Space as an invention of biological organisms

The question of the nature of space around us has occupied thinkers since the dawn of humanity, with scientists and philosophers today implicitly assuming that space is something that exists objectively. Here we show that this does not have to be the case: the notion of space could emerge when biological organisms seek an economic representation of their sensorimotor flow. The emergence of spatial notions does not necessitate the existence of real physical space, but only requires the presence of sensorimotor invariants called `compensable' sensory changes. We show mathematically and then in simulations that na\"ive agents making no assumptions about the existence of space are able to learn these invariants and to build the abstract notion that physicists call rigid displacement, which is independent of what is being displaced. Rigid displacements may underly perception of space as an unchanging medium within which objects are described by their relative positions. Our findings suggest that the question of the nature of space, currently exclusive to philosophy and physics, should also be addressed from the standpoint of neuroscience and artificial intelligence

Use of shuttle for life sciences

The use of the space shuttle in carrying out biological and medical research programs, with emphasis on the sortie module, is examined. Detailed descriptions are given of the goals of space life science disciplines, how the sortie can meet these goals, and what shuttle design features are necessary for a viable biological and medical experiment program. Conclusions show that the space shuttle sortie module is capable of accommodating all biological experiments contemplated at this time except for those involving large specimens or large populations of small animals; however, these experiments can be done with a specially designed module. It was also found that at least two weeks is required to do a meaningful survey of biological effects

Space and related biological and instrumentation studies

Research and experimental effort was carried out on high-density photo-optical recorder design, implantable pH electrodes and the mangetic/doppler blood-flow sensor

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Multi-point monitoring of nitrous oxide emissions and aeration efficiency in a full-scale conventional activated sludge tank

In this work the biological tank of a WRRF in Italy was monitored placing five floating hoods on a plug-flow-like biological aerated tank surface in order to capture emission dynamics in both time and space domains. The five hoods report which location is more responsible for N2O production at a certain moment of the day. Moreover, with this experimental investigation, a spatial shift in N2O production towards the end of the biological tank could be detected. This provides important insights in the changes in biological dynamics especially with varying incoming load

Asymmetries arising from the space-filling nature of vascular networks

Cardiovascular networks span the body by branching across many generations of vessels. The resulting structure delivers blood over long distances to supply all cells with oxygen via the relatively short-range process of diffusion at the capillary level. The structural features of the network that accomplish this density and ubiquity of capillaries are often called space-filling. There are multiple strategies to fill a space, but some strategies do not lead to biologically adaptive structures by requiring too much construction material or space, delivering resources too slowly, or using too much power to move blood through the system. We empirically measure the structure of real networks (18 humans and 1 mouse) and compare these observations with predictions of model networks that are space-filling and constrained by a few guiding biological principles. We devise a numerical method that enables the investigation of space-filling strategies and determination of which biological principles influence network structure. Optimization for only a single principle creates unrealistic networks that represent an extreme limit of the possible structures that could be observed in nature. We first study these extreme limits for two competing principles, minimal total material and minimal path lengths. We combine these two principles and enforce various thresholds for balance in the network hierarchy, which provides a novel approach that highlights the trade-offs faced by biological networks and yields predictions that better match our empirical data.Comment: 17 pages, 15 figure

The development of medical and biological semiconductor detectors eighth quarterly pro- gress report

Medical and biological semiconductor detectors for manned space flight mission

A temporal logic approach to modular design of synthetic biological circuits

We present a new approach for the design of a synthetic biological circuit whose behaviour is specified in terms of signal temporal logic (STL) formulae. We first show how to characterise with STL formulae the input/output behaviour of biological modules miming the classical logical gates (AND, NOT, OR). Hence, we provide the regions of the parameter space for which these specifications are satisfied. Given a STL specification of the target circuit to be designed and the networks of its constituent components, we propose a methodology to constrain the behaviour of each module, then identifying the subset of the parameter space in which those constraints are satisfied, providing also a measure of the robustness for the target circuit design. This approach, which leverages recent results on the quantitative semantics of Signal Temporal Logic, is illustrated by synthesising a biological implementation of an half-adder

Bounded Coordinate-Descent for Biological Sequence Classification in High Dimensional Predictor Space

We present a framework for discriminative sequence classification where the learner works directly in the high dimensional predictor space of all subsequences in the training set. This is possible by employing a new coordinate-descent algorithm coupled with bounding the magnitude of the gradient for selecting discriminative subsequences fast. We characterize the loss functions for which our generic learning algorithm can be applied and present concrete implementations for logistic regression (binomial log-likelihood loss) and support vector machines (squared hinge loss). Application of our algorithm to protein remote homology detection and remote fold recognition results in performance comparable to that of state-of-the-art methods (e.g., kernel support vector machines). Unlike state-of-the-art classifiers, the resulting classification models are simply lists of weighted discriminative subsequences and can thus be interpreted and related to the biological problem