Lasing oscillation in a three-dimensional photonic crystal nanocavity with a complete bandgap

We demonstrate lasing oscillation in a three-dimensional photonic crystal nanocavity. The laser is realized by coupling a cavity mode, which is localized in a complete photonic bandgap and exhibits the highest quality factor of ~38,500, with high-quality semiconductor quantum dots. We show a systematic change in the laser characteristics, including the threshold and the spontaneous emission coupling factor by controlling the crystal size, which consequently changes the strength of photon confinement in the third dimension. This opens up many interesting possibilities for realizing future ultimate light sources and three-dimensional integrated photonic circuits and for more fundamental studies of physics in the field of cavity quantum electrodynamics.Comment: 14 pages, 4 figure

Structural subnetwork evolution across the life-span: rich-club, feeder, seeder

The impact of developmental and aging processes on brain connectivity and the connectome has been widely studied. Network theoretical measures and certain topological principles are computed from the entire brain, however there is a need to separate and understand the underlying subnetworks which contribute towards these observed holistic connectomic alterations. One organizational principle is the rich-club - a core subnetwork of brain regions that are strongly connected, forming a high-cost, high-capacity backbone that is critical for effective communication in the network. Investigations primarily focus on its alterations with disease and age. Here, we present a systematic analysis of not only the rich-club, but also other subnetworks derived from this backbone - namely feeder and seeder subnetworks. Our analysis is applied to structural connectomes in a normal cohort from a large, publicly available lifespan study. We demonstrate changes in rich-club membership with age alongside a shift in importance from 'peripheral' seeder to feeder subnetworks. Our results show a refinement within the rich-club structure (increase in transitivity and betweenness centrality), as well as increased efficiency in the feeder subnetwork and decreased measures of network integration and segregation in the seeder subnetwork. These results demonstrate the different developmental patterns when analyzing the connectome stratified according to its rich-club and the potential of utilizing this subnetwork analysis to reveal the evolution of brain architectural alterations across the life-span

dyschronic, a Drosophila Homolog of a Deaf-Blindness Gene, Regulates Circadian Output and Slowpoke Channels

Many aspects of behavior and physiology are under circadian control. In Drosophila, the molecular clock that regulates rhythmic patterns of behavior has been extensively characterized. In contrast, genetic loci involved in linking the clock to alterations in motor activity have remained elusive. In a forward-genetic screen, we uncovered a new component of the circadian output pathway, which we have termed dyschronic (dysc). dysc mutants exhibit arrhythmic locomotor behavior, yet their eclosion rhythms are normal and clock protein cycling remains intact. Intriguingly, dysc is the closest Drosophila homolog of whirlin, a gene linked to type II Usher syndrome, the leading cause of deaf-blindness in humans. Whirlin and other Usher proteins are expressed in the mammalian central nervous system, yet their function in the CNS has not been investigated. We show that DYSC is expressed in major neuronal tracts and regulates expression of the calcium-activated potassium channel SLOWPOKE (SLO), an ion channel also required in the circadian output pathway. SLO and DYSC are co-localized in the brain and control each other's expression post-transcriptionally. Co-immunoprecipitation experiments demonstrate they form a complex, suggesting they regulate each other through protein–protein interaction. Furthermore, electrophysiological recordings of neurons in the adult brain show that SLO-dependent currents are greatly reduced in dysc mutants. Our work identifies a Drosophila homolog of a deaf-blindness gene as a new component of the circadian output pathway and an important regulator of ion channel expression, and suggests novel roles for Usher proteins in the mammalian nervous system

Biodegradable starch-based composites: effect of micro and nanoreinforcements on composite properties

Thermoplastic starch (TPS) matrix was reinforced with various kenaf bast cellulose nanofiber loadings (0–10 wt%). Thin films were prepared by casting and evaporating the mixture of aqueous suspension of nanofibers (NFs), starch, and glycerol which underwent gelatinization process at the same time. Moreover, raw fibers (RFs) reinforced TPS films were prepared with the same contents and conditions. The effects of filler type and loading on different characteristics of prepared materials were studied using transmission and scanning electron microscopies, X-ray diffractometry, Fourier transform infrared spectroscopy, thermogravimetric analysis, differential scanning calorimetry, and moisture absorption analysis. Obtained results showed a homogeneous dispersion of NFs within the TPS matrix and strong association between the filler and matrix. Moreover, addition of nanoreinforcements decreased the moisture sensitivity of the TPS film significantly. About 20 % decrease in moisture content at equilibrium was observed with addition of 10 wt% NFs while this value was only 5.7 % for the respective RFs reinforced film

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND: The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS: We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS: The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION: Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms

Effects of sample handling and storage on quantitative lipid analysis in human serum

There is sparse information about specific storage and handling protocols that minimize analytical error and variability in samples evaluated by targeted metabolomics. Variance components that affect quantitative lipid analysis in a set of human serum samples were determined. The effects of freeze-thaw, extraction state, storage temperature, and freeze-thaw prior to density-based lipoprotein fractionation were quantified. The quantification of high abundance metabolites, representing the biologically relevant lipid species in humans, was highly repeatable (with coefficients of variation as low as 0.01 and 0.02) and largely unaffected by 1–3 freeze-thaw cycles (with 0–8% of metabolites affected in each lipid class). Extraction state had effects on total lipid class amounts, including decreased diacylglycerol and increased phosphatidylethanolamine in thawed compared with frozen samples. The effects of storage temperature over 1 week were minimal, with 0–4% of metabolites affected by storage at 4°C, −20°C, or −80°C in most lipid classes, and 19% of metabolites in diacylglycerol affected by storage at −20°C. Freezing prior to lipoprotein fractionation by density ultracentrifugation decreased HDL free cholesterol by 37% and VLDL free fatty acid by 36%, and increased LDL cholesterol ester by 35% compared with fresh samples. These findings suggest that density-based fractionation should preferably be undertaken in fresh serum samples because up to 37% variability in HDL and LDL cholesterol could result from a single freeze-thaw cycle. Conversely, quantitative lipid analysis within unfractionated serum is minimally affected even with repeated freeze-thaw cycles

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

Deep-Inelastic Inclusive ep Scattering at Low x and a Determination of alpha_s

A precise measurement of the inclusive deep-inelastic e^+p scattering cross section is reported in the kinematic range 1.5<= Q^2 <=150 GeV^2 and 3*10^(-5)<= x <=0.2. The data were recorded with the H1 detector at HERA in 1996 and 1997, and correspond to an integrated luminosity of 20 pb^(-1). The double differential cross section, from which the proton structure function F_2(x,Q^2) and the longitudinal structure function F_L(x,Q^2) are extracted, is measured with typically 1% statistical and 3% systematic uncertainties. The measured partial derivative (dF_2(x,Q^2)/dln Q^2)_x is observed to rise continuously towards small x for fixed Q^2. The cross section data are combined with published H1 measurements at high Q^2 for a next-to-leading order DGLAP QCD analysis.The H1 data determine the gluon momentum distribution in the range 3*10^(-4)<= x <=0.1 to within an experimental accuracy of about 3% for Q^2 =20 GeV^2. A fit of the H1 measurements and the mu p data of the BCDMS collaboration allows the strong coupling constant alpha_s and the gluon distribution to be simultaneously determined. A value of alpha _s(M_Z^2)=0.1150+-0.0017 (exp) +0.0009-0.0005 (model) is obtained in NLO, with an additional theoretical uncertainty of about +-0.005, mainly due to the uncertainty of the renormalisation scale.Comment: 68 pages, 24 figures and 18 table

Towards the understanding of microRNA and environmental factor interactions and their relationships to human diseases

Increasing studies have shown that the interactions between microRNAs (miRNAs) and environmental factors (EFs) play critical roles in determining phenotypes and diseases. In this study, we revealed a number of important biological insights by analyzing and modeling of miRNA-EF interactions and their relationships with human diseases. We demonstrated that the miRNA signatures of EFs could provide new information on EFs. More importantly, we quantitatively showed that the miRNA signatures of drug/radiation could be used as indicators for evaluating the results of cancer treatments. Finally, we developed a computational model that could efficiently identify the possible relationship between EF and human diseases. Meanwhile, we provided a website (http://cmbi.hsc.pku.edu.cn/miren) for the main results of this study. This study elucidates the mechanisms of EFs, presents a framework for predicting the results of cancer treatments, and develops a model that illustrates the relationships between EFs and human diseases

BPGA- an ultra-fast pan-genome analysis pipeline

Recent advances in ultra-high-throughput sequencing technology and metagenomics have led to a paradigm shift in microbial genomics from few genome comparisons to large-scale pan-genome studies at different scales of phylogenetic resolution. Pan-genome studies provide a framework for estimating the genomic diversity of the dataset, determining core (conserved), accessory (dispensable) and unique (strain-specific) gene pool of a species, tracing horizontal gene-flux across strains and providing insight into species evolution. The existing pan genome software tools suffer from various limitations like limited datasets, difficult installation/requirements, inadequate functional features etc. Here we present an ultra-fast computational pipeline BPGA (Bacterial Pan Genome Analysis tool) with seven functional modules. In addition to the routine pan genome analyses, BPGA introduces a number of novel features for downstream analyses like core/pan/MLST (Multi Locus Sequence Typing) phylogeny, exclusive presence/absence of genes in specific strains, subset analysis, atypical G + C content analysis and KEGG & COG mapping of core, accessory and unique genes. Other notable features include minimum running prerequisites, freedom to select the gene clustering method, ultra-fast execution, user friendly command line interface and high-quality graphics outputs. The performance of BPGA has been evaluated using a dataset of complete genome sequences of 28 Streptococcus pyogenes strains