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Deciding where to forage must not only account for variations in habitat quality but also where others might forage. Recent studies have suggested that when individuals remember recent foraging outcomes, negative frequency-dependent learning can allow them to avoid resources exploited by others (indirect competition). This process can drive the emergence of consistent differences in resource use (resource partitioning) at the population level. However, indirect cues of competition can be difficult for individuals to sense. Here, we propose that information pooling through collective decision-making—i.e. collective intelligence—can allow populations of group-living animals to more effectively partition resources relative to populations of solitary animals. We test this hypothesis by simulating (i) individuals preferring to forage where they were recently successful and (ii) cohesive groups that choose one resource using a majority rule. While solitary animals can partially avoid indirect competition through negative frequency-dependent learning, resource partitioning is more likely to emerge in populations of group-living animals. Populations of larger groups also better partition resources than populations of smaller groups, especially in environments with more choices. Our results give insight into the value of long- versus short-term memory, home range sizes and the evolution of specialization, optimal group sizes and territoriality. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of collective and emergent sensing with a flying robot swarm in which social interactions among individuals lead to following the gradient of a scalar field in the environment without the need of any gradient sensing capability. We proposed two methods—desired distance modulation and speed modulation—with and without alignment control. In the former, individuals modulate their desired distance to their neighbors and in the latter, they modulate their speed depending on the social interactions with their neighbors and measurements from the environment. Methods are systematically tested using two metrics with different scalar field models, swarm sizes and swarm densities. Experiments are conducted using: (1) a kinematic simulator, (2) a physics-based simulator, and (3) real nano-drone swarm. Results show that using the proposed methods, a swarm—composed of individuals lacking gradient sensing ability—is able to follow the gradient in a scalar field successfully. Results show that when individuals modulate their desired distances, alignment control is not needed but it still increases the performance. However, when individuals modulate their speed, alignment control is needed for collective motion. Real nano-drone experiments reveal that the proposed methods are applicable in real-life scenarios. [ABSTRACT FROM AUTHOR]

This study investigates the efficacy of collective sensing as a mechanism for unveiling disinformation in group interaction. Small group interactions were simulated to experiment on the effects of a group reaction to incentivized deceptive behavior when initiated by social influencers. We use multilevel modeling to examine the individual communication data nested within group interactions. The study advances the use of computational efficacy to support the supposition of collective sensing—by analyzing individual social actors’ communicative language and interaction within group contexts. Language-action cues as stigmergic signals were systemically extracted, compared and analyzed within groups as well as between groups. The results demonstrate that patterns of group communication become more concentrated and expressive after a social influencer becomes deceptive, even when the act of deception itself is not obvious to any individual. That is, individuals in the group characterize deceptive situations differently, but communication patterns depict the group’s ability to collectively sense deception from circulating disinformation. The study confirms our postulation of using collective sensing to detect deceptive influences in a group.

Identification of human activity considering social interactions and group dynamics non-intrusively has been one of the fundamental problems and a challenging area of research. In real life, it is required for designing human-centric applications like assisted living, health care, and creating a smart home environment. As human beings spend 90% of time indoors, such a system will be helpful to monitor the behavioral anomalies of the inhabitants. Existing approaches have used intrusive or invasive methods like camera or wearable devices. In this work, we present a device-free, non-invasive, and non-intrusive sensing framework called HumanSense using an array of heterogeneous sensor grid for human activity monitoring. The sensor grids, comprising the ultrasonic and sound sensors, have been deployed for collective sensing combining a person's physical activity and verbal interaction information. The proposed system senses a stream of events when the occupant(s) perform different physical activities categorized as atomic and group activities like sitting, standing, and walking. Simultaneously, it also tracks person-person verbal interactions such as monologue and discussion. Both information are then integrated into a single framework to understand the overall behavioral scenario of the indoor environment. The experimental results have shown that HumanSense can detect different activities with accuracy more than 90% and also improves overall identification accuracy compared to existing works. Our developed system can be further evolved into ready-to-deploy smart sensing panels which can be effective for human activity monitoring in an indoor environment. [ABSTRACT FROM AUTHOR]

This paper discusses a novel collective sensing approach using autonomous sensors specially designed to monitor gas leaks and search for gas sources. The proposed collective behavior aims to improve the gas-source search by sharing information between mobile sensors and reducing the risks associated with gas leakage. The group acts as a composite sensor that can move independently to search for an optimal sensing zone. The autonomous searching behavior is bio-inspired by colonies of bacteria that continuously seek energy sources throughout their existence. Each sensor makes its own autonomous search decision, considering the group sense, to move in the direction of a better energy source. The collective approach is based on autonomous agents sharing information to achieve a collective sense of gas perception and utilizes more intelligent searching. The method is evaluated in a cyber-physical system specially developed to safely experiment with gases and mobile sensors while reproducing the realistic dynamic behavior of the gas. Experiments are performed to clarify the collective gas-sensing contributions, and the gas search is compared through multiple mobile sensors with and without collective sensing. The proposed approach is evaluated in an unhealthy environment to elucidate its effectiveness. In addition to presenting the related differences between collective and individual sensory approaches, this work contributes with analyzes of the scalability of mobile gas sensing systems. This work also contributed as a simulated semi-physical experimental system to test algorithms’ performance before applying it to practice. [ABSTRACT FROM AUTHOR]

The artist duo knowbotiq were invited in 2019/20 by the Timespan Heritage and Art Center in the Scottish Highlands to develop a project on the ecologies of the peat bogs. The resulting project, „Thulu thu thu, before the sun harms you", traced and unfolded past, present and future entanglements and narratives of materialities in the peat land, it's inscribed asymmetric violence and the impacts of racialized capitalism. The artistic unfolding of the land developed performative acts of „collective sensing" - groups of people connected through a multitudinous object of braided plant fibers (Thulhu) to apprehend the vibrations of the peatlands. Their explorations were accompanied by „sonic incantations" - knowbotiq invited other artists for producing a fractured series of sounds, songs and readings. The incantations were articulated from various planetary positions, invoking the inhumane politics of the Anthropocene. [ABSTRACT FROM AUTHOR]

Even within small organs like pancreatic islets, different endocrine cell types and subtypes form a heterogeneous collective to sense the chemical composition of the extracellular solution and compute an adequate hormonal output. Erroneous cellular processing and hormonal output due to challenged heterogeneity result in various disorders with diabetes mellitus as a flagship metabolic disease. Here we attempt to address the aforementioned functional heterogeneity with comparing pairwise cell-cell cross-correlations obtained from simultaneous measurements of cytosolic calcium responses in hundreds of islet cells in an optical plane to statistical properties of correlations predicted by the random matrix theory (RMT). We find that the bulk of the empirical eigenvalue spectrum is almost completely described by RMT prediction, however, the deviating eigenvalues that exist below and above RMT spectral edges suggest that there are local and extended modes driving the correlations. We also show that empirical nearest neighbor spacing of eigenvalues follows universal RMT properties regardless of glucose stimulation, but that number variance displays clear separation from RMT prediction and can differentiate between empirical spectra obtained under non-stimulated and stimulated conditions. We suggest that RMT approach provides a sensitive tool to assess the functional cell heterogeneity and its effects on the spatio-temporal dynamics of a collective of beta cells in pancreatic islets in physiological resting and stimulatory conditions, beyond the current limitations of molecular and cellular biology.

In an organization, individuals prefer to form various formal and informal groups for mutual interactions. Therefore, ubiquitous identification of such groups and understanding their dynamics are important to monitor activities, behaviors, and well-being of the individuals. In this paper, we develop a lightweight, yet near-accurate, methodology, called GroupSense, to identify various interacting groups based on collective sensing through users’ smartphones. Group detection from sensor signals is not straightforward because users in proximity may not always be under the same group. Therefore, we use acoustic context extracted from audio signals to infer the interaction pattern among the subjects in proximity. We have developed an unsupervised and lightweight mechanism for user group detection by taking cues from network science and measuring the cohesivity of the detected groups regarding modularity. Taking modularity into consideration, GroupSense can efficiently eliminate incorrect groups, as well as adapt the mechanism depending on the role played by the proximity and the acoustic context in a specific scenario. The proposed method has been implemented and tested under many real-life scenarios in an academic institute environment, and we observe that GroupSense can identify user groups with on an average $0.9(\pm 0.14)$ 0. 9 (± 0. 14) $F_1$ F 1 -Score even in a noisy environment. [ABSTRACT FROM AUTHOR]

Even within small organs like pancreatic islets, different endocrine cell types and subtypes form a heterogeneous collective to sense the chemical composition of the extracellular solution and compute an adequate hormonal output. Erroneous cellular processing and hormonal output due to challenged heterogeneity result in various disorders with diabetes mellitus as a flagship metabolic disease. Here we attempt to address the aforementioned functional heterogeneity with comparing pairwise cell-cell cross-correlations obtained from simultaneous measurements of cytosolic calcium responses in hundreds of islet cells in an optical plane to statistical properties of correlations predicted by the random matrix theory (RMT). We find that the bulk of the empirical eigenvalue spectrum is almost completely described by RMT prediction, however, the deviating eigenvalues that exist below and above RMT spectral edges suggest that there are local and extended modes driving the correlations. We also show that empirical nearest neighbor spacing of eigenvalues follows universal RMT properties regardless of glucose stimulation, but that number variance displays clear separation from RMT prediction and can differentiate between empirical spectra obtained under non-stimulated and stimulated conditions. We suggest that RMT approach provides a sensitive tool to assess the functional cell heterogeneity and its effects on the spatio-temporal dynamics of a collective of beta cells in pancreatic islets in physiological resting and stimulatory conditions, beyond the current limitations of molecular and cellular biology. [ABSTRACT FROM AUTHOR]

Including members of the public in the development of effective environmental monitoring systems is gaining traction. This research assesses the potential for a hybrid monitoring system for the case of coral at the Great Barrier Reef. Based on a review of citizen-derived data sources, the paper first develops a framework and then populates it with five datasets. These are then compared based on data volumes, type of data, spatial coverage, and bleaching patterns. The results reveal the inherent difficulties – both in terms of quantity and quality – for collective sensing data (Twitter in this case) and more structured human sensors approaches (Eye on the Reef Sightings). However, more targeted approaches, such as CoralWatch and tourism-operator based data collection, emerged as important contributors to information generation on the state of coral. Citizen-based data that either deliver a high data density per location, a wide geographic coverage, or regular observations over time are particularly valuable. Recommendations are made for developing a hybrid monitoring system that integrates citizen-derived with professionally collected data. • Framework for differentiating various approaches to citizen-derived data • Comparison of five data sources for coral at the Great Barrier Reef • Collective sensing data are insufficient in quantity and quality. • Structured citizen science programs can add considerable value. • A hybrid monitoring system could greatly assist the marine park authority. [ABSTRACT FROM AUTHOR]

Major part of a pancreatic islet is composed of β-cells that secrete insulin, a key hormone regulating influx of nutrients into all cells in a vertebrate organism to support nutrition, housekeeping or energy storage. β-cells constantly communicate with each other using both direct, short-range interactions through gap junctions, and paracrine long-range signaling. However, how these cell interactions shape collective sensing and cell behavior in islets that leads to insulin release is unknown. When stimulated by specific ligands, primarily glucose, β-cells collectively respond with expression of a series of transient Ca2+ changes on several temporal scales. Here we reanalyze a set of Ca2+ spike trains recorded in acute rodent pancreatic tissue slice under physiological conditions. We found strongly correlated states of co-spiking cells coexisting with mostly weak pairwise correlations widespread across the islet. Furthermore, the collective Ca2+ spiking activity in islet shows on-off intermittency with scaling of spiking amplitudes, and stimulus dependent autoassociative memory features. We use a simple spin glass-like model for the functional network of a β-cell collective to describe these findings and argue that Ca2+ spike trains produced by collective sensing of β-cells constitute part of the islet metabolic code that regulates insulin release and limits the islet size.

Collective sensing is an emergent phenomenon which enables individuals to estimate a hidden property of the environment through the observation of social interactions. Previous work on collective sensing shows that gregarious individuals obtain an evolutionary advantage by exploiting collective sensing when competing against solitary individuals. This work addresses the question of whether collective sensing allows for the emergence of groups from a population of individuals without predetermined behaviors. It is assumed that group membership does not lessen competition on the limited resources in the environment, e.g., groups do not improve foraging efficiency. Experiments are run in an agent-based evolutionary model of a foraging task, where the fitness of the agents depends on their foraging strategy. The foraging strategy of agents is determined by a neural network, which does not require explicit modeling of the environment and of the interactions between agents. Experiments demonstrate that gregarious behavior is not the evolutionary-fittest strategy if resources are abundant, thus invalidating previous findings in a specific region of the parameter space. In other words, resource scarcity makes gregarious behavior so valuable as to make up for the increased competition over the few available resources. Furthermore, it is shown that a population of solitary agents can evolve gregarious behavior in response to a sudden scarcity of resources, thus individuating a possible mechanism that leads to gregarious behavior in nature. The evolutionary process operates on the whole parameter space of the neural networks; hence, these behaviors are selected among an unconstrained set of behavioral models. [ABSTRACT FROM AUTHOR]

The data we are typically using in geographic information systems is typically somewhat 'outdated', as soon as it gets created. The spatial data we are using represents typically a specific state of a place or area in the physical world at a certain moment in time. These data is very valuable for a vast amount of spatial analysis and applications but is not in sync with the 'current' situation on site (Artz, 2013). Combining new location aware Internet of Thing technologies (IoT) and GIS data shared via Spatial data Infrastructures give us now opportunities to integrate up-to-date ness into our GIS applications and to reflect its current status. These applications are being characterized by a continuous stream of events including position, phenomenon values (e.g. temperature, humidity etc.) flowing from sensors or data feeds interlinked with building-plans, basemaps etc. [ABSTRACT FROM AUTHOR]

Exploring how flocks of soaring migrants manage to achieve and maintain coordination while exploiting thermal updrafts is important for understanding how collective movements can enhance the sensing of the surrounding environment. Here we examined the structural organization of a group of circling white storks (Ciconia ciconia) throughout their migratory journey from Germany to Spain. We analysed individual high-resolution GPS trajectories of storks during circling events, and evaluated each bird's flight behaviour in relation to its flock members. Within the flock, we identified subgroups that synchronize their movements and coordinate switches in their circling direction within thermals. These switches in direction can be initiated by any individual of the subgroup, irrespective of how advanced its relative vertical position is, and occur at specific horizontal locations within the thermal allowing the storks to remain within the thermal. Using the motion of all flock members, we were able to examine the dynamic variation of airflow within the thermals and to determine the specific environmental conditions surrounding the flock. With an increasing amount of highresolution GPS tracking, we may soon be able to use these animals as distributed sensors providing us with a new means to obtain a detailed knowledge of our environment. [ABSTRACT FROM AUTHOR]

Major part of a pancreatic islet is composed of β-cells that secrete insulin, a key hormone regulating influx of nutrients into all cells in a vertebrate organism to support nutrition, housekeeping or energy storage. β-cells constantly communicate with each other using both direct, short-range interactions through gap junctions, and paracrine long-range signaling. However, how these cell interactions shape collective sensing and cell behavior in islets that leads to insulin release is unknown. When stimulated by specific ligands, primarily glucose, β-cells collectively respond with expression of a series of transient Ca2+ changes on several temporal scales. Here we reanalyze a set of Ca2+ spike trains recorded in acute rodent pancreatic tissue slice under physiological conditions. We found strongly correlated states of co-spiking cells coexisting with mostly weak pairwise correlations widespread across the islet. Furthermore, the collective Ca2+ spiking activity in islet shows on-off intermittency with scaling of spiking amplitudes, and stimulus dependent autoassociative memory features. We use a simple spin glass-like model for the functional network of a β-cell collective to describe these findings and argue that Ca2+ spike trains produced by collective sensing of β-cells constitute part of the islet metabolic code that regulates insulin release and limits the islet size. [ABSTRACT FROM AUTHOR]

Current construction hazard identification mostly relies on safety managers' ability to identify hazards using their prior knowledge about them. Consequently, numerous latent hazards remain unidentified, which poses significant risks to construction workers. To advance current hazard identification capabilities, this study examines the feasibility of harnessing and analyzing collective patterns of workers' bodily responses (balance, gait, etc.) to identify safety hazards on a jobsite. To test the hypothesis that the abnormality of workers' bodily responses in one location highly correlates with the likelihood of a safety hazard in that location, this project collected data on the bodily responses of 10 subjects who participated in five experiments. These test subjects wore inertial measurement unit (IMU) sensors on their body. Then the collected response data were analyzed using three metrics [average, standard deviation, and Shapiro-Wilk statistic (W)]. The data showed that the normality of workers' bodily response distributions--represented as a W statistic--highly correlated with hazard locations in every experiment, which implies that workers' bodily responses in hazardous areas are more irregularly distributed than in nonhazardous areas. This outcome demonstrates an opportunity for utilizing workers' collective bodily responses to identify safety hazards in diverse construction environments. [ABSTRACT FROM AUTHOR]

Ubiquitous geo-sensing enables context-aware analyses of physical and social phenomena, i.e., analyzing one phenomenon in the context of another. Although such context-aware analysis can potentially enable a more holistic understanding of spatio-temporal processes, it is rarely documented in the scientific literature yet. In this paper we analyzed the collective human behavior in the context of the weather. We therefore explored the complex relationships between these two spatio-temporal phenomena to provide novel insights into the dynamics of urban systems. Aggregated mobile phone data, which served as a proxy for collective human behavior, was linked with the weather data from climate stations in the case study area, the city of Udine, Northern Italy. To identify and characterize potential patterns within the weather-human relationships, we developed a hybrid approach which integrates several spatio-temporal statistical analysis methods. Thereby we show that explanatory factor analysis, when applied to a number of meteorological variables, can be used to differentiate between normal and adverse weather conditions. Further, we measured the strength of the relationship between the ‘global’ adverse weather conditions and the spatially explicit effective variations in user-generated mobile network traffic for three distinct periods using the Maximal Information Coefficient (MIC). The analyses result in three spatially referenced maps of MICs which reveal interesting insights into collective human dynamics in the context of weather, but also initiate several new scientific challenges.

Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond “hard infrastructure” by addressing “humans as sensors”, mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.

Behavioral sensing systems collect data from smartphones, wearables, and other devices with the aim of analyzing and making sense of them. In this work, we present InCense IoT, a collective sensing system which uses mobile and ubiquitous sensors for collecting behavior data of groups of participants in shared spaces. This paper describes the concept of collective sensing, an implementation onto InCense called InCense IoT, innovative features, advantages over individual-centric sensing systems. Finally, this paper presents results of a use case using it in monitoring behaviors in mother-child interactions.

This paper presents a collective sensing approach that integrates imperfect Volunteered Geographic Information (VGI) obtained through Citizen Science (CS) tree mapping projects with very high resolution (VHR) optical remotely sensed data for low-cost, fine-scale, and accurate mapping of trees in urban orchards. To this end, an individual tree crown (ITC) detection technique utilizing template matching (TM) was developed for extracting urban orchard trees from VHR optical imagery. To provide the training samples for the TM algorithm, remotely sensed VGI about trees including the crowdsourced data about ITC locations and their crown diameters was adopted in this study. A data quality assessment of the proposed approach in the study area demonstrated that the detected trees had a very high degree of completeness (92.7%), a high thematic accuracy (false discovery rate (FDR) = 0.090, false negative rate (FNR) = 0.073, and F1 score (F1) = 0.918), and a fair positional accuracy (root mean square error(RMSE) = 1.02 m). Overall, the proposed approach based on the crowdsourced training samples generally demonstrated a promising ITC detection performance in our pilot project.

This paper proves that mobile phone usage data is an easy to use, cheap and most importantly, reliable predictor of motorway incidents. Using econometric modelling, this paper provides a proof of concept of how mobile phone usage data can be utilised to detect motorway incidents. Greater Amsterdam is used here as a case study and the results suggest that mobile phone usage data can be utilised for the development of an early warning system to support road traffic incident management. [ABSTRACT FROM AUTHOR]

Ubiquitous geo-sensing enables context-aware analyses of physical and social phenomena, i.e., analyzing one phenomenon in the context of another. Although such context-aware analysis can potentially enable a more holistic understanding of spatio-temporal processes, it is rarely documented in the scientific literature yet. In this paper we analyzed the collective human behavior in the context of the weather. We therefore explored the complex relationships between these two spatio-temporal phenomena to provide novel insights into the dynamics of urban systems. Aggregated mobile phone data, which served as a proxy for collective human behavior, was linked with the weather data from climate stations in the case study area, the city of Udine, Northern Italy. To identify and characterize potential patterns within the weather-human relationships, we developed a hybrid approach which integrates several spatio-temporal statistical analysis methods. Thereby we show that explanatory factor analysis, when applied to a number of meteorological variables, can be used to differentiate between normal and adverse weather conditions. Further, we measured the strength of the relationship between the 'global' adverse weather conditions and the spatially explicit effective variations in user-generated mobile network traffic for three distinct periods usingthe Maximal Information Coefficient (MIC). The analyses result in three spatially referenced maps of MICs which reveal interesting insights into collective human dynamics in the context of weather, but also initiate several new scientific challenges. [ABSTRACT FROM AUTHOR]

Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond "hard infrastructure" by addressing "humans as sensors", mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures. [ABSTRACT FROM AUTHOR]

Even within small organs like pancreatic islets, different endocrine cell types and subtypes form a heterogeneous collective to sense the chemical composition of the extracellular solution and compute an adequate hormonal output. Erroneous cellular processing and hormonal output due to challenged heterogeneity result in various disorders with diabetes mellitus as a flagship metabolic disease. Here we attempt to address the aforementioned functional heterogeneity with comparing pairwise cell-cell cross-correlations obtained from simultaneous measurements of cytosolic calcium responses in hundreds of islet cells in an optical plane to statistical properties of correlations predicted by the random matrix theory (RMT). We find that the bulk of the empirical eigenvalue spectrum is almost completely described by RMT prediction, however, the deviating eigenvalues that exist below and above RMT spectral edges suggest that there are local and extended modes driving the correlations. We also show that empirical nearest neighbor spacing of eigenvalues follows universal RMT properties regardless of glucose stimulation, but that number variance displays clear separation from RMT prediction and can differentiate between empirical spectra obtained under non-stimulated and stimulated conditions. We suggest that RMT approach provides a sensitive tool to assess the functional cell heterogeneity and its effects on the spatio-temporal dynamics of a collective of beta cells in pancreatic islets in physiological resting and stimulatory conditions, beyond the current limitations of molecular and cellular biology., (Copyright © 2019 Korošak and Slak Rupnik.)

This paper presents a collective sensing approach that integrates imperfect Volunteered Geographic Information (VGI) obtained through Citizen Science (CS) tree mapping projects with very high resolution (VHR) optical remotely sensed data for low-cost, fine-scale, and accurate mapping of trees in urban orchards. To this end, an individual tree crown (ITC) detection technique utilizing template matching (TM) was developed for extracting urban orchard trees from VHR optical imagery. To provide the training samples for the TM algorithm, remotely sensed VGI about trees including the crowdsourced data about ITC locations and their crown diameters was adopted in this study. A data quality assessment of the proposed approach in the study area demonstrated that the detected trees had a very high degree of completeness (92.7%), a high thematic accuracy (false discovery rate (FDR) = 0.090, false negative rate (FNR) = 0.073, and F 1 score ( F 1 ) = 0.918), and a fair positional accuracy ( root   mean   square   error (R M S E) = 1.02 m). Overall, the proposed approach based on the crowdsourced training samples generally demonstrated a promising ITC detection performance in our pilot project. [ABSTRACT FROM AUTHOR]

Cells constantly sense their chemical and mechanical environments. We study the effect of mechanics on the ATP-induced collective calcium response of fibroblast cells in experiments that mimic various tissue environments. We find that closely packed two-dimensional cell cultures on a soft polyacrylamide gel (Young's modulus E = 690 Pa) contain more cells exhibiting calcium oscillations than cultures on a rigid substrate (E = 36 000 Pa). Calcium responses of cells on soft substrates show a slower decay of calcium level relative to those on rigid substrates. Actin enhancement and disruption experiments for the cell cultures allow us to conclude that actin filaments determine the collective Ca(2+) oscillatory behaviour in the culture. Inhibition of gap junctions results in a decrease of the oscillation period and reduced correlation of calcium responses, which suggests additional complexity of signalling upon cell-cell contact. Moreover, the frequency of calcium oscillations is independent of the rigidity of the substrate but depends on ATP concentration. We compare our results with those from similar experiments on individual cells. Overall, our observations show that collective chemical signalling in cell cultures via calcium depends critically on the mechanical environment., (© 2015 The Author(s) Published by the Royal Society. All rights reserved.)

Pervasive presence of location-sharing services made it possible for researchers to gain an unprecedented access to the direct records of human activity in space and time. This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012, we estimate the volume of international travelers by country of residence. Mobility profiles of different nations were examined based on such characteristics as mobility rate, radius of gyration, diversity of destinations, and inflow-outflow balance. Temporal patterns disclose the universally valid seasons of increased international mobility and the particular character of international travels of different nations. Our analysis of the community structure of the Twitter mobility network reveals spatially cohesive regions that follow the regional division of the world. We validate our result using global tourism statistics and mobility models provided by other authors and argue that Twitter is exceptionally useful for understanding and quantifying global mobility patterns.