25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Sensing Human Activity and Interaction Patterns through Movement Observations

Human beings have a fundamental need for social interaction according to Maslow’s hierarchy of needs. Human interaction contributes to individual psychological development and well-being, and enhances social cohesion more broadly. Among others, two scientific fields that are interested in human interaction in space and time progressed in separate paths but emerged from similar inspirations and aim at similar end goals. The first is activity analysis with a focus on developing models of activity participation and travel behavior using travel surveys. These are commonly referred to as activity-based approaches in travel demand analysis. The second is the study of human movement in space and time using geometric relationships that evolve over time and space derived from the observations of moving individuals, a tradition followed mostly by geographic information scientists. Travel behavior researchers recognize human interaction as one of the most important determinants affecting people's daily time allocation and travel. However, inter-individual interactions, particularly inter-household ones, in travel behavior research have received less attention while these interactions contribute to a significant amount of travel. Traditional travel surveys are usually limited in sample size and observation period due to expensive costs. They also often lack sufficient information regarding inter-household interactions. Besides human interactions when individuals engage in activities at specific locations, it is also crucial to consider potential interactions in shared spaces, such as when their activity spaces or paths intersect during movement. In recent years, the increased availability of diverse and fine-grained movement data offers an unprecedented opportunity to study human movement and interaction at an unparalleled level of fine granularity. However, there is a shortage of advanced computational methods to trace and understand human interactions present in fine-grained and large-volume movement data. It also remains unclear to what extent the knowledge gained from individual movement data can contribute to our understanding of collective behaviors across different socio-geographical contexts. The primary goal of this dissertation is to develop and evaluate new computational methods for identifying and quantifying spatiotemporal patterns of human daily activities and interactions, using both conventional travel diaries and massive movement data of individuals. By doing so, this dissertation explores important questions to understand heterogeneity in people’s daily activity-travel patterns and interactions across different socio-geographical spaces

Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps

The locations where taxicabs pick up and drop off passengers are crucial to understanding the dynamics of taxi trip demand. Investigating their spatial distribution and derived dynamic features is still a key task in the fields of urban geography and transportation. Such investigations are urgently needed, considering the competition created by new communication technology services, specifically e-hailing apps such as Uber, Didi and Kuaidi. For example, a subsidy war between two e-hailing apps occurred in China in 2014. However, how the pick-up and drop-off locations of taxicabs change during subsidy wars is still an open question. This paper introduces a methodological framework that can be used to derive the pick-up and drop-off dynamics of taxicabs. It also proposes three indexes that can be used to assess the dynamics of the pick-up and drop-off locations at the city and sub-district scales, namely the numbers of daily pick ups and drop offs per taxi, average transfer distance per unit area of weighted mean centers of pick-up and drop-off locations, and degree of dispersion in the spatial distribution of pick-up and drop-off locations. This paper employs data from taxicabs in the city of Shenzhen to uncover the dynamics of their pick-up and drop-off locations. The results show that the methodological framework and the indexes introduced are powerful tools for uncovering the dynamics of the pick-up and drop-off locations of taxicabs in urban environments

Untangling the relationships among residential environment, destination choice, and daily walk accessibility

Accessibility to opportunities is strongly correlated with travel behaviors. This research, in particular, centered on individual daily accessibility, which quantifies the total number of opportunities of each type that can be reached within a certain travel time from the destinations an individual visited in a day. Existing studies rarely focused on the interrelationships among people’s socio-demographic characteristics, residential built environment, destination choice, and daily accessibility simultaneously. Using survey data from California, the present study develops a daily accessibility indicator summarizing the individual daily total number of opportunities that is reachable within a given travel time based on individual daily activities. Subsequently, a structural equation model jointly estimates the relationships among these factors showing that people’s social status and the residential built environment influence directly daily accessibility to retail opportunities significantly. The residential built environment has no significant direct impact on daily ocean-view open space accessibility. Both social status and residential built environment have a significant indirect impact on daily accessibility via the mediator number of distinct destinations showing the importance of designing cities with increased destination variety that are better served by non-motorized networks and public transportation

Understanding the Effect of an E-Hailing App Subsidy War on Taxicab Operation Zones

Understanding taxicab operation behaviors under various management or market policies (i.e., subsidies) is critical to making informed operating decisions for e-hailing companies and for government surveillance. This paper investigates the change of taxicab operation zones in context of an e-hailing app subsidy war in China, which is an important perspective that reflects changes in taxicab behavior, such as how the operation zones of taxicabs under the e-hailing app subsidy war change and how this change affects their trip distance and cruising time. To investigate this issue, this paper utilizes three indexes to elucidate the change of taxicab operation zones, namely, the repetition ratio of operation zone pairs, the area, and the degree of dispersion in the spatial distribution. A case study using taxicab trajectories during all of the important periods of the e-hailing app subsidy war in Shenzhen, China, was conducted and produced several valuable findings; for example, with respect to taxicabs as a whole, the proportion of habitual operation zone pairs among operation zone pairs in neighboring periods is relatively stable under any subsidy policy, and changes in the operation zones have little effect on changes in the average daily trip distance and average daily cruising time. Four groups of taxicabs divided according to initial change patterns in the operation zones present different change patterns during the subsidy war. By comparing these changes before and after the subsidy war, this paper finds that the subsidy war influences the taxicabs in groups I and II, while it has little influence on the taxicabs in groups III and IV, although all groups were affected during the subsidy war. For the taxicab groups in the period with the highest subsidy, the average daily trip distance and average daily cruising time decreased, whereas, in other periods, they presented different patterns

ORTEGA v1.0: an open-source Python package for context-aware interaction analysis using movement data

Abstract Background Interaction analysis via movement in space and time contributes to understanding social relationships among individuals and their dynamics in ecological systems. While there is an exciting growth in research in computational methods for interaction analysis using movement data, there remain challenges regarding reproducibility and replicability of the existing approaches. The current movement interaction analysis tools are often less accessible or tested for broader use in ecological research. Results To address these challenges, this paper presents ORTEGA, an Object-oRiented TimE-Geographic Analytical tool, as an open-source Python package for analyzing potential interactions between pairs of moving entities based on the observation of their movement. ORTEGA is developed based on one of the newly emerged time-geographic approaches for quantifying space-time interaction patterns among animals. A case study is presented to demonstrate and evaluate the functionalities of ORTEGA in tracing dynamic interaction patterns in animal movement data. Besides making the analytical code and data freely available to the community, the developed package also offers an extension of the existing theoretical development of ORTEGA for incorporating a context-aware ability to inform interaction analysis. Conclusions ORTEGA contributes two significant capabilities: (1) the functions to identify potential interactions (e.g., encounters, concurrent interactions, delayed interactions) from movement data of two or more entities using a time-geographic-based approach; and (2) the capacity to compute attributes of potential interaction events including start time, end time, interaction duration, and difference in movement parameters such as speed and moving direction, and also contextualize the identified potential interaction events

Exploring the impact of COVID-19 pandemic on Americans time use related subjective wellbeing

In this study, the most recent American Time Use Surveys containing reported activity-based emotions and sensations information before (10,378 respondents in 2013) and during (6,902 respondents in 2021) the COVID-19 pandemic are used to assess if time use related individuals' subjective wellbeing (SWB) decreased in the pandemic. Given that the coronavirus has been shown to strongly influence activity decisions and social interactions, sequence analysis is applied to find daily time allocation patterns and changes in daily time allocation. Then, those derived daily patterns and other activity-travel factors, as well as social and demographic, temporal, spatial, and other contextual characteristics are added as explanatory variables in regression models of SWB measures. This provides a holistic framework of exploring the direct and indirect effects (via activity-travel schedules) of the recent pandemic on SWB while controlling for contexts such as the life assessments, daily schedule of activities, and living environment. The results show that respondents in the COVID year reported a new time allocation pattern that has a substantial amount of time at home, and they experienced more negative emotions. Three relatively happier daily patterns in 2021 contained substantial amounts of outdoor and indoor activities. In addition, no significant correlation was observed between metropolitan areas and individuals' SWB in 2021. However, comparisons among states show Texas and Florida residents experienced more positive wellbeing presumably due to fewer COVID-related restrictions

Uncovering Spatial Inequality in Taxi Services in the Context of a Subsidy War among E-Hailing Apps

Spatial inequalities in urban public transportation are a major concern in many countries but little of this research has focused specifically on taxi services. The taxi situation has grown more complex, as traditional ride-for-hire services face growing competition from e-hailing apps like Uber in the U.S., or Didi and Kuaidi in China. In 2014, Didi and Kuaidi triggered a nationwide subsidy war, with possible effects on the spatial inequality of taxi services. Taxi trajectory data from Shenzhen collected during the subsidy war shows that this competition reduced spatial inequality in the inner city but aggravated it in the outer city. In this study, a measure of service rate to depict the quantity of taxi services is proposed to calculate a Gini coefficient for evaluating change in the spatial inequality of taxi services. The Theil index and its decomposition were used to distinguish the contribution of Traffic Analysis Zones (TAZs) in the inner and the outer city and compare them to the overall spatial inequality of taxi services in Shenzhen, TAZs in the outer city had greater inequality in taxi services than the inner city. Furthermore, the primary contributor to overall inequality in taxi services was inequality within, rather than between, the inner and outer city. Moreover, the mean values for the changed service rates in the inner city were always larger than the outer city, and the inner city had a more equitable changed service rate than the outer city. These results could serve as a foundation for improving taxi services citywide

Expression of Cucumber mosaic virus suppressor 2b alters FWA methylation and its siRNA accumulation in Arabidopsis thaliana

The Cucumber mosaic virus (CMV) suppressor 2b co-localizes with AGO4 in cytoplasmic and nuclear fractions of Arabidopsis thaliana. Biochemical fractionation of A. thaliana cellular extracts revealed that 2b and AGO4 coexist in multiple size exclusions. 2b transgenic A. thaliana exhibited an enhanced accumulation of 24nt siRNAs from flowering wageningen (FWA) and other heterochromatic loci. These plants also exhibited hypo-methylation of an endogenous- as well as transgene-FWA promoter at non-CG sites. In corroboration, both transgenic 2b and CMV infection affected the regulation of transposons which mimics the ago4 phenotype. In conclusion, 2b perturbs plant defense by interfering with AGO4-regulated transcriptional gene silencing