Tracking Target Signal Strengths on a Grid using Sparsity

Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation, and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to \emph{linear} state and measurement equations, which bypass data association and can afford state estimation via sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through $\ell_1$-norm regularization, and the other invokes sparsity as an extra measurement. Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators. Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates. The novel TSSG trackers do not require knowing the number of targets or their signal strengths, and exhibit considerably lower complexity than the benchmark hidden Markov model filter, especially for a large number of targets. Numerical simulations demonstrate that sparsity-cognizant trackers enjoy improved root mean-square error performance at reduced complexity when compared to their sparsity-agnostic counterparts.Comment: Submitted to IEEE Trans. on Signal Processin

A CdZnTeSe Gamma Spectrometer Trained by Deep Convolutional Neural Network for Radioisotope Identification

We report the implementation of a deep convolutional neural network to train a high-resolution room-temperature CdZnTeSe based gamma ray spectrometer for accurate and precise determination of gamma ray energies for radioisotope identification. The prototype learned spectrometer consists of a NI PCI 5122 fast digitizer connected to a pre-amplifier to recognize spectral features in a sequence of data. We used simulated preamplifier pulses that resemble actual data for various gamma photon energies to train a CNN on the equivalent of 90 seconds worth of data and validated it on 10 seconds worth of simulated data

Prevalence of Metabolic Syndrome among the Patients Attending for Master Health Check-up in Family Medicine Department

Introduction: Metabolic syndrome (MetS) is a cluster of conditions that occur together, increasing risk of heart disease, stroke and type 2 diabetes. The prevalence of MetS is increasing worldwide. The aim of the study is to determine the prevalence of MetS among patients attending for Master Health check-up at Family Medicine outpatient department, to find out common component and see the association of body mass index with MetS. Methods: This cross-sectional observational study was done at family medicine outpatient department over a period of six months. There were total of 854 participants involved in the study and each subject was interviewed, anthropometric measured, biochemical parameter recorded in the Performa. The MetS was diagnosed according to modified National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria. Results: The MetS was diagnosed in 53.9 %( 95% CI:50.56%-57.24%)of the study population on the basis of modified NCEP-ATP III criteria, with prevalence significantly higher among males (58.3%) than in females (48.6%)(P value <0.01). Abdominal obesity (70.7%) was the most common morbidity followed by increased fasting blood sugar (57.1%), high level of triglyceride (45.4%), high blood pressure (45.0%) and low level of high density lipoproteins (41.0%). Prevalence of metabolic syndrome was significantly (p-value=0.000) high among obesity (82.5%) and overweight (67.6%) individuals than those with normal weight (38.7%) and under-weight (7.1%). Conclusion: The metabolic syndrome was seen in more than half of study population, with significantly higher among males than in females. The most common component in both genders was abdominal obesity. Presence of any one component should alert the primary care physician to look for other components so that definitive diagnosis can be made and timely intervention can be started with dietary measures, regular exercises and medical treatment

Dry eye in systemic sclerosis patients: Novel methods to monitor disease activity

Background: In systemic sclerosis (SSc) patients, dry eye syndrome (DES) is the most frequent ocular feature. The aim of this study was to investigate ocular DES-related SSc patients and to establish any correlation with the severity of the disease. Methods: Retrospectively, data from 60 patients with SSc underwent ophthalmic examination, where non-invasive film tear break-up time (NIF-TBUT), tear film lipid layer thickness (LLT), anesthetic-free Schirmer test I, tear osmolarity measurement (TearLab System), and modified Rodnan skin score (mRSS) data were collected. The visual analog scale (VAS) and Symptom Assessment in Dry Eye (SANDE) methods were utilized. The results were correlated with mRSS and the duration of SSc. Results: Severe DES occurred in 84% of cases, and was more severe in women. The eyelids were involved in 86.6%, secondary to meibomian gland disease (MGD). A direct correlation was found between the tear osmolarity (mean 328.51 ± 23.8 SD) and skin score (mRSS) (r = 0.79; p &lt; 0.01). Significantly reduced NIF-TBUT, LLT, and Schirmer test I values were observed in the case of severe skin involvement. Conclusions: SSc patients show lipid tear dysfunction related to the severity and duration of the disease due to inflammation and the subsequent atrophy of the meibomian glands

Development of a clinical prediction model for the onset of functional decline in people aged 65-75 years: Pooled analysis of four European cohort studies

Background: Identifying those people at increased risk of early functional decline in activities of daily living (ADL) is essential for initiating preventive interventions. The aim of this study is to develop and validate a clinical prediction model for onset of functional decline in ADL in three years of follow-up in older people of 65-75 years old. Methods: Four population-based cohort studies were pooled for the analysis: ActiFE-ULM (Germany), ELSA (United Kingdom), InCHIANTI (Italy), LASA (Netherlands). Included participants were 65-75 years old at baseline and reported no limitations in functional ability in ADL at baseline. Functional decline was assessed with two items on basic ADL and three items on instrumental ADL. Participants who reported at least some limitations at three-year follow-up on any of the five items were classified as experiencing functional decline. Multiple logistic regression analysis was used to develop a prediction model, with subsequent bootstrapping for optimism-correction. We applied internal-external cross-validation by alternating the data from the four cohort studies to assess the discrimination and calibration across the cohorts. Results: Two thousand five hundred sixty community-dwelling people were included in the analyses (mean age 69.7 ± 3.0 years old, 47.4% female) of whom 572 (22.3%) reported functional decline at three-year follow-up. The final prediction model included 10 out of 22 predictors: age, handgrip strength, gait speed, five-repeated chair stands time (non-linear association), body mass index, cardiovascular disease, diabetes, chronic obstructive pulmonary disease, arthritis, and depressive symptoms. The optimism-corrected model showed good discrimination with a C statistic of 0.72. The calibration intercept was 0.06 and the calibration slope was 1.05. Internal-external cross-validation showed consistent performance of the model across the four cohorts. Conclusions: Based on pooled cohort data analyses we were able to show that the onset of functional decline in ADL in three years in older people aged 65-75 years can be predicted by specific physical performance measures, age, body mass index, presence of depressive symptoms, and chronic conditions. The prediction model showed good discrimination and calibration, which remained stable across the four cohorts, supporting external validity of our findings

Insights into the single-particle composition, size, mixing state, and aspect ratio of freshly emitted mineral dust from field measurements in the Moroccan Sahara using electron microscopy

The chemical and morphological properties of mineral dust aerosols emitted by wind erosion from arid and semi-arid regions influence climate, ocean, and land ecosystems; air quality; and multiple socio-economic sectors. However, there is an incomplete understanding of the emitted dust particle size distribution (PSD) in terms of its constituent minerals that typically result from the fragmentation of soil aggregates during wind erosion. The emitted dust PSD affects the duration of particle transport and thus each mineral's global distribution, along with its specific effect upon climate. This lack of understanding is largely due to the scarcity of relevant in situ measurements in dust sources. To advance our understanding of the physicochemical properties of the emitted dust PSD, we present insights into the elemental composition and morphology of individual dust particles collected during the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) field campaign in the Moroccan Sahara in September 2019. We analyzed more than 300 000 freshly emitted individual particles by performing offline analysis in the laboratory using scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectrometry (EDX). Eight major particle-type classes were identified with clay minerals making up the majority of the analyzed particles both by number and mass, followed by quartz, whereas carbonates and feldspar contributed to a lesser extent. We provide an exhaustive analysis of the PSD and potential mixing state of different particle types, focusing largely on iron-rich (Fe oxide-hydroxides) and feldspar particles, which are key to the effects of dust upon radiation and clouds, respectively. Nearly pure or externally mixed Fe oxide-hydroxides are present mostly in diameters smaller than 2 µm, with the highest fraction below 1 µm at about 3.75 % abundance by mass. Fe oxide-hydroxides tend to be increasingly internally mixed with other minerals, especially clays, as particle size increases; i.e., the volume fraction of Fe oxide-hydroxides in aggregates decreases with particle size. Pure (externally mixed) feldspar represented 3.2 % of all the particles by mass, of which we estimated about a 10th to be K-feldspar. The externally mixed total feldspar and K-feldspar abundances are relatively invariant with particle size, in contrast to the increasing abundance of feldspar-like (internally mixed) aggregates with particle size with mass fractions ranging from 5 % to 18 %. We also found that overall the median aspect ratio is rather constant across particle size and mineral groups, although we obtain slightly higher aspect ratios for internally mixed particles. The detailed information on the composition of freshly emitted individual dust particles and quantitative analysis of their mixing state presented here can be used to constrain climate models including mineral species in their representation of the dust cycle.</p

Instability, investment, disasters, and demography: natural disasters and fertility in Italy (1820–1962) and Japan (1671–1965)

This article examines whether natural disasters affect fertility—a topic little explored but of policy importance given relevance to policies regarding disaster insurance, foreign aid, and the environment. The identification strategy uses historic regional data to exploit natural variation within each of two countries: one European country—Italy (1820–1962), and one Asian country—Japan (1671–1965). The choice of study settings allows consideration of Jones’ (The European miracle, Cambridge University Press, Cambridge, 1981) theory that preindustrial differences in income and population between Asia and Europe resulted from the fertility response to different environmental risk profiles. According to the results, short-run instability, particularly that arising from the natural environment, appears to be associated with a decrease in fertility—thereby suggesting that environmental shocks and economic volatility are associated with a decrease in investment in the population size of future generations. The results also show that, contrary to Jones’ (The European miracle, Cambridge University Press, Cambridge, 1981) theory, differences in fertility between Italy and Japan cannot be explained away by disaster proneness alone. Research on the effects of natural disasters may enable social scientists and environmentalists alike to better predict the potential effects of the increase in natural disasters that may result from global climate change

MARS spectral molecular imaging of lamb tissue: data collection and image analysis

Spectral molecular imaging is a new imaging technique able to discriminate and quantify different components of tissue simultaneously at high spatial and high energy resolution. Our MARS scanner is an x-ray based small animal CT system designed to be used in the diagnostic energy range (20 to 140 keV). In this paper, we demonstrate the use of the MARS scanner, equipped with the Medipix3RX spectroscopic photon-processing detector, to discriminate fat, calcium, and water in tissue. We present data collected from a sample of lamb meat including bone as an illustrative example of human tissue imaging. The data is analyzed using our 3D Algebraic Reconstruction Algorithm (MARS-ART) and by material decomposition based on a constrained linear least squares algorithm. The results presented here clearly show the quantification of lipid-like, water-like and bone-like components of tissue. However, it is also clear to us that better algorithms could extract more information of clinical interest from our data. Because we are one of the first to present data from multi-energy photon-processing small animal CT systems, we make the raw, partial and fully processed data available with the intention that others can analyze it using their familiar routines. The raw, partially processed and fully processed data of lamb tissue along with the phantom calibration data can be found at [http://hdl.handle.net/10092/8531].Comment: 11 pages, 6 fig