A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination

By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks

High recombination rates and hotspots in a Plasmodium falciparum genetic cross

Using the universal P2/P8 primers, we were able to obtain the gene segments of chromo-helicase-DNA binding protein (CHD)-Z and CHD-W from ten species of ardeid birds including Chinese egret (Egretta eulophotes), little egret (E. garzetta), eastern reef egret (E. sacra), great egret (Ardea alba), grey heron (A. cinerea), Chinese pond-heron (Ardeola bacchus), cattle egret (Bubulcus ibis), black-crowned night-heron (Nycticorax nycticorax), cinnamon bittern (Ixobrychus cinnamomeus) and yellow bittern (I. sinensis). Based on conserved regions inside the P2/P8-derived sequences, we designed new PCR primers for sex identification in these ardeid species. Using agarose gel electrophoresis, the PCR products showed two bands for females (140 bp derived from CHD-W and the other 250 bp from CHD-ZW), whereas the males showed only the 250 bp band. The results indicated that our new primers could be used for accurate and convenient sex identification in ardeid species.National Natural Science Foundation of China[30970380, 40876077]; Fujian Natural Science Foundation of China[2008S0007, 2009J01195

iTRAQ Analysis of Complex Proteome Alterations in 3xTgAD Alzheimer's Mice: Understanding the Interface between Physiology and Disease

Alzheimer's disease (AD) is characterized by progressive cognitive impairment associated with accumulation of amyloid β-peptide, synaptic degeneration and the death of neurons in the hippocampus, and temporal, parietal and frontal lobes of the cerebral cortex. Analysis of postmortem brain tissue from AD patients can provide information on molecular alterations present at the end of the disease process, but cannot discriminate between changes that are specifically involved in AD versus those that are simply a consequence of neuronal degeneration. Animal models of AD provide the opportunity to elucidate the molecular changes that occur in brain cells as the disease process is initiated and progresses. To this end, we used the 3xTgAD mouse model of AD to gain insight into the complex alterations in proteins that occur in the hippocampus and cortex in AD. The 3xTgAD mice express mutant presenilin-1, amyloid precursor protein and tau, and exhibit AD-like amyloid and tau pathology in the hippocampus and cortex, and associated cognitive impairment. Using the iTRAQ stable-isotope-based quantitative proteomic technique, we performed an in-depth proteomic analysis of hippocampal and cortical tissue from 16 month old 3xTgAD and non-transgenic control mice. We found that the most important groups of significantly altered proteins included those involved in synaptic plasticity, neurite outgrowth and microtubule dynamics. Our findings have elucidated some of the complex proteome changes that occur in a mouse model of AD, which could potentially illuminate novel therapeutic avenues for the treatment of AD and other neurodegenerative disorders

NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience

Allele Summation of Diabetes Risk Genes Predicts Impaired Glucose Tolerance in Female and Obese Individuals

INTRODUCTION: Single nucleotide polymorphisms (SNPs) in approximately 40 genes have been associated with an increased risk for type 2 diabetes (T2D) in genome-wide association studies. It is not known whether a similar genetic impact on the risk of prediabetes (impaired glucose tolerance [IGT] or impaired fasting glycemia [IFG]) exists. METHODS: In our cohort of 1442 non-diabetic subjects of European origin (normal glucose tolerance [NGT] n = 1046, isolated IFG n = 142, isolated IGT n = 140, IFG+IGT n = 114), an impact on glucose homeostasis has been shown for 9 SNPs in previous studies in this specific cohort. We analyzed these SNPs (within or in the vicinity of the genes TCF7L2, KCNJ11, HHEX, SLC30A8, WFS1, KCNQ1, MTNR1B, FTO, PPARG) for association with prediabetes. RESULTS: The genetic risk load was significantly associated with the risk for IGT (p = 0.0006) in a model including gender, age, BMI and insulin sensitivity. To further evaluate potential confounding effects, we stratified the population on gender, BMI and insulin sensitivity. The association of the risk score with IGT was present in female participants (p = 0.008), but not in male participants. The risk score was significantly associated with IGT (p = 0.008) in subjects with a body mass index higher than 30 kg/m(2) but not in non-obese individuals. Furthermore, only in insulin resistant subjects a significant association between the genetic load and the risk for IGT (p = 0.01) was found. DISCUSSION: We found that T2D genetic risk alleles cause an increased risk for IGT. This effect was not present in male, lean and insulin sensitive subjects, suggesting a protective role of beneficial environmental factors on the genetic risk

Glucose-Raising Genetic Variants in MADD and ADCY5 Impair Conversion of Proinsulin to Insulin

Recent meta-analyses of genome-wide association studies revealed new genetic loci associated with fasting glycemia. For several of these loci, the mechanism of action in glucose homeostasis is unclear. The objective of the study was to establish metabolic phenotypes for these genetic variants to deliver clues to their pathomechanism.) and insulin resistance (HOMA-IR, Matsuda-Index) were assessed.. on proinsulin-to-insulin conversion. These effects may also be related to neighboring regions of the genome

Characterization Of Inpaint Residuals In Interferometric Measurements of the Epoch Of Reionization

Radio Frequency Interference (RFI) is one of the systematic challenges preventing 21cm interferometric instruments from detecting the Epoch of Reionization. To mitigate the effects of RFI on data analysis pipelines, numerous inpaint techniques have been developed to restore RFI corrupted data. We examine the qualitative and quantitative errors introduced into the visibilities and power spectrum due to inpainting. We perform our analysis on simulated data as well as real data from the Hydrogen Epoch of Reionization Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural network that capable of inpainting RFI corrupted data in interferometric instruments. We train our network on simulated data and show that our network is capable at inpainting real data without requiring to be retrained. We find that techniques that incorporate high wavenumbers in delay space in their modeling are best suited for inpainting over narrowband RFI. We also show that with our fiducial parameters Discrete Prolate Spheroidal Sequences (DPSS) and CLEAN provide the best performance for intermittent ``narrowband'' RFI while Gaussian Progress Regression (GPR) and Least Squares Spectral Analysis (LSSA) provide the best performance for larger RFI gaps. However we caution that these qualitative conclusions are sensitive to the chosen hyperparameters of each inpainting technique. We find these results to be consistent in both simulated and real visibilities. We show that all inpainting techniques reliably reproduce foreground dominated modes in the power spectrum. Since the inpainting techniques should not be capable of reproducing noise realizations, we find that the largest errors occur in the noise dominated delay modes. We show that in the future, as the noise level of the data comes down, CLEAN and DPSS are most capable of reproducing the fine frequency structure in the visibilities of HERA data.Comment: 26 pages, 18 figure