India's Pink-Eye Mystery: Decoding the 2023 Conjunctivitis Outbreak.

The pink eye outbreak in 2023 was caused due to humid weather conditions in most regions of India. The most affected states include Delhi, Gujrat (21% cases), Maharashtra (30%), Himachal Pradesh (4%) and Karnataka (4%). The epidemiological data indicates that males have a high prevalence rate as compared to females; urban areas were most affected, and professionals as well as students were the population group that had the highest prevalence rate. The most common clinical manifestations were the presence of red eye, eye discharge, grittiness, and eyelashes being stuck together. One of the hallmarks of histopathology is a cobblestone formation of flattened nodules with central vascular centers. Conjunctivitis is a virusmediated immune response accompanied by inflammation, which proceeds the immune reaction, giving rise to vasodilation, pseudo membrane formation, and conjunctivital discharge. The gold standard for the diagnosis of Conjunctivitis is the Adenoplus kit using PCR technology; apart from this slit lamp biomicroscope can be used for the evaluation. It is the need of the hour to spread awareness about the Pink Eye disease and the measures to prevent it.

Intrinsic dimensionality of human behavioral activity data.

Patterns of spatial behavior dictate how we use our infrastructure, encounter other people, or are exposed to services and opportunities. Understanding these patterns through the analysis of data commonly available through commodity smartphones has become an important arena for innovation in both academia and industry. The resulting datasets can quickly become massive, indicating the need for concise understanding of the scope of the data collected. Some data is obviously correlated (for example GPS location and which WiFi routers are seen). Codifying the extent of these correlations could identify potential new models, provide guidance on the amount of data to collect, and even provide actionable features. However, identifying correlations, or even the extent of correlation, is difficult because the form of the correlation must be specified. Fractal-based intrinsic dimensionality directly calculates the minimum number of dimensions required to represent a dataset. We provide an intrinsic dimensionality analysis of four smartphone datasets over seven input dimensions, and empirically demonstrate an intrinsic dimension of approximately two.

Multiscale entropy rate analysis of complex mobile agents.

Accurate prediction of the motion of objects is a central scientific goal. For deterministic or stochastic processes, models exist which characterize motion with a high degree of reliability. For complex systems, or those where objects have a degree of agency, characterizing motion is far more challenging. The information entropy rate of motion through a discrete space can place a limit on the predictability of even the most complex or history-dependent actor, but the variability in measured encountered locations is inexorably tied to the spatial and temporal resolutions of those measurements. This relation depends on the path of the actor in ways that can be used to derive a general law in closed form relating the mobility entropy rate to different spatial and temporal resolutions, and the path properties within each cell along the path. Correcting for spatial and temporal effects through regression yields the path properties and a measure of mobility entropy rate robust to changes in dimension, allowing comparison of mobility entropy rates between datasets. Employing this measure on empirical datasets yields novel findings, from the similarity of taxicabs to drifters, to the predictable motions of undergraduates, to the browsing habits of Canadian moose.

How many days are enough?: capturing routine human mobility.

Wedding mobile phone sensor technology and human spatial behaviour has great potential. The ubiquity of Global Positioning Systems (GPS) technology has made gathering data about human mobility simpler, more precise, and with higher fidelity, providing minute-by-minute records of the locations of cohorts from dozens of participants. While this data provides a strong basis for Geographic Information Science research, it also constitutes an invasion of the participants' privacy and can provide more information than researchers require to answer their questions. As an ethical and practical consideration, researchers should gather only as much data as they need. In this paper, we take three weeks of GPS traces from over a hundred student participants in mobile phone-based tracking studies and show that fewer than 14 days of data is necessary to establish complete activity spaces. We define 'complete' as the point at which marginal information gains become negligible according to a pairwise temporal analysis of the Kullback-Leibler (KL) divergence of the spatial (bivariate) histogram through time. For the fixed level of information difference, observable in the data, impacts due to individual variability, population composition, and spatial resolution are evident. However, all populations at each level of resolution examined in the paper demonstrated convergence to low divergence levels occurred within a matter of days, and to negligible information gain in less than two weeks. The methods described in the paper represent a novel metric useful to understand the interaction between measurements and information in human mobility.

A Theoretical Basis for Entropy-Scaling Effects in Human Mobility Patterns.

Characterizing how people move through space has been an important component of many disciplines. With the advent of automated data collection through GPS and other location sensing systems, researchers have the opportunity to examine human mobility at spatio-temporal resolution heretofore impossible. However, the copious and complex data collected through these logging systems can be difficult for humans to fully exploit, leading many researchers to propose novel metrics for encapsulating movement patterns in succinct and useful ways. A particularly salient proposed metric is the mobility entropy rate of the string representing the sequence of locations visited by an individual. However, mobility entropy rate is not scale invariant: entropy rate calculations based on measurements of the same trajectory at varying spatial or temporal granularity do not yield the same value, limiting the utility of mobility entropy rate as a metric by confounding inter-experimental comparisons. In this paper, we derive a scaling relationship for mobility entropy rate of non-repeating straight line paths from the definition of Lempel-Ziv compression. We show that the resulting formulation predicts the scaling behavior of simulated mobility traces, and provides an upper bound on mobility entropy rate under certain assumptions. We further show that this formulation has a maximum value for a particular sampling rate, implying that optimal sampling rates for particular movement patterns exist.

Flow cytometric immunophenotyping and cell block immunocytochemistry in the diagnosis of primary Non-Hodgkin's Lymphoma by fine-needle aspiration: Experience from a tertiary care center.

BACKGROUND: Accurate diagnosis of Non-Hodgkin's Lymphoma (NHL) on fine-needle aspiration (FNA) specimen is challenging and requires ancillary testing. AIM: The feasibility of flow cytometric immunophenotyping (FCI) along with cell block immunocytochemistry (CB-ICC) as adjunct techniques in the diagnosis of NHL as per the current World Health Organization (WHO) classification was evaluated. MATERIALS AND METHODS: All cases of suspected lymphoma underwent FNA, and the sample was triaged for light microscopic evaluation, FCI, and CB-ICC, and each case was classified as per the current WHO classification. RESULTS: A total of 65 cases was analyzed which included 40 B-cell, 21 T-cell, and 4 unclassifiable lymphomas. Of 61 cases, FCI alone was contributory in 74% (45/61) cases whereas CB-ICC alone was contributory in 65.5% (40/61) cases in typing the lymphoma. In 11.4% (7/61) cases, the lymphoma could not be classified by either technique. Thus, in a total of 88.5% (54/61) cases a combination of FCI and CB-ICC from FNA enabled a diagnosis of lymphoma with its subtyping. CONCLUSION: Flow cytometric immunophenotyping and ICC on CBs are feasible on FNA material and are very useful in a suspected case of NHL especially when a biopsy may not be possible or feasible.