Exploring the Relationship between Rising Temperatures and the Number of Climate-Related Natural Disasters in China

Warming has strongly influenced the quantity and variability of natural disasters around the globe. This study aims to characterize the varying patterns between rising temperatures and climate-related natural disasters in China from 1951 to 2010. We examined the overall trend in the patterns of an 11-year cycle, and climate-related natural disaster responses to periods of rising and dropping temperature. We used Morlet wavelet analysis to determine the length of a temperature cycle period, and the arc elasticity coefficient to assess the number of climate-related natural disasters in response to the changing temperature. We found that: (1) the overall relationship between temperature and the number of climate-related natural disasters was positive; (2) however, on the cycle level, the pattern of climate-related natural disasters was found to be independent of temperature variation; (3) on the rise-drop level, temperature increases were associated with declines in the number of climate-related natural disasters. Moreover, as temperature decreased, the number of climate-related natural disasters increased substantially, such that temperature had a more considerable influence on the quantity of climate-related natural disasters during the temperature-drop period. Findings in this study can help enhance the dissemination of warning and mitigation efforts to combat natural disasters in the changing climate.

A quantitative model of cellular decision making in direct neuronal reprogramming

The direct reprogramming of adult skin fibroblasts to neurons is thought to be controlled by a small set of interacting gene regulators. Here, we investigate how the interaction dynamics between these regulating factors coordinate cellular decision making in direct neuronal reprogramming. We put forward a quantitative model of the governing gene regulatory system, supported by measurements of mRNA expression. We found that nPTB needs to feed back into the direct neural conversion network most likely via PTB in order to accurately capture quantitative gene interaction dynamics and correctly predict the outcome of various overexpression and knockdown experiments. This was experimentally validated by nPTB knockdown leading to successful neural conversion. We also proposed a novel analytical technique to dissect system behaviour and reveal the influence of individual factors on resulting gene expression. Overall, we demonstrate that computational analysis is a powerful tool for understanding the mechanisms of direct (neuronal) reprogramming, paving the way for future models that can help improve cell conversion strategies.

Coupling between Blood Pressure and Subarachnoid Space Width Oscillations during Slow Breathing

The precise mechanisms connecting the cardiovascular system and the cerebrospinal fluid (CSF) are not well understood in detail. This paper investigates the couplings between the cardiac and respiratory components, as extracted from blood pressure (BP) signals and oscillations of the subarachnoid space width (SAS), collected during slow ventilation and ventilation against inspiration resistance. The experiment was performed on a group of 20 healthy volunteers (12 females and 8 males; BMI =22.1±3.2 kg/m2; age 25.3±7.9 years). We analysed the recorded signals with a wavelet transform. For the first time, a method based on dynamical Bayesian inference was used to detect the effective phase connectivity and the underlying coupling functions between the SAS and BP signals. There are several new findings. Slow breathing with or without resistance increases the strength of the coupling between the respiratory and cardiac components of both measured signals. We also observed increases in the strength of the coupling between the respiratory component of the BP and the cardiac component of the SAS and vice versa. Slow breathing synchronises the SAS oscillations, between the brain hemispheres. It also diminishes the similarity of the coupling between all analysed pairs of oscillators, while inspiratory resistance partially reverses this phenomenon. BP–SAS and SAS–BP interactions may reflect changes in the overall biomechanical characteristics of the brain.

Relationship between influenza and dengue outbreaks, and subsequent bacterial sepsis in French Guiana: A time series analysis

Background: Influenza has been shown to increase the risk for severe bacterial infection, in the tropics the seasonality of influenza epidemics is less marked, and this may not be the case. Dengue is often followed by prolonged asthenia and some physicians hypothesized increased susceptibility to infections based on anecdotal observations.

Design and Methods: Time series of influenza and dengue surveillance were confronted bacterial sepsis admissions to test the hypotheses. Monthly surveillance data on influenza and dengue and aggregated sepsis data in Cayenne hospital were matched between 24/10/2007 and 27/09/2016. An ARIMA (1,0,1) model was used.

Results The series of the number of monthly cases of sepsis was positively associated with the monthly number of cases of influenza at time t (β=0.001, p=0.0359). Forecasts were imperfectly correlated with sepsis since influenza is not the only risk factor for sepsis. None of the ARIMA models showed a significant link between the dengue series and the sepsis series.

Conclusions: There was thus no link between dengue epidemics and sepsis, but it was estimated that for every 1,000 cases of flu there was one additional case of sepsis. In this tropical setting, influenza was highly seasonal, and improved vaccination coverage could have benefits on sepsis.

Annual Partitioning Patterns of Labyrinthulomycetes Protists Reveal Their Multifaceted Role in Marine Microbial Food Webs

Heterotrophic microbes play a key role in remineralizing organic material in the coastal ocean. While there is a significant body of literature examining heterotrophic bacterioplankton and phytoplankton communities, much less is known about the diversity, dynamics, and ecology of eukaryotic heterotrophs. Here, we focus on the Labyrinthulomycetes, a fungus-like protistan group whose biomass can exceed that of the bacterioplankton in coastal waters. We examined their diversity and community structure in a weekly temperate coastal ocean time series. Their seasonal community patterns were related to temperature, insolation, dissolved inorganic carbon, fungal abundance, ammonia, chlorophyll a, pH, and other environmental variables. Similar to the bacterioplankton, annual community patterns of the Labyrinthulomycetes were dominated by a few persistent taxa with summer or winter preferences. However, like the patterns of fungi at this site, the majority of the Labyrinthulomycetes phylotypes occurred mostly as short, reoccurring, season-specific blooms. Furthermore, some specific phylotypes of Labyrinthulomycetes displayed time-lagged correlations or cooccurrences with bacterial, algal, or fungal phylotypes, suggesting their potentially multifaceted involvement in the marine food webs. Overall, this study reports niche partitioning between closely related Labyrinthulomycetes and identifies distinct ecotypes and temporal patterns compared to bacterioplankton and fungi.IMPORTANCE Increasing evidence has shown that heterotrophic microeukaryotes are an important component in global marine ecosystems, while their diversity and ecological functions remain largely unknown. Without appropriately incorporating these organisms into the food web models, our current understanding of marine microbial community ecology is incomplete, which may further hamper broader studies of biogeochemistry and climate change. This study focuses on a major group of unicellular fungus-like protists (Labyrinthulomycetes) and reveals their distinct annual community patterns relative to fungi and bacteria. Results of our observations provide new information on the community structure and ecology of this protistan group and shed light on the intricate ecological roles of unicellular heterotrophic eukaryotes in the coastal oceans.

Excess Deaths during Influenza and Coronavirus Disease and Infection-Fatality Rate for Severe Acute Respiratory Syndrome Coronavirus 2, the Netherlands

Since the 2009 influenza pandemic, the Netherlands has used a weekly death monitoring system to estimate deaths in excess of expectations. We present estimates of excess deaths during the ongoing coronavirus disease (COVID-19) epidemic and 10 previous influenza epidemics. Excess deaths per influenza epidemic averaged 4,000. The estimated 9,554 excess deaths (41% in excess) during the COVID-19 epidemic weeks 12–19 of 2020 appeared comparable to the 9,373 excess deaths (18%) during the severe influenza epidemic of 2017–18. However, these deaths occurred in a shorter time, had a higher peak, and were mitigated by nonpharmaceutical control measures. Excess deaths were 1.8-fold higher than reported laboratory-confirmed COVID-19 deaths (5,449). Based on excess deaths and preliminary results from seroepidemiologic studies, we estimated the infection-fatality rate to be 1%. Monitoring of excess deaths is crucial for timely estimates of disease burden for influenza and COVID-19. Our data complement laboratory-confirmed COVID-19 death reports and enable comparisons between epidemics.

Recommended Sampling Intervals for Arsenic in Private Wells

Geogenic arsenic in drinking water is a worldwide problem. For private well owners, testing (e.g., private or government laboratory) is the main method to determine arsenic concentration. However, the temporal variability of arsenic concentrations is not well characterized and it is not clear how often private wells should be tested. To answer this question, three datasets, two new and one publicly available, with temporal arsenic data were utilized: 6370 private wells from New Jersey tested at least twice since 2002, 2174 wells from the USGS NAWQA database, and 391 private wells sampled 14 years apart from Bangladesh. Two arsenic drinking water standards are used for the analysis: 10 µg/L, the WHO guideline and EPA standard or maximum contaminant level (MCL) and 5 µg/L, the New Jersey MCL. A rate of change was determined for each well and these rates were used to predict the temporal change in arsenic for a range of initial arsenic concentrations below an MCL. For each MCL and initial concentration, the probability of exceeding an MCL over time was predicted. Results show that to limit a person to below a 5% chance of drinking water above an MCL, wells that are ½ an MCL and above should be tested every year and wells below ½ an MCL should be tested every 5 years. These results indicate that one test result below an MCL is inadequate to ensure long-term compliance. Future recommendations should account for temporal variability when creating drinking water standards and guidance for private well owners.

Trends in Medical and Surgical Admission Length of Stay by Race/Ethnicity and Socioeconomic Status: A Time Series Analysis

BACKGROUND

Length of stay (LOS), a metric of hospital efficiency, differs by race/ethnicity and socioeconomic status (SES) and longer LOS is associated with adverse health outcomes. Historically, projects to improve LOS efficiency have yielded LOS reductions by 0.3 to 0.7 days per admission.

OBJECTIVE

To assess differences in average adjusted length of stay (aALOS) over time by race/ethnicity, and SES stratified by discharge destination (home or non-home).

METHOD

Data were obtained from 2009-2014 Healthcare Cost and Utilization Project State Inpatient Datasets for New York, New Jersey, and Florida. Multivariate generalized linear models were used to examine trends in aALOS differences by race/ethnicity, and by high vs low SES patients (defined first vs fourth quartile of median income by zip code) controlling for patient, disease and hospital characteristics.

RESULTS

For those discharged home, racial/ethnic and SES aALOS differences remained stable from 2009 to 2014. However, among those discharged to non-home destinations, Black vs White aALOS differences increased from 0.21 days in Q1 2009, (95% confidence interval (CI): 0.13 to 0.30) to 0.32 days in Q3 2013, (95% CI: 0.23 to 0.40), and for low vs high SES patients from 0.03 days in Q1 2009 (95% CI: -0.04 to 0.1) to 0.26 days, (95% CI: 0.19 to 0.34). Notably, for patients not discharged home, racial/ethnic and SES aALOS differences increased and persisted after Q3 2011, coinciding with the introduction of the Affordable Care Act (ACA).

CONCLUSION

Further research to understand the ACA's policy impact on hospital efficiencies, and relationship to racial/ethnic and SES differences in LOS is warranted.

Quantifying Model Error in P-technique Factor Analysis

P-technique factor analysis is an exploratory factor model for multivariate time series data. Assessing model fit of P-technique factor models is non-trivial because time series data are correlated at nearby time points. We present a test statistic that is appropriate for P-technique factor analysis. In addition, the test statistic allows researchers to quantify the amount of model error. We explore the statistical properties of the test statistic with simulated data and we illustrate its use with an empirical study of personality states. Results of the simulation study include (1) the empirical distributions of the test statistic approximately followed their respective theoretical chi-square distributions, (2) the empirical Type I error rates of the test of perfect fit are close to the nominal level and the empirical Type I error rates of the test of close fit are slightly lower than the nominal level, and (3) the empirical power rates of the test of perfect fit are satisfactory but the empirical power rates of the test of close fit are only satisfactory for small models.

The Convex Mixture Distribution: Granger Causality for Categorical Time Series

We present a framework for learning Granger causality networks for multivariate categorical time series based on the mixture transition distribution (MTD) model. Traditionally, MTD is plagued by a nonconvex objective, non-identifiability, and presence of local optima. To circumvent these problems, we recast inference in the MTD as a convex problem. The new formulation facilitates the application of MTD to high-dimensional multivariate time series. As a baseline, we also formulate a multi-output logistic autoregressive model (mLTD), which while a straightforward extension of autoregressive Bernoulli generalized linear models, has not been previously applied to the analysis of multivariate categorial time series. We establish identifiability conditions of the MTD model and compare them to those for mLTD. We further devise novel and efficient optimization algorithms for MTD based on our proposed convex formulation, and compare the MTD and mLTD in both simulated and real data experiments. Finally, we establish consistency of the convex MTD in high dimensions. Our approach simultaneously provides a comparison of methods for network inference in categorical time series and opens the door to modern, regularized inference with the MTD model.

Spatial and dosimetric evaluation of residual distortions of prostate and seminal vesicle bed after image-guided definitive and postoperative radiotherapy of prostate cancer with endorectal balloon

Purpose

To quantify daily residual deviations from the planned geometry after image‐guided prostate radiotherapy with endorectal balloon and to evaluate their effect on the delivered dose distribution.

Methods

Daily kV‐CBCT imaging was used for online setup‐correction in six degrees of freedom (6‐dof) for 24 patients receiving definitive (12 RT_def patients) or postoperative (12 RT_postop patients) radiotherapy with endorectal balloon (overall 739 CBCTs). Residual deviations were evaluated using several spatial and dosimetric variables, including: (a) posterior Hausdorff distance HD^post (=maximum distance between planned and daily CTV contour), (b) point P_worst with largest HD^post over all fractions, (c) equivalent uniform dose using a cell survival model (EUD_SF) and the generalized EUD concept (gEUD_a with parameter a = −7 and a = −20). EUD values were determined for planned (EUDSFplan), daily (EUDSFind), and delivered dose distributions (EUDSFaccum) for plans with 6 mm (=clinical plans) and 2 mm CTV‐to‐PTV margin. Time series analyses of interfractional spatial and dosimetric deviations were conducted.

Results

Large HD^post values ≥ 12.5 mm (≥15 mm) were observed in 20/739 (5/739) fractions distributed across 7 (3) patients. Points P_worst were predominantly located at the posterior CTV boundary in the seminal vesicle region (16/24 patients, 6/7 patients with HD^post ≥ 12.5 mm). Time series analyses revealed a stationary white noise characteristic of HD^post and relative dose at P_worst. The EUD_SF difference between planned and accumulated dose distributions was < 5.4% for all 6‐mm plans. Evaluating 2‐mm plans, EUD_SF deteriorated by < 10% (<5%) in 75% (58.5%) of the patients. EUDSFaccum was well described by the median value of the EUDSFind distribution. PTV margin calculation at P_worst yielded 8.8 mm.

Conclusions

Accumulated dose distributions in prostate radiotherapy with endorectal balloon are forgiving of considerable residual distortions after 6‐dof patient setup if they are observed in a minority of fractions and the median value of EUDSFind determined per fraction stays within 95% of prescribed dose. Common PTV margin calculations are overly conservative because after online correction of translational and rotational errors only residual deformations need to be included. These results provide guidelines regarding online navigation, margin optimization, and treatment adaptation strategies.

Time Trends of the Public's Attention Toward Suicide During the COVID-19 Pandemic: Retrospective, Longitudinal Time-Series Study

Background

The COVID-19 pandemic has overwhelmed health care systems around the world. Emerging evidence has suggested that substantially few patients seek help for suicidality at clinical settings during the COVID-19 pandemic, which has elicited concerns of an imminent mental health crisis as the course of the pandemic continues to unfold. Clarifying the relationship between the public’s attention to knowledge about suicide and the public’s attention to knowledge about the COVID-19 pandemic may provide insight into developing prevention strategies for a putative surge of suicide in relation to the impact of the COVID-19 pandemic.

Objective

The goal of this retrospective, longitudinal time-series study is to understand the relationship between temporal trends of interest for the search term “suicide” and those of COVID-19–related terms, such as “social distancing,” “school closure,” and “lockdown.”

Methods

We used the Google Trends platform to collect data on daily interest levels for search terms related to suicide, several other mental health-related issues, and COVID-19 over the period between February 14, 2020 and May 13, 2020. A correlational analysis was performed to determine the association between the search term ‘‘suicide’’ and COVID-19–related search terms in 16 countries. The Mann-Kendall test was used to examine significant differences between interest levels for the search term “suicide” before and after school closure.

Results

We found that interest levels for the search term “suicide” statistically significantly inversely correlated with interest levels for the search terms “COVID-19” or “coronavirus” in nearly all countries between February 14, 2020 and May 13, 2020. Additionally, search interest for the term ‘‘suicide’’ significantly and negatively correlated with that of many COVID-19–related search terms, and search interest varied between countries. The Mann-Kendall test was used to examine significant differences between search interest levels for the term “suicide” before and after school closure. The Netherlands (P=.19), New Zealand (P=.003), the United Kingdom (P=.006), and the United States (P=.049) showed significant negative trends in interest levels for suicide in the 2-week period preceding school closures. In contrast, interest levels for suicide had a significant positive trend in Canada (P<.001) and the United States (P=.002) after school closures.

Conclusions

The public’s attention to suicide might inversely correlate with the public’s attention to COVID-19–related issues. Additionally, several anticontagion policies, such as school closure, might have led to a turning point for mental health crises, because the attention to suicidality increased after restrictions were implemented. Our results suggest that an increased risk of suicidal ideation may ensue due to the ongoing anticontagion policies. Timely intervention strategies for suicides should therefore be an integral part of efforts to flatten the epidemic curve.

Reduction of COVID-19 Incidence and Nonpharmacologic Interventions: Analysis Using a US County-Level Policy Data Set

BACKGROUND

Worldwide, nonpharmacologic interventions (NPIs) have been the main tool used to mitigate the COVID-19 pandemic. This includes social distancing measures (closing businesses, closing schools, and quarantining symptomatic persons) and contact tracing (tracking and following exposed individuals). While preliminary research across the globe has shown these policies to be effective, there is currently a lack of information on the effectiveness of NPIs in the United States.

OBJECTIVE

The purpose of this study was to create a granular NPI data set at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases.

METHODS

Using a standardized crowdsourcing methodology, we collected time-series data on 7 key NPIs for 1320 US counties.

RESULTS

This open-source data set is the largest and most comprehensive collection of county NPI policy data and meets the need for higher-resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (P<.001). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases (P=.004). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R=0.21) and elected leadership (R=0.22).

CONCLUSIONS

This study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this data set will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.

Bleach correction ImageJ plugin for compensating the photobleaching of time-lapse sequences

During the capturing of the time-lapse sequence of fluorescently labeled samples, fluorescence intensity exhibits decays. This phenomenon is known as 'photobleaching' and is a widely known problem in imaging in life sciences. The photobleaching can be attenuated by tuning the imaging set-up, but when such adjustments only partially work, the image sequence can be corrected for the loss of intensity in order to precisely segment the target structure or to quantify true intensity dynamics. We implemented an ImageJ plugin that allows the user to compensate for the photobleaching to estimate the non-bleaching condition with choice of three different algorithms: simple ratio, exponential fitting, and histogram matching methods. The histogram matching method is a novel algorithm for photobleaching correction. This article presents details and characteristics of each algorithm based on application to actual image sequences.

Assessing antiretroviral therapy success in HIV/AIDS morbidity and mortality trends in Brazil, 1990-2017: an interrupted time series study

To describe the trends of HIV/AIDS metrics related to the burden of disease for Brazil between 1990 and 2017 we conducted a timeseries analysis for HIV/AIDS indicators by extracting data from the Global Burden of Disease study. We calculated traditional prevalence, incidence and mortality rates, the number of years lost by HIV-related deaths (YLL) and disability (YLD), and disability-adjusted life years (DALY). We estimated time series models and assessed the impact of highly active antiretroviral therapy (HAART) on the same indicators. In the set of disability-adjusted life years (DALY), the highest weight of its magnitude was due to YLL. There was a decline, especially after 1996, of DALY, mortality and YLL for HIV/AIDS. However, YLD, incidence, and prevalence increased over the same period. Also, the analysis of interrupted time series showed that the introduction of HAART into health policy had a significant impact on indicators, especially for DALY and YLL. We need to assess the quality of life of people living with HIV, especially among older adults. In addition, we need to focus on primary prevention, emphasizing methods to avoid infection and public policies should reflect this.

Using network analysis to identify seasonal patterns and key nodes for risk-based surveillance of pig diseases in Italy

The description of the pattern of livestock movements between herds provides essential information for both improving risk-based surveillance and to understand the likely spread of infectious diseases. This study provides a description of the temporal pattern of pig movements recorded in Italy on a 4-year period (2013-2016). Data, provided by the National Livestock registry, were described by social network analysis and the application of a walk-trap algorithm for community detection. Our results show a highly populated community located in Northern Italy, which is the focal point of the Italian industrial pig production and as a general pattern an overall decline of medium and backyard farms and an increase in the number of large farms, in agreement with the trend observed by other EU pig-producing countries. A seasonal pattern of all the parameters evaluated, including the number of active nodes in both the intensive and smaller production systems, emerged: that is characterized by a higher number of movements in spring and autumn, linked with the breeding and production cycle as pigs moved from the growing to the finishing phase and with periods of increased slaughtering at Christmas and Easter. The same pattern was found when restricting the analysis to imported pig batches. Outbreaks occurring during these periods would have a greater impact on the spread of infectious diseases; therefore, targeted surveillance may be appropriate. Finally, potential super-spreader nodes have been identified and represent 0.47% of the total number of pig holdings (n = 477). Those nodes are present during the whole study period with a similar ranking in their potential of being super-spreaders. Most of them were in Northern Italy, but super-spreaders with high mean out-degree centrality were also located in other Regions. Seasonality, communities and super-spreaders should be considered when planning surveillance activity and when applying disease control strategies.

Effects of the COVID-19 Pandemic on Spontaneous Reporting: Global and National Time-series Analyses

Purpose

The COVID-19 pandemic has been widely reported to present stress to medical systems globally and to disrupt the lives of patients and health care practitioners (HCPs). Given that spontaneous reporting heavily relies on both HCPs and patients, an understandable question is whether the stress of the pandemic has diminished spontaneous reporting. Herein, the hypothesis that the COVID-19 pandemic has negatively affected the spontaneous reporting of adverse drug events was assessed.

Methods

Spontaneous-report counts from 119 weeks (January 1, 2018, to April 12, 2020) were identified using Pfizer's safety database and were analyzed. Autoregressive integrated moving-average models were fitted to aggregated and disaggregated time series (TSs). Model residuals were charted on individual-value and moving-range charts and exponentially weighted moving-average charts for the identification of statistically unexpected changes associated with the pandemic.

Findings

Overall, the reporting of serious adverse events showed no unexpected decline. Total global reporting declined, driven by HCP reporting (of both serious and nonserious events), starting after week 8 of 2020 and exceeding model expectations by week 15 of 2020, suggesting the pandemic as an assignable cause. However, reporting remained within longer-term historical ranges. The TS from Japan was the only national TS that showed a significant decline, and an unusual periodicity related to national holidays. A few countries, notably Taiwan, showed unexpected statistical increases in reporting associated with the pandemic, commencing as early as week 3 of 2020. In the literature, the reporting of adverse drug events was stable. Ancillary findings included prevalent year-end/beginning reporting minima, with more reports from HCPs than from consumers.

Implications

Using data from a large-scale and diverse safety database from a pharmaceutical company, a significant global decline in total reporting was detected, driven by HCPs, not consumers, and reports of nonserious events, consistent with the pandemic as an assignable cause, but the reporting remained within long-term ranges, suggesting relative durability. Importantly, the analyses found no unexpected decline in overall reporting of serious events. Future avenues of research include the use of data from large-scale, publicly available spontaneous reporting systems for assessing the generalizability of the present findings and whether they correlate with impaired signal detection, as well as a follow-up analysis of whether the effects on spontaneous reporting abate after the pandemic.

Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption

Data from smart grids are challenging to analyze due to their very large size, high dimensionality, skewness, sparsity, and number of seasonal fluctuations, including daily and weekly effects. With the data arriving in a sequential form the underlying distribution is subject to changes over the time intervals. Time series data streams have their own specifics in terms of the data processing and data analysis because, usually, it is not possible to process the whole data in memory as the large data volumes are generated fast so the processing and the analysis should be done incrementally using sliding windows. Despite the proposal of many clustering techniques applicable for grouping the observations of a single data stream, only a few of them are focused on splitting the whole data streams into the clusters. In this article we aim to explore individual characteristics of electricity usage and recommend the most suitable tariff to the customer so they can benefit from lower prices. This work investigates various algorithms (and their improvements) what allows us to formulate the clusters, in real time, based on smart meter data.

Utilization of Time Series Tools in Life-sciences and Neuroscience

Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals', other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.

Uncovering ecological state dynamics with hidden Markov models

Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or 'hidden'. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.

Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection

We present an unsupervised method to detect anomalous time series among a collection of time series. To do so, we extend traditional Kernel Density Estimation for estimating probability distributions in Euclidean space to Hilbert spaces. The estimated probability densities we derive can be obtained formally through treating each series as a point in a Hilbert space, placing a kernel at those points, and summing the kernels (a "point approach"), or through using Kernel Density Estimation to approximate the distributions of Fourier mode coefficients to infer a probability density (a "Fourier approach"). We refer to these approaches as Functional Kernel Density Estimation for Anomaly Detection as they both yield functionals that can score a time series for how anomalous it is. Both methods naturally handle missing data and apply to a variety of settings, performing well when compared with an outlyingness score derived from a boxplot method for functional data, with a Principal Component Analysis approach for functional data, and with the Functional Isolation Forest method. We illustrate the use of the proposed methods with aviation safety report data from the International Air Transport Association (IATA).

Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data

In a previous study, air sampling using vortex air samplers combined with species-specific amplification of pathogen DNA was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic uncertainty. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of pathogen abundance data. In particular, the analyses suggested that the length of time series data that it is practical or cost-effective to collect may limit the ability to definitively classify the uncertainty in the data. Over the two years of the study, five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating whether the pathogen abundance data were increasing revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves and (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence was positive.

Personalized Predictive Models for Identifying Clinical Deterioration Using LSTM in Emergency Departments

Early detection of deterioration at hospitals could be beneficial in terms of reducing mortality and morbidity rates and costs. In this paper, we present a model based on Long Short-Term Memory (LSTM) neural network used in deep learning to predict the illness severity of patients in advance. Hence, by predicting health severity, this model can be used to identify deteriorating patients. Our proposed model utilizes continuous monitored vital signs, including heart rate, respiratory rate, oxygen saturation, and blood pressure automatically collected from patients during hospitalization. In this study, a short-time prediction using a sliding window approach is applied. The performance of the proposed model was compared with the Multi-Layer Perceptron (MLP) neural network, a feedforward class of neural network, based on R2 score and Root Mean Square Error (RMSE) metrics. The results showed that the LSTM has a better performance and could predict the illness severity of patients more accurately.

Sampling Properties of color Independent Component Analysis

Independent Component Analysis (ICA) offers an effective data-driven approach for blind source extraction encountered in many signal and image processing problems. Although many ICA methods have been developed, they have received relatively little attention in the statistics literature, especially in terms of rigorous theoretical investigation for statistical inference. The current paper aims at narrowing this gap and investigates the statistical sampling properties of the colorICA (cICA) method. The cICA incorporates the correlation structure within sources through parametric time series models in the frequency domain and outperforms several existing ICA alternatives numerically. We establish the consistency and asymptotic normality of the cICA estimates, which then enables statistical inference based on the estimates. These asymptotic properties are further validated using simulation studies.