DNA resection in eukaryotes: deciding how to fix the break

DNA double-strand breaks are repaired by different mechanisms, including homologous recombination and nonhomologous end-joining. DNA-end resection, the first step in recombination, is a key step that contributes to the choice of DSB repair. Resection, an evolutionarily conserved process that generates single-stranded DNA, is linked to checkpoint activation and is critical for survival. Failure to regulate and execute this process results in defective recombination and can contribute to human disease. Here, I review recent findings on the mechanisms of resection in eukaryotes, from yeast to vertebrates, provide insights into the regulatory strategies that control it, and highlight the consequences of both its impairment and its deregulation

Solving the mu problem with a heavy Higgs boson

We discuss the generation of the mu-term in a class of supersymmetric models characterized by a low energy effective superpotential containing a term lambda S H_1 H_2 with a large coupling lambda~2. These models generically predict a lightest Higgs boson well above the LEP limit of 114 GeV and have been shown to be compatible with the unification of gauge couplings. Here we discuss a specific example where the superpotential has no dimensionful parameters and we point out the relation between the generated mu-term and the mass of the lightest Higgs boson. We discuss the fine-tuning of the model and we find that the generation of a phenomenologically viable mu-term fits very well with a heavy lightest Higgs boson and a low degree of fine-tuning. We discuss experimental constraints from collider direct searches, precision data, thermal relic dark matter abundance, and WIMP searches finding that the most natural region of the parameter space is still allowed by current experiments. We analyse bounds on the masses of the superpartners coming from Naturalness arguments and discuss the main signatures of the model for the LHC and future WIMP searches.Comment: Extended discussion of the LHC phenomenology, as published on JHEP plus an addendum on the existence of further extremal points of the potential. 47 pages, 16 figure

Cellular Radiosensitivity: How much better do we understand it?

Purpose: Ionizing radiation exposure gives rise to a variety of lesions in DNA that result in genetic instability and potentially tumorigenesis or cell death. Radiation extends its effects on DNA by direct interaction or by radiolysis of H2O that generates free radicals or aqueous electrons capable of interacting with and causing indirect damage to DNA. While the various lesions arising in DNA after radiation exposure can contribute to the mutagenising effects of this agent, the potentially most damaging lesion is the DNA double strand break (DSB) that contributes to genome instability and/or cell death. Thus in many cases failure to recognise and/or repair this lesion determines the radiosensitivity status of the cell. DNA repair mechanisms including homologous recombination (HR) and non-homologous end-joining (NHEJ) have evolved to protect cells against DNA DSB. Mutations in proteins that constitute these repair pathways are characterised by radiosensitivity and genome instability. Defects in a number of these proteins also give rise to genetic disorders that feature not only genetic instability but also immunodeficiency, cancer predisposition, neurodegeneration and other pathologies. Conclusions: In the past fifty years our understanding of the cellular response to radiation damage has advanced enormously with insight being gained from a wide range of approaches extending from more basic early studies to the sophisticated approaches used today. In this review we discuss our current understanding of the impact of radiation on the cell and the organism gained from the array of past and present studies and attempt to provide an explanation for what it is that determines the response to radiation

Abnormal social reward processing in autism as indexed by pupillary responses to happy faces

Background: Individuals with Autism Spectrum Disorders (ASD) typically show impaired eye contact during social interactions. From a young age, they look less at faces than typically developing (TD) children and tend to avoid direct gaze. However, the reason for this behavior remains controversial; ASD children might avoid eye contact because they perceive the eyes as aversive or because they do not find social engagement through mutual gaze rewarding. Methods: We monitored pupillary diameter as a measure of autonomic response in children with ASD (n = 20, mean age = 12.4) and TD controls (n = 18, mean age = 13.7) while they looked at faces displaying different emotions. Each face displayed happy, fearful, angry or neutral emotions with the gaze either directed to or averted from the subjects. Results: Overall, children with ASD and TD controls showed similar pupillary responses; however, they differed significantly in their sensitivity to gaze direction for happy faces. Specifically, pupillary diameter increased among TD children when viewing happy faces with direct gaze as compared to those with averted gaze, whereas children with ASD did not show such sensitivity to gaze direction. We found no group differences in fixation that could explain the differential pupillary responses. There was no effect of gaze direction on pupil diameter for negative affect or neutral faces among either the TD or ASD group. Conclusions: We interpret the increased pupillary diameter to happy faces with direct gaze in TD children to reflect the intrinsic reward value of a smiling face looking directly at an individual. The lack of this effect in children with ASD is consistent with the hypothesis that individuals with ASD may have reduced sensitivity to the reward value of social stimuli

Ecstasy use and depression: A 4-year longitudinal study among an Australian general community sample

RATIONALE: Longitudinal, population-based studies can better assess the relationship of ecstasy use with depression. OBJECTIVES: We examined whether change in ecstasy use was associated with change in depressive symptoms/probable depression over a 4-year period, among a large Australian sample. METHODS: The Personality and Total Health project is a longitudinal general community study of Australians from Canberra and Queanbeyan. Data from the youngest cohort when aged 24-30 (N = 2, 128) and 4 years later (N = 1, 977) was included. The Goldberg depression scale and the Brief Patient Health Questionnaire measured depressive symptoms and probable depression, respectively. Multilevel growth models also considered demographics, psychosocial characteristics, and other drug use. RESULTS: Ecstasy use was not associated with long-term depressive symptoms or greater odds of depression in multivariate analyses. Users had more self-reported depressive symptoms when using ecstasy compared to not using. However, differences between people who had and had not ever used ecstasy largely accounted for this. Other factors were more important in the prediction of depression. CONCLUSIONS: It would be premature to conclude that ecstasy use is not related to the development of long-term depressive symptoms, given the relatively low level of ecstasy and other drug use in this community sample. Results showed that other factors need to be considered when investigating ecstasy use and depression

L2-norm multiple kernel learning and its application to biomedical data fusion

<p>Abstract</p> <p>Background</p> <p>This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as <it>L</it>_∞, <it>L</it>₁, and <it>L</it>₂MKL. In particular, <it>L</it>₂MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing <it>L</it>_∞MKL method. In real biomedical applications, <it>L</it>₂MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.</p> <p>Results</p> <p>We provide a theoretical analysis of the relationship between the <it>L</it>₂optimization of kernels in the dual problem with the <it>L</it>₂coefficient regularization in the primal problem. Understanding the dual <it>L</it>₂problem grants a unified view on MKL and enables us to extend the <it>L</it>₂method to a wide range of machine learning problems. We implement <it>L</it>₂MKL for ranking and classification problems and compare its performance with the sparse <it>L</it>_∞and the averaging <it>L</it>₁MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. <it>L</it>₂MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel <it>L</it>₂MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing.</p> <p>Conclusions</p> <p>This paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in <it>L</it>_∞MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing <it>L</it>₂kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL.</p> <p>Availability</p> <p>The MATLAB code of algorithms implemented in this paper is downloadable from <url>http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html</url>.</p

Mottness at finite doping and charge instabilities in cuprates

The intrinsic instability of underdoped copper oxides towards inhomogeneous states is one of the central puzzles of the physics of correlated materials. The influence of the Mott physics on the doping-temperature phase diagram of copper oxides represents a major issue that is subject of intense theoretical and experimental effort. Here, we investigate the ultrafast electron dynamics in prototypical single-layer Bi-based cuprates at the energy scale of the O-2p\u2192Cu-3d charge-transfer (CT) process. We demonstrate a clear evolution of the CT excitations from incoherent and localized, as in a Mott insulator, to coherent and delocalized, as in a conventional metal. This reorganization of the high-energy degrees of freedom occurs at the critical doping pcr 430.16 irrespective of the temperature, and it can be well described by dynamical mean field theory calculations. We argue that the onset of the low-temperature charge instabilities is the low-energy manifestation of the underlying Mottness that characterizes the p<pcr region of the phase diagram. This discovery sets a new framework for theories of charge order and low-temperature phases in underdoped copper oxides. ArXI

Adults with autism overestimate the volatility of the sensory environment.

Insistence on sameness and intolerance of change are among the diagnostic criteria for autism spectrum disorder (ASD), but little research has addressed how people with ASD represent and respond to environmental change. Here, behavioral and pupillometric measurements indicated that adults with ASD are less surprised than neurotypical adults when their expectations are violated, and decreased surprise is predictive of greater symptom severity. A hierarchical Bayesian model of learning suggested that in ASD, a tendency to overlearn about volatility in the face of environmental change drives a corresponding reduction in learning about probabilistically aberrant events, thus putatively rendering these events less surprising. Participant-specific modeled estimates of surprise about environmental conditions were linked to pupil size in the ASD group, thus suggesting heightened noradrenergic responsivity in line with compromised neural gain. This study offers insights into the behavioral, algorithmic and physiological mechanisms underlying responses to environmental volatility in ASD

HLA Genes, Islet Autoantibodies and Residual C-Peptide at the Clinical Onset of Type 1 Diabetes Mellitus and the Risk of Retinopathy 15 Years Later

HLA genes, islet autoantibodies and residual C-peptide were studied to determine the independent association of each exposure with diabetic retinopathy (DR), 15 years after the clinical onset of type 1 diabetes in 15-34 year old individuals.The cohort was identified in 1992 and 1993 by the Diabetes Incidence Study in Sweden (DISS), which investigates incident cases of diabetes for patients between 15 and 34 years of age. Blood samples at diagnosis were analyzed to determine HLA genotype, islet autoantibodies and serum C-peptide. In 2009, fundus photographs were obtained from patient records. Study measures were supplemented with data from the Swedish National Diabetes Registry.The prevalence of DR was 60.2% (148/246). Autoantibodies against the 65 kD isoform of glutamate decarboxylase (GADA) at the onset of clinical diabetes increased the risk of DR 15 years later, relative risk 1.12 for each 100 WHO units/ml, [95% CI 1.02 to 1.23]. This equates to risk estimates of 1.27, [95% CI 1.04 to 1.62] and 1.43, [95% CI 1.06 to 1.94] for participants in the highest 25(th) (GADA>233 WHO units/ml) and 5(th) percentile (GADA>319 WHO units/ml) of GADA, respectively. These were adjusted for duration of diabetes, HbA(1c), treated hypertension, sex, age at diagnosis, HLA and C-peptide. Islet cell autoantibodies, insulinoma-antigen 2 autoantibodies, residual C-peptide and the type 1 diabetes associated haplotypes DQ2, DQ8 and DQ6 were not associated with DR.Increased levels of GADA at the onset of type 1 diabetes were associated with DR 15 years later. These results, if confirmed, could provide additional insights into the pathogenesis of the most common microvascular complication of diabetes and lead to better risk stratification for both patient screenings and DR treatment trials

Evolutionary history of Serpulaceae (Basidiomycota): molecular phylogeny, historical biogeography and evidence for a single transition of nutritional mode

<p>Abstract</p> <p>Background</p> <p>The fungal genus <it>Serpula </it>(Serpulaceae, Boletales) comprises several saprotrophic (brown rot) taxa, including the aggressive house-infecting dry rot fungus <it>Serpula lacrymans</it>. Recent phylogenetic analyses have indicated that the ectomycorrhiza forming genera <it>Austropaxillus </it>and <it>Gymnopaxillus </it>cluster within <it>Serpula</it>. In this study we use DNA sequence data to investigate phylogenetic relationships, historical biogeography of, and nutritional mode transitions in Serpulaceae.</p> <p>Results</p> <p>Our results corroborate that the two ectomycorrhiza-forming genera, <it>Austropaxillus </it>and <it>Gymnopaxillus</it>, form a monophyletic group nested within the saprotrophic genus <it>Serpula</it>, and that the <it>Serpula </it>species <it>S. lacrymans </it>and <it>S. himantioides </it>constitute the sister group to the <it>Austropaxillus</it>-<it>Gymnopaxillus </it>clade. We found that both vicariance (Beringian) and long distance dispersal events are needed to explain the phylogeny and current distributions of taxa within Serpulaceae. Our results also show that the transition from brown rot to mycorrhiza has happened only once in a monophyletic Serpulaceae, probably between 50 and 22 million years before present.</p> <p>Conclusions</p> <p>This study supports the growing understanding that the same geographical barriers that limit plant- and animal dispersal also limit the spread of fungi, as a combination of vicariance and long distance dispersal events are needed to explain the present patterns of distribution in Serpulaceae. Our results verify the transition from brown rot to ECM within Serpulaceae between 50 and 22 MyBP.</p