on the comprehensive study of stress-indicating biomarkers and the techniques along with principles of the biosensors used for continuous stress detection. The precise overview of wearable stress monitoring systems is also sectioned to pave a pathway for possible future research studies.

Cr and CeO2 promoted Ni/SBA-15 framework for hydrogen production by steam reforming of glycerol

Ni/SBA-15 meso-structured catalysts modified with chromium and CeO2 (Ni-Cr-CeO2/SBA-15) were utilized to produce hydrogen from glycerol steam reforming (GSR). The catalysts were synthesized by a one-pot hydrothermal process and extensively characterized by analytical techniques such as N2 adsorption-desorption (BET), H2-temperature programmed reduction (H2-TPR), powder X-ray diffraction (PXRD), inductively coupled plasma-optical emission spectrometry (ICP-OES), and transmission electron microscopy (TEM). The low-angle XRD reflections affirmed that the catalysts were crystalline and possessed a 2D-ordered porosity. The BET results depicted that all the catalysts exhibited a good surface area ranging from 633 to 792m2/g, and the pore sizes were consistently in the mesoporous range (between 3 and 5 nm). TEM analysis of both calcined and spent catalysts revealed that the metal active sites were embedded in the hybrid CeO2-SiO2 support. Overall, the Ni-based catalysts exhibited higher glycerol conversion -12Ni-SBA-15-99.9%, 12Ni3CeO2-SBA-15-89.4%, and 8Ni4Cr3CeO2-SBA-15-99.7%. Monometallic 12Ni/SBA-15 performed exceptionally well, while 12Cr/SBA-15 performed poorly with the highest 71.48% CO selectivity. For short-term GSR reactions, CeO2 addition to 12Ni/SBA-15 did not have any effect, whereas Cr addition resulted in a 32% decrease in H2 selectivity. The long-term stability studies of 12Ni-SBA-15 showed H2 selectivity of ~ 64% and ~ 98% glycerol conversion. However, its activity was short-lived. After 20-30 h, the H2 selectivity and conversion dropped precipitously to 40%. The doping of mesoporous Ni/SBA-15 with Cr and CeO2 remarkably enhanced the long-term stability of the catalyst for 12Ni3CeO2-SBA-15, and 8Ni4Cr3CeO2-SBA-15 catalyst which showed ~ 58% H2 selectivity and ~ 100% conversion for the entire 60 h. Interestingly, Cr and CeO2 seem to improve the shelf-life of Ni-SBA-15 via different mechanistic pathways. CeO2 mitigated Ni poisoning through coke oxidation whereas Cr bolstered the catalyst stability via maintaining a well-defined pore size, structural rigidity, and integrity of the heterogeneous framework, thereby restricting structural collapse, and hence retard sintering of the Ni active sites during the long-term 60 h of continuous reaction. Hydrogen generation from renewable biomass like glycerol could potentially serve as a sustainable energy source and could substantially help reduce the carbon footprint of the environment.

Evaluation of physiological responses to mental workload in n-back and arithmetic tasks

Working memory tasks, such as n-back and arithmetic tasks, are frequently used in studying mental workload. The present study investigated and compared the sensitivity of several physiological measures at three levels of difficulty of n-back and arithmetic tasks. The results showed significant differences in fixation duration and pupil diameter among three task difficulty levels for both n-back and arithmetic tasks. Pupil diameters increase with increasing mental workload, whereas fixation duration decreases. Blink duration and heart rate (HR) were significantly increased as task difficulty increased in the n-back task, while root mean square of successive differences (RMSSD) and standard deviation of R-R intervals (SDNN) were significantly decreased in the arithmetic task. On the other hand, blink rate and Galvanic Skin Response (GSR) were not sensitive enough to assess the differences in task difficulty for both tasks. All significant physiological measures yielded significant differences between low and high task difficulty except for SDNN.Practitioner summary: This study aimed to assess the sensitivity levels of several physiological measures of mental workload in n-back and arithmetic tasks. It showed that pupil diameter was the most sensitive in both tasks. This study also found that most physiological indices are sensitive to an extreme change in task difficulty levels.

False-negative probability in the SEM/EDS automated discovery of iGSR particles: A Bayesian approach

The automated search software integrated with a scanning electron microscope (SEM/EDS) has been the standard tool for detecting inorganic gunshot residues (iGSR) for several decades. The detection of these particles depends on various factors such as collection, preservation, contamination with organic matter, and the method for sample analysis. This article focuses on the influence of equipment resolution setup on the backscattered electron images of the sample. The pixel size of these images plays a crucial role in determining the detectability of iGSR particles, especially those with sizes close to the pixel size. In this study, we calculated the probability of missing all characteristic iGSR particles in a sample using an SEM/EDS automated search and how it depends on the image pixel resolution setup. We developed and validated an iGSR particle detection model that links particle size with equipment registers and applied it to 320 samples analyzed by a forensic science laboratory. Our results show that the probability of missing all characteristic iGSR particles due to their size is below 5% for pixel sizes below 0.32 μm2 . These findings indicate that pixel sizes as large as twice the one commonly used in laboratory casework, that is, 0.16 μm2 , are effective for initial sample scanning, yielding good detection rates of characteristic particles that could exponentially reduce laboratory workload.

Monitoring Inattention in Construction Workers Caused by Physical Fatigue Using Electrocardiograph (ECG) and Galvanic Skin Response (GSR) Sensors

Physical fatigue is frequent for heavy manual laborers like construction workers, but it causes distraction and may lead to safety incidents. The purpose of this study is to develop predictive models for monitoring construction workers' inattention caused by physical fatigue utilizing electrocardiograph (ECG) and galvanic skin response (GSR) sensors. Thirty participants were invited to complete an attention-demanding task under non-fatigued and physically fatigued conditions. Supervised learning algorithms were utilized to develop models predicting their attentional states, with heart rate variability (HRV) features derived from ECG signals and skin electric activity features derived from GSR signals as data inputs. The results demonstrate that using HRV features alone could obtain a prediction accuracy of 88.33%, and using GSR features alone could achieve an accuracy of 76.67%, both through the KNN algorithm. The accuracy increased to 96.67% through the SVM algorithm when combining HRV and GSR features. The findings indicate that ECG sensors used alone or in combination with GSR sensors can be applied to monitor construction workers' inattention on job sites. The findings would provide an approach for detecting distracted workers at job sites. Additionally, it might reveal the relationships between workers' physiological features and attention.

Affective Impressions Recognition under Different Colored Lights Based on Physiological Signals and Subjective Evaluation Method

The design of the light environment plays a critical role in the interaction between people and visual objects in space. Adjusting the space's light environment to regulate emotional experience is more practical for the observers under lighting conditions. Although lighting plays a vital role in spatial design, the effects of colored lights on individuals' emotional experiences are still unclear. This study combined physiological signal (galvanic skin response (GSR) and electrocardiography (ECG)) measurements and subjective assessments to detect the changes in the mood states of observers under four sets of lighting conditions (green, blue, red, and yellow). At the same time, two sets of abstract and realistic images were designed to discuss the relationship between light and visual objects and their influence on individuals' impressions. The results showed that different light colors significantly affected mood, with red light having the most substantial emotional arousal, then blue and green. In addition, GSR and ECG measurements were significantly correlated with impressions evaluation results of interest, comprehension, imagination, and feelings in subjective evaluation. Therefore, this study explores the feasibility of combining the measurement of GSR and ECG signals with subjective evaluations as an experimental method of light, mood, and impressions, which provided empirical evidence for regulating individuals' emotional experiences.

A study of dispersion of gunshot residue from a frequently used Serbian ammunition cal. 7.65 mm to support selected aspects of casework in North Macedonia

The subject of this research was the inorganic gunshot residue component collected from shooting patterns obtained on woven cotton cloth using a Pietro Beretta model 70 pistol, cal. 7.65 mm and Serbian ammunition for the following muzzle-to-target distances: 25, 50, 75, 100 and 125 cm. For each distance, three rounds of shooting were performed. Particles were lifted within a 10 cm radius of the projectile entrance and automatically analyzed using a scanning electron microscope coupled with an energy dispersion X-ray spectrometry. The obtained data on the populations of particles were analyzed taking into account their numbers, chemical classes and sizes. The results showed an apparent maximum incidence within all particles containing barium at about 50 cm distance. Also, lead particles revealed a distinct behaviour, being dominant at a 25 cm distance, falling below the other chemical classes, and finally becoming dominant again at 125 cm. The analysis of the frequency of occurrence of particles sorted according to their sizes confirmed that the small particle population is the largest, and their distribution in function of the equivalent circle diameter is exponential-like. The obtained results provided knowledge on the distribution of particles in the vicinity of the tested firearm and ammunition cal. 7.65 mm which generally corroborates with similarly studied GSR distributions obtained for the use of pistols cal. 9 mm. This information, together with the examinations of gunshot damages and other types of residues such as soot or unburned propellant grains may support qualitative inferences on shooting distance estimation, especially in cases, when the firearm and cartridges are not available to perform test shooting. In such cases even roughly estimated shooting distance can be helpful, e.g. for confirming or excluding the possibility of self-inflicted injuries or suicide and infer on the mutual position of the shooting stage actors. An example of casework that illustrates intermediate shooting distance estimation is presented.

Associations between emotions and psychophysiological states and confirmation bias in question formulation in ongoing simulated investigative interviews of child sexual abuse

Introduction

In forensic settings interviewers are advised to ask as many open-ended questions as possible. However, even experts may have difficulty following this advice potentially negatively impacting an investigation. Here, we sought to investigate how emotions and psychophysiological parameters are associated with question formulation in real time in an ongoing (simulated) child sexual abuse (CSA) interview.

Method

In a experimental study, psychology students (N = 60, Mage = 22.75) conducted two interviews with child avatars, while their emotions (anger, sadness, disgust, surprise and relief), GSR and heart rate (HR) were registered.

Results

First, we found that general emotionality related to CSA and perceived realness of the avatars was associated with stronger overall emotional reactions. Second, we found that closed (vs. open) questions were preceded by more facially observable anger, but not disgust, sadness, surprise or relief. Third, closed (vs. open) questions were preceded by higher GSR resistance and lower heart rate.

Discussion

Results suggest for the first time that emotions and psychophysiological states can drive confirmation bias in question formulation in real time in CSA.

Voltammetric analysis of luminescent markers in gunshot residues

Currently, SEM-EDS is used to detect gunshot residue (GSR) from the presence of Ba, Pb, and Sb in the sample. However, the development of new nontoxic ammunition (NTA) has prevented conventional metals from being found. In this work, we aim to determine the presence of an inorganic luminescent chemical marker based on rare earth in gunshot residues using the technique of squarewave voltammetry (SWV). After firing, the luminescent complex [(Eu₂ Zr)(btc)₃ (Hbtc)_0.5 .6H₂ O], which is used as a chemical marker, can be detected under a UV lamp. An aqueous solution with 0.1 mol L^-1 KCl as supporting electrolyte can be easily collected on carbon paste electrode surfaces for SWV analysis A = 100 mV, f = 10 Hz, and step potential of 5 mV are required. The luminescent marker incorporated into the carbon paste electrode showed two anodic peak currents in the region of 0.4 V (vs Ag/AgCl) and at 0.75 V (vs Ag/AgCl) and also a cathodic one in 0.4 V (vs Ag/AgCl). SEM-EDS was able to analyze the same voltammetric results for conventional and nontoxic ammunition containing the luminescent marker. Therefore, voltammetry and SEM-EDS are valid for detecting the new residue marker in GSR. Despite this, the electrochemical method is still more advantageous because of its low cost and lack of expensive equipment and supplies in forensic laboratories.

Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages

In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.

Examining the relationship of personality traits with online teaching using emotive responses and physiological signals

In the education sector, there is a rapid increase in using online teaching and learning scenarios. Making these scenarios more effective is the main purpose of this study. Though there are a lot of factors that affect it, however, the primary focus is to find out the relationship between a teacher's personality and their liking for online teaching. To conduct the study, a framework has been proposed which is a mixed design of self-reported (emotions and personality) data and physiological responses of a teacher. In self-reported data, along with teachers, learners' perception of a teacher's personality is also considered which explores their relationship with online teaching. The final results reveal that teachers with a high level of agreeableness, conscientiousness, and openness personality traits are more comfortable with online teaching as compared to extraversion and neuroticism traits. To validate the self-reported data analysis, the physiological responses of teachers were recorded that ensure the authenticity of the collected data. It also ensures that the physiological responses along with emotions are also good indicators of personality recognition.

A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation

Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger’s predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human factors, such as metabolic rate, clothing, etc., which do not necessarily reflect the actual human thermal comfort. Therefore, as electronic sensor devices have become widely used, we propose to develop a thermal sensation (TS) model that takes in humans’ physiological signals for consideration in addition to the environment parameters. We conduct climate chamber experiments to collect physiological signals and personal TS under different environments. The collected physiological signals are ECG, EEG, EMG, GSR, and body temperatures. As a preliminary study, we conducted experiments on young subjects under static behaviors by controlling the room temperature, fan speed, and humidity. The results show that our physiological-signal-based TS model performs much better than the PMV model, with average RMSEs 0.75 vs. 1.07 (lower is better) and R² 0.77 vs. 0.43 (higher is better), respectively, meaning that our model prediction has higher accuracy and better explainability. The experiments also ranked the importance of physiological signals (as EMG, body temperature, ECG, and EEG, in descending order) so they can be selectively adopted according to the feasibility of signal collection in different application scenarios. This study demonstrates the usefulness of physiological signals in TS prediction and motivates further thorough research on wider scenarios, such as ages, health condition, static/motion/sports behaviors, etc.

The use and understanding of forensic reports by judicial actors-The field of gunshot residue expertise as an example

A comprehensive review was conducted on 42 closed court cases for which at least one gunshot residue (GSR) expertise had been requested. The aim of this study is to examine the use and understanding of the GSR findings by criminal justice professionals, the contribution to/relationship with other (forensic) elements of the case, and ultimately to assess the place of the GSR expertise in judicial decisions. The study shows that, in the vast majority of cases, the court appeared to interpret and use the GSR findings correctly, although some minor misuses were identified, mainly when the court incorrectly mentioned the expert's words, using the prosecutor's fallacy. In the end, a higher percentage of conviction rate (+23% points) was observed when incriminating GSR findings were obtained. Surprisingly, in half of the cases examined in which a guilty verdict was reached, GSR evidence was the only forensic evidence in the file.

On the intersection between data quality and dynamical modelling of large-scale fMRI signals

Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connectivity matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.

Finding GSR evidence on used towels

When a firearm is discharged, gunshot residue (GSR) is produced and may be deposited on a shooter's body and his close vicinity. The tendency of GSR to drop off easily from the shooter may hinder forensic detection; this well-known phenomenon is accelerated by various common physical activities so that the number of particles detected on a suspect decreases over time. After shooting incidents, suspects will often try to cover their tracks by taking a shower. In these cases, it was assumed that no GSR will be detected upon examining the suspect's hand and hair. In the present study, we provide a way to overcome this loss of evidence by taking advantage of another occurrence, namely secondary transfer. Our participants were asked to take a shower after shooting a firearm. Samples were thereafter collected from the used bath towels and were found to contain up to a few dozen particles characteristic of GSR, including very large particles (>45 µm). The detection of GSR on a suspect's towel may provide significant forensic evidence aiding an investigation. When a shooter tries to remove evidence by taking a shower, sampling the towels that he may have used can preserve important evidence and connect a suspect to a shooting incident.

Unusual soot pattern from suicidal handgun wound

Soot is deposited from 20-30 cm from the muzzle of most handguns and arranged concentrically around the entry wound. We examined a case of a self-inflicted gunshot wound from a 9-mm pistol that left an unusual pattern of soot deposition consisting of two circular 3-mm deposits of soot located 2 cm from the entry wound. Examination of the weapon and test-firing it against a cloth reproduced the soot deposits, which were caused by two ports on the top of the barrel. Examination of the weapon and comparing the barrel with the entry wound and surrounding skin may provide important information about the type of weapon, the muzzle-to-target distance, and atypical soot deposits.

The Sample Size Matters: To What Extent the Participant Reduction Affects the Outcomes of a Neuroscientific Research. A Case-Study in Neuromarketing Field

The sample size is a crucial concern in scientific research and even more in behavioural neurosciences, where besides the best practice it is not always possible to reach large experimental samples. In this study we investigated how the outcomes of research change in response to sample size reduction. Three indices computed during a task involving the observations of four videos were considered in the analysis, two related to the brain electroencephalographic (EEG) activity and one to autonomic physiological measures, i.e., heart rate and skin conductance. The modifications of these indices were investigated considering five subgroups of sample size (32, 28, 24, 20, 16), each subgroup consisting of 630 different combinations made by bootstrapping n (n = sample size) out of 36 subjects, with respect to the total population (i.e., 36 subjects). The correlation analysis, the mean squared error (MSE), and the standard deviation (STD) of the indexes were studied at the participant reduction and three factors of influence were considered in the analysis: the type of index, the task, and its duration (time length). The findings showed a significant decrease of the correlation associated to the participant reduction as well as a significant increase of MSE and STD (p < 0.05). A threshold of subjects for which the outcomes remained significant and comparable was pointed out. The effects were to some extents sensitive to all the investigated variables, but the main effect was due to the task length. Therefore, the minimum threshold of subjects for which the outcomes were comparable increased at the reduction of the spot duration.

Effects of neoadjuvant therapies on genetic regulation of targeted pathways in ER+ primary ductal breast carcinoma: A meta-analysis of microarray datasets

Breast cancer arises as a result of multiple interactions between environmental and genetic factors. Conventionally, breast cancer is treated based on histopathological and clinical features. DNA technologies like the human genome microarray are now partially integrated into clinical practice and are used for developing new "personalized medicines" and "pharmacogenetics" for improving the efficiency and safety of cancer medications. We investigated the effects of four established therapies-for ER+ ductal breast cancer-on the differential gene expression. The therapies included single agent tamoxifen, two-agent docetaxel and capecitabine, or combined three-agents CAF (cyclophosphamide, doxorubicin, and fluorouracil) and CMF (cyclophosphamide, methotrexate, and fluorouracil). Genevestigator 8.1.0 was used to compare five datasets from patients with infiltrating ductal carcinoma, untreated or treated with selected drugs, to those from the healthy control. We identified 74 differentially expressed genes involved in three pathways, i.e., apoptosis (extrinsic and intrinsic), oxidative signaling, and PI3K/Akt signaling. The treatments affected the expression of apoptotic genes (TNFRSF10B [TRAIL], FAS, CASP3/6/7/8, PMAIP1 [NOXA], BNIP3L, BNIP3, BCL2A1, and BCL2), the oxidative stress-related genes (NOX4, XDH, MAOA, GSR, GPX3, and SOD3), and the PI3K/Akt pathway gene (ERBB2 [HER2]). Breast cancer treatments are complex with varying drug responses and efficacy among patients. This necessitates identifying novel biomarkers for predicting the drug response, using available data and new technologies. GSR, NOX4, CASP3, and ERBB2 are potential biomarkers for predicting the treatment response in primary ER+ ductal breast carcinoma.

Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli

The galvanic skin response (GSR; also widely known as electrodermal activity (EDA)) is a signal for stress-related studies. Given the sparsity of studies related to the GSR and the variety of devices, this study was conducted at the Human Health Activity Laboratory (H2AL) with 17 healthy subjects to determine the variability in the detection of changes in the galvanic skin response among a test group with heterogeneous respondents facing pleasant and unpleasant stimuli, correlating the GSR biosignals measured from different body sites. We experimented with the right and left wrist, left fingers, the inner side of the right foot using Shimmer3GSR and Empatica E4 sensors. The results indicated the most promising homogeneous places for measuring the GSR, namely, the left fingers and right foot. The results also suggested that due to a significantly strong correlation among the inner side of the right foot and the left fingers, as well as the moderate correlations with the right and left wrists, the foot may be a suitable place to homogenously measure a GSR signal in a test group. We also discuss some possible causes of weak and negative correlations from anomalies detected in the raw data possibly related to the sensors or the test group, which may be considered to develop robust emotion detection systems based on GRS biosignals.

MicroRNA-362-5p promotes the proliferation and inhibits apoptosis of trophoblast cells via targeting glutathione-disulfide reductase

Gestational diabetes mellitus (GDM), a common complication of pregnancy, harms the health of pregnant women and fetuses. MicroRNAs (miRNAs) dysregulation in placenta is involved in GDM. Herein, we explored the roles of miR-362-5p in GDM. After high glucose (HG) treated HTR-8/SVneo cells, CCK-8 and flow cytometry were conducted to assess the capability of the proliferation and apoptosis, respectively. The data demonstrated that HG inhibited proliferation and induced apoptosis of HTR-8/SVneo cells. MiR-362-5p level was reduced in HG-treated cells and placenta tissues of GDM patients, measured by qPCR. Overexpressed miR-362-5p accelerated the proliferation and restrained apoptosis of HG-treated cells. Furthermore, glutathione-disulfide reductase (GSR) was verified as a target of miR-362-5p, through TargetScan database and dual-luciferase reporter assay. GSR was upregulated in GDM placenta tissues and was negatively regulated by miR-362-5p. Enforced GSR level abolished the effects of miR-362-5p overexpression on the proliferation and apoptosis of HTR-8/SVneo cells. Furthermore, miR-362-5p increased p-PI3K, p-AKT and bcl-2, while reduced bax and cleaved caspase3, which were abolished by GSR. In conclusion, miR-362-5p promoted cell proliferation and inhibited apoptosis via targeting GSR and activating PI3K/AKT pathway. The findings mentioned above suggested that miR-362-5p might be a therapy target of GDM.

Dietary exposure to methyl mercury chloride induces alterations in hematology, biochemical parameters, and mRNA expression of antioxidant enzymes and metallothionein in Nile tilapia

Methyl mercury chloride "MMC" (CH₃ClHg) is an ubiquitous environmental toxicant that causes a variety of adverse effects. In the present study, we investigated the effects of sub-chronic toxicity of MMC on Nile tilapia (Oreochromis niloticus) through the evaluation of growth performance and hematological, biochemical, and oxidative stress biomarkers. From 150 healthy fish, five equally sized treatment groups were created: a control (CT) group fed with a basal diet and four MMC treatment groups exposed to 0.5, 1, 1.5, and 2 mg of MMC per kg of basal diet for 60 days. MMC exposure significantly reduced the growth performance and survival of O. niloticus and decreased red blood cell count and hemoglobin concentration. Treated fish exhibited normocytic normochromic anemia in addition to leucopenia, lymphopenia, granulocytopenia, and monocytopenia. Moreover, MMC exposure significantly affected liver function, including a reduction in the total protein levels while increasing cholesterol and triglyceride levels. It also markedly increased the production of stress biomarkers such as glucose and cortisol levels. Furthermore, MMC significantly elevated the levels of hepatic enzymes, induced tissue damage, and caused inflammation, as indicated by the upregulation of mRNA expression of hepatic metallothionein. Finally, MMC exposure induced oxidative stress by altering the antioxidant status of the liver and downregulating the mRNA expression of superoxide dismutase, glutathione peroxidase, and glutathione S-reductase. In conclusion, MMC toxicity induced hematological and biochemical alterations, leading to an enhanced state of oxidative stress in O. niloticus.

Gunshot residue detection in stagnant water: SEM-EDX or ICP-MS? A preliminary study

The identification of gunshot residue (GSR) on wounds enables the differentiation of entry and exit wounds. Unfortunately, studies analyzing GSR on degraded bodies have been poorly documented, and no data exist regarding GSR detection after stagnant water immersion. The aim of this preliminary experimental study was to detect GSR on wounds altered in stagnant water, using scanning electron microscopy coupled with energy-dispersive X (SEM-EDX) and inductively coupled plasma mass spectrometry (ICP-MS). Shots were performed on sheep limbs with a 22LR at a distance of 20 cm. The limbs were then submerged in stagnant water and analyzed on days 0, 6, and 14. SEM-EDX was performed on previously dehydrated wounds. For ICP-MS analysis, the wounds were rubbed with a cotton swab that was then analyzed. In the SEM studies, a higher number of particles were detected in entry wounds compared to exit wounds under every set of experimental conditions. Unfortunately, SEM-EDX failed to detect GSR particles, even on day 0. ICP-MS enabled the detection of Pb, Sb, and Ba at every stage with higher quantities on entry than in exit. These elements remained detectable following limb immersion. ICP-MS enabled differentiate entry from exit wounds, even after immersion in stagnant water. Nevertheless, when manually swabbing the wounds, quantities of matter collected is highly variable. ICP-MS is a more suitable technique than SEM-EDX for GSR identification of wounds after decomposition in stagnant water; however, standardization is needed.

Identification of Metabolic-Associated Genes for the Prediction of Colon and Rectal Adenocarcinoma

Background and Aim

Uncontrolled proliferation is the most prominent biological feature of tumors. In order to rapidly proliferate, tumor cells regulate their metabolic behavior by controlling the expression of metabolism-related genes (MRGs) to maximize the utilization of available nutrients. In this study, we aimed to construct prognosis models for colorectal adenocarcinoma (COAD) and rectum adenocarcinoma (READ) using MRGs to predict the prognoses of patients.

Methods

We first acquired the gene expression profiles of COAD and READ from the TCGA database, and then utilized univariate Cox analysis, Lasso regression, and multivariable Cox analysis to identify the MRGs for risk models.

Results

Eight genes (CPT1C, PLCB2, PLA2G2D, GAMT, ENPP2, PIP4K2B, GPX3, and GSR) in the colon cancer risk model and six genes (TDO2, PKLR, GAMT, EARS2, ACO1, and WAS) in the rectal cancer risk model were identified successfully. Multivariate Cox analysis indicated that these two models could accurately and independently predict overall survival (OS) for patients with COAD or READ. Furthermore, functional enrichment analysis was used to identify the metabolism pathway of MRGs in the risk models and analyzed these genes comprehensively. Then, we verified the prognosis model in independent COAD cohorts (GSE17538) and detected the correlations of the protein expression levels of GSR and ENPP2 with prognosis for COAD or READ.

Conclusion

In this study, 14 MRGs were identified as potential prognostic biomarkers and therapeutic targets for colorectal cancer.

The risk of inter-stub contamination during SEM/EDS analysis of gunshot residue particles

Detecting gunshot residue (GSR) particles on samples collected from individuals or their belongings can connect them to a shooting event. Scanning electron microscopy combined with energy-dispersive X-ray spectrometry (SEM/EDX) is currently the most common forensic method for detecting and characterizing GSR. At the forensic laboratory of the Israel Police, one inch (25 mm) diameter sticky stubs are used to collect samples from suspects' hands, hair, clothes and vehicles. To maximize testing capacity, stubs of samples collected from several different cases and persons may be analyzed side by side in a single run. This has raised concern in court that a clean sample taken from an innocent person may be contaminated during the analysis by GSR particles from an adjacent sample transferred inside the SEM chamber. several experiments were conducted where stubs that were known to contain GSR particles were run adjacent to stubs that were known to be clean. Not a single event of GSR particle transfer was detected, even when a clean stub was surrounded on all sides by stubs containing a total of over 100,000 particles. Thus, the probability of transfer of a single particle is at most 1:100,000. Since the total number of GSR particles found per run is usually three orders of magnitude lower than 100,000, we conclude that the risk of inter-stub contamination is highly negligible.

Interpol review of gunshot residue 2016-2019

This review paper covers the forensic-relevant literature in gunshot residue analysis from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.

Emotion matters: Different psychophysiological responses to expressive and non-expressive full-body movements

We explore dance video clip stimuli as a means to test human observers' accuracy in detecting genuine emotional expressivity in full-body movements. Stimuli of every-day-type full-body expressions of emotions usually use culturally very recognizable actions (e.g. fist shaking for anger, etc). However, expressive dance movement stimuli can be created to contain fully abstract movements. The expressivity results from subtle variations in the body movements of the expressor, and emotions cannot be recognised by observers via particular actions (e.g. fist shaking, etc). Forty-one participants watched and rated 24 pairs of short dance videos -from a published normalised dance stimuli library- in randomised order (N = 48). Of each carefully matched pair, one version of the full-body movement sequence had been danced to be emotionally genuinely expressive (clip a), while the other version of the same sequence (clip b) had been danced -while technically correct- without any emotional expressivity. Participants rated (i) expressivity (to test their accuracy; block 1), and (ii) how much they liked each movement (an implicit measure to test their emotional response ("liking"); block 2). Participants rated clips that were intended to be expressive as more expressive (part 1: expressivity ratings), and liked those expressive clips more than the non-expressive clips (part 2: liking ratings). Besides, their galvanic skin response differed, depending on the category of clips they were watching (expressive vs. non-expressive), and this relationship was modulated by interceptive accuracy and arts experience. Results are discussed in relation to the Body Precision Hypothesis and the Hypothesis of Constructed Emotion.

Eyes Closing and Drowsiness in Human Subjects Decrease Baseline Galvanic Skin Response and Active Palmar Sweating: Relationship Between Galvanic Skin and Palmar Perspiration Responses

We previously constructed a perspiration ratemeter for the measurement of palmar sweating in human subjects. Although galvanic skin response (GSR) has been used to evaluate emotional responses in human subjects, little is known about the relationships between the phasic and baseline components in GSR and active palmar sweating. From the aforementioned, we aimed to investigate the relationships in human subjects with handgrip exercise and eyes closing or opening. Fifteen healthy volunteers (mean age: 26.9 ± 8.7 years) participated in the present experiments. We investigated the effects of maximal handgrip exercise, eyes closing or opening, and self-awareness of drowsy on the GSR, active palmar sweating, R-R interval in electrocardiograph (ECG), and percentage of α wave in EEG. The faster phasic component in GSR completely agreed with the starting point of active palmar sweating. Handgrip exercise induced significantly faster spike in GSR, active palmar sweating, and decrease in R-R interval in ECG. Eyes closing produced significant decreases in baseline GSR and active palmar sweating in all human subjects. The percentage of α wave in electroencephalograph (EEG) also increased. In contrast, eyes opening increased significantly the baseline GSR and active palmar sweating. In the equivalent electrical model of human skin, the eyes closing-mediated time-dependent decrease in the baseline GSR completely agreed with the hypothesis that the palmar skin voltage only in the model decreased time dependently to 0.4 of the control during 6 min. The self-awareness of drowsy in mid-night working with computer produced similar decreases in baseline GSR and active palmar sweating to the responses with eyes closing in all human subjects. In conclusion, the faster spike in GSR completely agreed with the starting point of active palmar sweating. Eyes closing and opening or self-awareness of drowsy significantly produced changes in baseline GSR and active palmar sweating, which may become useful tools for evaluating clearness or drowsiness in human subjects.

Analysis of Consciousness Level Using Galvanic Skin Response during Therapeutic Effect

The neurological status of patients in the Intensive Care Units (ICU) is determined by the Glasgow Coma Scale (GCS). Patients in coma are thought to be unaware of what is happening around them. However, many studies show that the family plays an important role in the recovery of the patient and is a great emotional resource. In this study, Galvanic Skin Response (GSR) signals were analyzed from 31 patients with low consciousness levels between GCS 3 and 8 to determine relationship between consciousness level and GSR signals as a new approach. The effect of family and nurse on unconscious patients was investigated by GSR signals recorded with a new proposed protocol. The signals were recorded during conversation and touching of the patient by the nurse and their families. According to numerical results, the level of consciousness can be separated using GSR signals. Also, it was found that family and nurse had statistically significant effects on the patient. Patients with GCS 3,4, and 5 were considered to have low level of consciousness, while patients with GCS 6,7, and 8 were considered to have high level of consciousness. According to our results, it is obtained lower GSR amplitude in low GCS (3, 4, 5) compared to high GCS (7, 8). It was concluded that these patients were aware of therapeutic affect although they were unconscious. During the classification stage of this study, the class imbalance problem, which is common in medical diagnosis, was solved using Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN) and random oversampling methods. In addition, level of consciousness was classified with 92.7% success using various decision tree algorithms. Random Forest was the method which provides higher accuracy compared to all other methods. The obtained results showed that GSR signal analysis recorded in different stages gives very successful GCS score classification performance according to literature studies.

Hybrid System of Emotion Evaluation in Physiotherapeutic Procedures

Nowadays, the dynamic development of technology allows for the design of systems based on various information sources and their integration into hybrid expert systems. One of the areas of research where such systems are especially helpful is emotion analysis. The sympathetic nervous system controls emotions, while its function is directly reflected by the electrodermal activity (EDA) signal. The presented study aimed to develop a tool and propose a physiological data set to complement the psychological data. The study group consisted of 41 students aged from 19 to 26 years. The presented research protocol was based on the acquisition of the electrodermal activity signal using the Empatica E4 device during three exercises performed in a prototype Disc4Spine system and using the psychological research methods. Different methods (hierarchical and non-hierarchical) of subsequent data clustering and optimisation in the context of emotions experienced were analysed. The best results were obtained for the k-means classifier during Exercise 3 (80.49%) and for the combination of the EDA signal with negative emotions (80.48%). A comparison of accuracy of the k-means classification with the independent division made by a psychologist revealed again the best results for negative emotions (78.05%).

Filtering and model-based analysis independently improve skin-conductance response measures in the fMRI environment: Validation in a sample of women with PTSD

Numerous methods exist for the pre-processing and analysis of skin-conductance response (SCR) data, but there is incomplete consensus on suitability and implementation, particularly with regard to signal filtering in conventional peak score (PS) analysis. This is particularly relevant when SCRs are measured during fMRI, which introduces additional noise and signal variability. Using SCR-fMRI data (n = 65 women) from a fear conditioning experiment, we compare the impact of three nested data processing methods on analysis using conventional PS as well as psychophysiological modeling. To evaluate the different methods, we quantify effect size to recover a benchmark contrast of interest, namely, discriminating SCR magnitude to a conditioned stimulus (CS+) relative to a CS not followed by reinforcement (CS-). Findings suggest that low-pass filtering reduces PS sensitivity (Δd = -20%), while band-pass filtering improves PS sensitivity (Δd = +27%). We also replicate previous findings that a psychophysiological modeling approach yields superior sensitivity to detect contrasts of interest than even the most sensitive PS method (Δd = +110%). Furthermore, we present preliminary evidence that filtering differences may account for a portion of exclusions made on commonly applied metrics, such as below zero discrimination. Despite some limitations of our sample and experimental design, it appears that SCR processing pipelines that include band-pass filtering, ideally with model-based SCR quantification, may increase the validity of SCR response measures, maximize research productivity, and decrease sampling bias by reducing data exclusion.

Autonomic and Electrophysiological Evidence for Reduced Auditory Habituation in Autism

It is estimated that nearly 90% of children on the autism spectrum exhibit sensory atypicalities. What aspects of sensory processing are affected in autism? Although sensory processing can be studied along multiple dimensions, two of the most basic ones involve examining instantaneous sensory responses and how the responses change over time. These correspond to the dimensions of 'sensitivity' and 'habituation'. Results thus far have indicated that autistic individuals do not differ systematically from controls in sensory acuity/sensitivity. However, data from studies of habituation have been equivocal. We have studied habituation in autism using two measures: galvanic skin response (GSR) and magneto-encephalography (MEG). We report data from two independent studies. The first study, was conducted with 13 autistic and 13 age-matched neurotypical young adults and used GSR to assess response to an extended metronomic sequence. The second study involved 24 participants (12 with an ASD diagnosis), different from those in study 1, spanning the pre-adolescent to young adult age range, and used MEG. Both studies reveal consistent patterns of reduced habituation in autistic participants. These results suggest that autism, through mechanisms that are yet to be elucidated, compromises a fundamental aspect of sensory processing, at least in the auditory domain. We discuss the implications for understanding sensory hypersensitivities, a hallmark phenotypic feature of autism, recently proposed theoretical accounts, and potential relevance for early detection of risk for autism.

Evaluation of the Simultaneous Analysis of Organic and Inorganic Gunshot Residues Within a Large Population Data Set Using Electrochemical Sensors*, †

The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen-printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4-dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1 µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field-testing technique and assist with decisions in case management.

CNN and LSTM-Based Emotion Charting Using Physiological Signals

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence-High Arousal, High Valence-Low Arousal, Low Valence-High Arousal, and Low Valence-Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.

Identification and clinical validation of metastasis-associated biomarkers based on large-scale samples in colon-adenocarcinoma

AIM

Distant metastasis is the main cause of death in patients with colon-adenocarcinoma(COAD). Due to the lack of effective molecular markers and treatment, the prognosis of patients with metastatic colon cancer is still rather poor.

METHODS

Metastatic related signature (MRS) of stage I and stage IV in colon cancer were identified from different cohorts. Univariate cox regression is used to analyze the relationship between MRS and the overall survival. L1000FWD and DGIdb databases are used to identify molecular drugs. Expression and functional experimental validation of the hub MRS were carried out.

RESULTS

16 MRS were identified, of which 14 MRS was significantly correlated with overall survival. Further functional enrichment analysis showed that MRS was significantly involved with important biological functions such as cell migration, and apoptosis. As important metastatic related genes, GSR, FAS and CYP1B1 have significant interaction with drug molecules. Further studies have confirmed that the expression of FAS and GSR is low, and inhibition of its expression can promote the metastasis of COAD. CYP1B1 expression is highly expressed, and inhibition of its expression can attenuate the malignant biological behavior of colon cancer.

CONCLUSION

Our research could increase the understanding of the mechanism of colon cancer metastasis and provide theoretical basis for the treatment of metastatic colon cancer.

Technical note: presence of gunshot residue in and around a police station

Previous studies on the transference of gunshot residue (GSR) have shown that GSR can be transferred to surfaces through everyday activities and can persist on surfaces. Being that all police departments operate differently and have different spaces, GSR can be transferred and accumulates in different areas. Samples were collected from persons and surfaces in and around the Scranton Police Department and tested by scanning electron microscopy to identify GSR. Surfaces included police car seats, gun holsters, clothing around holsters, and belts around holsters. The results of the study showed that of the 25 samples collected, 40% contained at least one particle that was "characteristic of primer GSR", 64% contained at least one particle that was "consistent with primer GSR", and 92% contained at least one particle considered "commonly associated with primer GSR". This research characterizes where GSR is transferred within and around the police department. This data can be used to implement cleaning procedures or methods for decontamination. This study continues to strengthen the body of knowledge surrounding transferring of GSR.

Conduction of a round-robin test on a real sample for the identification of gunshot residues by SEM/EDX

A round-robin test on the identification of GSR particles by SEM/EDX and involving eleven Institutes was conducted on a real sample, in order to evaluate the possibilities/limitations of using such sample to get additional information (compared to the analysis of the usual synthetic sample used within the framework of the ENFSI proficiency test) about the performances of the SEM/EDX systems. Each Institute was asked to analyse this sample following its own standard operating procedure, and by using all the systems in house, whenever available. Between each Institute, a check of the sample was performed by the organizing Institute (NICC), in order inter alia to monitor any degradation and/or contamination of the sample. A total of about 30 analyses were performed on the sample. For each particle of interest identified on the real sample, the detection effectiveness was monitored, as well as the classification allotted by each Institute. The Institutes were also asked to report some of their measurement parameters, and to send the results as they would have been communicated in their own case report. A quite good agreement was observed with regard to the classification of the particles of interest, since a broad consensus was reached for approximately 75% of these particles. A different classification risk exists for some classes, the barium/antimony classes being probably the most critical, as traces of lead may cause the particles to shift (or not) from the consistent with GSR upper-class to the characteristic of GSR upper-class; in the end, the decision to shift from one class to another strongly depends on local rules. At the end of the campaign, a survey sent to collect experience and lessons learned from this exercise showed that analysing a real sample definitively offers an added value, especially in terms of classification process (during the automatic run and when performing the manual review) of particles.

Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem

One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts, and its removal via GSR improves various quality-control metrics, enhances the anatomical specificity of functional-connectivity patterns, and can increase the behavioral variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified visually from a matrix of colour-coded signal intensities, called a carpet plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparently global changes are greatly diminished. Here, using three independent datasets, we show that reordering carpet plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants, and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in carpet plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. Using task fMRI data across 47 contrasts from 7 tasks in the Human Connectome Project, we also present evidence that DiCER is more successful than GSR in preserving the spatial structure of expected task-related activation patterns. Our findings indicate that care must be exercised when examining WSDs (and their possible removal) in rsfMRI data, and that DiCER is a viable alternative to GSR for removing anatomically widespread and temporally coherent signals. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER.

Genomic and Phenotypic Analyses Reveal Mechanisms Underlying Homing Ability in Pigeon

The homing pigeon was selectively bred from the domestic pigeon for a homing ability over long distances, a very fascinating but complex behavioral trait. Here, we generate a total of 95 whole genomes from diverse pigeon breeds. Comparing the genomes from the homing pigeon population with those from other breeds identifies candidate positively selected genes, including many genes involved in the central nervous system, particularly spatial learning and memory such as LRP8. Expression profiling reveals many neuronal genes displaying differential expression in the hippocampus, which is the key organ for memory and navigation and exhibits significantly larger size in the homing pigeon. In addition, we uncover a candidate gene GSR (encoding glutathione-disulfide reductase) experiencing positive selection in the homing pigeon. Expression profiling finds that GSR is highly expressed in the wattle and visual pigment cell layer, and displays increased expression levels in the homing pigeon. In vitro, a magnetic field stimulates increases in calcium ion concentration in cells expressing pigeon GSR. These findings support the importance of the hippocampus (functioning in spatial memory and navigation) for homing ability, and the potential involvement of GSR in pigeon magnetoreception.

On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights

Automobiles for our roadways are increasingly using advanced driver assistance systems. The adoption of such new technologies requires us to develop novel perception systems not only for accurately understanding the situational context of these vehicles, but also to infer the driver's awareness in differentiating between safe and critical situations. This manuscript focuses on the specific problem of inferring driver awareness in the context of attention analysis and hazardous incident activity. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally employed computer vision systems provides exciting opportunities to explore the limitations of these sensor modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing- and deep learning-based tools. We show that our methods outperform previously reported studies to classify driver attention and detecting hazardous/non-hazardous situations for short time scales of two seconds. We use EEG and vision data for high resolution temporal classification (two seconds) while additionally also employing PPG and GSR over longer time periods. We evaluate our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) while the other was collected by us (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.

Feasibility of an accelerated PVAL method for the collection of GSR and biological traces

The polyvinyl alcohol method (PVAL) is known as an effective technique to thoroughly collect traces of gunshot residue (GSR) from different surfaces, e.g., from hands or gunshot wounds. Despite obvious advantages over other methods using adhesive tapes, PVAL is still not widely accepted and applied in routine case work due to a required acquisition time of at least 15 to 20 min for a single shooting hand. In this study, the feasibility of a modified procedure taking 6 to 8 min per sample is tested within the frame of an experimental setting including (1) the collection of GSR from experimental gunshots with a semi-automatic pistol and lead-containing primer ammunition and (2) a simple experimental setting involving dry and moist artificial blood traces. In a third step, samples of four gun-associated suicide cases and one attempted suicide case were taken and analyzed. Furthermore, an exemplary implementation into a work flow of modern instrumental techniques of GSR analysis is presented.

An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data

In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user's emotions from physiological data. Among a myriad of target emotions, boredom, in particular, has been suggested to cause not only medical issues but also challenges in various facets of daily life. However, to the best of our knowledge, no previous studies have used electroencephalography (EEG) and galvanic skin response (GSR) together for boredom classification, although these data have potential features for emotion classification. To investigate the combined effect of these features on boredom classification, we collected EEG and GSR data from 28 participants using off-the-shelf sensors. During data acquisition, we used a set of stimuli comprising a video clip designed to elicit boredom and two other video clips of entertaining content. The collected samples were labeled based on the participants' questionnaire-based testimonies on experienced boredom levels. Using the collected data, we initially trained 30 models with 19 machine learning algorithms and selected the top three candidate classifiers. After tuning the hyperparameters, we validated the final models through 1000 iterations of 10-fold cross validation to increase the robustness of the test results. Our results indicated that a Multilayer Perceptron model performed the best with a mean accuracy of 79.98% (AUC: 0.781). It also revealed the correlation between boredom and the combined features of EEG and GSR. These results can be useful for building accurate affective computing systems and understanding the physiological properties of boredom.

How Neurophysiological Measures Can be Used to Enhance the Evaluation of Remote Tower Solutions

New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgment from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter-operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload. In this study, we aimed to demonstrate: (i) the higher resolution of neurophysiological measures in comparison to subjective ones; and (ii) how the simultaneous employment of neurophysiological measures and behavioral ones could allow a holistic assessment of operational tools. In this regard, we tested the effectiveness of an electroencephalography (EEG)-based neurophysiological index (W_EEG index) in comparing two different solutions (i.e., Normal and Augmented) in terms of experienced workload. In this regard, 16 professional air traffic controllers (ATCOs) have been asked to perform two operational scenarios. Galvanic Skin Response (GSR) has also been recorded to evaluate the level of arousal (i.e., operator involvement) during the two scenarios execution. NASA-TLX questionnaire has been used to evaluate the perceived workload, and an expert was asked to assess performance achieved by the ATCOs. Finally, reaction times on specific operational events relevant for the assessment of the two solutions, have also been collected. Results highlighted that the Augmented solution induced a local increase in subjects performance (Reaction times). At the same time, this solution induced an increase in the workload experienced by the participants (W_EEG). Anyhow, this increase is still acceptable, since it did not negatively impact the performance and has to be intended only as a consequence of the higher engagement of the ATCOs. This behavioral effect is totally in line with physiological results obtained in terms of arousal (GSR), that increased during the scenario with augmentation. Subjective measures (NASA-TLX) did not highlight any significant variation in perceived workload. These results suggest that neurophysiological measure provide additional information than behavioral and subjective ones, even at a level of few seconds, and its employment during the pre-operational activities (e.g., design process) could allow a more holistic and accurate evaluation of new solutions.

Bullet wipe on the uppermost textile layer of gunshot entrance sites: may it be absent due to pre-existing blood staining?

Most gunshot entrance sites on human victims are localized in clothed body regions. Except for the use of lead-free ammunition, a positive color reaction of the sodium rhodizonate test indicates a primary target hit by the bullet. Any lead residue pattern in the area around the entrance hole allows approximate conclusions as to the firing distance in close and intermediate range shots, whereas the presence of a bullet wipe denotes an entrance site. A criminal case gave rise to an experimental study to clarify whether a blood-soaked garment being shot at as a primary target may lack a bullet wipe around the entrance hole. Distant-range shots were fired with a semi-automatic pistol (Heckler & Koch, Mod. USP Compact, cal. 9-mm Luger) using cartridges with jacketed round-nose bullets and a Sinoxid primer containing lead styphnate. In fabrics saturated with fluid blood, a wide area around the bullet entrance was densely covered with rhodizonate-positive microparticles simulating gunshot residues (GSR) from a close-range shot. In shots to fabrics oversaturated with blood, a typical bullet wipe was lacking, whereas lead-containing particles were spotted in the periphery. The results are discussed with respect to the aberrant appearance of bullet entrance sites in blood-soaked fabrics.