Types Of Temporal Patterns Research Articles

A comparative analysis of the potential of carbon emission reductions from shared micro-mobility

Shared micro-mobility systems (SMSs) have recently experienced rapid growth, providing new green mobility options to reduce energy use. To better understand SMS’s environmental impact, this paper comparatively examines the potential of carbon emission reductions from dockless shared bikes and e-bikes in three Chinese cities at different economic development stages (i.e., Binjiang, Wucheng and Xiangshan) using massive user-generated trips. Results show that shared bikes and e-bikes in the three cities reduced 41.51 and 31.84 tonnes of CO2 emissions over a week, respectively. The trip-level environmental benefit of shared e-bikes is 155.11 g of CO2, higher than that of shared bikes (132.03 g). Most of the reduced carbon emissions are from substituting driving trips, but shared e-bikes even generate extra carbon emissions by substituting public transit and walking. Patterns of carbon emission reductions show spatiotemporal heterogeneity. Spatially, reduced carbon emissions are concentrated in central areas of Wucheng/Xiangshan and more dispersedly distributed in Binjiang, which is a more economically vibrant city. From a temporal perspective, SMS’s carbon reduction patterns are similar to typical temporal usage patterns. The Dining and Shopping trips contribute the most to SMS’s carbon reductions in all cities. Binjiang has more carbon emissions reduced from the Transfer and Working trips, and shared bike trips with the Home and Schooling purposes reduced relatively more carbon emissions in Wucheng. Our analysis helps us better understand the environmental impact of SMSs across various urban contexts and inform relevant transport planning to achieve carbon neutrality in cities.

Evaluating the timing of bovine respiratory disease morbidity and mortality in feedlot cattle by first treatment, fatal disease onset, and days from first treatment to death

Little research has evaluated risk factors influencing the timing of bovine respiratory disease (BRD); the main focus of previ­ous BRD research has been on environmental and demographic factors influencing disease frequency. The study objective was to descriptively evaluate typical BRD temporal patterns for cat­tle in U.S. Great Plains feedyard systems.

Evaluation of First Treatment Timing, Fatal Disease Onset, and Days from First Treatment to Death Associated with Bovine Respiratory Disease in Feedlot Cattle

Bovine respiratory disease (BRD) is a frequent beef cattle syndrome. Improved understanding of the timing of BRD events, including subsequent deleterious outcomes, promotes efficient resource allocation. This study's objective was to determine differences in timing distributions of initial BRD treatments (Tx1), days to death after initial treatment (DTD), and days after arrival to fatal disease onset (FDO). Individual animal records for the first BRD treatment (n = 301,721) or BRD mortality (n = 19,332) were received from 25 feed yards. A subset of data (318-363 kg; steers/heifers) was created and Wasserstein distances were used to compare temporal distributions of Tx1, FDO, and DTD across genders (steers/heifers) and the quarter of arrival. Disease frequency varied by quarter with the greatest Wasserstein distances observed between Q2 and Q3 and between Q2 and Q4. Cattle arriving in Q3 and Q4 had earlier Tx1 events than in Q2. Evaluating FDO and DTD revealed the greatest Wasserstein distance between cattle arriving in Q2 and Q4, with cattle arriving in Q2 having later events. Distributions of FDO varied by gender and quarter and typically had wide distributions with the largest 25-75% quartiles ranging from 20 to 80 days (heifers arriving in Q2). The DTD had right-skewed distributions with 25% of cases occurring by days 3-4 post-treatment. Results illustrate temporal disease and outcome patterns are largely right-skewed and may not be well represented by simple arithmetic means. Knowledge of typical temporal patterns allows cattle health managers to focus disease control efforts on the correct groups of cattle at the appropriate time.

SPATIO-TEMPORAL ANALYSIS AND MODELLING OF DENGUE INCIDENCES IN QUEZON CITY USING ORDINARY LEAST SQUARES AND SPATIAL REGRESSION

Abstract. This research examined monthly dengue incidences per barangay (village) in Quezon City, Philippines over a period of six years (2010–2015) to determine the relative significance of environmental variables on dengue prevalence. The data were subjected to correlation analysis, spatial autocorrelation assessment, multiple factor analysis, ordinary least squares (OLS) and spatial regression. Local Indicators of Spatial Autocorrelation (LISA) Moran’s I cluster maps indicate significant (p=0.05, 0.01) High-High clustering and Low-Low clustering in the northern and southern parts of the city, respectively. Monthly total cases indicated increasing trend staring from May/June, peaking at around August/September, and declining afterwards to lower levels in November/December. This corresponds to the typical temporal rainfall pattern. Dengue cases were found to be positively correlated (α=0.05) with Population (R=0.84), Informal_Settlements (IS) (R=0.716), Very_Low_Density_Residential (VLDR) (R=0.512), Open_Spaces (OS) (R=0.339), Mean_Rainfall (RFM) (R=0.637), and Mean Elevation (EM) (R=0.498). Dengue incidence (DI) was negatively correlated with Mean Air Temperature (ATM) (R=−0.3 to 0.5). Based on factor analysis, the dengue incidences were closely related to these variables, though factors F1 and F2 accounted for only 28% of the data variability. Ordinary Least Square (OLS) regression analysis of DI with general land use (LU) classes (e.g., no subclasses in residential areas) identified only IS and OS, explaining 43% of the variability of DI, with IS having twice as much influence on DI compared to OS. When residential subclasses are considered, VLDR was added to the model, slightly increasing R-squared to 0.452. Considering, in addition, EM, RFM, and ATM, the R-squared improved to 0.589, with RFM and EM considered more influential on dengue compared to IS and OS. ATM was however removed due to multicollinearity. The use of Spatial Error regression (SER) and Spatial Lag regression (SLR) produced improved models relative to the OLS model with R-squared of 0.676 and 0.667, respectively. This indicates the importance of spatial dependence. This can be explained by the fact that mosquitos fly over considerably long distances, traversing across the different barangays. The SER model (AIC=1359.89; SE=26.9584) is slightly better than the SLR model (AIC=1366.05; SE= 27.3399).

Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion

Basic vital signs such as heart and respiratory rates (HR and RR) are essential bio-indicators. Their longitudinal in-home collection enables prediction and detection of disease onset and change, providing for earlier health intervention. In this article, we propose a robust, non-touch vital signs monitoring system using a pair of co-located Ultra-Wide Band (UWB) and depth sensors. By extensive manual examination, we identify four typical temporal and spectral signal patterns and their suitable vital sign estimators. We devise a probabilistic weighted framework (PWF) that quantifies evidence of these patterns to update the weighted combination of estimator output to track the vital signs robustly. We also design a “heatmap”-based signal quality detector to exclude the disturbed signal from inadvertent motions. To monitor multiple co-habiting subjects in-home, we build a two-branch long short-term memory (LSTM) neural network to distinguish between individuals and their activities, providing activity context crucial to disambiguating critical from normal vital sign variability. To achieve reliable context annotation, we carefully devise the feature set of the consecutive skeletal poses from the depth data, and develop a probabilistic tracking model to tackle non-line-of-sight (NLOS) cases. Our experimental results demonstrate the robustness and superior performance of the individual modules as well as the end-to-end system for passive and context-aware vital sign monitoring.

Detecting time-evolving phenotypic components of adverse reactions against BNT162b2 SARS-CoV-2 vaccine via non-negative tensor factorization

SummarySymptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms. Here, we attempt to extract the dynamic changes in vaccine adverse reaction symptoms as a small number of interpretable components by using non-negative tensor factorization. We recruited healthcare workers who received two doses of the BNT162b2 mRNA COVID-19 vaccine at Chiba University Hospital and collected information on adverse reactions using a smartphone/web-based platform. We analyzed the adverse-reaction data after each dose obtained for 1,516 participants who received two doses of vaccine. The non-negative tensor factorization revealed four time-evolving components that represent typical temporal patterns of adverse reactions for both doses. These components were differently associated with background factors and post-vaccine antibody titers. These results demonstrate that complex adverse reactions against vaccines can be explained by a limited number of time-evolving components identified by tensor factorization.

Dynamic multivariate analysis for pollution assessment and river habitat conservation in the Vietnamese La Buong watershed.

Analysis of temporal patterns of high-dimensional time-series water quality data is essential for pollution management worldwide. This study has applied dynamic factor analysis (DFA) and cluster analysis (CA) to analyze time-series water quality data monitored at the five stations installed along the La Buong river in Southern Vietnam. Application of the DFA identified two types of temporal patterns, one of the run-off driven parameters (total suspended solid (TSS), turbidity, and iron) and the other of diffuse source pollution. The association of the variables like BOD5 and COD at most stations to the run-off-driven parameters revealed their sharing of drivers. On the contrary, separating variables like phosphate (PO43) at the three upstream stations from the run-off patterns suggested their local point-source origin. The DFA-derived factors were later used in the time-point CA to explore the seasonality of water quality parameters and their pollution intensities compared to regulatory levels. The result suggested intensification in wet season of Fe, TSS, BOD5, and COD concentrations at most sites, which are unobservable in run-off detached parameters like reactive nitrogen, phosphate (PO43-), and E. coli. These findings generated robust insights to support water quality management for river habitat conservation.

Spatial and temporal evolution of drought characteristics across the Yellow River basin

Drought disasters restrict economic and social development. Understanding the evolution of the spatiotemporal characteristics of drought plays a vital role in basin risk response and management. Complex empirical orthogonal function analysis is widely used in meteorological research and is an important tool for studying the spatial-temporal distribution structure of arid fields. This research aims to take the Yellow River Basin (YRB) as an example, separate the temporal and spatial distribution structure of drought based on the CEOF method, and explore the abrupt changes, trends, and periodic characteristics of different temporal and spatial distribution patterns. In addition, the cross-wavelet analysis reveals the lagging correlation relationship between the distribution pattern of the arid field in the basin and the Southern Oscillation factor. Results indicated that: (1) there are three typical spatial and temporal distribution patterns (full basin type, southeast-northwest reversed type and east-west reversed type) of the interannual scale drought in the YRB; (2) interannual drought evolution have obvious spatial propagation characteristics. (3) There are correlations between the three types and the Southern Oscillation on the temporal-frequency scale, and there is a time lag of 1-2a with the Southern Oscillation factor.

Decompression of Multimorbidity Along the Disease Trajectories of Diabetes Mellitus Patients.

Multimorbidity, the presence of two or more diseases in a patient, is maybe the greatest health challenge for the aging populations of many high-income countries. One of the main drivers of multimorbidity is diabetes mellitus (DM) due to its large number of risk factors and complications. Yet, we currently have very limited understanding of how to quantify multimorbidity beyond a simple counting of diseases and thereby inform prevention and intervention strategies tailored to the needs of elderly DM patients. Here, we conceptualize multimorbidity as typical temporal progression patterns of multiple diseases, so-called trajectories, and develop a framework to perform a matched and sex-specific comparison between DM and non-diabetic patients. We find that these disease trajectories can be organized into a multi-level hierarchy in which DM patients progress from relatively healthy states with low mortality to high-mortality states characterized by cardiovascular diseases, chronic lower respiratory diseases, renal failure, and different combinations thereof. The same disease trajectories can be observed in non-diabetic patients, however, we find that DM patients typically progress at much higher rates along their trajectories. Comparing male and female DM patients, we find a general tendency that females progress faster toward high multimorbidity states than males, in particular along trajectories that involve obesity. Males, on the other hand, appear to progress faster in trajectories that combine heart diseases with cerebrovascular diseases. Our results show that prevention and efficient management of DM are key to achieve a compression of morbidity into higher patient ages. Multidisciplinary efforts involving clinicians as well as experts in machine learning and data visualization are needed to better understand the identified disease trajectories and thereby contribute to solving the current multimorbidity crisis in healthcare.

Analysing spatio-temporal process and parameter dynamics in models to characterise contrasting catchments

The relevance of hydrological processes varies in space and time resulting in typical temporal patterns for catchments. Contrasting catchments moreover differ in their catchment metrics. Hydrological models claim to be able to reproduce typical temporal patterns of dominant processes using site-specific model parameters. Thus, patterns of temporal dynamics in dominant modelled processes and their corresponding dominant parameters are a fingerprint of how a model represents the hydrological behaviour of a catchment and how these process patterns vary between contrasting catchments.In this study, we demonstrate how catchment metrics, modelled processes and parameter dominances can be jointly used to characterise catchments. We assess how catchment characteristics are represented in spatio-temporal process dynamics in models and how to understand the reasons for hydrological (dis)similarity among catchments along a landscape gradient. For this purpose, catchment metrics which characterise contrasting landscapes (lowland, mid-range mountain and alpine catchments) are related to dominant processes and parameters which were provided by a temporally resolved sensitivity analysis (TEDPAS) and simulations of a hydrological model.Our study shows that the applied model is able to represent the different processes and their seasonal variability according to the specific hydrological conditions of the study catchments. By analysing catchment metrics, modelled processes and model parameters jointly, we show that the largest differences are identified for the alpine catchment, whilst similarities are found among the other catchments. Following a landscape gradient, high flow phases are dominated by different flow components. In contrast, the model shows groundwater dominance in low flow phases in non-alpine catchments while in the alpine catchment low flows in winter are mainly controlled by snow processes. The joint analysis of catchment metrics, temporal dynamics of dominant processes and parameters can therefore be used to better disentangle similarities and differences among catchments from different landscapes.

Prolonged ovarian acyclicity is associated with a higher likelihood of developing hyperprolactinemia in zoo female African elephants.

Hyperprolactinemia is a common disorder of the hypothalamic-pituitary axis, and a cause of ovarian dysfunction in women. Currently, over half of non-cycling African elephant females in North America also are hyperprolactinemic, suggesting a similar link between these two conditions may exist. The objective of this study was to determine the relationship between acyclicity and prolactin status by comparing mean prolactin concentrations of bi-weekly samples collected over a 1-year period in 2012 with 20 years of historical weekly progestagen data to assess cyclicity. Females were categorized as: 1) non-cycling with an average prolactin concentration of 15 ng/ml or greater (HIGH; n = 17); 2) non-cycling with an average prolactin concentration below 15 ng/ml (LOW; n = 16); and 3) typical temporal patterns of progestagen and prolactin secretion (NORMAL; n = 45), and evaluated based on length of time (in years) they had experienced ovarian inactivity. Results showed that the majority of HIGH prolactin elephants had been acyclic for at least 5 years, and in a number of cases (n = 9) for over 10 years. By contrast, most of the LOW prolactin elephants had experienced acyclicity for less than 5 years. Finally, there was a positive association between duration of acyclicity and mean prolactin concentrations, with an increase in the likelihood of having higher prolactin concentrations the longer an individual was acyclic. This study highlights the importance of longitudinal hormonal datasets to examine temporal changes in biological functioning and better understand the etiology of infertility problems.

Dynamics of shallow lake cover types in relation to Paraná River flood pulses: assessment with multitemporal Landsat data

The presence of shallow lakes (SL) is typical of large floodplains with wetland landscapes. By taking into account the dynamic behavior of SL, multitemporal remote sensing data can be used to delineate and monitor water extent and SL cover types. The aim of this study was to analyze the spatial and temporal patterns of SL coverages related to flood pulses in the Lower Parana River floodplain. SL were delineated with a frequency analysis over a 1987–2015 time series of the Normalized Difference Vegetation Index derived from Landsat 5-TM and 8-OLI data. A total of 303 SL were identified (22% of the study area). Most of the SL were small, only eight were larger than 1.5 km2 (66% of the total SL area). Five dominant SL cover types were identified by using multitemporal Landsat data. The dynamics of these cover types were analyzed along seven flood pulses and we described six typical temporal patterns that repeated and characterized most of the SL areas. Vegetation quickly recovered after floods, except for extreme floods related to the occurrence of El Nino-Southern Oscillation events. The historical Landsat data turn out to be valuable tools for the reconstructions of historical processes in floodplains of large rivers.

Identification of typical diurnal patterns for clear-sky climatology of surface urban heat islands

Understanding the diurnal dynamics of surface urban heat islands (SUHIs) is an indispensable step towards their full interpretation at multiple time scales. However, because of the tradeoff between the spatial and temporal resolutions of satellite-derived land surface temperature (LST) data, the climatology, variety, and taxonomy of diurnal SUHI (DSUHI) patterns remain largely unknown for numerous cities with different bioclimates. By combining daily MODIS LST data with a newly developed four-parameter diurnal temperature cycle (DTC) model, we selected 354 Chinese megacities located in different bioclimatic zones to examine the characteristics of the DSUHI descriptors and systematically investigate the prevalent DSUHI temporal patterns.The DSUHI variations demonstrate that both the daily maximum and minimum SUHI intensity (SUHII) can occur during most periods of the day, although these intensities are more likely to occur in the early morning and noon/afternoon. Our results also reveal that both strong SUHIs (SUHII > 3 K) and surface urban cool islands (SUCIs) (SUHII < 0 K) are more prevalent than those identified directly through the four MODIS transits. According to the SUHI dynamics, five typical DSUHI temporal patterns are identified: standard-spoon, weak-spoon, quasi-spoon, inverse-spoon, and straight-line patterns. A gradient was found with spoon-like patterns (DSUHI dynamics typically with a daytime valley and a roughly constant trend or a small peak at night) in North China and inverse-spoon (DSUHI dynamics with a typical daytime peak and a constant trend at night) or straight-line patterns (DSUHI dynamics virtually unchanged all day) in South China. The DSUHI shapes were found to be greatly controlled by the urban-rural contrast in the normalized difference vegetation index (NDVI) and urban geometry. Our results not only advance our understanding of the diurnal climatology of SUHIs but also provide a basis for urban surface heat mitigation by identifying the possible timing of the mitigation requirement.

Occurrence, sources and health risk of polycyclic aromatic hydrocarbons in soils around oil wells in the border regions between oil fields and suburbs

The Yellow River Delta (YRD) is a typical region where oil fields generally overlap cities and towns, leading to complex soil contamination from both the oil fields and human activities. To clarify the distribution, speciation, potential sources and health risk of polycyclic aromatic hydrocarbons (PAHs) in soils of border regions between oil fields and suburbs of the YRD, 138 soil samples (0–20 cm) were collected among 12 sampling sites located around oil wells with different extraction histories. The 16 priority control PAHs (16PAHs), as selected by the United States Environmental Protection Agency (USEPA), were extracted via an accelerated solvent extraction and detected by GC-MS. The results showed that soils of the study area were generally polluted by the 16PAHs. Among these pollutions, chrysene and phenanthrene were the dominant components, and 4-ring PAHs were the most abundant. A typical temporal distribution pattern of the 16PAHs was revealed in soils from different sampling sites around oil wells with different exploitation histories. The concentrations of total 16PAHs and high-ring PAHs (HPAHs) both increased with the extraction time of the nearby oil wells. Individual PAH ratios and PCA method revealed that the 16PAHs in soil with newly developed oil wells were mainly from petroleum pollutants, whereas PAHs in soils around oil wells with a long exploitation history were probably from petroleum contamination; combustion of petroleum, fuel, and biomass; and degradation and migration of PAHs from petroleum. Monte Carlo simulation was used to evaluate the health risks of the 7 carcinogenic PAHs and 9 non-carcinogenic PAHs in the study area. The results indicated that ingestion and dermal contact were the predominant pathways of exposure to PAH residues in soils. Both the carcinogenic and non-carcinogenic burden of the 16PAHs in soils of the oil field increased significantly with exploitation time of nearby oil wells.

Acknowledging temporal diversity in sustainability transformations at the nexus of interconnected systems

The language of the global sustainability discourse in science and society is laden with time rhetoric, and time has been identified as one major contextual condition for sustainability transformations. Still, time and temporal dynamics are often not explicitly considered or conceptualized in research and strategy-building towards sustainability transformations in social-ecological and socio-technical systems: While existing approaches to time such as the concept of time ecology mainly inform system knowledge, many transformation concepts are found to lack an in-depth integration of time. This sets up the challenge to acknowledge and operationalize temporal system dynamics in transformation processes, in order to be able to plan and act purposefully towards long-term sustainable development. In this article, we take a first step to meet this challenge and propose an approach towards operationalizing and integrating the findings of time ecology for these sustainability transformation concepts. The presented time-in-transformations-approach consists of three subsequent steps. Each step informs specific features of the transformation process and is operationalized on the basis of insights from time ecology. By applying these steps to the exemplary case of the mineral-energy nexus, we show how our approach might enable a better temporal system understanding and support a structured reflection on human perception of change and typical temporal patterns. The approach leads to two main outcomes: First, it can inform how to design and carry out interventions in a way that they consider a system’s temporal resilience and transformability. Second, an active consideration of temporal dynamics can inform strategy-building towards coherent sustainability transformations across sectors and regulatory levels, spatial and temporal scales. Therefore, it can contribute to a shared and operational understanding of temporal diversity and may thus meaningfully complement existing approaches to analyze, assess and purposefully intervene into systems across sector boundaries.

Tracking the evolution of temporal patterns of usage in bicycle-Sharing systems using nonnegative matrix factorization on multiple sliding windows

ABSTRACTBicycle-Sharing Systems (BSS) are growing quickly in popularity all over the world. In this article, we propose a method based on Nonnegative Matrix Factorization to study the typical temporal patterns of usage of the BSS of Lyon, France, by studying logs of rentals. First, we show how this approach allows us to understand the spatial and temporal usage of the system. Second, we show how we can track the evolution of these temporal patterns over several years, and how this information can be used to better understand the BSS, but also changes in the city itself, by considering the stations as social sensors.

Genome-wide identification, characterization of the MADS-box gene family in Chinese jujube and their involvement in flower development

MADS-box genes encode transcription factors that are involved in plant development control (particularly in floral organogenesis) and signal transduction pathways, though a comprehensive analysis of MADS-box family proteins in Chinese jujube (Ziziphus jujuba Mill.) is still missing. Here, we report a genome-wide analysis of the MADS-box gene family in Chinese jujube. Based on phylogenetic analyses, 52 jujube MADS-box genes were classified into 25 MIKCC-type, 3 MIKC*-type, 16 Mα, 5 Mβ and 3 Mγ genes. 37 genes were randomly distributed across all 12 putative chromosomes. We found that the type II genes are more complex than the type I genes and that tandem duplications have occurred in three groups of MADS-box genes. Meanwhile, some gene pairs in the same clade displayed similar or distinct expression profiles, suggesting possible functional redundancy or divergence. MIKCC-type genes exhibited typical temporal and spatial expression patterns in the four whorls of floral tissues. The expressions of B, C/D and E-type genes were significantly suppressed in phyllody as compared to flower, providing valuable evidence for their involvement in flower development. This study is the first comprehensive analysis of the MADS-box family in jujube, and provides valuable information for elucidating molecular regulation mechanism of jujube flower development.

Analyzing year-to-year changes in public transport passenger behaviour using smart card data

In recent years, there has been increased interest in using completely anonymized data from smart card collection systems to better understand the behavioural habits of public transport passengers. Such an understanding can benefit urban transport planners as well as urban modelling by providing simulation models with realistic mobility patterns of transit networks. In particular, the study of temporal activities has elicited substantial interest. In this regard, a number of methods have been developed in the literature for this type of analysis, most using clustering approaches. This paper presents a two-level generative model that applies the Gaussian mixture model to regroup passengers based on their temporal habits in their public transportation usage. The strength of the proposed methodology is that it can model a continuous representation of time instead of having to employ discrete time bins. For each cluster, the approach provides typical temporal patterns that enable easy interpretation. The experiments are performed on five years of data collected by the Société de transport de l’Outaouais. The results demonstrate the efficiency of the proposed approach in identifying a reduced set of passenger clusters linked to their fare types. A five-year longitudinal analysis also shows the relative stability of public transport usage.

Two-year temporal response of benthic macrofauna and sediments to hypoxia in a tropical semi-enclosed bay (Cienfuegos, Cuba).

Hypoxia is the depletion of dissolved oxygen below 2 mg O(2)/L. Relatively few studies on hypoxia and its effects on benthic macrofauna have been done in tropical marine ecosystems. This study describes the temporal response of the water column, sediments and macrofauna to seasonal hypoxia in a semi-enclosed bay (Cienfuegos, Caribbean Sea). The Calisito site was sampled monthly from June 2010 until February 2012, yielding 21 sampling times. At each sampling event water and sediment samples were collected for measuring the abiotic variables (temperature, salinity, dissolved oxygen, nutrients, redox potential discontinuity, silt/clay and organic matter content) and macrofauna (abundance and species richness). Temperature and surface salinity followed a typical temporal pattern during the summer/rainy and the winter/dry periods. Salinity stratification occurred in the rainy period, lasting three months in 2010 and six months in 2011. The bottom water dissolved oxygen indicated hypoxic and anoxic events during the wet periods of 2010 and 2011 associated with salinity stratification, low hydrodynamics and oxidation of the accumulated organic matter. Over the study period, 817 individuals were collected and identified. Polychaetes were the dominant group in terms of abundance (57 % of total) followed by mollusks (41%). Hypoxia (and occasionally anoxia) caused strong deleterious effects on the abundance and species richness of macrofaunal communities in the study site. The most abundant polychaetes were opportunistic species with high tolerance to hypoxic conditions: Prionospio steenstrupi, Polydora sp.and Paraprionospio pinnata. Most of them colonized relatively fast once hypoxia ended. Persistent species such as Caecum pulchellum and Parvanachis obesa were present during hypoxia with fluctuating densities and apparently recover to higher abundances when normoxic conditions are re-established. Macoma tenta and Tellina consobrina colonized approximately 1-2 months later than the first polychaete peak during normoxia. Probably, the deleterious effects of hypoxia on the macrofauna were intensified by negative interspecific relationships such as competition by suitable space and predation. The recolonization of macrofauna depended possibly on local transport by currents within the bay because the connection with the Caribbean Sea is relatively limited. In summary, seasonal hypoxia in Cienfuegos Bay influences the water and sediment geochemistry and reduces both the abundance and diversity of macrofauna.

A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources.

BackgroundHealth level seven version 2.5 (HL7 v2.5) is a widespread messaging standard for information exchange between clinical information systems. By applying Semantic Web technologies for handling HL7 v2.5 messages, it is possible to integrate large-scale clinical data with life science knowledge resources.ObjectiveShowing feasibility of a querying method over large-scale resource description framework (RDF)-ized HL7 v2.5 messages using publicly available drug databases.MethodsWe developed a method to convert HL7 v2.5 messages into the RDF. We also converted five kinds of drug databases into RDF and provided explicit links between the corresponding items among them. With those linked drug data, we then developed a method for query expansion to search the clinical data using semantic information on drug classes along with four types of temporal patterns. For evaluation purpose, medication orders and laboratory test results for a 3-year period at the University of Tokyo Hospital were used, and the query execution times were measured.ResultsApproximately 650 million RDF triples for medication orders and 790 million RDF triples for laboratory test results were converted. Taking three types of query in use cases for detecting adverse events of drugs as an example, we confirmed these queries were represented in SPARQL Protocol and RDF Query Language (SPARQL) using our methods and comparison with conventional query expressions were performed. The measurement results confirm that the query time is feasible and increases logarithmically or linearly with the amount of data and without diverging.ConclusionsThe proposed methods enabled query expressions that separate knowledge resources and clinical data, thereby suggesting the feasibility for improving the usability of clinical data by enhancing the knowledge resources. We also demonstrate that when HL7 v2.5 messages are automatically converted into RDF, searches are still possible through SPARQL without modifying the structure. As such, the proposed method benefits not only our hospitals, but also numerous hospitals that handle HL7 v2.5 messages. Our approach highlights a potential of large-scale data federation techniques to retrieve clinical information, which could be applied as applications of clinical intelligence to improve clinical practices, such as adverse drug event monitoring and cohort selection for a clinical study as well as discovering new knowledge from clinical information.