GATA Factors Regulate Inner Ear Development and Midbrain Neurogenesis

The zinc-finger transcription factors GATA2 and GATA3 in vertebrates belong to the six-member family that are essential regulators in the development of various organs. The aim of this study was to gain new information of the roles of GATA2 and GATA3 in inner ear morphogenesis and of the function of GATA2 in neuronal fate specification in the midbrain using genetically modified mouse and chicken embryos as models. A century ago the stepwise process of inner ear epithelial morphogenesis was described, but the molecular players regulating the cellular differentiation of the otic epithelium are still not fully resolved. This study provided novel data on GATA factor roles in several developmental processes during otic development. The expression analysis in chicken suggested that GATA2 and GATA3 possess redundant roles during otic cup and vesicle formation, but complementary cell-type specific functions during vestibular and cochlear morphogenesis. The comparative analysis between mouse and chicken Gata2 and Gata3 expression revealed many conserved aspects, especially during later stages of inner ear development, while the expression was more divergent at early stages. Namely, expression of both Gata genes was initiated earlier in chicken than mouse otic epithelium relative to the morphogenetic stages. Likewise, important differences concerning Gata3 expression in the otic cup epithelium were detected between mouse and chicken, suggesting that distinct molecular mechanisms regulate otic vesicle closure in different vertebrate species. Temporally distinct Gata2 and Gata3 expression was also found during otic ganglion formation in mouse and chicken. Targeted inactivation of Gata3 in mouse embryos caused aberrant morphology of the otic vesicle that in severe cases was disrupted into two parts, a dorsal and a ventral vesicle. Detailed analyses of Gata3 mutant embryos unveiled a crucial role for GATA3 in the initial inner ear morphogenetic event, the invagination of the otic placode. A large-scale comparative expression analysis suggested that GATA3 could control cell adhesion and motility in otic epithelium, which could be important for early morphogenesis. GATA3 was also identified as the first factor to directly regulate Fgf10 expression in the otic epithelium and could thus influence the development of the semicircular ducts. Despite the serious problems in the early inner ear development, the otic sensory fate establishment and some vestibular hair cell differentiation was observable in pharmacologically rescued Gata3-/- embryos. Cochlear sensory differentiation was, however, completely blocked so that no auditory hair cells were detected. In contrast to the early morphogenetic phenotype in Gata3-/- mutants, conditional inactivation of Gata2 in mouse embryos resulted in a relatively late growth defect of the three semicircular ducts. GATA2 was required for the proliferation of the vestibular nonsensory epithelium to support growing of the three ducts. Concurrently, with the role in epithelial semicircular ducts, GATA2 was also required for the mesenchymal cell clearance from the vestibular perilymphatic region between the membranous labyrinth and bony capsule. The gamma-aminobutyric acid-secreting (GABAergic) neurons in the midbrain are clinically relevant since they contribute to fear, anxiety, and addiction regulation. The molecular mechanisms regulating the GABAergic neuronal development, however, are largely unknown. Using tissue-specific mutagenesis in mice, GATA2 was characterized as a critical determinant of the GABAergic neuronal fate in the midbrain. In Gata2-deficient mouse midbrain, GABAergic neurons were not produced, instead the Gata2-mutant cells acquired a glutamatergic neuronal phenotype. Gain-of-function experiments in chicken also revealed that GATA2 was sufficient to induce GABAergic differentiation in the midbrain.

Vehicle Routing Problem with Load Compatibility Constraints

The major contribution of this paper is to introduce load compatibility constraints in the mathematical model for the capacitated vehicle routing problem with pickup and deliveries. The employee transportation problem in the Indian call centers and transportation of hazardous materials provided the motivation for this variation. In this paper we develop a integer programming model for the vehicle routing problem with load compatibility constraints. Specifically two types of load compatability constraints are introduced, namely mutual exclusion and conditional exclusion. The model is demonstrated with an application from the employee transportation problem in the Indian call centers.

Cohésion modale et subordination : Le conditionnel et le jussif finnois au miroir de la valeur sémantique et discursive du subjonctif français

Modal cohesion and subordination. The Finnish conditional and jussive moods in comparison to the French subjunctive This study examines verb moods in subordinate clauses in French and Finnish. The first part of the analysis deals with the syntax and semantics of the French subjunctive, mood occurring mostly in subordinate positions. The second part investigates Finnish verb moods. Although subordinate positions in Finnish grammar have no special finite verb form, certain uses of Finnish verb moods have been compared to those of subjunctives and conjunctives in other languages. The present study focuses on the subordinate uses of the Finnish conditional and jussive (i.e. the third person singular and plural of the imperative mood). The third part of the analysis discusses the functions of subordinate moods in contexts beyond complex sentences. The data used for the analysis include 1834 complex sentences gathered from newspapers, online discussion groups and blog texts, as well as audio-recorded interviews and conversations. The data thus consist of both written and oral texts as well as standard and non-standard variants. The analysis shows that the French subjunctive codes theoretical modality. The subjunctive does not determine the temporal and modal meaning of the event, but displays the event as virtual. In a complex sentence, the main clause determines the temporal and modal space within which the event coded by the subjunctive clause is interpreted. The subjunctive explicitly indicates that the space constructed in the main clause extends its scope over the subordinate clause. The subjunctive can therefore serve as a means for creating modal cohesion in the discourse. The Finnish conditional shares the function of making explicit the modal link between the components of a complex construction with the French subjunctive, but the two moods differ in their semantics. The conditional codes future time and can therefore occur only in non-factual or counterfactual contexts, whereas the event expressed by French subjunctive clauses can also be interpreted as realized. Such is the case when, for instance, generic and habitual meaning is involved. The Finnish jussive mood is used in a relatively limited number of subordinate clause types, but in these contexts its modal meaning is strikingly close to that of the French subjunctive. The permissive meaning, typical of the jussive in main clause positions, is modified in complex sentences so that it entails inter-clausal relation, namely concession. Like the French subjunctive, the jussive codes theoretical modal meaning with no implication of the truth value of the proposition. Finally, the analysis shows that verb moods mark modal cohesion, not only on the syntagmatic level (namely in complexe sentences), but also on the paradigmatic axis of discourse in order to create semantic links over entire segments of talk. In this study, the subjunctive thus appears, not as an empty category without function, as it is sometimes described, but as an open form that conveys the temporal and modal meanings emerging from the context.

A Geostatistical Study pf Alumina-Content in Ores of An Indian Iron-Ore Deposit

Mining and blending operations in the high grade iron ore deposit under study are performed to optimize recovery with minimal alumina content while maintaining required levels of other chemical component and a proper mix of ore types. In the present work the regionalisation of alumina in the ores has been studied independently and its effects on global and local recoverable tonnage as well as on alternatives of mining operations have been evaluated. The global tonnage recovery curves for blocks (20m x 20m x 12m) obtained by simulation closely approximated the curves obtained theoretically using a change of support under the discretised gaussian model. Variations in block size up to 80m x 20m x 12m did not affect the recovery as the horizontal dimensions of the blocks are small in relation to the range of the variogram. A comparison of the local tonnage recovery curves obtained through multiple conditional simulations made with that obtained by the method of uniform conditioning of block grades on an estimate of panel 100m x 100m x 12m panel grade reveals comparable results only in panels which have been well conditioned and possesing an ensemble simulation mean close to the ordinary kriged value for the panel. Study of simple alternative sequence of mining on the conditionally simulated deposit shows that concentration of mining operations simultaneously on a single bench enhances the fluctuation in alumina values of ore mined.

Adaptive Keeper Design for Dynamic Logic Circuits Using Rate Sensing Technique

The increasing variability in device leakage has made the design of keepers for wide OR structures a challenging task. The conventional feedback keepers (CONV) can no longer improve the performance of wide dynamic gates for the future technologies. In this paper, we propose an adaptive keeper technique called rate sensing keeper (RSK) that enables faster switching and tracks the variation across different process corners. It can switch upto 1.9x faster (for 20 legs) than CONV and can scale upto 32 legs as against 20 legs for CONV in a 130-nm 1.2-V process. The delay tracking is within 8% across the different process corners. We demonstrate the circuit operation of RSK using a 32 x 8 register file implemented in an industrial 130-nm 1.2-V CMOS process. The performance of individual dynamic logic gates are also evaluated on chip for various keeper techniques. We show that the RSK technique gives superior performance compared to the other alternatives such as Conditional Keeper (CKP) and current mirror-based keeper (LCR).

density matrix renormalization group study of polyacene

We study the nature of excited states of long polyacene oligomers within a Pariser-Parr-Pople (PPP) Hamiltonian using the Symmetrized Density Matrix Renormalization Group (SDMRG) technique. We find a crossover between the two-photon state and the lowest dipole allowed excited state as the system size is increased from tetracene to pentacene. The spin-gap is the smallest gap. We also study the equilibrium geome tries in the ground and excited states from bond orders and bond-bond correlation functions. We find that the Peierls instability in the ground state of polyacene is conditional both from energetics and structure factors computed froth correlation functions.

Maximum Margin Classifiers with Specified False Positive and False Negative Error Rates

This paper addresses the problem of maximum margin classification given the moments of class conditional densities and the false positive and false negative error rates. Using Chebyshev inequalities, the problem can be posed as a second order cone programming problem. The dual of the formulation leads to a geometric optimization problem, that of computing the distance between two ellipsoids, which is solved by an iterative algorithm. The formulation is extended to non-linear classifiers using kernel methods. The resultant classifiers are applied to the case of classification of unbalanced datasets with asymmetric costs for misclassification. Experimental results on benchmark datasets show the efficacy of the proposed method.

Clustering Based Large Margin Classification: A Scalable Approach using SOCP Formulation

This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture distributions, where each component of the mixture has a spherical covariance, the second order statistics of the components can be estimated efficiently using clustering algorithms like BIRCH. For each cluster, the second order moments are used to derive a second order cone constraint via a Chebyshev-Cantelli inequality. This constraint ensures that any data point in the cluster is classified correctly with a high probability. This leads to a large margin SOCP formulation whose size depends on the number of clusters rather than the number of training data points. Hence, the proposed formulation scales well for large datasets when compared to the state-of-the-art classifiers, Support Vector Machines (SVMs). Experiments on real world and synthetic datasets show that the proposed algorithm outperforms SVM solvers in terms of training time and achieves similar accuracies.

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

Evolving Random Geometric Graph Models for Mobile Wireless Networks

We consider evolving exponential RGGs in one dimension and characterize the time dependent behavior of some of their topological properties. We consider two evolution models and study one of them detail while providing a summary of the results for the other. In the first model, the inter-nodal gaps evolve according to an exponential AR(1) process that makes the stationary distribution of the node locations exponential. For this model we obtain the one-step conditional connectivity probabilities and extend it to the k-step case. Finite and asymptotic analysis are given. We then obtain the k-step connectivity probability conditioned on the network being disconnected. We also derive the pmf of the first passage time for a connected network to become disconnected. We then describe a random birth-death model where at each instant, the node locations evolve according to an AR(1) process. In addition, a random node is allowed to die while giving birth to a node at another location. We derive properties similar to those above.

Generalized distributive law for ML decoding of STBCs

The Generalized Distributive Law (GDL) is a message passing algorithm which can efficiently solve a certain class of computational problems, and includes as special cases the Viterbi's algorithm, the BCJR algorithm, the Fast-Fourier Transform, Turbo and LDPC decoding algorithms. In this paper GDL based maximum-likelihood (ML) decoding of Space-Time Block Codes (STBCs) is introduced and a sufficient condition for an STBC to admit low GDL decoding complexity is given. Fast-decoding and multigroup decoding are the two algorithms used in the literature to ML decode STBCs with low complexity. An algorithm which exploits the advantages of both these two is called Conditional ML (CML) decoding. It is shown in this paper that the GDL decoding complexity of any STBC is upper bounded by its CML decoding complexity, and that there exist codes for which the GDL complexity is strictly less than the CML complexity. Explicit examples of two such families of STBCs is given in this paper. Thus the CML is in general suboptimal in reducing the ML decoding complexity of a code, and one should design codes with low GDL complexity rather than low CML complexity.

A computational study of heat transfer, soot production and transport in an acetylene-air laminar diffusion flame in the field of a line vortex

The present work involves a computational study of soot formation and transport in case of a laminar acetylene diffusion flame perturbed by a co nvecting line vortex. The topology of the soot contours (as in an earlier experimental work [4]) have been investigated. More soot was produced when vortex was introduced from the air si de in comparison to a fuel side vortex. Also the soot topography was more diffused in case of the air side vortex. The computational model was found to be in good agreement with the ex perimental work [4]. The computational simulation enabled a study of the various parameters affecting soot transport. Temperatures were found to be higher in case of air side vortex as compared to a fuel side vortex. In case of the fuel side vortex, abundance of fuel in the vort ex core resulted in stoichiometrically rich combustion in the vortex core, and more discrete so ot topography. Overall soot production too was low. In case of the air side vortex abundan ce of air in the core resulted in higher temperatures and more soot yield. Statistical techniques like probability density fun ction, correlation coefficient and conditional probability function were introduced to explain the transient dependence of soot yield and transport on various parameters like temperature, a cetylene concentration.

A computational study of flame dynamics on soot production and transport for an acetylene-air laminar diffusion flame in the field of a convecting line vortex

The present work involves a computational study of soot formation and transport in case of a laminar acetylene diffusion flame perturbed by a convecting line vortex. The topology of the soot contours (as in an earlier experimental work [4]) have been investigated. More soot was produced when vortex was introduced from the air side in comparison to a fuel side vortex. Also the soot topography was more diffused in case of the air side vortex. The computational model was found to be in good agreement with the experimental work [4]. The computational simulation enabled a study of the various parameters affecting soot transport. Temperatures were found to be higher in case of air side vortex as compared to a fuel side vortex. In case of the fuel side vortex, abundance of fuel in the vort ex core resulted in stoichiometrically rich combustion in the vortex core, and more discrete soot topography. Overall soot production too was low. In case of the air side vortex abundance of air in the core resulted in higher temperatures and more soot yield. Statistical techniques like probability density function, correlation coefficient and conditional probability function were introduced to explain the transient dependence of soot yield and transport on various parameters like temperature, a cetylene concentration.

Generalized distributive law for ML decoding of space-time block codes

The problem of designing good space-time block codes (STBCs) with low maximum-likelihood (ML) decoding complexity has gathered much attention in the literature. All the known low ML decoding complexity techniques utilize the same approach of exploiting either the multigroup decodable or the fast-decodable (conditionally multigroup decodable) structure of a code. We refer to this well-known technique of decoding STBCs as conditional ML (CML) decoding. In this paper, we introduce a new framework to construct ML decoders for STBCs based on the generalized distributive law (GDL) and the factor-graph-based sum-product algorithm. We say that an STBC is fast GDL decodable if the order of GDL decoding complexity of the code, with respect to the constellation size, is strictly less than M-lambda, where lambda is the number of independent symbols in the STBC. We give sufficient conditions for an STBC to admit fast GDL decoding, and show that both multigroup and conditionally multigroup decodable codes are fast GDL decodable. For any STBC, whether fast GDL decodable or not, we show that the GDL decoding complexity is strictly less than the CML decoding complexity. For instance, for any STBC obtained from cyclic division algebras which is not multigroup or conditionally multigroup decodable, the GDL decoder provides about 12 times reduction in complexity compared to the CML decoder. Similarly, for the Golden code, which is conditionally multigroup decodable, the GDL decoder is only half as complex as the CML decoder.

Robust formulations for clustering-based large-scale classification

Chebyshev-inequality-based convex relaxations of Chance-Constrained Programs (CCPs) are shown to be useful for learning classifiers on massive datasets. In particular, an algorithm that integrates efficient clustering procedures and CCP approaches for computing classifiers on large datasets is proposed. The key idea is to identify high density regions or clusters from individual class conditional densities and then use a CCP formulation to learn a classifier on the clusters. The CCP formulation ensures that most of the data points in a cluster are correctly classified by employing a Chebyshev-inequality-based convex relaxation. This relaxation is heavily dependent on the second-order statistics. However, this formulation and in general such relaxations that depend on the second-order moments are susceptible to moment estimation errors. One of the contributions of the paper is to propose several formulations that are robust to such errors. In particular a generic way of making such formulations robust to moment estimation errors is illustrated using two novel confidence sets. An important contribution is to show that when either of the confidence sets is employed, for the special case of a spherical normal distribution of clusters, the robust variant of the formulation can be posed as a second-order cone program. Empirical results show that the robust formulations achieve accuracies comparable to that with true moments, even when moment estimates are erroneous. Results also illustrate the benefits of employing the proposed methodology for robust classification of large-scale datasets.