Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Variation in Epigenetic Pathways Affects the Specification of Female Gamete Precursors in Arabidopsis[OPEN]

Comparative analysis in Arabidopsis ecotypes, hybrids, and tetraploids revealed the presence of natural variation in early reproductive development that depends on the function of epigenetic pathways. In angiosperms, the transition to the female gametophytic phase relies on the specification of premeiotic gamete precursors from sporophytic cells in the ovule. In Arabidopsis thaliana, a single diploid cell is specified as the premeiotic female gamete precursor. Here, we show that ecotypes of Arabidopsis exhibit differences in megasporogenesis leading to phenotypes reminiscent of defects in dominant mutations that epigenetically affect the specification of female gamete precursors. Intraspecific hybridization and polyploidy exacerbate these defects, which segregate quantitatively in F2 populations derived from ecotypic hybrids, suggesting that multiple loci control cell specification at the onset of female meiosis. This variation in cell differentiation is influenced by the activity of ARGONAUTE9 (AGO9) and RNA-DEPENDENT RNA POLYMERASE6 (RDR6), two genes involved in epigenetic silencing that control the specification of female gamete precursors. The pattern of transcriptional regulation and localization of AGO9 varies among ecotypes, and abnormal gamete precursors in ovules defective for RDR6 share identity with ectopic gamete precursors found in selected ecotypes. Our results indicate that differences in the epigenetic control of cell specification lead to natural phenotypic variation during megasporogenesis. We propose that this mechanism could be implicated in the emergence and evolution of the reproductive alternatives that prevail in flowering plants.
Pre-reordering Model of Chinese Special Sentences for Patent Machine Translation

Chinese prepositions play an important role in sentence reordering, especially in patent texts. In this paper, a rule-based model is proposed to deal with the long distance reordering of sentences with special prepositions. We firstly identify the prepositions and their syntax levels. After that, sentences are parsed and transformed to be much closer to English word order with reordering rules. After integrating our method into a patent MT system, the reordering and translation results of source language are effectively improved.
Positioning of necrotic lobular intraepithelial neoplasias (LIN, grade 3) within the sequence of breast carcinoma progression

Lobular intraepithelial neoplasia Grade 3 (LIN3) is a recently recognized variant of intraepithelial lobular neoplasia (LIN) of the breast composed of either uniform, generally small cells with massive lobular distension, pleomorphic cells, signet‐ring cells, or any cell type with necrosis. In contrast to classic forms of LIN, there is no consensus on therapeutic strategies for LIN3. In part this is due to the paucity of molecular data that could assist in defining the relationship of LIN3 to classic LIN and carcinomas. In this study we have employed array comparative genomic hybridization to determine the patterns of chromosomal aberrations in nine LIN3 lesions. By comparison to array CGH data of 13 classic LIN lesions, we demonstrate that classic LIN and LIN3 share several recurrent changes, in particular gains of 1q and losses of 16q. Both aberrations are known to appear early in tumorigenesis and to be associated with good prognosis. However, apart from this overlap, there were a number of karyotypic features that were observed exclusively in LIN3. Clearly, this lesion was characterized by a significantly higher number of DNA copy number changes (9 vs. 31 on average), a considerable complexity of chromosomal rearrangements with more than 16 breakpoints in one chromosome and overlapping high copy amplifications encompassing a number of known oncogenes. Our data suggest that, at the genetic level, LIN3 represents a highly advanced lesion with considerable resemblance to carcinomas and, therefore, might represent the transition state from an intraepithelial neoplasm to breast carcinoma. © 2010 Wiley‐Liss, Inc.
Reduced Cortical BDNF Expression and Aberrant Memory in Carf Knock-Out Mice

Transcription factors are a key point of convergence between the cell-intrinsic and extracellular signals that guide synaptic development and brain plasticity. Calcium-response factor (CaRF) is a unique transcription factor first identified as a binding protein for a calcium-response element in the gene encoding brain-derived neurotrophic factor (Bdnf). We have now generated Carf knock-out (KO) mice to characterize the function of this factor in vivo. Intriguingly, Carf KO mice have selectively reduced expression of Bdnf exon IV-containing mRNA transcripts and BDNF protein in the cerebral cortex, whereas BDNF levels in the hippocampus and striatum remain unchanged, implicating CaRF as a brain region-selective regulator of BDNF expression. At the cellular level, Carf KO mice show altered expression of GABAergic proteins at striatal synapses, raising the possibility that CaRF may contribute to aspects of inhibitory synapse development. Carf KO mice show normal spatial learning in the Morris water maze and normal context-dependent fear conditioning. However they have an enhanced ability to find a new platform location on the first day of reversal training in the water maze and they extinguish conditioned fear more slowly than their wild-type littermates. Finally, Carf KO mice show normal short-term (STM) and long-term memory (LTM) in a novel object recognition task, but exhibit impairments during the remote memory phase of testing. Together, these data reveal novel roles for CaRF in the organization and/or function of neural circuits that underlie essential aspects of learning and memory.
A pattern search and implicit filtering algorithm for solving linearly constrained minimization problems with noisy objective functions

ABSTRACT PSIFA – Pattern Search and Implicit Filtering Algorithm – is a derivative-free algorithm that has been designed for linearly constrained problems with noise in the objective function. It combines some elements of the pattern search approach of Lewis and Torczon with ideas from the method of implicit filtering of Kelley enhanced with a further analysis of the current face and a simple extrapolation strategy for updating the step length. The feasible set is explored by PSIFA without any particular assumption about its description, being the equality constraints handled in their original formulation. Besides, compact bounds for the variables are not mandatory. The global convergence analysis is presented, encompassing the degenerate case, under mild assumptions. Numerical experiments with linearly constrained problems from the literature were performed. Additionally, problems with the feasible set defined by polyhedral 3D cones with several degrees of degeneration at the solution were addressed, including noisy functions that are not covered by the theoretical hypotheses. To put PSIFA in perspective, comparative tests have been prepared, with encouraging results.
Diet‐dependent modulation of gastro‐oesphageal vagal afferent mechanosensitivity by endogenous nitric oxide

Nitric oxide (NO) is an important signalling molecule found in the gastrointestinal tract, which can modulate the mechanosensitivity of gastro‐oesophageal vagal afferents. It is known that the effects of NO on food intake depend on feeding status, but the effect of NO on gastro‐oesophageal vagal afferents in different feeding conditions is unknown. We show that in the fed state endogenous NO inhibits mucosal afferents through a pathway involving nicotinamide adenine dinucleotide phosphate (NADPH) oxidase. In mice fasted overnight endogenous NO potentiates the response of mucosal stroking‐ and tension‐sensitive afferents to mechanical stimuli via a mechanism involving hyperpolarisation‐activated cyclic nucleotide‐gated 3 (HCN3) channels. These results show the role of NO in the peripheral modulation of gastro‐oesophageal vagal afferents is dynamic and dependent on feeding status.
The Septins Function in G1 Pathways that Influence the Pattern of Cell Growth in Budding Yeast

The septins are a conserved family of proteins that have been proposed to carry out diverse functions. In budding yeast, the septins become localized to the site of bud emergence in G1 but have not been thought to carry out important functions at this stage of the cell cycle. We show here that the septins function in redundant mechanisms that are required for formation of the bud neck and for the normal pattern of cell growth early in the cell cycle. The Shs1 septin shows strong genetic interactions with G1 cyclins and is directly phosphorylated by G1 cyclin-dependent kinases, consistent with a role in early cell cycle events. However, Shs1 phosphorylation site mutants do not show genetic interactions with the G1 cyclins or obvious defects early in the cell cycle. Rather, they cause an increased cell size and aberrant cell morphology that are dependent upon inhibitory phosphorylation of Cdk1 at the G2/M transition. Shs1 phosphorylation mutants also show defects in interaction with the Gin4 kinase, which associates with the septins during G2/M and plays a role in regulating inhibitory phosphorylation of Cdk1. Phosphorylation of Shs1 by G1 cyclin-dependent kinases plays a role in events that influence Cdk1 inhibitory phosphorylation.
Calculation of Zero-Field Splittings, g-Values, and the Relativistic Nephelauxetic Effect in Transition Metal Complexes. Application to High-Spin Ferric Complexes.

Equations are derived and discussed that allow the computation of zero-field splitting (ZFS) tensors in transition metal complexes for any value of the ground-state total spin S. An effective Hamiltonian technique is used and the calculation is carried to second order for orbitally nondegenerate ground states. The theory includes contributions from excited states of spin S and S +/- 1. This makes the theory more general than earlier treatments. Explicit equations are derived for the case where all states are well described by single-determinantal wave functions, for example restricted open shell Hartree-Fock (HF) and spin-polarized HF or density functional (DFT) calculation schemes. Matrix elements are evaluated for many electron wave functions that result from a molecular orbital (MO) treatment including configuration interaction (CI). A computational implementation in terms of bonded functions is outlined. The problem of ZFS in high-spin ferric complexes is treated at some length, and contributions due to low-symmetry distortions, anisotropic covalency, charge-transfer states, and ligand spin-orbit coupling are discussed. ROHF-INDO/S-CI results are presented for FeCl(4)(-) and used to evaluate the importance of the various terms. Finally, contributions to the experimentally observed reduction of the metal spin-orbit coupling constants (the relativistic nephelauxetic effect) are discussed. B3LYP and Hartree-Fock calculations for FeCl(4)(-) are used to characterize the change of the iron 3d radial function upon complex formation. It is found that the iron 3d radial distribution function is significantly expanded and that the expansion is anisotropic. This is interpreted as a combination of reduction in effective charge on the metal 3d electrons (central field covalence) together with expansive promotion effects that are a necessary consequence of chemical bond formation. The (3d) values that are important in the interpretation of magnetic data are up to 15% reduced from their free-ion value before any metal-ligand orbital mixing (symmetry-restricted covalency) is taken into account. Thus the use of free-ion values for spin-orbit coupling and related constants in the analysis of experimental data leads to values for MO coefficients that overestimate the metal-ligand covalency.
Scalable Inference in Dynamic Mixture Models

Previous work on inference for dynamic mixture models has so far been directed to models that follow a simple Brownian motion diffusion over time and pursued a batch inference approach. We generalize the underlying dynamics model to follow a Gaussian process, introducing a novel class of dynamic priors for mixture models. Further, we propose a stochastic variational inference scheme and compare our approach to previous solutions in terms of runtime complexity and test error.
Visualization and 3D Reconstruction of Flame Cells of Taenia solium (Cestoda)

Background Flame cells are the terminal cells of protonephridial systems, which are part of the excretory systems of invertebrates. Although the knowledge of their biological role is incomplete, there is a consensus that these cells perform excretion/secretion activities. It has been suggested that the flame cells participate in the maintenance of the osmotic environment that the cestodes require to live inside their hosts. In live Platyhelminthes, by light microscopy, the cells appear beating their flames rapidly and, at the ultrastructural, the cells have a large body enclosing a tuft of cilia. Few studies have been performed to define the localization of the cytoskeletal proteins of these cells, and it is unclear how these proteins are involved in cell function. Methodology/Principal Findings Parasites of two different developmental stages of T. solium were used: cysticerci recovered from naturally infected pigs and intestinal adults obtained from immunosuppressed and experimentally infected golden hamsters. Hamsters were fed viable cysticerci to recover adult parasites after one month of infection. In the present studies focusing on flame cells of cysticerci tissues was performed. Using several methods such as video, confocal and electron microscopy, in addition to computational analysis for reconstruction and modeling, we have provided a 3D visual rendition of the cytoskeletal architecture of Taenia solium flame cells. Conclusions/Significance We consider that visual representations of cells open a new way for understanding the role of these cells in the excretory systems of Platyhelminths. After reconstruction, the observation of high resolution 3D images allowed for virtual observation of the interior composition of cells. A combination of microscopic images, computational reconstructions and 3D modeling of cells appears to be useful for inferring the cellular dynamics of the flame cell cytoskeleton.
Nodes’ clustering in WDM star networks with real-time traffic

This paper proposes a novel scheduling scheme, namely node clustering with prioritized scheduling (NOC-PS), which is designed to handle real-time traffic in wavelength division multiplexing (WDM) star networks. NOC-PS is based on clustering techniques, while it takes into account the priority information of data packets. The clustering process aims at organizing the network nodes into groups in terms of their packetspsila requests per channel. Then, NOC-PS rearranges the nodespsila service order beginning from the cluster with long-length requests and ending to the cluster with short-length requests. The novelty of NOC-PS is that it applies separate clustering to nodes with high and low priority packets, while it also differentiates their scheduling, since high-priority packets have the privilege of being scheduled prior to low-priority ones. The simulation results indicate that the proposed scheme leads to a significantly higher throughput-delay performance for real-time traffic, without sacrificing the performance of non-real-time traffic.
A corpus-based approach for automated LOINC mapping

OBJECTIVE To determine whether the knowledge contained in a rich corpus of local terms mapped to LOINC (Logical Observation Identifiers Names and Codes) could be leveraged to help map local terms from other institutions. METHODS We developed two models to test our hypothesis. The first based on supervised machine learning was created using Apache's OpenNLP Maxent and the second based on information retrieval was created using Apache's Lucene. The models were validated by a random subsampling method that was repeated 20 times and that used 80/20 splits for training and testing, respectively. We also evaluated the performance of these models on all laboratory terms from three test institutions. RESULTS For the 20 iterations used for validation of our 80/20 splits Maxent and Lucene ranked the correct LOINC code first for between 70.5% and 71.4% and between 63.7% and 65.0% of local terms, respectively. For all laboratory terms from the three test institutions Maxent ranked the correct LOINC code first for between 73.5% and 84.6% (mean 78.9%) of local terms, whereas Lucene's performance was between 66.5% and 76.6% (mean 71.9%). Using a cut-off score of 0.46 Maxent always ranked the correct LOINC code first for over 57% of local terms. CONCLUSIONS This study showed that a rich corpus of local terms mapped to LOINC contains collective knowledge that can help map terms from other institutions. Using freely available software tools, we developed a data-driven automated approach that operates on term descriptions from existing mappings in the corpus. Accurate and efficient automated mapping methods can help to accelerate adoption of vocabulary standards and promote widespread health information exchange.
Construction And Control Of A Three-Dimensional Vocal Tract Model

We present a novel 3D vocal tract model and a method to control the articulatory movements of the model. The vocal tract model consists of 7 wireframe meshes that represent the three dimensional surfaces of the articulators and the vocal tract walls. 23 parameters determine the shape of the meshes. The articulatory movements in terms of the parameter curves are generated from a gestural description of an utterance. The work presented here is an integral part of a complete articulatory speech synthesizer for high quality synthesis
Duration Mismatch Compensation Using Four-Covariance Model and Deep Neural Network for Speaker Verification

Duration mismatch between enrollment and test utterances still remains a major concern for reliability of real-life speaker recognition applications. Two approaches are proposed here to deal with this case when using the i-vector representation. The first one is an adaptation of Gaussian Probabilistic Linear Discriminant Analysis (PLDA) modeling, which can be extended to the case of any shift between i-vectors drawn from two distinct distributions. The second one attempts to map i-vectors of truncated segments of an utterance to the i-vector of the full segment, by the use of deep neural networks (DNN). Our results show that both new approaches outperform the standard PLDA by about 10 % relative, noting that these back-end methods could complement those quantifying the i-vector uncertainty during its extraction process, in the case of duration gap.
Investigation of thermal behaviour and mechanical property of the functionalised graphene oxide/epoxy resin nanocomposites

Graphite oxide (GO) and functionalised graphite oxide (FGO) were successfully prepared with -NH2-terminated GO in the paper, and their chemical structures were characterised with Fourier transform infrared (FTIR), Energy-Dispersive X-ray Spectroscopy (EDS), UV spectrum analysis and XRD, their microstructures were researched by a scanning electron microscope (SEM) and a transmission electron microscope (TEM), and their thermal properties were characterised by TG. The result showed the carbon residue of FGO was 82.1% and the residual char of GO was 48%, the composite materials were prepared with curing for epoxy resin. The thermal stability, mechanical properties, and morphology after impacting tests of composite materials were investigated using thermogravimetric analysis, tensile and charpy impact tests and SEM. The result showed when the 0.2% FGO was filled into the epoxy, the tensile strength was 55.4 MPa, the impact strength was 17.3 KJ/m2, the flexural strength was 82 MPa, and the flexural modulus was 2760 MPa. The mechanical properties of composite materials were higher than those of pure epoxy and improved the strength and toughness of epoxy nanocomposites.
Artificial bee colony algorithm for optimal design of power system stabilizer

A newly developed heuristic global optimization algorithm, artificial bee colony (ABC) algorithm, is applied for simultaneous coordinated tuning of the power system stabilizers (PSSs) in the multi-machine power system. ABC is a population based optimization algorithm inspired by the foraging behavior of bee colony and proven its superior capabilities, such as faster convergence and better global minimum achievement. To investigate the robustness and ability of the proposed algorithm in damping stabilizers design, a 2-area 4-machine system under different operating conditions is considered. The performance of the proposed ABC is compared with the genetic algorithm (GA) through eigenvalue analysis and nonlinear time-domain simulation. The simulation studies show that the stabilizers designed by ABC perform better than those by GA in damping the power system low-frequency oscillations and enhance greatly the dynamic stability of the power system.
Requirement of Heavy Neurofilament Subunit in the Development of Axons with Large Calibers

Neurofilaments (NFs) are prominent components of large myelinated axons. Previous studies have suggested that NF number as well as the phosphorylation state of the COOH-terminal tail of the heavy neurofilament (NF-H) subunit are major determinants of axonal caliber. We created NF-H knockout mice to assess the contribution of NF-H to the development of axon size as well as its effect on the amounts of low and mid-sized NF subunits (NF-L and NF-M respectively). Surprisingly, we found that NF-L levels were reduced only slightly whereas NF-M and tubulin proteins were unchanged in NF-H–null mice. However, the calibers of both large and small diameter myelinated axons were diminished in NF-H–null mice despite the fact that these mice showed only a slight decrease in NF density and that filaments in the mutant were most frequently spaced at the same interfilament distance found in control. Significantly, large diameter axons failed to develop in both the central and peripheral nervous systems. These results demonstrate directly that unlike losing the NF-L or NF-M subunits, loss of NF-H has only a slight effect on NF number in axons. Yet NF-H plays a major role in the development of large diameter axons.
Scalable Dense Linear Algebra on Heterogeneous Hardware

Design of systems exceeding 1 Pflop/s and the push toward 1 Eflop/s, forced a dramatic shift in hardware design. Various physical and engineering constraints resulted in introduction of massive parallelism and functional hybridization with the use of accelerator units. This paradigm change brings about a serious challenge for application developers, as the management of multicore proliferation and heterogeneity rests on software. And it is reasonable to expect, that this situation will not change in the foreseeable future. This chapter presents a methodology of dealing with this issue in three common scenarios. In the context of shared-memory multicore installations, we show how high performance and scalability go hand in hand, when the well-known linear algebra algorithms are recast in terms of Direct Acyclic Graphs (DAGs), which are then transparently scheduled at runtime inside the Parallel Linear Algebra Software for Multicore Architectures (PLASMA) project. Similarly, Matrix Algebra on GPU and Multicore Architectures (MAGMA) schedules DAG-driven computations on multicore processors and accelerators. Finally, Distributed PLASMA (DPLASMA), takes the approach to distributed-memory machines with the use of automatic dependence analysis and the Direct Acyclic Graph Engine (DAGuE) to deliver high performance at the scale of many thousands of cores.