A Comparison of Facial Emotion Recognition in Patients with Temporal Lobe Epilepsy and Non-epileptics

Abstract Introduction: Temporal lobe epilepsy (TLE) is related to the mesial temporal lobe structures such as the hippocampus, amygdala, and Parahippocampal gyrus. In patients with TLE, the amygdala complex is a component of the temporal lobe that is damaged. Previous studies on emotional processing have proven deficits due to amygdala damage in these patients. The present study compares the facial emotion recognition in patients with temporal lobe epilepsy and healthy controls. It was hypothesized that the TLE group have more dysfunctions than non- people with epilepsy. Methods: In this comparative study, 120 subjects, including 60 patients with a definite diagnosis of the temporal lobe and 60 non-epileptic individuals, were recruited using purposive sampling. The patient group was chosen from the Chamran hospital and Iranian Epilepsy Association, Tehran, Iran. The research data were collected by the Ekman computer test of facial emotion recognition. This test uses 36 images to measure the six basic emotions (i.e., happiness, disgust, anger, fear, sadness, and surprise); these images were adapted from the Ekman and Friesen series of images. The data were analyzed using multivariate analysis of variance by SPSS Statistics 19-IBM in two levels of response accuracy and reaction time in TLE patients and healthy individuals. Results: Data analysis showed a significant difference in the response accuracy of facial expressions of happiness, disgust, anger, fear, sadness, and surprise in patients with TLE (P < 0.01). Furthermore, recognizing emotions of fear, disgust, and anger in patients with TLE was more inadequate. When it came to the reaction time of emotion recognition, the TLE patients showed a higher functional impairment than the healthy group (P < 0.01). The reactions to fear and disgust were notably slower than other emotions. Conclusions: The results showed more inaccurate facial emotion recognition of fear, disgust, and anger inferred from facial expressions. Moreover, the reaction time response of facial emotion recognition for all six emotions was slower, compared to non-epileptics. Assessing the emotional recognition dysfunction through this measurement can facilitate recognizing the emotional deficiency regarding social communication in TLE patients. Psychological dysfunction can be a predictor of not a good response to the treatment, more frequency of seizures, and worse quality of life in these patients

Information Theoretic Identification and Compensation of Nonlinear Devices

Breaking the anonymity of different wireless users with the purpose of decreasing internet crime rates is addressed in this thesis by considering radiometric identification techniques. Minute imperfections and non-idealities in the different transmitter components, especially the inherent nonlinearity in power amplifiers, result in variations in their Volterra series representations which could be utilized as a signature. For a two user scenario, signal processing algorithms based on generalized likelihood ratio test(GLRT) and the classical likelihood ratio test are introduced and the resulting receiver decision rules and performance curves are presented. These algorithms consider the high signal to noise ratio(SNR) case where we have available the input samples completely at the receiver which is a practical assumption for most cases. Volterra series are widely used in behavioral modeling of power amplifiers. To validate the existence of these variations in the Volterra series representation of power amplifiers, process variations are introduced as major sources. The plausibility of our techniques are justified by deriving and comparing the Volterra coefficients for the fast and slow process corners. Finally,an information theoretic framework is presented where the amount of mutual information of the output about the Volterra coefficients represents the amount of anonymity taken from users. Here, some results for the low SNR case are presented to prove the achievability of some information about individual systems using our hardware anonymity breaking techniques

Advanced methodologies for the modeling of metabolic pathway systems based on time series data

Metabolic pathways are series of enzyme-catalyzed chemical reactions that take place within a cell. These biochemical pathways can be quite elaborate and highly regulated with numerous positive or negative feedback or feed-forward mechanisms, which produce complex dynamical behaviors. Time series data have been more readily available in recent years as a result of the development of new measurement techniques. These techniques offer novel options for inferring the intricate regulatory structure of the metabolic pathways, analyzing the design and function of biological modules, as well as making predictions based on this analysis. The first objective of the proposed research is to advance mathematical methodologies for the study of metabolic and signaling pathways where time series data are available. The second objective is the application of these methodological advances toward a deeper understanding of the glycolytic pathway in the dairy bacterium Lactococcus lactis.Ph.D

Quantitative mechanistic model reveals key determinants of placental IgG transfer and informs prenatal immunization strategies.

Transplacental antibody transfer is crucially important in shaping neonatal immunity. Recently, prenatal maternal immunization has been employed to boost pathogen-specific immunoglobulin G (IgG) transfer to the fetus. Multiple factors have been implicated in antibody transfer, but how these key regulators work together to elicit selective transfer is pertinent to engineering vaccines for mothers to optimally immunize their newborns. Here, we present the first quantitative mechanistic model to uncover the determinants of placental antibody transfer and inform personalized immunization approaches. We identified placental FcγRIIb expressed by endothelial cells as a limiting factor in receptor-mediated transfer, which plays a key role in promoting preferential transport of subclasses IgG1, IgG3, and IgG4, but not IgG2. Integrated computational modeling and in vitro experiments reveal that IgG subclass abundance, Fc receptor (FcR) binding affinity, and FcR abundance in syncytiotrophoblasts and endothelial cells contribute to inter-subclass competition and potentially inter- and intra-patient antibody transfer heterogeneity. We developed an in silico prenatal vaccine testbed by combining a computational model of maternal vaccination with this placental transfer model using the tetanus, diphtheria, and acellular pertussis (Tdap) vaccine as a case study. Model simulations unveiled precision prenatal immunization opportunities that account for a patient's anticipated gestational length, placental size, and FcR expression by modulating vaccine timing, dosage, and adjuvant. This computational approach provides new perspectives on the dynamics of maternal-fetal antibody transfer in humans and potential avenues to optimize prenatal vaccinations that promote neonatal immunity

A comparison of facial emotion recognition in patients with early- and late-onset temporal lobe epilepsy

Background: Epilepsy is accompanied by a series of clinical manifestations of frequent and abnormal discharges of brain neurons. Early onset of epilepsy can normally cause severe cognitive, emotional and social impairments. Therefore, the purpose of the present study is to compare the recognition of facial emotions in patients with early- and late-onset temporal lobe epilepsy. Materials and Methods: In a causal-comparative study, after definitive diagnosis of temporal lobe epilepsy, 80 patients with temporal lobe epilepsy included 40 early- and 40 late-onset are recruited in the study by using purposive convenience sampling. The research instruments were by Ekman test of facial emotion recognition and clinical psychiatric interview based on DSM-V. SPSS 19 analyzed data using multivariate analysis of variance. Results: The results showed significant differences in response accuracy and reaction time of facial emotion recognition between the two groups of early- and late-onset temporal lobe epilepsy (p<0.01). These differences were significant in the response accuracy for recognition of sadness, as well significant differences represented in the reaction time for all six basic emotions (happy, sadness, fear, disgust, anger and surprise). Conclusion: Patients with early-onset temporal lobe epilepsy performed poorly in recognizing sadness. Furthermore, these patients had a longer reaction time in recognizing facial emotions such as; fear, sadness, anger, disgust, happiness and surprise than patients with late-onset temporal lobe epilepsy. &nbsp

Emotion regulation difficulties and cognitive emotional regulation in patients with temporal lobe epilepsy

Objective: Temporal lobe epilepsy (TLE) is one of the most prevalent types of complex partial epilepsy in adults. Due to the damage to the amygdala, patients with TLE struggle with emotional problems. The purpose of this study was to investigate the functions of emotion regulation and cognitive-emotional self-regulation in patients with TLE compared to non-epileptic individuals. Method: In this study, 80 patients with TLE were recruited based on some inclusion criteria and compared with 80 non-epileptics by considering their emotional functions. Questionnaires for evaluating difficulties in emotion regulation (Gratz & Roemer) and cognitive emotional regulation (Garnefski) were given to the participants of the study. Finally, data analysis was performed using multivariate analysis of variance (MANOVA) by SPSS 19 Statistics. Results: The results of MANOVA test showed a significant difference between the components of difficulties in emotion regulation as well as cognitive-emotional regulation between patients with TLE and the healthy group (P<0.01). Conclusion: Patients with TLE had more difficulties in emotion regulation and higher negative cognitive emotional regulation strategies than healthy subjects. &nbsp

GeoTyper: Automated Pipeline from Raw scRNA-Seq Data to Cell Type Identification

The cellular composition of the tumor microenvironment can directly impact cancer progression and the efficacy of therapeutics. Understanding immune cell activity, the body's natural defense mechanism, in the vicinity of cancerous cells is essential for developing beneficial treatments. Single cell RNA sequencing (scRNA-seq) enables the examination of gene expression on an individual cell basis, providing crucial information regarding both the disturbances in cell functioning caused by cancer and cell-cell communication in the tumor microenvironment. This novel technique generates large amounts of data, which require proper processing. Various tools exist to facilitate this processing but need to be organized to standardize the workflow from data wrangling to visualization, cell type identification, and analysis of changes in cellular activity, both from the standpoint of malignant cells and immune stromal cells that eliminate them. We aimed to develop a standardized pipeline (GeoTyper, https://github.com/celineyayifeng/GeoTyper) that integrates multiple scRNA-seq tools for processing raw sequence data extracted from NCBI GEO, visualization of results, statistical analysis, and cell type identification. This pipeline leverages existing tools, such as Cellranger from 10X Genomics, Alevin, and Seurat, to cluster cells and identify cell types based on gene expression profiles. We successfully tested and validated the pipeline on several publicly available scRNA-seq datasets, resulting in clusters corresponding to distinct cell types. By determining the cell types and their respective frequencies in the tumor microenvironment across multiple cancers, this workflow will help quantify changes in gene expression related to cell-cell communication and identify possible therapeutic targets.Comment: 6 pages, 3 figures, IEEE conferenc

Evaluation of the Areas Involved in Visual Cortex in Parkinson\u27s Disease Using Diffusion Tensor Imaging

Parkinson\u27s disease (PD) is a progressive neurodegenerative disorder assumed to involve different areas of CNS and PNS. In PD patients and in primates with experimental Parkinsonism indicating that retinal dopamine deficiency is an important factor in the pathogenesis of PD visual dysfunction. Visual signs and symptoms of PD may include defects in eye movement, pupillary function, and in more complex visual tasks. In this study, we evaluated the areas involved in visual cortex in PD by diffusion tensor imaging to assess the structural change in PD

IgG3 collaborates with IgG1 and IgA to recruit effector function in RV144 vaccinees

While the RV144 HIV vaccine trial led to moderately reduced risk of HIV acquisition, emerging data from the HVTN702 trial point to the critical need to reexamine RV144-based correlates of reduced risk of protection. While in RV144, the induction of V2-binding, non-IgA, IgG3 antibody responses with nonneutralizing functions were linked to reduced risk of infection, the interactions between these signatures remain unclear. Thus, here we comprehensively profile the humoral immune response in 300 RV144 vaccinees to decipher the relationships between humoral biomarkers of protection. We found that vaccine-specific IgG1, IgG3, and IgA were highly correlated. However, ratios of IgG1:IgG3:IgA provided insights into subclass/isotype polyclonal functional regulation. For instance, in the absence of high IgG1 levels, IgG3 antibodies exhibited limited functional activity, pointing to IgG3 as a critical contributor, but not sole driver, of effective antiviral humoral immunity. Higher IgA levels were linked to enhanced antibody effector function, including neutrophil phagocytosis (ADNP), complement deposition (ADCD), and antibody-dependent NK degranulation (CD107a), some of which were increased in infected vaccinees in a case/control data set, suggesting that IgA-driven functions compromised immunity. These data highlight the interplay between IgG1, IgG3, and IgA, pointing to the need to profile the relationships between subclass/isotype selection