A general model of hierarchical fractal scale-free networks.

We propose a general model of unweighted and undirected networks having the scale-free property and fractal nature. Unlike the existing models of fractal scale-free networks (FSFNs), the present model can systematically and widely change the network structure. In this model, an FSFN is iteratively formed by replacing each edge in the previous generation network with a small graph called a generator. The choice of generators enables us to control the scale-free property, fractality, and other structural properties of hierarchical FSFNs. We calculate theoretically various characteristic quantities of networks, such as the exponent of the power-law degree distribution, fractal dimension, average clustering coefficient, global clustering coefficient, and joint probability describing the nearest-neighbor degree correlation. As an example of analyses of phenomena occurring on FSFNs, we also present the critical point and critical exponents of the bond-percolation transition on infinite FSFNs, which is related to the robustness of networks against edge removal. By comparing the percolation critical points of FSFNs whose structural properties are the same as each other except for the clustering nature, we clarify the effect of the clustering on the robustness of FSFNs. As demonstrated by this example, the present model makes it possible to elucidate how a specific structural property influences a phenomenon occurring on FSFNs by varying systematically the structures of FSFNs. Finally, we extend our model for deterministic FSFNs to a model of non-deterministic ones by introducing asymmetric generators and reexamine all characteristic quantities and the percolation problem for such non-deterministic FSFNs.

Identification of intrinsic long-range degree correlations in complex networks.

Many real-world networks exhibit degree-degree correlations between nodes separated by more than one step. Such long-range degree correlations (LRDCs) can be fully described by one joint and four conditional probability distributions with respect to degrees of two randomly chosen nodes and shortest path distance between them. While LRDCs are induced by nearest-neighbor degree correlations (NNDCs) between adjacent nodes, some networks possess intrinsic LRDCs which cannot be generated by NNDCs. Here we develop a method to extract intrinsic LRDC in a correlated network by comparing the probability distributions for the given network with those for nearest-neighbor correlated random networks. We also demonstrate the utility of our method by applying it to several real-world networks.

Effects of Casein Hydrolysate Ingestion on Thermoregulatory Responses in Healthy Adults during Exercise in Heated Conditions: A Randomized Crossover Trial.

Food ingestion has been shown to affect thermoregulation during exercise, while the impact of protein degradant consumption remains unclear. We investigated the effects of casein hydrolysate ingestion on thermoregulatory responses during exercise in the heat. In a randomized, placebo-controlled, double-blind, crossover trial, five men and five women consumed either 5 g of casein hydrolysate or placebo. Thirty minutes after ingestion, participants cycled at 60% VO2max until voluntary exhaustion wearing a hot-water (43 °C) circulation suit. Exercise time to exhaustion, body core temperature, forearm sweat rate, and forearm cutaneous vascular conductance did not differ different between the conditions. However, chest sweat rate and mean skin temperature increased upon casein hydrolysate ingestion compared with placebo during exercise. Increased chest sweat rate upon casein hydrolysate ingestion was associated with elevated sudomotor sensitivity to increasing body core temperature, but not the temperature threshold for initiating sweating. A positive correlation was found between chest sweat rate and plasma total amino acid concentration during exercise. These results suggest that casein hydrolysate ingestion enhances sweating heterogeneously by increasing peripheral sensitivity of the chest's sweating mechanism and elevating skin temperature during exercise in the heat. However, the physiological link between plasma amino acid concentration and sweat rate remains unclear.

General formulation of long-range degree correlations in complex networks.

We provide a general framework for analyzing degree correlations between nodes separated by more than one step (i.e., beyond nearest neighbors) in complex networks. One joint and four conditional probability distributions are introduced to fully describe long-range degree correlations with respect to degrees k and k^{'} of two nodes and shortest path length l between them. We present general relations among these probability distributions and clarify the relevance to nearest-neighbor degree correlations. Unlike nearest-neighbor correlations, some of these probability distributions are meaningful only in finite-size networks. Furthermore, as a baseline to determine the existence of intrinsic long-range degree correlations in a network other than inevitable correlations caused by the finite-size effect, the functional forms of these probability distributions for random networks are analytically evaluated within a mean-field approximation. The utility of our argument is demonstrated by applying it to real-world networks.

[Antimicrobial activities of macrolides against recent clinical isolates, and analysis of resistant mechanisms].

We examined antibacterial activities of 4 kinds of macrolides, erythromycin (EM), clarithromycin (CAM), azithromycin (AZM) and rokitamycin (RKM), against 6 bacterial species of clinical strains isoleted in 2002. Bacterial isolates used were each 50 strains of methicillin-susceptible Staphylococcus aureus (MSSA), Streptococcus pyogenes, Streptococcus agalactiae, Moraxella (Branhamella) catarrhalis, Haemophilus influenzae and 43 strains of Streptococcus pneumoniae. S. agalactiae were derived from gynecological samples, and other species were isolated from respiratory specimens. Antimicrobial activities against S. aureus, S. pyogenes, S. agalactiae, M. catarrhalis and H. influenzae of 14-membered macrolides, such as EM and CAM, were higher than those of 16-membered macrolide, RKM. By contrast, against S. pneumoniae, RKM was more effective than 14-membered macrolides. Six, three and four strains of S. aureus, S. pyogenes and S. agalactiae, respectively, were resistant to macrolides. Thirty-five among 43 pneumococcal isolates were resistant, and 15 of the 35 were highly-resistant, MIC of > 128 micrograms/ml, to any one of EM, CAM or AZM. Isolation frequency of resistant strains to RKM was lower than those to 14- and 15-membered macrolides: only one strain was highly-resistant and 12 were intermediately-resistant. No resistant strain was recognized in M. catarrhalis and H. influenzae. Further, we analyzed the resistant mechanisms, methylation or efflux, of macrolide resistant strains by the double-disk method. Methylation was major mechanism in S. aureus, and in S. pyogenes, all of the resistance was caused by methylation. In S. agalactiae and S. pneumoniae, methylation and efflux shared about half and half.