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In Virtual Reality (VR), a higher level of presence positively influences the experience and engagement of a user. There are several parameters that are responsible for generating different levels of presence in VR, including but not limited to, graphical fidelity, multi-sensory stimuli, and embodiment. However, standard methods of measuring presence, including self-reported questionnaires, are biased. This research focuses on developing a robust model, via machine learning, to detect different levels of presence in VR using multimodal neurological and physiological signals, including electroencephalography and electrodermal activity. An experiment has been undertaken whereby participants (N = 22) were each exposed to three different levels of presence (high, medium, and low) in a random order in VR. Four parameters within each level, including graphics fidelity, audio cues, latency, and embodiment with haptic feedback, were systematically manipulated to differentiate the levels. A number of multi-class classifiers were evaluated within a three-class classification problem, using a One-vs-Rest approach, including Support Vector Machine, k-Nearest Neighbour, Extra Gradient Boosting, Random Forest, Logistic Regression, and Multiple Layer Perceptron. Results demonstrated that the Multiple Layer Perceptron model obtained the highest macro average accuracy of 93 ± 0.03 % . Posthoc analysis revealed that relative band power, which is expressed as the ratio of power in a specific frequency band to the total baseline power, in both the frontal and parietal regions, including beta over theta and alpha ratio, and differential entropy were most significant in detecting different levels of presence.

Plasma cell segmentation is the first stage of a computer assisted automated diagnostic tool for multiple myeloma (MM). Owing to large variability in biological cell types, a method for one cell type cannot be applied directly on the other cell types. In this paper, we present PCSeg Tool for plasma cell segmentation from microscopic medical images. These images were captured from bone marrow aspirate slides of patients with MM. PCSeg has a robust pipeline consisting of a pre-processing step, the proposed modified multiphase level set method followed by post-processing steps including the watershed and circular Hough transform to segment clusters of cells of interest and to remove unwanted cells. Our modified level set method utilizes prior information about the probability densities of regions of interest (ROIs) in the color spaces and provides a solution to the minimal-partition problem to segment ROIs in one of the level sets of a two-phase level set formulation. PCSeg tool is tested on a number of microscopic images and provides good segmentation results on single cells as well as efficient segmentation of plasma cell clusters.

Individual characterization of subjects based on their functional connectome (FC), termed “FC fingerprinting”, has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.

A room temperature microfabrication technique using SU8, an epoxy-based highly functional photoresist as a sacrificial layer, is developed to obtain suspended aligned carbon nanotube beams. The humidity-sensing characteristics of aligned suspended single-walled carbon nanotube films are studied. A comparative study between suspended and non-suspended architectures is done by recording the resistance change in the nanotubes under humidity. For the tests, the humidity was varied from 15% to 98% RH. A comparative study between suspended and non-suspended devices shows that the response and recovery times of the suspended devices was found to be almost 3 times shorter than the non-suspended devices. The suspended devices also showed minimal hysteresis even after 10 humidity cycles, and also exhibit enhanced sensitivity. Repeatability tests were performed by subjecting the sensors to continuous humidification cycles. All tests reported here have been performed using pristine non-functionalized nanotubes.

Analysis of functional magnetic resonance imaging (fMRI) data has revealed that brain regions can be grouped into functional brain networks (fBNs) or communities. A community in fMRI analysis signifies a group of brain regions coupled functionally with one another. In neuroimaging, functional connectivity (FC) measure can be utilized to quantify such functionally connected regions for disease diagnosis and hence, signifies the need of devising novel FC estimation methods. In this paper, we propose a novel method of learning FC by constraining its rank and the sum of non-zero coefficients. The underlying idea is that fBNs are sparse and can be embedded in a relatively lower dimension space. In addition, we propose to extract overlapping networks. In many instances, communities are characterized as combinations of disjoint brain regions, although recent studies indicate that brain regions may participate in more than one community. In this paper, large-scale overlapping fBNs are identified on resting state fMRI data by employing non-negative matrix factorization. Our findings support the existence of overlapping brain networks.

Lenvatinib significantly prolonged progression‐free survival (PFS) versus placebo in patients with radioiodine‐refractory differentiated thyroid cancer (RR‐DTC) in the phase 3 Study of (E7080) Lenvatinib in Differentiated Cancer of the Thyroid (SELECT) trial. This subanalysis evaluated the efficacy and safety of lenvatinib in Japanese patients who participated in SELECT. Outcomes for Japanese patients (lenvatinib, n = 30; placebo, n = 10) were assessed in relationship to the SELECT population (lenvatinib, n = 261; placebo, n = 131). The primary endpoint was PFS; secondary endpoints included overall survival, overall response rate, and safety. Lenvatinib PFS benefit was shown in Japanese patients (median PFS: lenvatinib, 16.5 months; placebo, 3.7 months), although significance was not reached, presumably due to sample size (hazard ratio, 0.39; 95% confidence interval, 0.10–1.57; P = 0.067). Overall response rates were 63.3% and 0% for lenvatinib and placebo, respectively. No significant difference was found in overall survival. The lenvatinib safety profile was similar between the Japanese and overall SELECT population, except for higher incidences of hypertension (any grade: Japanese, 87%; overall, 68%; grade ≥3: Japanese, 80%; overall, 42%), palmar–plantar erythrodysesthesia syndrome (any grade: Japanese, 70%; overall, 32%; grade ≥3: Japanese, 3%; overall, 3%), and proteinuria (any grade: Japanese, 63%; overall, 31%; grade ≥3: Japanese, 20%; overall, 10%). Japanese patients had more dose reductions (Japanese, 90%; overall, 67.8%), but fewer discontinuations due to adverse events (Japanese, 3.3%; overall, 14.2%). There was no difference in lenvatinib exposure between the Japanese and overall SELECT populations after adjusting for body weight. In Japanese patients with radioiodine‐refractory differentiated thyroid cancer, lenvatinib showed similar clinical outcomes to the overall SELECT population. Some differences in adverse event frequencies and dose modifications were observed. Clinical trial registration no.: NCT01321554. Lenvatinib significantly prolonged progression‐free survival (PFS) vs placebo in patients with radioiodine‐refractory differentiated thyroid cancer (RR‐DTC) in the phase 3 SELECT trial. This subanalysis evaluated the efficacy and safety of lenvatinib in Japanese patients from SELECT. In Japanese patients with RR‐DTC, lenvatinib demonstrated similar clinical outcomes as the overall SELECT population. Some differences in adverse event frequencies and dose modifications were observed.

To define and differentiate relevant aspects of blood–brain barrier transport and distribution in order to aid research methodology in brain drug delivery. Pharmacokinetic parameters relative to the rate and extent of brain drug delivery are described and illustrated with relevant data, with special emphasis on the unbound, pharmacologically active drug molecule. Drug delivery to the brain can be comprehensively described using three parameters: K p,uu (concentration ratio of unbound drug in brain to blood), CL in (permeability clearance into the brain), and V u,brain (intra-brain distribution). The permeability of the blood–brain barrier is less relevant to drug action within the CNS than the extent of drug delivery, as most drugs are administered on a continuous (repeated) basis. K p,uu can differ between CNS-active drugs by a factor of up to 150-fold. This range is much smaller than that for log BB ratios ( K p ), which can differ by up to at least 2,000-fold, or for BBB permeabilities, which span an even larger range (up to at least 20,000-fold difference). Methods that measure the three parameters K p,uu , CL in , and V u,brain can give clinically valuable estimates of brain drug delivery in early drug discovery programmes.

Time-varying neurophysiological activity has been classically explored using correlation based sliding window analysis. However, this method employs only lower order statistics to track dynamic functional connectivity of the brain. We introduce recursive dynamic functional connectivity (rdFC) that incorporates higher order statistics to generate a multi-order connectivity pattern by analyzing neurophysiological data at multiple time scales. The technique builds a hierarchical graph between various temporal scales as opposed to traditional approaches that analyze each scale independently. We examined more than a million rdFC patterns obtained from morphologically diverse EEGs of 2378 subjects of varied age and neurological health. Spatiotemporal evaluation of these patterns revealed three dominant connectivity patterns that represent a universal underlying correlation structure seen across subjects and scalp locations. The three patterns are both mathematically equivalent and observed with equal prevalence in the data. The patterns were observed across a range of distances on the scalp indicating that they represent a spatially scale-invariant correlation structure. Moreover, the number of patterns representing the correlation structure has been shown to be linked with the number of nodes used to generate them. We also show evidence that temporal changes in the rdFC patterns are linked with seizure dynamics.

Abnormal expression patterns of regulatory small non-coding RNA (sncRNA) molecules such as microRNAs (miRs), piwi-interacting RNAs (piRNAs), and small nucleolar RNAs (snoRNAs) play an important role in the development and progression of cancer. Identification of clinically relevant sncRNA signatures could, therefore, be of tremendous translational value. In the present study, genome-wide small RNA sequencing identified a unique pattern of differential regulation of eight miRs in Chronic Lymphocytic Leukemia (CLL). Among these, three were up-regulated (miR-1295a, miR-155, miR-4524a) and five were down-regulated (miR-30a, miR-423, miR-486*, let-7e, and miR-744) in CLL. Altered expression of all these eight differentially expressed miRs (DEMs) was validated by RQ-PCR. Besides, seven novel sequences identified to have elevated expression levels in CLL turned out to be transfer RNA (tRNA)/piRNAs (piRNA-30799, piRNA-36225)/snoRNA (SNORD43) related. Multivariate analysis showed that miR-4524a (HR: 1.916, 95% CI: 1.080–3.4, p value: 0.026) and miR-744 (HR: 0.415, 95% CI: 0.224–0.769, p value: 0.005) were significantly associated with risk and time to first treatment. Further investigations could help establish the scope of integration of these DEM markers into risk stratification designs and prognostication approaches for CLL.

Traditional statistical tests have limitations in analyzing cause-and-effect relationships. Directed acyclic graphs (DAGs) offer a structured representation of causality. This study aimed to utilize DAGs to explore the causal impact of epidemiological factors on 30-day mortality among patients following their first acute ST-elevation myocardial infarction (STEMI). The study employs data from the North India (NORIN)-STEMI study registry, comprising 3,192 first-time STEMI patients collected prospectively from two tertiary care hospitals in Delhi, India. Continuous optimization structure learning using the Non-combinatorial Optimization via Trace Exponential and Augmented Lagrangian for Structure Learning (NOTEARS) method is applied to learn the DAG. Additionally, a permutation testing framework is proposed for the statistical validation of the links of the DAG. Among 2,946 first-time STEMI patients, 246 (7.7%) experienced mortality during the study period. A t-test revealed that age was significantly different between the survival and mortality groups within 30 days post-STEMI (p<0.0001). Patients who died within 30 days had a higher mean age (59.90±13.89 years). Furthermore, the study identified a statistically significant association between mortality and HbA1c, triglycerides, smoking, sex, education, occupation, socioeconomic status, physical activity, overall stress, and hypertension. Our DAG reveals causal relationships and identifies confounding variables affecting mortality after STEMI. Sex is identified as a significant factor influencing mortality both directly and indirectly. This influence occurs through its effects on age, alcohol consumption, stress, hypertension, and socioeconomic status. Additionally, sex is recognized as a confounding factor whose impact on mortality is modified by other factors.