Self-Replicating Strands that Self-Assemble into User-Specified Meshes

It has been argued that a central objective of nanotechnology is to make products inexpensively, and that self-replication is an effective approach to very low-cost manufacturing. The research presented here is intended to be a step towards this vision. In previous work (JohnnyVon 1.0), we simulated machines that bonded together to form self-replicating strands. There were two types of machines (called types 0 and 1), which enabled strands to encode arbitrary bit strings. However, the information encoded in the strands had no functional role in the simulation. The information was replicated without being interpreted, which was a significant limitation for potential manufacturing applications. In the current work (JohnnyVon 2.0), the information in a strand is interpreted as instructions for assembling a polygonal mesh. There are now four types of machines and the information encoded in a strand determines how it folds. A strand may be in an unfolded state, in which the bonds are straight (although they flex slightly due to virtual forces acting on the machines), or in a folded state, in which the bond angles depend on the types of machines. By choosing the sequence of machine types in a strand, the user can specify a variety of polygonal shapes. A simulation typically begins with an initial unfolded seed strand in a soup of unbonded machines. The seed strand replicates by bonding with free machines in the soup. The child strands fold into the encoded polygonal shape, and then the polygons drift together and bond to form a mesh. We demonstrate that a variety of polygonal meshes can be manufactured in the simulation, by simply changing the sequence of machine types in the seed

Self-Replication and Self-Assembly for Manufacturing

It has been argued that a central objective of nanotechnology is to make products inexpensively, and that self-replication is an effective approach to very low-cost manufacturing. The research presented here is intended to be a step towards this vision. We describe a computational simulation of nanoscale machines floating in a virtual liquid. The machines can bond together to form strands (chains) that self-replicate and self-assemble into user-specified meshes. There are four types of machines and the sequence of machine types in a strand determines the shape of the mesh they will build. A strand may be in an unfolded state, in which the bonds are straight, or in a folded state, in which the bond angles depend on the types of machines. By choosing the sequence of machine types in a strand, the user can specify a variety of polygonal shapes. A simulation typically begins with an initial unfolded seed strand in a soup of unbonded machines. The seed strand replicates by bonding with free machines in the soup. The child strands fold into the encoded polygonal shape, and then the polygons drift together and bond to form a mesh. We demonstrate that a variety of polygonal meshes can be manufactured in the simulation, by simply changing the sequence of machine types in the seed

JohnnyVon: Self-Replicating Automata in Continuous Two-Dimensional Space

JohnnyVon is an implementation of self-replicating automata in continuous two-dimensional space. Two types of particles drift about in a virtual liquid. The particles are automata with discrete internal states but continuous external relationships. Their internal states are governed by finite state machines but their external relationships are governed by a simulated physics that includes brownian motion, viscosity, and spring-like attractive and repulsive forces. The particles can be assembled into patterns that can encode arbitrary strings of bits. We demonstrate that, if an arbitrary “seed” pattern is put in a “soup” of separate individual particles, the pattern will replicate by assembling the individual particles into copies of itself. We also show that, given sufficient time, a soup of separate individual particles will eventually spontaneously form self-replicating patterns. We discuss the implications of JohnnyVon for research in nanotechnology, theoretical biology, and artificial life

Self-Replicating Machines in Continuous Space with Virtual Physics

JohnnyVon is an implementation of self-replicating machines in continuous two-dimensional space. Two types of particles drift about in a virtual liquid. The particles are automata with discrete internal states but continuous external relationships. Their internal states are governed by finite state machines but their external relationships are governed by a simulated physics that includes Brownian motion, viscosity, and spring-like attractive and repulsive forces. The particles can be assembled into patterns that can encode arbitrary strings of bits. We demonstrate that, if an arbitrary "seed" pattern is put in a "soup" of separate individual particles, the pattern will replicate by assembling the individual particles into copies of itself. We also show that, given sufficient time, a soup of separate individual particles will eventually spontaneously form self-replicating patterns. We discuss the implications of JohnnyVon for research in nanotechnology, theoretical biology, and artificial life

A Fish Habitat Classification Model for the Upper and Middle Sections of the Bay of Quinte, Lake Ontario

A fish habitat classification model was developed and applied to the upper and middle sections of the Bay of Quinte, Lake Ontario. Available habitat inventories were assembled in a GIS database, bringing bathymetric, shoreline, substrate, and vegetation data together in a series of layers. The classification model was developed in four steps. In the first step, the Defensible Methods (DM) model developed by Minns et al. (2000) was used to estimate suitability values in all habitat patches for a set of nine fish groups each with three life stages. The fish groups were formed from the assemblage of fish species present in the Bay of Quinte by combining them according to thermal and vegetation preferences, and combinations of size and age-at-maturity. Different methods of combining the 27 suitability indices were examined to allow designation of each unique habitat patch to low, medium or high suitability categories for fish. The K-means clustering technique was selected for classifying habitat patches into three suitability categories, thereby exploiting natural breaks in the cumulative distributions of suitability values and maintaining consistency with underlying habitat features. In the second step, the spatially rare habitats for each fish group by life stage combination were used to identify habitat patches that are important for particular fish groups and life stages but which had been classified as medium or low suitability in the first classification step. Criteria for recognizing rarity were used to reassign habitat patches rated low or medium in step one to the high class. In the third step, local expert knowledge of important fish habitats gathered from anglers and fishers were used to develop an expert classification. This expert mapping of important fishing areas was compared with that obtained via suitability and rarity ratings and then, in step four, used to upgrade some areas from low or medium to high. The final habitat classification model is a mixture of suitability, rarity and expert ratings. The habitat suitability class assignments obtained in step one were not changed appreciably by steps two and three. The combined suitability-rarity ratings showed good agreement with the local expert ratings. Important fishing areas either overlapped suitable areas or were close by where fisher access would be restricted by depth or vegetation density. The final habitat classification for the Bay of Quinte provides a context for both conservation and restoration efforts. Periodic updating of the classification system will be needed as conditions change, e.g., as a result of climate change or as the effects of the zebra mussel invasion on macrophytes and substrates mature, or as data on other habitat elements becomes available, e.g., seasonal and spatially thermal habitat maps. Further effort is needed to understand the procedures used by government agencies at different levels to integrate the knowledge embodied in habitat maps into on-going fisheries and fish habitat management

Advances in prevention and therapy of neonatal dairy calf diarrhoea : a systematical review with emphasis on colostrum management and fluid therapy

Neonatal calf diarrhoea remains the most common cause of morbidity and mortality in preweaned dairy calves worldwide. This complex disease can be triggered by both infectious and non-infectious causes. The four most important enteropathogens leading to neonatal dairy calf diarrhoea are Escherichia coli, rota-and coronavirus, and Cryptosporidium parvum. Besides treating diarrhoeic neonatal dairy calves, the veterinarian is the most obvious person to advise the dairy farmer on prevention and treatment of this disease. This review deals with prevention and treatment of neonatal dairy calf diarrhoea focusing on the importance of a good colostrum management and a correct fluid therapy

The role of probiotics on the microbiota: effect on obesity

The microbiota and the human host maintain a symbiotic association. Nowadays, metagenomic analyses are providing valuable knowledge on the diversity and functionality of the gut microbiota. However, with regard to the definition of a “healthy microbiota” and the characterization of the dysbiosis linked to obesity, there is still not a clear answer. Despite this fact, attempts have been made to counteract obesity through probiotic supplementation. A literature search of experimental studies relevant to the topic was performed in PubMed database with the keywords “probiotic” and “obesity” and restricted to those with “Lactobacillus” or “Bifidobacterium” in the title. So far, evidence of an antiobesity effect of different lactobacilli and bifidobacteria has been mainly obtained from animal models of dietary-induced obesity. Using these experimental models, a substantial number of studies have reported reductions in weight gain and, in particular, fat tissue mass at different locations following administration of bacteria, as compared with controls. Antiatherogenic and anti-inflammatory effects—including regulation of expression of lipogenic and lipolytic genes in the liver, reduction in liver steatosis, improvement of blood lipid profile and glucose tolerance, decreased endotoxemia, and regulation of inflammatory pathways—are also reported in many of them. The number of human studies focused on probiotic administration for obesity management is still very scarce, and it is too soon to judge their potential efficacy, especially when considering the fact that the actions of probiotics are always strain specific and the individual response varies according to intrinsic factors, the overall composition of diet, and their interactions

Canada goose nest survival at rural wetlands in north-central Iowa

The last comprehensive nest survival study of the breeding giant Canada goose (Branta canadensis maxima) population in Iowa, USA, was conducted \u3e30 years ago during a period of population recovery, during which available nesting habitat consisted primarily of artificial nest structures. Currently, Iowa\u27s resident goose population is stable and nests in a variety of habitats. We analyzed the effects of available habitat on nest survival and how nest survival rates compared with those of the expanding goose population studied previously to better understand how to maintain a sustainable Canada goose population in Iowa. We documented Canada goose nest survival at rural wetland sites in north-central Iowa. We monitored 121 nests in 2013 and 149 nests in 2014 at 5 Wildlife Management Areas (WMAs) with various nesting habitats, including islands, muskrat (Ondatra zibethicus) houses, and elevated nest structures. We estimated daily nest-survival rate using the nest survival model in Program MARK. Survival was influenced by year, site, stage, presence of a camera, nest age, and an interaction between nest age and stage. Nest success rates for the 28-day incubation period by site and year combination ranged from 0.10 to 0.84. Nest survival was greatest at sites with nest structures (β = 17.34). Nest survival was negatively affected by lowered water levels at Rice Lake WMA (2013 β = −0.77, nest age β = −0.07). Timing of water-level drawdowns for shallow lake restorations may influence nest survival rates. Published 2016. This article is a U.S. Government work and is in the public domain in the USA

Bifidobacterium breve UCC2003 induces a distinct global transcriptomic programme in neonatal murine intestinal epithelial cells

The underlying health-driving mechanisms of Bifidobacterium during early life are not well understood, particularly how this microbiota member may modulate the intestinal barrier via programming of intestinal epithelial cells (IECs). We investigated the impact of Bifidobacterium breve UCC2003 on the transcriptome of neonatal murine IECs. Small IECs from two-week-old neonatal mice administered B. breve UCC2003 or PBS (control) were subjected to global RNA-Seq, and differentially expressed genes, pathways and affected cell types determined. We observed extensive regulation of the IEC transcriptome with ∼4,000 genes significantly up-regulated, including key genes linked with epithelial barrier function. Enrichment of cell differentiation pathways were observed, along with an overrepresentation of stem cell marker genes, indicating an increase in the regenerative potential of the epithelial layer. In conclusion, B. breve UCC2003 plays a central role in driving intestinal epithelium homeostatic development during early life and suggests future avenues for next-stage clinical studies