More than a feeling: Scalp EEG and eye signals in conscious tactile perception.

Understanding the neural basis of consciousness is a fundamental goal of neuroscience, and sensory perception is often used as a proxy for consciousness in empirical studies. However, most studies rely on reported perception of visual stimuli. Here we present behavior, high density scalp EEG and eye metric recordings collected simultaneously during a novel tactile threshold perception task. We found significant N80, N140 and P300 event related potentials in perceived trials and in perceived versus not perceived trials. Significance was limited to a P100 and P300 in not perceived trials. We also found an increase in pupil diameter and blink rate and a decrease in microsaccade rate following perceived relative to not perceived tactile stimuli. These findings support the use of eye metrics as a measure of physiological arousal associated with conscious perception. Eye metrics may also represent a novel path toward the creation of tactile no-report tasks in the future.

Improving Regression Analysis with Imputation in a Longitudinal Study of Alzheimer's Disease.

Background: Missing data is prevalent in the Alzheimer's Disease Neuroimaging Initiative (ADNI). It is common to deal with missingness by removing subjects with missing entries prior to statistical analysis; however, this can lead to significant efficiency loss and sometimes bias. It has yet to be demonstrated that the imputation approach to handling this issue can be valuable in some longitudinal regression settings. Objective: The purpose of this study is to demonstrate the importance of imputation and how imputation is correctly done in ADNI by analyzing longitudinal Alzheimer's Disease Assessment Scale -Cognitive Subscale 13 (ADAS-Cog 13) scores and their association with baseline patient characteristics. Methods: We studied 1,063 subjects in ADNI with mild cognitive impairment. Longitudinal ADAS-Cog 13 scores were modeled with a linear mixed-effects model with baseline clinical and demographic characteristics as predictors. The model estimates obtained without imputation were compared with those obtained after imputation with Multiple Imputation by Chained Equations (MICE). We justify application of MICE by investigating the missing data mechanism and model assumptions. We also assess robustness of the results to the choice of imputation method. Results: The fixed-effects estimates of the linear mixed-effects model after imputation with MICE yield valid, tighter confidence intervals, thus improving the efficiency of the analysis when compared to the analysis done without imputation. Conclusions: Our study demonstrates the importance of accounting for missing data in ADNI. When deciding to perform imputation, care should be taken in choosing the approach, as an invalid one can compromise the statistical analyses.

Multimodal sentiment analysis leveraging the strength of deep neural networks enhanced by the XGBoost classifier.

Multimodal sentiment analysis, an increasingly vital task in the realms of natural language processing and machine learning, addresses the nuanced understanding of emotions and sentiments expressed across diverse data sources. This study presents the Hybrid LXGB (Long short-term memory Extreme Gradient Boosting) Model, a novel approach for multimodal sentiment analysis that merges the strengths of long short-term memory (LSTM) and XGBoost classifiers. The primary objective is to address the intricate task of understanding emotions across diverse data sources, such as textual data, images, and audio cues. By leveraging the capabilities of deep learning and gradient boosting, the Hybrid LXGB Model achieves an exceptional accuracy of 97.18% on the CMU-MOSEI dataset, surpassing alternative classifiers, including LSTM, CNN, DNN, and XGBoost. This study not only introduces an innovative model but also contributes to the field by showcasing its effectiveness and balance in capturing the nuanced spectrum of sentiments within multimodal datasets. The comparison with equivalent studies highlights the model's remarkable success, emphasizing its potential for practical applications in real-world scenarios. The Hybrid LXGB Model offers a unique and promising perspective in the realm of multimodal sentiment analysis, demonstrating the significance of integrating LSTM and XGBoost for enhanced performance.

Quality of Life Outcomes After Pediatric Otitis Media Surgery: A Systematic Review and Meta-Analysis.

OBJECTIVES: Systematically review of literature characterizing health-related quality of life (HRQoL) impact of surgery in pediatric otitis media (OM) patients, and meta-analysis of studies using the OM-6 questionnaire. DATA SOURCES: Pubmed, EMBASE, Cochrane Library, Scopus. REVIEW METHODS: A systematic review of literature of studies evaluating HRQoL outcomes for OM patients managed by surgery. Two investigators independently reviewed abstracts and full-length articles. Risk of bias was assessed using the MINORS criteria and Cochrane Risk of Bias 2 tool. RESULTS: The search yielded 1272 studies, 50 underwent full-text review and 23 met inclusion criteria. Non-randomized studies were of moderate to good quality, while randomized trials had a high risk of bias. Age ranged from 6 months to 15 years. Race and socioeconomic factors were inconsistently reported. There were 11 HRQoL outcome measure instruments of which four were disease-specific. Eleven studies used OM-6 and nine were included in the meta-analysis. Pooled analysis of five studies showed a mean OM-6 change of 1.79 (95% CI: 1.53-2.06; 95% PI: 0.92-2.67; I2 = 68%) 4-6 weeks after surgery; a mean change of 1.87 (95% CI: 1.15-2.58; 98%) after 6 months across two studies; and a mean change of 1.64 (1.02 to 2.27; -6.35 to 9.64; 98%) after 9-13 months across three studies. CONCLUSIONS: There is no consistency in HRQoL instruments used to evaluate pediatric OM surgery outcomes in current literature with few RCTs. Meta-analysis showed a clinically significant large improvement in HRQoL 4-6 weeks after tympanostomy tube placement. LEVEL OF EVIDENCE: N/A Laryngoscope, 2024.

α-Synuclein Conformations in Plasma Distinguish Parkinson's Disease from Dementia with Lewy Bodies.

Spread and aggregation of misfolded α-synuclein (aSyn) within the brain is the pathologic hallmark of Lewy body diseases (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). While evidence exists for multiple aSyn protein conformations, often termed "strains" for their distinct biological properties, it is unclear whether PD and DLB result from aSyn strain differences, and biomarkers that differentiate PD and DLB are lacking. Moreover, while pathological forms of aSyn have been detected outside the brain ( e.g., in skin, gut, blood), the functional significance of these peripheral aSyn species is unclear. Here, we developed assays using monoclonal antibodies selective for two different aSyn species generated in vitro - termed Strain A and Strain B - and used them to evaluate human brain tissue, cerebrospinal fluid (CSF), and plasma, through immunohistochemistry, enzyme-linked immunoassay, and immunoblotting. Surprisingly, we found that plasma aSyn species detected by these antibodies differentiated individuals with PD vs. DLB in a discovery cohort (UPenn, n=235, AUC 0.83) and a multi-site replication cohort (Parkinson's Disease Biomarker Program, or PDBP, n=200, AUC 0.72). aSyn plasma species detected by the Strain A antibody also predicted rate of cognitive decline in PD. We found no evidence for aSyn strains in CSF, and ability to template aSyn fibrillization differed for species isolated from plasma vs. brain, and in PD vs. DLB. Taken together, our findings suggest that aSyn conformational differences may impact clinical presentation and cortical spread of pathological aSyn. Moreover, the enrichment of these aSyn strains in plasma implicates a non-central nervous system source.

A systematic literature review on spam content detection and classification.

The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection.