Emergence as the conversion of information: a unifying theory.

Is reduction always a good scientific strategy? The existence of the special sciences above physics suggests not. Previous research has shown that dimensionality reduction (macroscales) can increase the dependency between elements of a system (a phenomenon called 'causal emergence'). Here, we provide an umbrella mathematical framework for emergence based on information conversion.

Evolution and emergence: higher order information structure in protein interactomes across the tree of life.

The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein-protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function.

The overfitted brain: Dreams evolved to assist generalization.

Understanding of the evolved biological function of sleep has advanced considerably in the past decade. However, no equivalent understanding of dreams has emerged. Contemporary neuroscientific theories often view dreams as epiphenomena, and many of the proposals for their biological function are contradicted by the phenomenology of dreams themselves.

A training course on interpersonal relationships using role play in a Master of Mental Health Care programme - The students' experiences.

Objectives: To study how Master Students of Mental Health Care experienced role play as an educational method that strengthened their relational competence.

Design: The study was qualitative with an exploratory descriptive design.

Settings: Relational competence training course during the Master of Mental Health Care programme INFORMANTS: Master students in a Mental Health Care programme METHODS: Data from open-ended questions were analysed using qualitative content analysis based on Graneheim and Lundman.

Falsification and consciousness.

The search for a scientific theory of consciousness should result in theories that are falsifiable. However, here we show that falsification is especially problematic for theories of consciousness. We formally describe the standard experimental setup for testing these theories.

Gene regulatory networks exhibit several kinds of memory: quantification of memory in biological and random transcriptional networks.

Gene regulatory networks (GRNs) process important information in developmental biology and biomedicine. A key knowledge gap concerns how their responses change over time. Hypothesizing long-term changes of dynamics induced by transient prior events, we created a computational framework for defining and identifying diverse types of memory in candidate GRNs.

Causal Geometry.

Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here, we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon.

Examining the Causal Structures of Deep Neural Networks Using Information Theory.

Deep Neural Networks (DNNs) are often examined at the level of their response to input, such as analyzing the mutual information between nodes and data sets. Yet DNNs can also be examined at the level of causation, exploring "what does what" within the layers of the network itself. Historically, analyzing the causal structure of DNNs has received less attention than understanding their responses to input.

Emergence of informative higher scales in biological systems: a computational toolkit for optimal prediction and control.

The biological sciences span many spatial and temporal scales in attempts to understand the function and evolution of complex systems-level processes, such as embryogenesis. It is generally assumed that the most effective description of these processes is in terms of molecular interactions. However, recent developments in information theory and causal analysis now allow for the quantitative resolution of this question.

Reduced Repertoire of Cortical Microstates and Neuronal Ensembles in Medically Induced Loss of Consciousness.

Medically induced loss of consciousness (mLOC) during anesthesia is associated with a macroscale breakdown of brain connectivity, yet the neural microcircuit correlates of mLOC remain unknown. To explore this, we applied different analytical approaches (t-SNE/watershed segmentation, affinity propagation clustering, PCA, and LZW complexity) to two-photon calcium imaging of neocortical and hippocampal microcircuit activity and local field potential (LFP) measurements across different anesthetic depths in mice, and to micro-electrode array recordings in human subjects. We find that in both cases, mLOC disrupts population activity patterns by generating (1) fewer discriminable network microstates and (2) fewer neuronal ensembles.

What Caused What? A Quantitative Account of Actual Causation Using Dynamical Causal Networks.

Actual causation is concerned with the question: "What caused what?" Consider a transition between two states within a system of interacting elements, such as an artificial neural network, or a biological brain circuit. Which combination of synapses caused the neuron to fire? Which image features caused the classifier to misinterpret the picture? Even detailed knowledge of the system's causal network, its elements, their states, connectivity, and dynamics does not automatically provide a straightforward answer to the "what caused what?" question. Counterfactual accounts of actual causation, based on graphical models paired with system interventions, have demonstrated initial success in addressing specific problem cases, in line with intuitive causal judgments.

Gender and leadership for health literacy to combat the epidemic rise of noncommunicable diseases.

Until recently, the noncommunicable diseases (NCDs) epidemic has been considered only a significant burden to men in high-income countries. However, latest figures indicate that half of all NCD-related deaths affect women, especially in low- and middle-income countries (LMICs), with global responses to the NCD epidemic overlooking the significance of women and girls in their approaches and programs. This case study highlights the burden of disease challenging South Africa that disproportionately affects women in the country and suggests that the country, along with other LMICs internationally, requires a shift in the gender-based leadership of health literacy and self-empowerment.

Can the macro beat the micro? Integrated information across spatiotemporal scales.

Causal interactions within complex systems such as the brain can be analyzed at multiple spatiotemporal levels. It is widely assumed that the micro level is causally complete, thus excluding causation at the macro level. However, by measuring effective information-how much a system's mechanisms constrain its past and future states-we recently showed that causal power can be stronger at macro rather than micro levels.

Synaptic refinement during development and its effect on slow-wave activity: a computational study.

Recent evidence suggests that synaptic refinement, the reorganization of synapses and connections without significant change in their number or strength, is important for the development of the visual system of juvenile rodents. Other evidence in rodents and humans shows that there is a marked drop in sleep slow-wave activity (SWA) during adolescence. Slow waves reflect synchronous transitions of neuronal populations between active and inactive states, and the amount of SWA is influenced by the connection strength and organization of cortical neurons.

Quantifying causal emergence shows that macro can beat micro.

Causal interactions within complex systems can be analyzed at multiple spatial and temporal scales. For example, the brain can be analyzed at the level of neurons, neuronal groups, and areas, over tens, hundreds, or thousands of milliseconds. It is widely assumed that, once a micro level is fixed, macro levels are fixed too, a relation called supervenience.

Ichthyobodo necator (Kinetoplastida)--a complex of sibling species.

Ichthyobodo necator is a parasitic flagellate that attacks fishes, causing disease problems in freshwater worldwide. Findings of similar flagellates in strictly marine fishes have indicated that ichthyobodiosis may be caused by more than 1 flagellate species. We obtained partial small subunit rDNA (ssu rDNA) sequences of 14 Ichthyobodo isolates originating from fishes in Norway, Japan, Singapore, South Africa and Brazil, and identified 8 strains or species, including 2 species infecting cultured salmon in Norway.