LSD1 is essential for oocyte meiotic progression by regulating CDC25B expression in mice

Mammalian oocytes are arrested at prophase I until puberty when hormonal signals induce the resumption of meiosis I and progression to meiosis II. Meiotic progression is controlled by CDK1 activity and is accompanied by dynamic epigenetic changes. Although the signalling pathways regulating CDK1 activity are well defined, the functional significance of epigenetic changes remains largely unknown. Here we show that LSD1, a lysine demethylase, regulates histone H3 lysine 4 di-methylation (H3K4me2) in mouse oocytes and is essential for meiotic progression. Conditional deletion of Lsd1 in growing oocytes results in precocious resumption of meiosis and spindle and chromosomal abnormalities. Consequently, most Lsd1-null oocytes fail to complete meiosis I and undergo apoptosis. Mechanistically, upregulation of CDC25B, a phosphatase that activates CDK1, is responsible for precocious meiotic resumption and also contributes to subsequent spindle and chromosomal defects. Our findings uncover a functional link between LSD1 and the major signalling pathway governing meiotic progression

A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence

A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context. Here, we call trivariate vine copulas to extend the bivariate meta-analysis of diagnostic test accuracy studies by accounting for disease prevalence. Our vine copula mixed model includes the trivariate generalized linear mixed model as a special case and can also operate on the original scale of sensitivity, specificity, and disease prevalence. Our general methodology is illustrated by re-analyzing the data of two published meta-analyses. Our study suggests that there can be an improvement on trivariate generalized linear mixed model in fit to data and makes the argument for moving to vine copula random effects models especially because of their richness, including reflection asymmetric tail dependence, and computational feasibility despite their three dimensionality

X-ray Insights into the Nature of Quasars with Redshifted Broad Absorption Lines

We present $Chandra$ observations of seven broad absorption line (BAL) quasars at $z=0.863$-2.516 with redshifted BAL troughs (RSBALs). Five of our seven targets were detected by $Chandra$ in 4-13 ks exposures with ACIS-S. The $\alpha_{\rm ox}$ values, $\Delta\alpha_{\rm ox}$ values, and spectral energy distributions of our targets demonstrate they are all X-ray weak relative to expectations for non-BAL quasars, and the degree of X-ray weakness is consistent with that of appropriately-matched BAL quasars generally. Furthermore, our five detected targets show evidence for hard X-ray spectral shapes with a stacked effective power-law photon index of $\Gamma_{\rm eff}=0.5^{+0.5}_{-0.4}$. These findings support the presence of heavy X-ray absorption ($N_{\rm H}\approx 2 \times 10^{23}$ cm$^{-2}$) in RSBAL quasars, likely by the shielding gas found to be common in BAL quasars more generally. We use these X-ray measurements to assess models for the nature of RSBAL quasars, finding that a rotationally-dominated outflow model is favored while an infall model also remains plausible with some stipulations. The X-ray data disfavor a binary quasar model for RSBAL quasars in general.Comment: 11 pages, 5 figures, and 3 table

Graphene-based photovoltaic cells for near-field thermal energy conversion

Thermophotovoltaic devices are energy-conversion systems generating an electric current from the thermal photons radiated by a hot body. In far field, the efficiency of these systems is limited by the thermodynamic Schockley-Queisser limit corresponding to the case where the source is a black body. On the other hand, in near field, the heat flux which can be transferred to a photovoltaic cell can be several orders of magnitude larger because of the contribution of evanescent photons. This is particularly true when the source supports surface polaritons. Unfortunately, in the infrared where these systems operate, the mismatch between the surface-mode frequency and the semiconductor gap reduces drastically the potential of this technology. Here we show that graphene-based hybrid photovoltaic cells can significantly enhance the generated power paving the way to a promising technology for an intensive production of electricity from waste heat.Comment: 5 pages, 4 figure

Proteomics: in pursuit of effective traumatic brain injury therapeutics

Effective traumatic brain injury (TBI) therapeutics remain stubbornly elusive. Efforts in the field have been challenged by the heterogeneity of clinical TBI, with greater complexity among underlying molecular phenotypes than initially conceived. Future research must confront the multitude of factors comprising this heterogeneity, representing a big data challenge befitting the coming informatics age. Proteomics is poised to serve a central role in prescriptive therapeutic development, as it offers an efficient endpoint within which to assess post-TBI biochemistry. We examine rationale for multifactor TBI proteomic studies and the particular importance of temporal profiling in defining biochemical sequences and guiding therapeutic development. Lastly, we offer perspective on repurposing biofluid proteomics to develop theragnostic assays with which to prescribe, monitor and assess pharmaceutics for improved translation and outcome for TBI patients

Evidence for solar cycles in a late Holocene speleothem record from Dongge Cave, China

The association between solar activity and Asian monsoon (AM) remains unclear. Here we evaluate the possible connection between them based on a precisely-dated, high-resolution speleothem oxygen isotope record from Dongge Cave, southwest China during the past 4.2 thousand years (ka). Without being adjusted chronologically to the solar signal, our record shows a distinct peak-to-peak correlation with cosmogenic nuclide 14C, total solar irradiance (TSI) and sunspot number (SN) at multi-decadal to centennial timescales. Further cross-wavelet analyses between our calcite δ18O and atmospheric 14C show statistically strong coherence at three typical periodicities of ~80, 200 and 340 years, suggesting important roles of solar activities in modulating AM changes at those timescales. Our result has further indicated a better correlation between our calcite δ18O record and atmospheric 14C than between our record and TSI. This better correlation may imply that the Sun–monsoon connection is dominated most likely by cosmic rays and oceanic circulation (both associated to atmospheric 14C), instead of the direct solar heating (TSI)

Graph embedding-based intelligent industrial decision for complex sewage treatment processes

Intelligent algorithms-driven industrial decision systems have been a general demand for modeling complex sewage treatment processes (STP). Existing researches modeled complex STP with the use of various neural network models, yet neglecting the fact that latent and occasional relations exist inside complex STP. To deal with the challenge, this paper proposes graph embedding-based intelligent industrial decision for complex STP (GE-STP). The graph embedding (GE) scheme is employed to enhance feature extraction and neural computing structure is utilized to simulate uncertain biochemical transformation inside STP. The introduction of GE can not only improves the fineness of feature spaces, but also improves the representative ability of models towards complex industrial processes. On this basis, the GE-STP is evaluated on a real-world data set collected from a realistic sewage treatment plant equipped with a set of Internet of Things devices. And some typical neural network models that have been utilized for modeling complex STP, are selected as baseline methods. Three groups of experiments show that efficiency of the GE-STP exceeds baselines about 6%–12%, and that the GE-STP is not susceptible to parameter changing

The Sloan Digital Sky Survey Reverberation Mapping Project: Rapid CIV Broad Absorption Line Variability

We report the discovery of rapid variations of a high-velocity CIV broad absorption line trough in the quasar SDSS J141007.74+541203.3. This object was intensively observed in 2014 as a part of the Sloan Digital Sky Survey Reverberation Mapping Project, during which 32 epochs of spectroscopy were obtained with the Baryon Oscillation Spectroscopic Survey spectrograph. We observe significant (>4sigma) variability in the equivalent width of the broad (~4000 km/s wide) CIV trough on rest-frame timescales as short as 1.20 days (~29 hours), the shortest broad absorption line variability timescale yet reported. The equivalent width varied by ~10% on these short timescales, and by about a factor of two over the duration of the campaign. We evaluate several potential causes of the variability, concluding that the most likely cause is a rapid response to changes in the incident ionizing continuum. If the outflow is at a radius where the recombination rate is higher than the ionization rate, the timescale of variability places a lower limit on the density of the absorbing gas of n_e > 3.9 x 10^5 cm^-3. The broad absorption line variability characteristics of this quasar are consistent with those observed in previous studies of quasars, indicating that such short-term variability may in fact be common and thus can be used to learn about outflow characteristics and contributions to quasar/host-galaxy feedback scenarios.Comment: 15 pages, 14 figures. Accepted for publication in the Astrophysical Journa

Modeling Disordered Regions in Proteins Using Rosetta

Protein structure prediction methods such as Rosetta search for the lowest energy conformation of the polypeptide chain. However, the experimentally observed native state is at a minimum of the free energy, rather than the energy. The neglect of the missing configurational entropy contribution to the free energy can be partially justified by the assumption that the entropies of alternative folded states, while very much less than unfolded states, are not too different from one another, and hence can be to a first approximation neglected when searching for the lowest free energy state. The shortcomings of current structure prediction methods may be due in part to the breakdown of this assumption. Particularly problematic are proteins with significant disordered regions which do not populate single low energy conformations even in the native state. We describe two approaches within the Rosetta structure modeling methodology for treating such regions. The first does not require advance knowledge of the regions likely to be disordered; instead these are identified by minimizing a simple free energy function used previously to model protein folding landscapes and transition states. In this model, residues can be either completely ordered or completely disordered; they are considered disordered if the gain in entropy outweighs the loss of favorable energetic interactions with the rest of the protein chain. The second approach requires identification in advance of the disordered regions either from sequence alone using for example the DISOPRED server or from experimental data such as NMR chemical shifts. During Rosetta structure prediction calculations the disordered regions make only unfavorable repulsive contributions to the total energy. We find that the second approach has greater practical utility and illustrate this with examples from de novo structure prediction, NMR structure calculation, and comparative modeling

MemBrain: Improving the Accuracy of Predicting Transmembrane Helices

Prediction of transmembrane helices (TMH) in α helical membrane proteins provides valuable information about the protein topology when the high resolution structures are not available. Many predictors have been developed based on either amino acid hydrophobicity scale or pure statistical approaches. While these predictors perform reasonably well in identifying the number of TMHs in a protein, they are generally inaccurate in predicting the ends of TMHs, or TMHs of unusual length. To improve the accuracy of TMH detection, we developed a machine-learning based predictor, MemBrain, which integrates a number of modern bioinformatics approaches including sequence representation by multiple sequence alignment matrix, the optimized evidence-theoretic K-nearest neighbor prediction algorithm, fusion of multiple prediction window sizes, and classification by dynamic threshold. MemBrain demonstrates an overall improvement of about 20% in prediction accuracy, particularly, in predicting the ends of TMHs and TMHs that are shorter than 15 residues. It also has the capability to detect N-terminal signal peptides. The MemBrain predictor is a useful sequence-based analysis tool for functional and structural characterization of helical membrane proteins; it is freely available at http://chou.med.harvard.edu/bioinf/MemBrain/