scholarly journals Multiple metals influence distinct properties of the Arabidopsis circadian clock

2021 ◽  
Author(s):  
Jessica Kate Hargreaves ◽  
Rachael Oakenfull ◽  
Amanda Davis ◽  
Freya Pullen ◽  
Marina Knight ◽  
...  
Keyword(s):  
Circadian Rhythms ◽  
Environmental Changes ◽  
Time Series Data ◽  
Environmental Contaminants ◽  
Series Data ◽  
Biological Time ◽  
Fitness Consequences ◽  
Wide Range ◽  
Agricultural Pollutants ◽  
External Signals

Circadian rhythms coordinate endogenous events with external signals, and are essential to biological function. When environmental contaminants affect these rhythms, the organism may experience fitness consequences such as reduced growth or increased susceptibility to pathogens. In their natural environment plants may be exposed to a wide range of industrial and agricultural pollutants. Here, we investigate how the addition of various metal salts to the environment can impact plant-circadian rhythms, via the promoter:luciferase system. The consequences of these environmental changes were found to be varied and complex. Therefore, in addition to Fourier-based analyses, we apply novel wavelet-based spectral hypothesis testing and clustering methodologies to organize and understand the data. We are able to classify broad sets of responses to environmental contaminants, including pollutants which increase, or decrease, the period, or which induce a lack of precision or disrupt any meaningful periodicity. The methods are general, and may be applied to discover common responses and hidden structures within a wide range of biological time series data.

scholarly journals RhythmicAlly: Your R and Shiny–Based Open-Source Ally for the Analysis of Biological Rhythms

2019 ◽  
Vol 34 (5) ◽  
pp. 551-561 ◽  
Author(s):  
Lakshman Abhilash ◽  
Vasu Sheeba
Keyword(s):  
Time Series ◽  
Open Source ◽  
Time Series Data ◽  
Biological Rhythms ◽  
Series Data ◽  
Biological Time ◽  
Source Program ◽  
Wide Range ◽  
Infradian Rhythms ◽  
User Friendly

Research on circadian rhythms often requires researchers to estimate period, robustness/power, and phase of the rhythm. These are important to estimate, owing to the fact that they act as readouts of different features of the underlying clock. The commonly used tools, to this end, suffer from being very expensive, having very limited interactivity, being very cumbersome to use, or a combination of these. As a step toward remedying the inaccessibility to users who may not be able to afford them and to ease the analysis of biological time-series data, we have written RhythmicAlly, an open-source program using R and Shiny that has the following advantages: (1) it is free, (2) it allows subjective marking of phases on actograms, (3) it provides high interactivity with graphs, (4) it allows visualization and storing of data for a batch of individuals simultaneously, and (5) it does what other free programs do but with fewer mouse clicks, thereby being more efficient and user-friendly. Moreover, our program can be used for a wide range of ultradian, circadian, and infradian rhythms from a variety of organisms, some examples of which are described here. The first version of RhythmicAlly is available on Github, and we aim to maintain the program with subsequent versions having updated methods of visualizing and analyzing time-series data.

scholarly journals The Economic Impact of Critical National Infrastructure Failure Due to Space Weather

2018 ◽  
Author(s):  
Edward J. Oughton
Keyword(s):  
Economic Impact ◽  
Space Weather ◽  
Time Series Data ◽  
Critical Infrastructure ◽  
Satellite Communications ◽  
Series Data ◽  
Future Research ◽  
Wide Range ◽  
Infrastructure Failure ◽  
National Infrastructure

Space weather is a collective term for different solar or space phenomena that can detrimentally affect technology. However, current understanding of space weather hazards is still relatively embryonic in comparison to terrestrial natural hazards such as hurricanes, earthquakes, or tsunamis. Indeed, certain types of space weather such as large Coronal Mass Ejections (CMEs) are an archetypal example of a low-probability, high-severity hazard. Few major events, short time-series data, and the lack of consensus regarding the potential impacts on critical infrastructure have hampered the economic impact assessment of space weather. Yet, space weather has the potential to disrupt a wide range of Critical National Infrastructure (CNI) systems including electricity transmission, satellite communications and positioning, aviation, and rail transportation. In the early 21st century, there has been growing interest in these potential economic and societal impacts. Estimates range from millions of dollars of equipment damage from the Quebec 1989 event, to some analysts asserting that losses will be in the billions of dollars in the wider economy from potential future disaster scenarios. Hence, the origin and development of the socioeconomic evaluation of space weather is tracked, from 1989 to 2017, and future research directions for the field are articulated. Since 1989, many economic analyzes of space weather hazards have often completely overlooked the physical impacts on infrastructure assets and the topology of different infrastructure networks. Moreover, too many studies have relied on qualitative assumptions about the vulnerability of CNI. By modeling both the vulnerability of critical infrastructure and the socioeconomic impacts of failure, the total potential impacts of space weather can be estimated, providing vital information for decision makers in government and industry. Efforts on this subject have historically been relatively piecemeal, which has led to little exploration of model sensitivities, particularly in relation to different assumption sets about infrastructure failure and restoration. Improvements may be expedited in this research area by open-sourcing model code, increasing the existing level of data sharing, and improving multidisciplinary research collaborations between scientists, engineers, and economists.

scholarly journals The Origins and Effects of Corporate Diversity Programs

2013 ◽  
Author(s):  
Frank Dobbin ◽  
Alexandra Kalev
Keyword(s):  
Equal Opportunity ◽  
Time Series Data ◽  
Diversity Training ◽  
Series Data ◽  
Mentoring Programs ◽  
Full Time ◽  
Wide Range ◽  
Diversity Programs ◽  
Corporate Diversity ◽  
The 1960S

Corporations have implemented a wide range of equal opportunity and diversity programs since the 1960s. This chapter reviews studies of the origins of these programs, surveys that assess the popularity of different programs, and research on the effects of programs on the workforce. Human resources managers championed several waves of innovations: corporate equal opportunity policies and recruitment and training programs in the 1960s; bureaucratic hiring and promotion policies and grievance mechanisms in the 1970s; diversity training, networking, and mentoring programs in the 1980s; and work/family and sexual harassment programs in the 1990s and beyond. It was those managers who designed equal opportunity and diversity programs, not lawyers or judges or government bureaucrats, thus corporate take-up of the programs remains very uneven. Statistical analyses of time-series data on the effects of corporate diversity measures reveal several patterns. Initiatives designed to quash managerial bias, through diversity training, diversity performance evaluations, and bureaucratic rules, have been broadly ineffective. By contrast, innovations designed to engage managers in promoting workforce integration—mentoring programs, diversity taskforces, and full-time diversity staffers—have led to increases in diversity in the most difficult job to integrate, management. The research has clear implications for corporate and public policy.

scholarly journals Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series

2020 ◽  
Vol 109 (11) ◽  
pp. 2029-2061
Author(s):  
Zahraa S. Abdallah ◽  
Mohamed Medhat Gaber
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Ensemble Classifier ◽  
Series Data ◽  
New Classification ◽  
Symbolic Representations ◽  
Classification Technique ◽  
Field Of Vision ◽  
Main Challenge ◽  
Wide Range

Abstract Time series classification (TSC) is a challenging task that attracted many researchers in the last few years. One main challenge in TSC is the diversity of domains where time series data come from. Thus, there is no “one model that fits all” in TSC. Some algorithms are very accurate in classifying a specific type of time series when the whole series is considered, while some only target the existence/non-existence of specific patterns/shapelets. Yet other techniques focus on the frequency of occurrences of discriminating patterns/features. This paper presents a new classification technique that addresses the inherent diversity problem in TSC using a nature-inspired method. The technique is stimulated by how flies look at the world through “compound eyes” that are made up of thousands of lenses, called ommatidia. Each ommatidium is an eye with its own lens, and thousands of them together create a broad field of vision. The developed technique similarly uses different lenses and representations to look at the time series, and then combines them for broader visibility. These lenses have been created through hyper-parameterisation of symbolic representations (Piecewise Aggregate and Fourier approximations). The algorithm builds a random forest for each lens, then performs soft dynamic voting for classifying new instances using the most confident eyes, i.e., forests. We evaluate the new technique, coined Co-eye, using the recently released extended version of UCR archive, containing more than 100 datasets across a wide range of domains. The results show the benefits of bringing together different perspectives reflecting on the accuracy and robustness of Co-eye in comparison to other state-of-the-art techniques.

scholarly journals Thermal Infrared and Ionospheric Anomalies of the 2017 Mw6.5 Jiuzhaigou Earthquake

2020 ◽  
Vol 12 (17) ◽  
pp. 2843
Author(s):  
Meijiao Zhong ◽  
Xinjian Shan ◽  
Xuemin Zhang ◽  
Chunyan Qu ◽  
Xiao Guo ◽  
...  
Keyword(s):  
Time Series Data ◽  
Longwave Radiation ◽  
Thermal Infrared ◽  
Geological Structure ◽  
Series Data ◽  
Electron Content ◽  
Jiuzhaigou Earthquake ◽  
Total Electron ◽  
Wide Range ◽  
First Time

Taking the 2017 Mw6.5 Jiuzhaigou earthquake as a case study, ionospheric disturbances (i.e., total electron content and TEC) and thermal infrared (TIR) anomalies were simultaneously investigated. The characteristics of the temperature of brightness blackbody (TBB), medium-wave infrared brightness (MIB), and outgoing longwave radiation (OLR) were extracted and compared with the characteristics of ionospheric TEC. We observed different relationships among the three types of TIR radiation according to seismic or aseismic conditions. A wide range of positive TEC anomalies occurred southern to the epicenter. The area to the south of the Huarong mountain fracture, which contained the maximum TEC anomaly amplitudes, overlapped one of the regions with notable TIR anomalies. We observed three stages of increasing TIR radiation, with ionospheric TEC anomalies appearing after each stage, for the first time. There was also high spatial correspondence between both TIR and TEC anomalies and the regional geological structure. Together with the time series data, these results suggest that TEC anomaly genesis might be related to increasing TIR.

EFFECTIVENESS OF ENVIRONMENTAL TREATIES: TREND ANALYSIS OF TREATY-BASED ENVIRONMENTAL INDICATORS

2005 ◽  
Vol 07 (03) ◽  
pp. 493-541 ◽  
Author(s):  
CHENAZ B. SEELARBOKUS
Keyword(s):  
Environmental Conditions ◽  
Environmental Changes ◽  
Time Series Data ◽  
International Environmental Agreements ◽  
Environmental Indicators ◽  
Series Data ◽  
Regime Effectiveness ◽  
Global Environmental ◽  
Data Limitations ◽  
Environmental Effectiveness

The literature on environmental regime effectiveness has shown a predilection for behaviour modification studies, whereby effectiveness is associated with a change in the behaviour of relevant actors. There has not been a systematic endeavour to link the implementation of international environmental agreements (IEAs) with improvement in environmental conditions. This article shifts away from the paradigm of behavioural analysis and focuses instead on linking IEA effectiveness with positive environmental changes in treaty-based environmental effectiveness indicators. Thirty-four treaty texts have been analysed to determine potential environmental indicators, and treaty secretariats have been contacted to collect time-series data on the selected indicators. Based on data gathered, trend lines are established for the environmental indicators to depict changes in related global environmental conditions. The results of this exercise show that viewing IEA effectiveness from the environmental modification perspective is promising, though there are serious data limitations still to be overcome.

Applying Circular Statistics to the Analysis of Monitoring Data

2007 ◽  
Vol 23 (4) ◽  
pp. 227-237 ◽  
Author(s):  
Thomas Kubiak ◽  
Cornelia Jonas
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Ambulatory Monitoring ◽  
Circular Data ◽  
Circular Statistics ◽  
Series Data ◽  
Cross Sectional ◽  
Psychological Variables ◽  
Wide Range ◽  
Time Series Approach

Abstract. Patterns of psychological variables in time have been of interest to research from the beginning. This is particularly true for ambulatory monitoring research, where large (cross-sectional) time-series datasets are often the matter of investigation. Common methods for identifying cyclic variations include spectral analyses of time-series data or time-domain based strategies, which also allow for modeling cyclic components. Though the prerequisites of these sophisticated procedures, such as interval-scaled time-series variables, are seldom met, their usage is common. In contrast to the time-series approach, methods from a different field of statistics, directional or circular statistics, offer another opportunity for the detection of patterns in time, where fewer prerequisites have to be met. These approaches are commonly used in biology or geostatistics. They offer a wide range of analytical strategies to examine “circular data,” i.e., data where period of measurement is rotationally invariant (e.g., directions on the compass or daily hours ranging from 0 to 24, 24 being the same as 0). In psychology, however, circular statistics are hardly known at all. In the present paper, we intend to give a succinct introduction into the rationale of circular statistics and describe how this approach can be used for the detection of patterns in time, contrasting it with time-series analysis. We report data from a monitoring study, where mood and social interactions were assessed for 4 weeks in order to illustrate the use of circular statistics. Both the results of periodogram analyses and circular statistics-based results are reported. Advantages and possible pitfalls of the circular statistics approach are highlighted concluding that ambulatory assessment research can benefit from strategies borrowed from circular statistics.

scholarly journals Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

2017 ◽  
Vol 21 ◽  
Author(s):  
Trung Duy Pham ◽  
Dat Tran ◽  
Wanli Ma
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Digital Data ◽  
Series Data ◽  
Biomedical Data ◽  
Protection Mechanism ◽  
Wide Range ◽  
Watermarking Scheme ◽  
Ownership Protection

In the biomedical and healthcare fields, the ownership protection of the outsourced data is becoming a challenging issue in sharing the data between data owners and data mining experts to extract hidden knowledge and patterns. Watermarking has been proved as a right-protection mechanism that provides detectable evidence for the legal ownership of a shared dataset, without compromising its usability under a wide range of data mining for digital data in different formats such as audio, video, image, relational database, text and software. Time series biomedical data such as Electroencephalography (EEG) or Electrocardiography (ECG) is valuable and costly in healthcare, which need to have owner protection when sharing or transmission in data mining application. However, this issue related to kind of data has only been investigated in little previous research as its characteristics and requirements. This paper proposes an optimized watermarking scheme to protect ownership for biomedical and healthcare systems in data mining. To achieve the highest possible robustness without losing watermark transparency, Particle Swarm Optimization (PSO) technique is used to optimize quantization steps to find a suitable one. Experimental results on EEG data show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as noise addition, low-pass filtering, and re-sampling.

scholarly journals Macroecological dynamics of gut microbiota

2018 ◽  
Author(s):  
Brian W. Ji ◽  
Ravi U. Sheth ◽  
Purushottam D. Dixit ◽  
Konstantine Tchourine ◽  
Dennis Vitkup
Keyword(s):  
Gut Microbiota ◽  
Environmental Changes ◽  
Time Series Data ◽  
Complex Dynamics ◽  
Series Data ◽  
Ecological Communities ◽  
Publicly Traded Companies ◽  
Scaling Relationships ◽  
Sequencing Technologies ◽  
Context Specific

The gut microbiome is now widely recognized as a dynamic ecosystem that plays an important role in health and disease1. While current sequencing technologies make it possible to estimate relative abundances of host-associated bacteria over time2, 3, the biological processes governing their dynamics remain poorly understood. Therefore, as in other ecological systems4, 5, it is important to identify quantitative relationships describing global aspects of gut microbiota dynamics. Here we use multiple high-resolution time series data obtained from humans and mice6–8 to demonstrate that despite their inherent complexity, gut microbiota dynamics can be characterized by several robust scaling relationships. Interestingly, these patterns are highly similar to those previously observed across diverse ecological communities and economic systems, including the temporal fluctuations of animal and plant populations9–12 and the performance of publicly traded companies13. Specifically, we find power law relationships describing short- and long-term changes in gut microbiota abundances, species residence and return times, and the connection between the mean and variance of species abundances. The observed scaling relationships are altered in mice receiving different diets and affected by context-specific perturbations in humans. We use these macroecological relationships to reveal specific bacterial taxa whose dynamics are significantly affected by dietary and environmental changes. Overall, our results suggest that a quantitative macroecological framework will be important for characterizing and understanding complex dynamics of microbial communities.

scholarly journals Smart Helmet: Thresh-Learner–Online Machine Learning on Data Streams

2019 ◽  
Vol 9 (1S3) ◽  
pp. 466-473
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Real Life ◽  
General Purpose ◽  
Streaming Data ◽  
Series Data ◽  
Dynamic Threshold ◽  
Wide Range ◽  
Set Up

Today, with an enormous generation and availability of time series data and streaming data, there is an increasing need for an automatic analyzing architecture to get fast interpretations and results. One of the significant potentiality of streaming analytics is to train and model each stream with unsupervised Machine Learning (ML) algorithms to detect anomalous behaviors, fuzzy patterns, and accidents in real-time. If executed reliably, each anomaly detection can be highly valuable for the application. In this paper, we propose a dynamic threshold setting system denoted as Thresh-Learner, mainly for the Internet of Things (IoT) applications that require anomaly detection. The proposed model enables a wide range of real-life applications where there is a necessity to set up a dynamic threshold over the streaming data to avoid anomalies, accidents or sending alerts to distant monitoring stations. We took the major problem of anomalies and accidents in coal mines due to coal fires and explosions. This results in loss of life due to the lack of automated alarming systems. We propose Thresh-Learner, a general purpose implementation for setting dynamic thresholds. We illustrate it through the Smart Helmet for coal mine workers which seamlessly integrates monitoring, analyzing and dynamic thresholds using IoT and analysis on the cloud.

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