How predation shapes the social interaction rules of shoaling fish

Predation is thought to shape the macroscopic properties of animal groups, making moving groups more cohesive and coordinated. Precisely how predation has shaped individuals' fine-scale social interactions in natural populations, however, is unknown. Using high-resolution tracking data of shoaling fish (Poecilia reticulata) from populations differing in natural predation pressure, we show how predation adapts individuals' social interaction rules. Fish originating from high predation environments formed larger, more cohesive, but not more polarized groups than fish from low predation environments. Using a new approach to detect the discrete points in time when individuals decide to update their movements based on the available social cues, we determine how these collective properties emerge from individuals' microscopic social interactions. We first confirm predictions that predation shapes the attraction–repulsion dynamic of these fish, reducing the critical distance at which neighbours move apart, or come back together. While we find strong evidence that fish align with their near neighbours, we do not find that predation shapes the strength or likelihood of these alignment tendencies. We also find that predation sharpens individuals' acceleration and deceleration responses, implying key perceptual and energetic differences associated with how individuals move in different predation regimes. Our results reveal how predation can shape the social interactions of individuals in groups, ultimately driving differences in groups' collective behaviour.</jats:p

How predation shapes the social interaction rules of shoaling fish

The 688 links comprising the human signaling network. (XLS 111 kb

Modeling glioblastoma growth patterns and their mechanistic origins

Glioblastoma (GBM) is the most common and aggressive primary brain cancer. GBM cells migrate away from the primary lesion and invade healthy brain tissue. The invading cells escape surgical resection, radiotherapy and develop resistance to chemotherapy. Consequently, despite treatment, recurrence is inevitable, and survival is only 14 months. For this purpose, we conducted four studies where we integrated experimental data from extensive patient material with image analysis and mathematical modeling. In study 1, we developed a tool, TargetTranslator, integrating different data modalities to identify new treatments. We implemented an image analysis pipeline to validate our results using a deep artificial neural network to quantify neuroblastoma cell differentiation. In study 2, we integrated the zebrafish and image analysis from study 1 to develop a high-throughput in vivo assay. Zebrafish were orthotopically injected with GBM cells, and each fish's tumor growth and vital status were automatically measured. We characterized the in vivo proliferation rate, survival, and treatment response to the drug marizomib for several patient-derived cell cultures. Light-sheet imaging also revealed two distinct growth types. The first set of cell cultures grew as bulk tumors, whereas the second set invaded vasculature as single cells. In study 3, we used the image analysis from study 1, coupled with an agent-based model to estimate in vitro cell migration and proliferation from single end-point images. The method was validated by a time series data set and applied to a large high-content drug screen of GBM cells. We identified three promising candidates for reducing GBM cell migration. The method can estimate migration on any end-point images of adherent cells without any additional experimental cost. Study 4 characterized the growth and invasive patterns of 45 patient-derived GBM cell cultures in orthogonal mouse xenografts. We found that up to four independent axes of variation could describe the phenotypes and were associated with distinct transcriptomic pathways. The transcriptomic pathways were in part associated with common genomic alterations and subtypes in GBM. We further identified a particularly aggressive GBM phenotype. In conclusion, this thesis was interdisciplinary and aimed to measure survival, invasion, and morphology from extensive patient material. The work had given us new insight into GBM invasion and growth and developed several scalable models suitable for evaluating new therapies

Modeling glioblastoma growth patterns and their mechanistic origins

Glioblastoma (GBM) is the most common and aggressive primary brain cancer. GBM cells migrate away from the primary lesion and invade healthy brain tissue. The invading cells escape surgical resection, radiotherapy and develop resistance to chemotherapy. Consequently, despite treatment, recurrence is inevitable, and survival is only 14 months. For this purpose, we conducted four studies where we integrated experimental data from extensive patient material with image analysis and mathematical modeling. In study 1, we developed a tool, TargetTranslator, integrating different data modalities to identify new treatments. We implemented an image analysis pipeline to validate our results using a deep artificial neural network to quantify neuroblastoma cell differentiation. In study 2, we integrated the zebrafish and image analysis from study 1 to develop a high-throughput in vivo assay. Zebrafish were orthotopically injected with GBM cells, and each fish's tumor growth and vital status were automatically measured. We characterized the in vivo proliferation rate, survival, and treatment response to the drug marizomib for several patient-derived cell cultures. Light-sheet imaging also revealed two distinct growth types. The first set of cell cultures grew as bulk tumors, whereas the second set invaded vasculature as single cells. In study 3, we used the image analysis from study 1, coupled with an agent-based model to estimate in vitro cell migration and proliferation from single end-point images. The method was validated by a time series data set and applied to a large high-content drug screen of GBM cells. We identified three promising candidates for reducing GBM cell migration. The method can estimate migration on any end-point images of adherent cells without any additional experimental cost. Study 4 characterized the growth and invasive patterns of 45 patient-derived GBM cell cultures in orthogonal mouse xenografts. We found that up to four independent axes of variation could describe the phenotypes and were associated with distinct transcriptomic pathways. The transcriptomic pathways were in part associated with common genomic alterations and subtypes in GBM. We further identified a particularly aggressive GBM phenotype. In conclusion, this thesis was interdisciplinary and aimed to measure survival, invasion, and morphology from extensive patient material. The work had given us new insight into GBM invasion and growth and developed several scalable models suitable for evaluating new therapies

Analysis and visualization of collective motion in football : Analysis of youth football using GPS and visualization of professional football

Football is one of the biggest sports in the world. Professional teams track their player's positions using GPS (Global Positioning System). This report is divided into two parts, both focusing on applying collective motion to football. % The goal of the first part was to both see if a set of cheaper GPS units could be used to analyze the collective motion of a youth football team. 15 football players did two experiments and played three versus three football matches against each other while wearing a GPS. The first experiment measured the player's ability to control the ball while the second experiment measured how well they were able to move together as a team. Different measurements were measured from the match and Spearman correlations were calculated between measurements from the experiments and matches. Players which had good ball control also scored more goals in the match and received more passes. However, they also took the middle position in the field which naturally is a position which receives more passes. Players which were correlated during the team experiment were also correlated with team-members in the match. But, this correlation was weak and the experiment should be done again with more players. The GPS did not work well in the team experiment but have potential to work well in experiments done on a normal-sized football field. % The goal of the second part of the report was to visualize collective motion, more specifically leader-follower relations, in football which can be used as a basis for further research. This is done by plotting the player's positions at each time step to a user interface. Between each player, a double pointed arrow is drawn, where each side of the arrow has a separate color and arrow width. The maximum time lag between the between the two players is shown as the "pointiness" of the arrow while the color of the arrow show the maximum time lag correlation. The user can change the metrics the correlations are based of. As a compliment to the lagged correlation, a lag score is defined which tell the user how strong the lagged correlation is

Analysis and visualization of collective motion in football : Analysis of youth football using GPS and visualization of professional football

Football is one of the biggest sports in the world. Professional teams track their player's positions using GPS (Global Positioning System). This report is divided into two parts, both focusing on applying collective motion to football. % The goal of the first part was to both see if a set of cheaper GPS units could be used to analyze the collective motion of a youth football team. 15 football players did two experiments and played three versus three football matches against each other while wearing a GPS. The first experiment measured the player's ability to control the ball while the second experiment measured how well they were able to move together as a team. Different measurements were measured from the match and Spearman correlations were calculated between measurements from the experiments and matches. Players which had good ball control also scored more goals in the match and received more passes. However, they also took the middle position in the field which naturally is a position which receives more passes. Players which were correlated during the team experiment were also correlated with team-members in the match. But, this correlation was weak and the experiment should be done again with more players. The GPS did not work well in the team experiment but have potential to work well in experiments done on a normal-sized football field. % The goal of the second part of the report was to visualize collective motion, more specifically leader-follower relations, in football which can be used as a basis for further research. This is done by plotting the player's positions at each time step to a user interface. Between each player, a double pointed arrow is drawn, where each side of the arrow has a separate color and arrow width. The maximum time lag between the between the two players is shown as the "pointiness" of the arrow while the color of the arrow show the maximum time lag correlation. The user can change the metrics the correlations are based of. As a compliment to the lagged correlation, a lag score is defined which tell the user how strong the lagged correlation is

Does productivity influence priority setting? A case study from the field of CVD prevention.

In this case study, different measures aimed at preventing cardiovascular diseases (CVD) in different target groups have been ranked based on cost per QALY from a health care sector perspective and from a societal perspective, respectively. The innovation in this study is to introduce a budget constraint and thereby show exactly which groups would be included or excluded in treatment or intervention programs based on the two perspectives. Approximately 90% of the groups are included in both perspectives. Mainly elderly women are excluded when the societal perspective is used and mainly middle-aged men are excluded when the health care sector perspective is used. Elderly women have a higher risk of CVD and generally lower income than middle-aged men. Thus the exclusion of older women in the societal perspective is not a trivial consequence since it is in conflict with the general interpretation of the "treatment according to need" rule, as well as societal goals regarding gender equality and fairness. On the other hand, the exclusion of working individuals in the health care perspective undermines a growth of public resources for future health care for the elderly. The extent and consequences of this conflict are unclear and empirical studies of this problem are rare