Comparing Clusterings in Space

This paper proposes a new method for comparing clusterings both partitionally and geometrically. Our approach is motivated by the following observation: the vast majority of previous techniques for comparing clusterings are entirely partitional, i.e., they examine assignments of points in set theoretic terms after they have been partitioned. In doing so, these methods ignore the spatial layout of the data, disregarding the fact that this information is responsible for generating the clusterings to begin with. We demonstrate that this leads to a variety of failure modes. Previous comparison techniques often fail to differentiate between significant changes made in data being clustered. We formulate a new measure for comparing clusterings that combines spatial and partitional information into a single measure using optimization theory. Doing so eliminates pathological conditions in previous approaches. It also simultaneously removes common limitations, such as that each clustering must have the same number of clusters or they are over identical datasets. This approach is stable, easily implemented, and has strong intuitive appeal. spatial properties as well as their cluster membership assignments. We view a clustering as a partition of a set of points located in a space with an associated distance function. This view is natural, since popular clustering algorithms, e.g., k-means, spectral clustering, affinity propagation, etc., take as input not only a collection of points to be clustered but also a distance function on the space in which the points lie. This distance function may be specified implicitly and it may be transformed by a kernel, but it must be defined one way or another and its properties are crucial to a clustering algorithm’s output. In contrast, almost all existing clustering comparison techniques ignore the distances between points, treating clusterings as partitions of disembodied atoms. While this approach has merit under some circumstances, it seems surprising to ignore the distance func

A Spatially Sensitive Kernel to Predict Cognitive Performance from Short-Term Changes in Neural Structure

This paper introduces a novel framework for performing machine learning onlongitudinal neuroimaging datasets. These datasets are characterized by theirsize, particularly their width (millions of features per data input). Specifically, we address the problem of detecting subtle, short-term changes inneural structure that are indicative of cognitive change and correlate withrisk factors for Alzheimer's disease. We introduce a new spatially-sensitivekernel that allows us to reason about individuals, as opposed to populations. In doing so, this paper presents the first evidence demonstrating that verysmall changes in white matter structure over a two year period can predictchange in cognitive function in healthy adults

Network Security Course Project Report

Computatio

Association of increased risk of asthma with elevated arginase & interleukin-13 levels in serum & rs2781666 G/T genotype of arginase I

Background & objectives: High expression of arginase gene and its elevated level in serum and bronchial lavage reported in animal models indicated an association with the pathogenesis of asthma. This study was undertaken to assess the serum arginase activity in symptomatic asthma patients and healthy controls and to correlate it with cytokine levels [interleukin (IL)-4 and IL-13] and arginase I (ARG1) gene polymorphism. Methods: Asthma was confirmed by lung function test according to the GINA guidelines in patients attending Allergy and Pulmonology Clinic, Bhagwan Mahavir Hospital and Research Centre, Hyderabad, India, a tertiary care centre, during 2013-2015. Serum arginase was analyzed using a biochemical assay, total IgE and cytokine levels by enzyme-linked immunosorbent assay and genotyping of ARG1 for single-nucleotide polymorphisms (SNPs) rs2781666 and rs60389358 using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Results: There was a significant two-fold elevation in the arginase activity in asthmatics as compared to healthy controls which correlated with disease severity. Non-atopic asthmatics showed elevated activity of arginase compared to atopics, indicating its possible role in intrinsic asthma. Levels of serum IL-13 and IL-4 were significantly high in asthma group which correlated with disease severity that was assessed by spirometry. A positive correlation was observed between arginase activity and IL-13 concentration. Genetic analysis of ARG1 SNPs revealed that rs2781666 G/T genotype, T allele and C-T haplotype (rs60389358 and rs2781666) were associated with susceptibility to asthma. Interpretation & conclusions: This study indicated that high arginase activity and IL-13 concentration in the serum and ARG1 rs2781666 G/T genotype might increase the risk of asthma in susceptible population. Further studies need to be done with a large sample to confirm these findings

Sleep-Dependent Improvement in Visuomotor Learning: A Causal Role for Slow Waves

STUDY OBJECTIVES: Sleep after learning often benefits memory consolidation, but the underlying mechanisms remain unclear. In previous studies, we found that learning a visuomotor task is followed by an increase in sleep slow wave activity (SWA, the electroencephalographic [EEG] power density between 0.5 and 4.5 Hz during non-rapid eye movement sleep) over the right parietal cortex. The SWA increase correlates with the postsleep improvement in visuomotor performance, suggesting that SWA may be causally responsible for the consolidation of visuomotor learning. Here, we tested this hypothesis by studying the effects of slow wave deprivation (SWD). DESIGN: After learning the task, subjects went to sleep, and acoustic stimuli were timed either to suppress slow waves (SWD) or to interfere as little as possible with spontaneous slow waves (control acoustic stimulation, CAS). SETTING: Sound-attenuated research room. PARTICIPANTS: Healthy subjects (mean age 24.6 +/- 1.0 years; n = 9 for EEG analysis, n = 12 for behavior analysis; 3 women). MEASUREMENTS AND RESULTS: Sleep time and efficiency were not affected, whereas SWA and the number of slow waves decreased in SWD relative to CAS. Relative to the night before, visuomotor performance significantly improved in the CAS condition (+5.93% +/- 0.88%) but not in the SWD condition (-0.77% +/- 1.16%), and the direct CAS vs SWD comparison showed a significant difference (P = 0.0007, n = 12, paired t test). Changes in visuomotor performance after SWD were correlated with SWA changes over right parietal cortex but not with the number of arousals identified using clinically established criteria, nor with any sign of "EEG lightening" identified using a novel automatic method based on event-related spectral perturbation analysis. CONCLUSION: These results support a causal role for sleep slow waves in sleep-dependent improvement of visuomotor performance

Unilateral Axillary Lymphadenopathy in Cancer Patients Post-COVID-19 Vaccination: Review and Case Series

Novel coronavirus-19 (COVID-19) variants continue to spread worldwide with the development of highly transmissible strains. Several guidelines addressing management of cancer patients during the COVID-19 pandemic have been published, primarily based upon expert opinion. The COVID-19 pandemic has affected all aspects of breast cancer care including screening, diagnosis, treatment, and long-term follow-up. Recent reports indicate that mRNA COVID-19 vaccines can provoke lymphadenopathy in both cancer patients and healthy individuals. Unilateral axillary lymphadenopathy (UAL) post-COVID-19 vaccination is a challenging presentation for cancer patients because of the potential for misinterpretation as malignancy. The World Health Organization’s target to vaccinate 70% of the world’s population by mid-2023 is likely to increase the incidence of post-COVID-19 vaccination UAL. In this article, we review the published evidence regarding UAL post-COVID-19 vaccination and present diverse cases of breast cancer patients where false-positive UAL post-COVID-19 vaccination proved to be a therapeutic challenge. The United Arab Emirates (UAE) vaccination program is well ahead of other countries in the world, having accomplished the target of 100% vaccination of the population with at least one dose. Therefore, an increasing number of recently vaccinated patients are likely to present with UAL, detected by surveillance imaging, post-vaccination. We have therefore made recommendations regarding the management of cancer patients with UAL post-COVID-19 vaccination in order to avoid misdiagnosis and unnecessary imaging or invasive biopsy procedures