Life cycle assessment to quantify the impact of technology improvements in bike‐sharing systems

The reduced environmental footprint of bicycle sharing systems (BSS) is one of the reasons for their rapid growth in popularity. BSS have evolved technologically, transitioning from smart dock systems to smart bicycle systems, and it is not clear if the increased use of electronics in BSS results in a net environmental benefit. This article provides an evaluation of the impact of incorporating additional technology into BSS and uses that analysis as guidance for future BSS development. By comparing the impacts of a private bicycle, a smart dock BSS, and smart bike BSS using a life cycle assessment (LCA), this work reveals breakeven points and tradeoffs between the technologies. This study is also the first published empirical LCA of a smart bike known to the authors. In the production phase, smart bikes generate approximately three times the amount of greenhouse gas (GHG) emissions compared to the smart dock bikes per kilometer ridden over the lifetime, and when considering the endpoint categories of human health, ecosystem, and resources, smart bikes have approximately 2.7 times the environmental impact. The results suggest that shifting from smart dock to smart bike requires an increase in ridership by a factor of 1.8 to overcome the increased environmental impact based on the GHG emissions. We find that smart docks become preferable at a population density between 1,030 residents/km² (in a bike friendly city) and 3,100 residents/km² (in a city that is less likely to bike).     

Determining travel fluxes in epidemic areas

Infectious diseases attack humans from time to time and threaten the lives and survival of people all around the world. An important strategy to prevent the spatial spread of infectious diseases is to restrict population travel. With the reduction of the epidemic situation, when and where travel restrictions can be lifted, and how to organize orderly movement patterns become critical and fall within the scope of this study. We define a novel diffusion distance derived from the estimated mobility network, based on which we provide a general model to describe the spatiotemporal spread of infectious diseases with a random diffusion process and a deterministic drift process of the population. We consequently develop a multi-source data fusion method to determine the population flow in epidemic areas. In this method, we first select available subregions in epidemic areas, and then provide solutions to initiate new travel flux among these subregions. To verify our model and method, we analyze the multi-source data from mainland China and obtain a new travel flux triggering scheme in the selected 29 cities with the most active population movements in mainland China. The testable predictions in these selected cities show that reopening the borders in accordance with our proposed travel flux will not cause a second outbreak of COVID-19 in these cities. The finding provides a methodology of re-triggering travel flux during the weakening spread stage of the epidemic.     

Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns

Self-organizing maps (SOM) constitute an alternative to classical clustering methods because of its linear run times and superior performance to deal with noisy data. Nevertheless, the clustering obtained with SOM is dependent on the relative sizes of the clusters. Here, we show how the combination of SOM with hierarchical clustering methods constitutes an excellent tool for exploratory analysis of massive data like DNA microarray expression patterns.     

The application of GIS and spatiotemporal analyses to investigations of unusual marine mammal strandings and mortality events

In 2006–2007, an unusually high number of harbor porpoises (Phocoena phocoena) stranded along the Washington and Oregon coastlines. Spatiotemporal analyses were used to examine their ability to detect clusters of porpoise strandings during an unusual mortality event (UME) in the Pacific Northwest using stranding location data. Strandings were evaluated as two separate populations, outer coast and inland waters. The presence of global clustering was evaluated using the Knox spatiotemporal test, and the presence of local clusters was investigated using a spatiotemporal scan statistic (space–time permutation). There was evidence of global clustering, but no local clustering, supporting the hypothesis that strandings were due to more varied etiologies instead of localized causes. Further analyses at subregional levels, and concurrently assessing environmental factors, might reveal additional geographic distribution patterns. This article describes the spatial analytical tools applied in this study and how they can help elucidate the spatiotemporal epidemiology of other UMEs and assist in determining their causes. More than one spatial analytical technique should be used if the study objective is to detect and describe clustering in time and space and to generate hypotheses regarding causation of marine mammal disease and stranding events.     

Spatiotemporal Cluster Patterns of Hand,Foot, and Mouth Disease at the County Level in Mainland China, 2008-2012

Background

Hand, foot, and mouth disease (HFMD) is known to be a highly contagious childhood illness. In recent years, the number of reported cases of HFMD has significantly increased in mainland China. This study aims at the epidemiological features, spatiotemporal patterns of HMFD at the county/district level in mainland China.

Methods

Data on reported HFMD cases for each county from 1 January 2008 to 31 December 2012 were obtained from the Chinese Center for Disease Control and Prevention. Cluster analysis, spatial autocorrelation, and retrospective scan methods were used to explore the spatiotemporal patterns of the disease.

Results

The annual incidences varied greatly among the counties, ranging from 0 to 74.31‰ with the median of 5.42‰ (interquartile range: 1.54‰–13.55‰) during 2008–2012 in mainland China. Counties close to provincial capital cities generally had higher incidences than rural counties. A seasonal distribution was observed between the northern and southern China, of which dual epidemic were shown in southern China and usually only one in northern China. Based on the global and local spatial autocorrelation analysis, we found that the spatial distribution of HFMD was presented a significant clustering pattern for each year (P<0.001), and hotspots of the disease were mostly distributed in coastal provinces of China. The retrospective scan statistic further identified the dynamics of spatiotemporal clustering areas of the disease, which were mainly distributed in the counties of eastern and southern China, as well as provincial capitals and their surrounding counties.

Conclusions

The spatiotemporal clustering areas of the disease identified in this way were relatively stable, and imminent public health planning and resource allocation should be focused within those areas.     

Childhood Diarrhea Exhibits Spatiotemporal Variation in Northwest Ethiopia: A SaTScan Spatial Statistical Analysis

Background

Childhood diarrhea continues to be a public health problem in developing countries, including Ethiopia. Detecting clusters and trends of childhood diarrhea is important to designing effective interventions. Therefore, this study aimed to investigate spatiotemporal clustering and seasonal variability of childhood diarrhea in northwest Ethiopia.

Methods

Retrospective record review of childhood diarrhea was conducted using quarterly reported data to the district health office for the seven years period beginning July 1, 2007. Thirty three districts were included and geo-coded in this study. Spatial, temporal and space-time scan spatial statistics were employed to identify clusters of childhood diarrhea. Smoothing using a moving average was applied to visualize the trends and seasonal pattern of childhood diarrhea. Statistical analyses were performed using Excel^® and SaTScan programs. The maps were plotted using ArcGIS 10.0.

Results

Childhood diarrhea in northwest Ethiopia exhibits statistical evidence of spatial, temporal, and spatiotemporal clustering, with seasonal patterns and decreasing temporal trends observed in the study area. A most likely purely spatial cluster was found in the East Gojjam administrative zone of Gozamin district (LLR = 7123.89, p <0.001). The most likely spatiotemporal cluster was detected in all districts of East Gojjam zone and a few districts of the West Gojjam zone (LLR = 24929.90, p<0.001), appearing from July 1, 2009 to June 30, 2011. One high risk period from July 1, 2008 to June 30, 2010 (LLR = 9655.86, p = 0.001) was observed in all districts. Peak childhood diarrhea cases showed a seasonal trend, occurring more frequently from January to March and April to June.

Conclusion

Childhood diarrhea did not occur at random. It has spatiotemporal variation and seasonal patterns with a decreasing temporal trend. Accounting for the spatiotemporal variation identified in the study areas is advised for the prevention and control of diarrhea.     

A comparative climate analysis of heat-related emergency 911 dispatches: Chicago, Illinois and Phoenix, Arizona USA 2003 to 2006

Research into the health impacts of heat has proliferated since 2000. Temperature increases could exacerbate the increased heat already experienced by urban populations due to urbanization. Heat-related mortality studies have found that hot southern cities in North America have not experienced the summer increases in mortality found in their more northern counterparts. Heat-related morbidity studies have not assessed this possible regional difference. This comparison study uses data from emergency 911 dispatches [referred to as heat-related dispatches (HRD)] identified by responders as heat-related for two United States cities located in different regions with very different climates: Chicago, Illinois in the upper midwest and Phoenix, Arizona in the southwest. Phoenix’s climate is hot and arid. Chicago’s climate is more temperate, but can also experience days with unusually high temperatures combined with high humidity. This study examines the relationships between rising HRD and daily temperatures: maximum (Tmax); apparent (ATmax): minimum (Tmin) and two energy balance indices (PET and UTCI). Phoenix had more HRD cumulatively, over a longer warm weather season, but did not experience the large spikes in HRD that occurred in Chicago, even though it was routinely subjected to much hotter weather conditions. Statistical analyses showed the strongest relationships to daily ATmax for both cities. Phoenix’s lack of HRD spikes, similar to the summer mortality patterns for southern cities, suggests an avenue for future research to better understand the dynamics of possible physiological or behavioral adaption that seems to reduce residents’ vulnerability to heat.     

Human mobility patterns predict divergent epidemic dynamics among cities

The epidemic dynamics of infectious diseases vary among cities, but it is unclear how this is caused by patterns of infectious contact among individuals. Here, we ask whether systematic differences in human mobility patterns are sufficient to cause inter-city variation in epidemic dynamics for infectious diseases spread by casual contact between hosts. We analyse census data on the mobility patterns of every full-time worker in 48 Canadian cities, finding a power-law relationship between population size and the level of organization in mobility patterns, where in larger cities, a greater fraction of workers travel to work in a few focal locations. Similarly sized cities also vary in the level of organization in their mobility patterns, equivalent on average to the variation expected from a 2.64-fold change in population size. Systematic variation in mobility patterns is sufficient to cause significant differences among cities in infectious disease dynamics—even among cities of the same size—according to an individual-based model of airborne pathogen transmission parametrized with the mobility data. This suggests that differences among cities in host contact patterns are sufficient to drive differences in infectious disease dynamics and provides a framework for testing the effects of host mobility patterns in city-level disease data.     

Red deer (<Emphasis Type="Italic">Cervus elaphus</Emphasis>) avoid mountain biking trails

Human outdoor activities commonly affect animal behaviour. Ungulates often avoid roads and trails and increase their avoidance with expanding and intensifying human recreational activity. Recently, mountain biking has become increasingly popular in many regions in Norway, but we still have limited knowledge about how mountain biking may affect wildlife. In this study, we used pellet-group counts and camera traps to study the effect of mountain biking on red deer abundance in Kaupanger, Norway. Pellet-group counts decreased close to biking trails, and it appeared that red deer avoided using areas within 40 m of such trails. Camera trap data showed that there was a tendency for deer abundance to decrease with increasing human activity (trail width) during daytime. Males reacted more strongly to this increasing activity than did females. Even with a small-scale approach, focusing on within-stand effects, we found that red deer were affected by mountain bikers and avoided areas close to biking trails. Our findings imply that any further increase in mountain biking may result in greater avoidance and, thus, less suitable habitat for red deer in forest areas.     

Scoring clustering solutions by their biological relevance

MOTIVATION: A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering gene expression data into homogeneous groups was shown to be instrumental in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on clustering algorithms for gene expression analysis, very few works addressed the systematic comparison and evaluation of clustering results. Typically, different clustering algorithms yield different clustering solutions on the same data, and there is no agreed upon guideline for choosing among them. RESULTS: We developed a novel statistically based method for assessing a clustering solution according to prior biological knowledge. Our method can be used to compare different clustering solutions or to optimize the parameters of a clustering algorithm. The method is based on projecting vectors of biological attributes of the clustered elements onto the real line, such that the ratio of between-groups and within-group variance estimators is maximized. The projected data are then scored using a non-parametric analysis of variance test, and the score's confidence is evaluated. We validate our approach using simulated data and show that our scoring method outperforms several extant methods, including the separation to homogeneity ratio and the silhouette measure. We apply our method to evaluate results of several clustering methods on yeast cell-cycle gene expression data. AVAILABILITY: The software is available from the authors upon request.     

A population genetic assessment of taxonomic species: The case of Lake Malawi cichlid fishes

Organisms sampled for population‐level research are typically assigned to species by morphological criteria. However, if those criteria are limited to one sex or life stage, or the organisms come from a complex of closely related forms, the species assignments may misdirect analyses. The impact of such sampling can be assessed from the correspondence of genetic clusters, identified only from patterns of genetic variation, to the species identified using only phenotypic criteria. We undertook this protocol with the rock‐dwelling mbuna cichlids of Lake Malawi, for which species within genera are usually identified using adult male coloration patterns. Given high local endemism of male colour patterns, and considerable allele sharing among species, there persists considerable taxonomic uncertainty in these fishes. Over 700 individuals from a single transect were photographed, genotyped and separately assigned: (a) to morphospecies using photographs; and (b) to genetic clusters using five widely used methods. Overall, the correspondence between clustering methods was strong for larger clusters, but methods varied widely in estimated number of clusters. The correspondence between morphospecies and genetic clusters was also strong for larger clusters, as well as some smaller clusters for some methods. These analyses generally affirm (a) adult male‐limited sampling and (b) the taxonomic status of Lake Malawi mbuna, as the species in our study largely appear to be well‐demarcated genetic entities. More generally, our analyses highlight the challenges for clustering methods when the number of populations is unknown, especially in cases of highly uneven sample sizes.     

Multilevel functional clustering analysis

Summary In this article, we investigate clustering methods for multilevel functional data, which consist of repeated random functions observed for a large number of units (e.g., genes) at multiple subunits (e.g., bacteria types). To describe the within- and between variability induced by the hierarchical structure in the data, we take a multilevel functional principal component analysis (MFPCA) approach. We develop and compare a hard clustering method applied to the scores derived from the MFPCA and a soft clustering method using an MFPCA decomposition. In a simulation study, we assess the estimation accuracy of the clustering membership and the cluster patterns under a series of settings: small versus moderate number of time points; various noise levels; and varying number of subunits per unit. We demonstrate the applicability of the clustering analysis to a real data set consisting of expression profiles from genes activated by immunity system cells. Prevalent response patterns are identified by clustering the expression profiles using our multilevel clustering analysis.     

Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa

Simple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations.     

Using Hierarchical Clustering and Dendrograms to Quantify the Clustering of Membrane Proteins

Cell biologists have developed methods to label membrane proteins with gold nanoparticles and then extract spatial point patterns of the gold particles from transmission electron microscopy images using image processing software. Previously, the resulting patterns were analyzed using the Hopkins statistic, which distinguishes nonclustered from modestly and highly clustered distributions, but is not designed to quantify the number or sizes of the clusters. Clusters were defined by the partitional clustering approach which required the choice of a distance. Two points from a pattern were put in the same cluster if they were closer than this distance. In this study, we present a new methodology based on hierarchical clustering to quantify clustering. An intrinsic distance is computed, which is the distance that produces the maximum number of clusters in the biological data, eliminating the need to choose a distance. To quantify the extent of clustering, we compare the clustering distance between the experimental data being analyzed with that from simulated random data. Results are then expressed as a dimensionless number, the clustering ratio that facilitates the comparison of clustering between experiments. Replacing the chosen cluster distance by the intrinsic clustering distance emphasizes densely packed clusters that are likely more important to downstream signaling events.     

Robust hierarchical state-space models reveal diel variation in travel rates of migrating leatherback turtles

1. Biological and statistical complexity are features common to most ecological data that hinder our ability to extract meaningful patterns using conventional tools. Recent work on implementing modern statistical methods for analysis of such ecological data has focused primarily on population dynamics but other types of data, such as animal movement pathways obtained from satellite telemetry, can also benefit from the application of modern statistical tools. 2. We develop a robust hierarchical state-space approach for analysis of multiple satellite telemetry pathways obtained via the Argos system. State-space models are time-series methods that allow unobserved states and biological parameters to be estimated from data observed with error. We show that the approach can reveal important patterns in complex, noisy data where conventional methods cannot. 3. Using the largest Atlantic satellite telemetry data set for critically endangered leatherback turtles, we show that the diel pattern in travel rates of these turtles changes over different phases of their migratory cycle. While foraging in northern waters the turtles show similar travel rates during day and night, but on their southward migration to tropical waters travel rates are markedly faster during the day. These patterns are generally consistent with diving data, and may be related to changes in foraging behaviour. Interestingly, individuals that migrate southward to breed generally show higher daytime travel rates than individuals that migrate southward in a non-breeding year. 4. Our approach is extremely flexible and can be applied to many ecological analyses that use complex, sequential data.     

Specific patterns of Cdc42 activity are related to distinct elements of T cell polarization

T cell polarization toward and within the cellular interface with an APC is critical for effective T cell activation. The Rho family GTPase Cdc42 is a central regulator of cellular polarization. Using live-cell imaging, we characterized the spatiotemporal patterns of Cdc42 activity and their physiological regulation. Using three independent means of experimental manipulation of Cdc42 activity, we established that Cdc42 is a critical regulator of T cell actin dynamics, TCR clustering, and cell cycle entry. Using quantification of three-dimensional data, we could relate distinct spatiotemporal patterns of Cdc42 activity to specific elements of T cell activation. This result suggests that Cdc42 activity in specific locations at specific times is most critical for its function in T cell activation.     

Detecting clusters of different geometrical shapes in microarray gene expression data

MOTIVATION: Clustering has been used as a popular technique for finding groups of genes that show similar expression patterns under multiple experimental conditions. Many clustering methods have been proposed for clustering gene-expression data, including the hierarchical clustering, k-means clustering and self-organizing map (SOM). However, the conventional methods are limited to identify different shapes of clusters because they use a fixed distance norm when calculating the distance between genes. The fixed distance norm imposes a fixed geometrical shape on the clusters regardless of the actual data distribution. Thus, different distance norms are required for handling the different shapes of clusters. RESULTS: We present the Gustafson-Kessel (GK) clustering method for microarray gene-expression data. To detect clusters of different shapes in a dataset, we use an adaptive distance norm that is calculated by a fuzzy covariance matrix (F) of each cluster in which the eigenstructure of F is used as an indicator of the shape of the cluster. Moreover, the GK method is less prone to falling into local minima than the k-means and SOM because it makes decisions through the use of membership degrees of a gene to clusters. The algorithmic procedure is accomplished by the alternating optimization technique, which iteratively improves a sequence of sets of clusters until no further improvement is possible. To test the performance of the GK method, we applied the GK method and well-known conventional methods to three recently published yeast datasets, and compared the performance of each method using the Saccharomyces Genome Database annotations. The clustering results of the GK method are more significantly relevant to the biological annotations than those of the other methods, demonstrating its effectiveness and potential for clustering gene-expression data. AVAILABILITY: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://dragon.kaist.ac.kr/gk.     

Behavioral Responses of North American Elk to Recreational Activity

ABSTRACT Off-road recreation on public lands in North America has increased dramatically in recent years. Wild ungulates are sensitive to human activities, but the effect of off-road recreation, both motorized and nonmotorized, is poorly understood. We measured responses of elk (Cervus elaphus) to recreational disturbance in northeast Oregon, USA, from April to October, 2003 and 2004. We subjected elk to 4 types of recreational disturbance: all-terrain vehicle (ATV) riding, mountain biking, hiking, and horseback riding. Motion sensors inside radiocollars worn by 13 female elk recorded resting, feeding, and travel activities at 5-minute intervals throughout disturbance and control periods. Elk fed and rested during control periods, with little time spent traveling. Travel time increased in response to all 4 disturbances and was highest in mornings. Elk travel time was highest during ATV exposure, followed by exposure to mountain biking, hiking, and horseback riding. Feeding time decreased during ATV exposure and resting decreased when we subjected elk to mountain biking and hiking disturbance in 2003. Our results demonstrated that activities of elk can be substantially affected by off-road recreation. Mitigating these effects may be appropriate where elk are a management priority. Balancing management of species like elk with off-road recreation will become increasingly important as off-road recreational uses continue to increase on public lands in North America.     

Does the Effectiveness of Control Measures Depend on the Influenza Pandemic Profile?

Background

Although strategies to contain influenza pandemics are well studied, the characterization and the implications of different geographical and temporal diffusion patterns of the pandemic have been given less attention.

Methodology/Main Findings

Using a well-documented metapopulation model incorporating air travel between 52 major world cities, we identified potential influenza pandemic diffusion profiles and examined how the impact of interventions might be affected by this heterogeneity. Clustering methods applied to a set of pandemic simulations, characterized by seven parameters related to the conditions of emergence that were varied following Latin hypercube sampling, were used to identify six pandemic profiles exhibiting different characteristics notably in terms of global burden (from 415 to >160 million of cases) and duration (from 26 to 360 days). A multivariate sensitivity analysis showed that the transmission rate and proportion of susceptibles have a strong impact on the pandemic diffusion. The correlation between interventions and pandemic outcomes were analyzed for two specific profiles: a fast, massive pandemic and a slow building, long-lasting one. In both cases, the date of introduction for five control measures (masks, isolation, prophylactic or therapeutic use of antivirals, vaccination) correlated strongly with pandemic outcomes. Conversely, the coverage and efficacy of these interventions only moderately correlated with pandemic outcomes in the case of a massive pandemic. Pre-pandemic vaccination influenced pandemic outcomes in both profiles, while travel restriction was the only measure without any measurable effect in either.

Conclusions

Our study highlights: (i) the great heterogeneity in possible profiles of a future influenza pandemic; (ii) the value of being well prepared in every country since a pandemic may have heavy consequences wherever and whenever it starts; (iii) the need to quickly implement control measures and even to anticipate pandemic emergence through pre-pandemic vaccination; and (iv) the value of combining all available control measures except perhaps travel restrictions.     

Unraveling bacterial fingerprints of city subways from microbiome 16S gene profiles

Background

Microbial communities can be location specific, and the abundance of species within locations can influence our ability to determine whether a sample belongs to one city or another. As part of the 2017 CAMDA MetaSUB Inter-City Challenge, next generation sequencing (NGS) data was generated from swipe samples collected from subway stations in Boston, New York City hereafter New York, and Sacramento. DNA was extracted and Illumina sequenced. Sequencing data was provided for all cities as part of 2017 CAMDA contest challenge dataset.

Results

Principal component analysis (PCA) showed clear clustering of the samples for the three cities, with a substantial proportion of the variance explained by the first three components. We ran two different classifiers and results were robust for error rate (<?6%) and accuracy (>?95%). The analysis of variance (ANOVA) demonstrated that overall, bacterial composition across the three cities is significantly different. A similar conclusion was reached using a novel bootstrap based test using diversity indices. Last but not least, a co-abundance association network analyses for the taxonomic levels “order”, “family”, and “genus” found different patterns of bacterial networks for the three cities.

Conclusions

Bacterial fingerprint can be useful to predict sample provenance. In this work prediction of provenance reported with over 95% accuracy. Association based network analysis, emphasized similarities between the closest cities sharing common bacterial composition. ANOVA showed different patterns of bacterial amongst cities, and these findings strongly suggest that bacterial signature across multiple cities are different. This work advocates a data analysis pipeline which could be followed in order to get biological insight from this data. However, the biological conclusions from this analysis is just an early indication out of a pilot microbiome data provided to us through CAMDA 2017 challenge and will be subject to change as we get more complete data sets in the near future. This microbiome data can have potential applications in forensics, ecology, and other sciences.

Reviewers

This article was reviewed by Klas Udekwu, Alexandra Graf, and Rafal Mostowy.