Neural development features: Spatio-temporal development of the Caenorhabditis elegans neuronal network

The nematode Caenorhabditis elegans, with information on neural connectivity, three-dimensional position and cell linage provides a unique system for understanding the development of neural networks. Although C. elegans has been widely studied in the past, we present the first statistical study from a developmental perspective, with findings that raise interesting suggestions on the establishment of long-distance connections and network hubs. Here, we analyze the neuro-development for temporal and spatial features, using birth times of neurons and their three-dimensional positions. Comparisons of growth in C. elegans with random spatial network growth highlight two findings relevant to neural network development. First, most neurons which are linked by long-distance connections are born around the same time and early on, suggesting the possibility of early contact or interaction between connected neurons during development. Second, early-born neurons are more highly connected (tendency to form hubs) than later born neurons. This indicates that the longer time frame available to them might underlie high connectivity. Both outcomes are not observed for random connection formation. The study finds that around one-third of electrically coupled long-range connections are late forming, raising the question of what mechanisms are involved in ensuring their accuracy, particularly in light of the extremely invariant connectivity observed in C. elegans. In conclusion, the sequence of neural network development highlights the possibility of early contact or interaction in securing long-distance and high-degree connectivity

Theory of Star Formation

We review current understanding of star formation, outlining an overall theoretical framework and the observations that motivate it. A conception of star formation has emerged in which turbulence plays a dual role, both creating overdensities to initiate gravitational contraction or collapse, and countering the effects of gravity in these overdense regions. The key dynamical processes involved in star formation -- turbulence, magnetic fields, and self-gravity -- are highly nonlinear and multidimensional. Physical arguments are used to identify and explain the features and scalings involved in star formation, and results from numerical simulations are used to quantify these effects. We divide star formation into large-scale and small-scale regimes and review each in turn. Large scales range from galaxies to giant molecular clouds (GMCs) and their substructures. Important problems include how GMCs form and evolve, what determines the star formation rate (SFR), and what determines the initial mass function (IMF). Small scales range from dense cores to the protostellar systems they beget. We discuss formation of both low- and high-mass stars, including ongoing accretion. The development of winds and outflows is increasingly well understood, as are the mechanisms governing angular momentum transport in disks. Although outstanding questions remain, the framework is now in place to build a comprehensive theory of star formation that will be tested by the next generation of telescopes.Comment: 120 pages, to appear in ARAA. No changes from v1 text; permission statement adde

Cisplatin-induced emesis: systematic review and meta-analysis of the ferret model and the effects of 5-HT3 receptor antagonists

PURPOSE: The ferret cisplatin emesis model has been used for ~30 years and enabled identification of clinically used anti-emetics. We provide an objective assessment of this model including efficacy of 5-HT(3) receptor antagonists to assess its translational validity. METHODS: A systematic review identified available evidence and was used to perform meta-analyses. RESULTS: Of 182 potentially relevant publications, 115 reported cisplatin-induced emesis in ferrets and 68 were included in the analysis. The majority (n = 53) used a 10 mg kg(−1) dose to induce acute emesis, which peaked after 2 h. More recent studies (n = 11) also used 5 mg kg(−1), which induced a biphasic response peaking at 12 h and 48 h. Overall, 5-HT(3) receptor antagonists reduced cisplatin (5 mg kg(−1)) emesis by 68% (45–91%) during the acute phase (day 1) and by 67% (48–86%) and 53% (38–68%, all P < 0.001), during the delayed phase (days 2, 3). In an analysis focused on the acute phase, the efficacy of ondansetron was dependent on the dosage and observation period but not on the dose of cisplatin. CONCLUSION: Our analysis enabled novel findings to be extracted from the literature including factors which may impact on the applicability of preclinical results to humans. It reveals that the efficacy of ondansetron is similar against low and high doses of cisplatin. Additionally, we showed that 5-HT(3) receptor antagonists have a similar efficacy during acute and delayed emesis, which provides a novel insight into the pharmacology of delayed emesis in the ferret

Developmental axon pruning mediated by BDNF-p75NTR–dependent axon degeneration

The mechanisms that regulate the pruning of mammalian axons are just now being elucidated. Here, we describe a mechanism by which, during developmental sympathetic axon competition, winning axons secrete brain-derived neurotrophic factor (BDNF) in an activity-dependent fashion, which binds to the p75 neurotrophin receptor (p75NTR) on losing axons to cause their degeneration and, ultimately, axon pruning. Specifically, we found that pruning of rat and mouse sympathetic axons that project to the eye requires both activity-dependent BDNF and p75NTR. p75NTR and BDNF are also essential for activity-dependent axon pruning in culture, where they mediate pruning by directly causing axon degeneration. p75NTR, which is enriched in losing axons, causes axonal degeneration by suppressing TrkA-mediated signaling that is essential for axonal maintenance. These data provide a mechanism that explains how active axons can eliminate less-active, competing axons during developmental pruning by directly promoting p75NTR-mediated axonal degeneration

Statistically validated networks in bipartite complex systems

Many complex systems present an intrinsic bipartite nature and are often described and modeled in terms of networks [1-5]. Examples include movies and actors [1, 2, 4], authors and scientific papers [6-9], email accounts and emails [10], plants and animals that pollinate them [11, 12]. Bipartite networks are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set. When one constructs a projected network with nodes from only one set, the system heterogeneity makes it very difficult to identify preferential links between the elements. Here we introduce an unsupervised method to statistically validate each link of the projected network against a null hypothesis taking into account the heterogeneity of the system. We apply our method to three different systems, namely the set of clusters of orthologous genes (COG) in completely sequenced genomes [13, 14], a set of daily returns of 500 US financial stocks, and the set of world movies of the IMDb database [15]. In all these systems, both different in size and level of heterogeneity, we find that our method is able to detect network structures which are informative about the system and are not simply expression of its heterogeneity. Specifically, our method (i) identifies the preferential relationships between the elements, (ii) naturally highlights the clustered structure of investigated systems, and (iii) allows to classify links according to the type of statistically validated relationships between the connected nodes.Comment: Main text: 13 pages, 3 figures, and 1 Table. Supplementary information: 15 pages, 3 figures, and 2 Table

Ventilatory muscle strength, diaphragm thickness and pulmonary function in world-class powerlifters.

Resistance training activates the ventilatory muscles providing a stimulus similar to ventilatory muscle training. We examined the effects of elite powerlifting training upon ventilatory muscle strength, pulmonary function and diaphragm thickness in world-class powerlifters (POWER) and a control group (CON) with no history of endurance or resistance training, matched for age, height and body mass

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies

Association between HCV infection and diabetes type 2 in Egypt: is it time to split up?

Purpose: There is a conflicting evidence about the association between hepatitis C virus (HCV) infection and diabetes mellitus. The objective of this study was to assess this association in Egypt, the country with the highest HCV prevalence in the world. Methods: The source of data was from the Egypt Demographic and Health Survey conducted in 2008. Using multivariable logistic regression analyses to account for known confounders, the association was investigated at two levels]: (1) HCV exposure (HCV antibody status) and diabetes mellitus and (2) diabetes mellitus and chronic HCV infection (HCV RNA status) among HCV-exposed individuals. Results: We found no evidence for an association between HCV antibody status and diabetes (adjusted odds ratio [OR] = 0.87; 95% confidence interval [CI], 0.63-1.19). However, among HCV-exposed individuals, we found an evidence for an association between diabetes and active HCV infection (adjusted OR = 2.44, 95% Cl, 1.30-4.57). Conclusions: Although it does not appear that HCV exposure and diabetes are linked, there might be an association between diabetes and chronic HCV infection. The HCV diabetes relationship may be more complex than previously anticipated. Therefore, a call for an "amicable divorce" to the HCV diabetes relationship could be premature. (C) 2015 The Authors. Published by Elsevier Inc