Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts

<p>Abstract</p> <p>Background</p> <p>Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods.</p> <p>Methods</p> <p>Our new methods for modeling daily chief complaint counts are based on a seasonal-trend decomposition procedure based on loess (STL) and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, we develop a new synoptic-scale (days to weeks) outbreak detection method and carry out a simulation study to compare detection performance to four well-known methods for nine outbreak scenarios.</p> <p>Result</p> <p>The components of the STL decomposition reveal insights into the variability of the Indiana ED data. Day-of-the-week components tend to peak Sunday or Monday, fall steadily to a minimum Thursday or Friday, and then rise to the peak. Yearly-seasonal components show seasonal influenza, some with bimodal peaks.</p> <p>Some inter-annual components increase slightly due to increasing patient populations. A new outbreak detection method based on the decomposition modeling performs well with 90 days or more of data. Control limits were set empirically so that all methods had a specificity of 97%. STL had the largest sensitivity in all nine outbreak scenarios. The STL method also exhibited a well-behaved false positive rate when run on the data with no outbreaks injected.</p> <p>Conclusion</p> <p>The STL decomposition method for chief complaint counts leads to a rapid and accurate detection method for disease outbreaks, and requires only 90 days of historical data to be put into operation. The visualization tools that accompany the decomposition and outbreak methods provide much insight into patterns in the data, which is useful for surveillance operations.</p

Why alternative teenagers self-harm: exploring the link between non-suicidal self-injury, attempted suicide and adolescent identity

Background: The term ‘self-harm’ encompasses both attempted suicide and non-suicidal self-injury (NSSI). Specific adolescent subpopulations such as ethnic or sexual minorities, and more controversially, those who identify as ‘Alternative’ (Goth, Emo) have been proposed as being more likely to self-harm, while other groups such as ‘Jocks’ are linked with protective coping behaviours (for example exercise). NSSI has autonomic (it reduces negative emotions) and social (it communicates distress or facilitates group ‘bonding’) functions. This study explores the links between such aspects of self-harm, primarily NSSI, and youth subculture.&lt;p&gt;&lt;/p&gt; Methods: An anonymous survey was carried out of 452 15 year old German school students. Measures included: identification with different youth cultures, i.e. Alternative (Goth, Emo, Punk), Nerd (academic) or Jock (athletic); social background, e.g. socioeconomic status; and experience of victimisation. Self-harm (suicide and NSSI) was assessed using Self-harm Behavior Questionnaire and the Functional Assessment of Self-Mutilation (FASM).&lt;p&gt;&lt;/p&gt; Results: An “Alternative” identity was directly (r ≈ 0.3) and a “Jock” identity inversely (r ≈ -0.1) correlated with self-harm. “Alternative” teenagers self-injured more frequently (NSSI 45.5% vs. 18.8%), repeatedly self-injured, and were 4–8 times more likely to attempt suicide (even after adjusting for social background) than their non-Alternative peers. They were also more likely to self-injure for autonomic, communicative and social reasons than other adolescents.&lt;p&gt;&lt;/p&gt; Conclusions: About half of ‘Alternative’ adolescents’ self-injure, primarily to regulate emotions and communicate distress. However, a minority self-injure to reinforce their group identity, i.e. ‘To feel more a part of a group’

Early Detection of Tuberculosis Outbreaks among the San Francisco Homeless: Trade-Offs Between Spatial Resolution and Temporal Scale

BACKGROUND: San Francisco has the highest rate of tuberculosis (TB) in the U.S. with recurrent outbreaks among the homeless and marginally housed. It has been shown for syndromic data that when exact geographic coordinates of individual patients are used as the spatial base for outbreak detection, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. We examine the effect of varying the spatial resolution in the TB data within the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed to achieve better performance measures. METHODS AND FINDINGS: We apply a variation of space-time permutation scan statistic to the TB data in which a patient's location is either represented by its exact coordinates or by the centroid of its census tract. We show that the detection sensitivity and timeliness of the method generally improve when exact locations are used to identify real TB outbreaks. When outbreaks are simulated, while the detection timeliness is consistently improved when exact coordinates are used, the detection sensitivity varies depending on the size of the spatial scanning window and the number of tracts in which cases are simulated. Finally, we show that when exact locations are used, smaller amount of historical data is required for training the model. CONCLUSION: Systematic characterization of the spatio-temporal distribution of TB cases can widely benefit real time surveillance and guide public health investigations of TB outbreaks as to what level of spatial resolution results in improved detection sensitivity and timeliness. Trading higher spatial resolution for better performance is ultimately a tradeoff between maintaining patient confidentiality and improving public health when sharing data. Understanding such tradeoffs is critical to managing the complex interplay between public policy and public health. This study is a step forward in this direction

The spike-timing-dependent learning rule to encode spatiotemporal patterns in a network of spiking neurons

We study associative memory neural networks based on the Hodgkin-Huxley type of spiking neurons. We introduce the spike-timing-dependent learning rule, in which the time window with the negative part as well as the positive part is used to describe the biologically plausible synaptic plasticity. The learning rule is applied to encode a number of periodical spatiotemporal patterns, which are successfully reproduced in the periodical firing pattern of spiking neurons in the process of memory retrieval. The global inhibition is incorporated into the model so as to induce the gamma oscillation. The occurrence of gamma oscillation turns out to give appropriate spike timings for memory retrieval of discrete type of spatiotemporal pattern. The theoretical analysis to elucidate the stationary properties of perfect retrieval state is conducted in the limit of an infinite number of neurons and shows the good agreement with the result of numerical simulations. The result of this analysis indicates that the presence of the negative and positive parts in the form of the time window contributes to reduce the size of crosstalk term, implying that the time window with the negative and positive parts is suitable to encode a number of spatiotemporal patterns. We draw some phase diagrams, in which we find various types of phase transitions with change of the intensity of global inhibition.Comment: Accepted for publication in Physical Review

Diabetic Kidney Disease in FVB/NJ Akita Mice: Temporal Pattern of Kidney Injury and Urinary Nephrin Excretion

Akita mice are a genetic model of type 1 diabetes. In the present studies, we investigated the phenotype of Akita mice on the FVB/NJ background and examined urinary nephrin excretion as a marker of kidney injury. Male Akita mice were compared with non-diabetic controls for functional and structural characteristics of renal and cardiac disease. Podocyte number and apoptosis as well as urinary nephrin excretion were determined in both groups. Male FVB/NJ Akita mice developed sustained hyperglycemia and albuminuria by 4 and 8 weeks of age, respectively. These abnormalities were accompanied by a significant increase in systolic blood pressure in 10-week old Akita mice, which was associated with functional, structural and molecular characteristics of cardiac hypertrophy. By 20 weeks of age, Akita mice developed a 10-fold increase in albuminuria, renal and glomerular hypertrophy and a decrease in the number of podocytes. Mild-to-moderate glomerular mesangial expansion was observed in Akita mice at 30 weeks of age. In 4-week old Akita mice, the onset of hyperglycemia was accompanied by increased podocyte apoptosis and enhanced excretion of nephrin in urine before the development of albuminuria. Urinary nephrin excretion was also significantly increased in albuminuric Akita mice at 16 and 20 weeks of age and correlated with the albumin excretion rate. These data suggest that: 1. FVB/NJ Akita mice have phenotypic characteristics that may be useful for studying the mechanisms of kidney and cardiac injury in diabetes, and 2. Enhanced urinary nephrin excretion is associated with kidney injury in FVB/NJ Akita mice and is detectable early in the disease process

Changes in N-Transforming Archaea and Bacteria in Soil during the Establishment of Bioenergy Crops

Widespread adaptation of biomass production for bioenergy may influence important biogeochemical functions in the landscape, which are mainly carried out by soil microbes. Here we explore the impact of four potential bioenergy feedstock crops (maize, switchgrass, Miscanthus X giganteus, and mixed tallgrass prairie) on nitrogen cycling microorganisms in the soil by monitoring the changes in the quantity (real-time PCR) and diversity (barcoded pyrosequencing) of key functional genes (nifH, bacterial/archaeal amoA and nosZ) and 16S rRNA genes over two years after bioenergy crop establishment. The quantities of these N-cycling genes were relatively stable in all four crops, except maize (the only fertilized crop), in which the population size of AOB doubled in less than 3 months. The nitrification rate was significantly correlated with the quantity of ammonia-oxidizing archaea (AOA) not bacteria (AOB), indicating that archaea were the major ammonia oxidizers. Deep sequencing revealed high diversity of nifH, archaeal amoA, bacterial amoA, nosZ and 16S rRNA genes, with 229, 309, 330, 331 and 8989 OTUs observed, respectively. Rarefaction analysis revealed the diversity of archaeal amoA in maize markedly decreased in the second year. Ordination analysis of T-RFLP and pyrosequencing results showed that the N-transforming microbial community structures in the soil under these crops gradually differentiated. Thus far, our two-year study has shown that specific N-transforming microbial communities develop in the soil in response to planting different bioenergy crops, and each functional group responded in a different way. Our results also suggest that cultivation of maize with N-fertilization increases the abundance of AOB and denitrifiers, reduces the diversity of AOA, and results in significant changes in the structure of denitrification community

Contrasting Diversity Patterns of Crenarchaeal, Bacterial and Fungal Soil Communities in an Alpine Landscape

International audienceBackground: The advent of molecular techniques in microbial ecology has aroused interest in gaining an understanding about the spatial distribution of regional pools of soil microbes and the main drivers responsible of these spatial patterns. Here, we assessed the distribution of crenarcheal, bacterial and fungal communities in an alpine landscape displaying high turnover in plant species over short distances. Our aim is to determine the relative contribution of plant species composition, environmental conditions, and geographic isolation on microbial community distribution. Methodology/Principal Findings: Eleven types of habitats that best represent the landscape heterogeneity were investigated. Crenarchaeal, bacterial and fungal communities were described by means of Single Strand Conformation Polymorphism. Relationships between microbial beta diversity patterns were examined by using Bray-Curtis dissimilarities and Principal Coordinate Analyses. Distance-based redundancy analyses and variation partitioning were used to estimate the relative contributions of different drivers on microbial beta diversity. Microbial communities tended to be habitat- specific and did not display significant spatial autocorrelation. Microbial beta diversity correlated with soil pH. Fungal beta- diversity was mainly related to soil organic matter. Though the effect of plant species composition was significant for all microbial groups, it was much stronger for Fungi. In contrast, geographic distances did not have any effect on microbial beta diversity. Conclusions/Significance: Microbial communities exhibit non-random spatial patterns of diversity in alpine landscapes. Crenarcheal, bacterial and fungal community turnover is high and associated with plant species composition through different set of soil variables, but is not caused by geographical isolation

Short Conduction Delays Cause Inhibition Rather than Excitation to Favor Synchrony in Hybrid Neuronal Networks of the Entorhinal Cortex

How stable synchrony in neuronal networks is sustained in the presence of conduction delays is an open question. The Dynamic Clamp was used to measure phase resetting curves (PRCs) for entorhinal cortical cells, and then to construct networks of two such neurons. PRCs were in general Type I (all advances or all delays) or weakly type II with a small region at early phases with the opposite type of resetting. We used previously developed theoretical methods based on PRCs under the assumption of pulsatile coupling to predict the delays that synchronize these hybrid circuits. For excitatory coupling, synchrony was predicted and observed only with no delay and for delays greater than half a network period that cause each neuron to receive an input late in its firing cycle and almost immediately fire an action potential. Synchronization for these long delays was surprisingly tight and robust to the noise and heterogeneity inherent in a biological system. In contrast to excitatory coupling, inhibitory coupling led to antiphase for no delay, very short delays and delays close to a network period, but to near-synchrony for a wide range of relatively short delays. PRC-based methods show that conduction delays can stabilize synchrony in several ways, including neutralizing a discontinuity introduced by strong inhibition, favoring synchrony in the case of noisy bistability, and avoiding an initial destabilizing region of a weakly type II PRC. PRCs can identify optimal conduction delays favoring synchronization at a given frequency, and also predict robustness to noise and heterogeneity

Reduced conditioned fear response in mice that lack Dlx1 and show subtype-specific loss of interneurons

The inhibitory GABAergic system has been implicated in multiple neuropsychiatric diseases such as schizophrenia and autism. The Dlx homeobox transcription factor family is essential for development and function of GABAergic interneurons. Mice lacking the Dlx1 gene have postnatal subtype-specific loss of interneurons and reduced IPSCs in their cortex and hippocampus. To ascertain consequences of these changes in the GABAergic system, we performed a battery of behavioral assays on the Dlx1 mutant mice, including zero maze, open field, locomotor activity, food intake, rotarod, tail suspension, fear conditioning assays (context and trace), prepulse inhibition, and working memory related tasks (spontaneous alteration task and spatial working memory task). Dlx1 mutant mice displayed elevated activity levels in open field, locomotor activity, and tail suspension tests. These mice also showed deficits in contextual and trace fear conditioning, and possibly in prepulse inhibition. Their learning deficits were not global, as the mutant mice did not differ from the wild-type controls in tests of working memory. Our findings demonstrate a critical role for the Dlx1 gene, and likely the subclasses of interneurons that are affected by the lack of this gene, in behavioral inhibition and associative fear learning. These observations support the involvement of particular components of the GABAergic system in specific behavioral phenotypes related to complex neuropsychiatric diseases