Recent human evolution has shaped geographical differences in susceptibility to disease

Background: Searching for associations between genetic variants and complex diseases has been a very active area of research for over two decades. More than 51,000 potential associations have been studied and published, a figure that keeps increasing, especially with the recent explosion of array-based Genome-Wide Association Studies. Even if the number of true associations described so far is high, many of the putative risk variants detected so far have failed to be consistently replicated and are widely considered false positives. Here, we focus on the world-wide patterns of replicability of published association studies.Results: We report three main findings. First, contrary to previous results, genes associated to complex diseases present lower degrees of genetic differentiation among human populations than average genome-wide levels. Second, also contrary to previous results, the differences in replicability of disease associated-loci between Europeans and East Asians are highly correlated with genetic differentiation between these populations. Finally, highly replicated genes present increased levels of high-frequency derived alleles in European and Asian populations when compared to African populations. Conclusions: Our findings highlight the heterogeneous nature of the genetic etiology of complex disease, confirm the importance of the recent evolutionary history of our species in current patterns of disease susceptibility and could cast doubts on the status as false positives of some associations that have failed to replicate across populations.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Variable Kernel estimates: On the impossibility of tuning the parameters

For the standard kernel density estimate, it is known that one can tune the bandwidth such that the expected L1 error is within a constant factor of the optimal L1 error (obtained when one is allowed to choose the bandwidth with knowledge of the density). In this paper, we pose the same problem for variable bandwidth kernel estimates where the bandwidths are allowed to depend upon the location. We show in particular that for positive kernels on the real line, for any data-based bandwidth, there exists a densityfor which the ratio of expected L1 error over optimal L1 error tends to infinity. Thus, the problem of tuning the variable bandwidth in an optimal manner is ``too hard''. Moreover, from the class of counterexamples exhibited in the paper, it appears thatplacing conditions on the densities (monotonicity, convexity, smoothness) does not help.

A U-shaped association between intensity of Internet use and adolescent health.

OBJECTIVE: To examine the relationship between different Internet-use intensities and adolescent mental and somatic health. METHODS: Data were drawn from the 2002 Swiss Multicenter Adolescent Survey on Health, a nationally representative survey of adolescents aged 16 to 20 years in post-mandatory school. From a self-administered anonymous questionnaire, 3906 adolescent boys and 3305 girls were categorized into 4 groups according to their intensity of Internet use: heavy Internet users (HIUs; >2 hours/day), regular Internet users (RIUs; several days per week and <= 2 hours/day), occasional users (<= 1 hour/week), and non-Internet users (NIUs; no use in the previous month). Health factors examined were perceived health, depression, overweight, headaches and back pain, and insufficient sleep. RESULTS: In controlled multivariate analysis, using RIUs as a reference, HIUs of both genders were more likely to report higher depressive scores, whereas only male users were found at increased risk of overweight and female users at increased risk of insufficient sleep. Male NIUs and female NIUs and occasional users also were found at increased risk of higher depressive scores. Back-pain complaints were found predominantly among male NIUs. CONCLUSIONS: Our study provides evidence of a U-shaped relationship between intensity of Internet use and poorer mental health of adolescents. In addition, HIUs were confirmed at increased risk for somatic health problems. Thus, health professionals should be on the alert when caring for adolescents who report either heavy Internet use or very little/none. Also, they should consider regular Internet use as a normative behavior without major health consequence. Pediatrics 2011;127:e330-e335

Coexistence of specialist and generalist species is shaped by dispersal and environmental factors.

Disentangling the mechanisms mediating the coexistence of habitat specialists and generalists has been a long-standing subject of investigation. However, the roles of species traits and environmental and spatial factors have not been assessed in a unifying theoretical framework. Theory suggests that specialist species are more competitive in natural communities. However, empirical work has shown that specialist species are declining worldwide due to habitat loss and fragmentation. We addressed the question of the coexistence of specialist and generalist species with a spatially explicit metacommunity model in continuous and heterogeneous environments. We characterized how species' dispersal abilities, the number of interacting species, environmental spatial autocorrelation, and disturbance impact community composition. Our results demonstrated that species' dispersal ability and the number of interacting species had a drastic influence on the composition of metacommunities. More specialized species coexisted when species had large dispersal abilities and when the number of interacting species was high. Disturbance selected against highly specialized species, whereas environmental spatial autocorrelation had a marginal impact. Interestingly, species richness and niche breadth were mainly positively correlated at the community scale but were negatively correlated at the metacommunity scale. Numerous diversely specialized species can thus coexist, but both species' intrinsic traits and environmental factors interact to shape the specialization signatures of communities at both the local and global scales.

Evidence against close linkage of the loci for fraXq of Martin-Bell syndrome and for factor IX.

Linkage between the loci for fraXq of Martin-Bell syndrome and factor IX was studied in nine families exhibiting this syndrome by means of a restriction fragment length polymorphism at the factor IX locus. Computer analysis of the data indicates there to be no evidence for close linkage between the syndrome and the factor IX locus.

Fine-Tuning Translation Kinetics Selection as the Driving Force of Codon Usage Bias in the Hepatitis A Virus Capsid

Hepatitis A virus (HAV), the prototype of genus Hepatovirus, has several unique biological characteristics that distinguish it from other members of the Picornaviridae family. Among these, the need for an intact eIF4G factor for the initiation of translation results in an inability to shut down host protein synthesis by a mechanism similar to that of other picornaviruses. Consequently, HAV must inefficiently compete for the cellular translational machinery and this may explain its poor growth in cell culture. In this context of virus/cell competition, HAV has strategically adopted a naturally highly deoptimized codon usage with respect to that of its cellular host. With the aim to optimize its codon usage the virus was adapted to propagate in cells with impaired protein synthesis, in order to make tRNA pools more available for the virus. A significant loss of fitness was the immediate response to the adaptation process that was, however, later on recovered and more associated to a re-deoptimization rather than to an optimization of the codon usage specifically in the capsid coding region. These results exclude translation selection and instead suggest fine-tuning translation kinetics selection as the underlying mechanism of the codon usage bias in this specific genome region. Additionally, the results provide clear evidence of the Red Queen dynamics of evolution since the virus has very much evolved to re-adapt its codon usage to the environmental cellular changing conditions in order to recover the original fitness.

Enhancement of the photoelectrochemical properties of Cl-doped ZnO nanowires by tuning their coaxial doping profile

Arrays of vertically aligned ZnO:Cl/ZnO core-shell nanowires were used to demonstrate that the control of the coaxial doping profile in homojunction nanostructures can improve their surface charge carrier transfer while conserving potentially excellent transport properties. It is experimentally shown that the presence of a ZnO shell enhances the photoelectrochemical properties of ZnO:Cl nanowires up to a factor 5. Likewise, the ZnO shell promotes the visible photoluminescence band in highly conducting ZnO:Cl nanowires. These lines of evidence are associated with the increase of the nanowires" surface depletion layer

Multiple roles of mouse Numb in tuning developmental cell fates.

BACKGROUND: Notch signaling regulates multiple differentiation processes and cell fate decisions during both invertebrate and vertebrate development. Numb encodes an intracellular protein that was shown in Drosophila to antagonize Notch signaling at binary cell fate decisions of certain cell lineages. Although overexpression experiments suggested that Numb might also antagonize some Notch activity in vertebrates, the developmental processes in which Numb is involved remained elusive. RESULTS: We generated mice with a homozygous inactivation of Numb. These mice died before embryonic day E11.5, probably because of defects in angiogenic remodeling and placental dysfunction. Mutant embryos had an open anterior neural tube and impaired neuronal differentiation within the developing cranial central nervous system (CNS). In the developing spinal cord, the number of differentiated motoneurons was reduced. Within the peripheral nervous system (PNS), ganglia of cranial sensory neurons were formed. Trunk neural crest cells migrated and differentiated into sympathetic neurons. In contrast, a selective differentiation anomaly was observed in dorsal root ganglia, where neural crest--derived progenitor cells had migrated normally to form ganglionic structures, but failed to differentiate into sensory neurons. CONCLUSIONS: Mouse Numb is involved in multiple developmental processes and required for cell fate tuning in a variety of lineages. In the nervous system, Numb is required for the generation of a large subset of neuronal lineages. The restricted requirement of Numb during neural development in the mouse suggests that in some neuronal lineages, Notch signaling may be regulated independently of Numb.

Biocompatibility of an x-shaped zirconium implant in deep sclerectomy in rabbits

ABSTRACT : Background : The aim of this study was to evaluate the midterm biocompatibility of a new x-shaped implant made of zirconium in an animal model of glaucoma surgery. Methods : Preoperatively, ultrasound biomicroscopy (UBM), intraocular pressure (IOP) and outflow facility (OF) data were acquired. Upon surgery, one eye was chosen randomly to receive an implant, while the other received none. Ten rabbits went through a 1-, 2-, 3-, 4- and 6-month follow-up. [OP was measured regularly, UBM performed at 1, 3 and 6 months after surgery. At the end of the follow-up, OF was again measured. Histology sections were analyzed. Results : For both groups IOP control was satisfactory, while OF initially increased at month 1 to resume preoperative values thereafter. Eyes with implants had larger filtration blebs which decreased faster than in eyes without the implant. Drainage vessel density, inflammatory cell number and fibrosis were higher in tissues near the implant. Conclusions : The zirconium implant initially promoted the positive effects of the surgery (IOP control, OF increase). Nevertheless, after several months, foreign body reactions and fibrosis had occurred on some implants that restrained the early benefit of such a procedure. Modifications of the zirconium implant geometry could enhance the overall success rate.

Tuning clustering in random networks with arbitrary degree distributions

We present a generator of random networks where both the degree-dependent clustering coefficient and the degree distribution are tunable. Following the same philosophy as in the configuration model, the degree distribution and the clustering coefficient for each class of nodes of degree k are fixed ad hoc and a priori. The algorithm generates corresponding topologies by applying first a closure of triangles and second the classical closure of remaining free stubs. The procedure unveils an universal relation among clustering and degree-degree correlations for all networks, where the level of assortativity establishes an upper limit to the level of clustering. Maximum assortativity ensures no restriction on the decay of the clustering coefficient whereas disassortativity sets a stronger constraint on its behavior. Correlation measures in real networks are seen to observe this structural bound.