Miracle-dz/UniFAR-FTUs huggingface dataset:
RETARDED PALEA 1 ( REP 1 ) Controls Palea Development and Floral Zygomorphy in Rice 1

Poaceae, one of the largest flowering plant families in angiosperms, evolved distinct inflorescence and flower morphology diverging from eudicots and other monocots. However, the mechanism underlying the specification of flower morphology in grasses remains unclear. Here we show that floral zygomorphy along the lemma-palea (Le/Pa) axis in rice (Oryza sativa) is partially or indirectly determined by the CYCLOIDEA (CYC)-like homolog RETARDED PALEA1 (REP1) which regulates palea identity and development. The REP1 gene is only expressed in palea primordium during early flower development, but during later floral stages is radially dispersed in stamens and the vascular bundles of the lemma and palea. The development of palea is significantly retarded in rep1 mutant, and its palea has five vascular bundles which is similar to the vascular pattern of the wild type lemma. Furthermore, ectopic expression of REP1 caused the asymmetrical over-differentiation of the palea cells, altering their floral asymmetry. This work therefore extends the function of the TCP gene family members in defining the diversification of floral morphology in grasses and suggests that a common conserved mechanism controlling floral zygomorphy by CYC-like genes exists in both eudicots and the grasses.
Integration of synchronous synaptic input in CA1 pyramidal neuron depends on spatial and temporal distributions of the input

Highly synchronized neural firing has been discussed in relation to learning and memory, for instance sharp‐wave activity in hippocampus. We were interested to study how a postsynaptic CA1 pyramidal neuron would integrate input of different levels of synchronicity. In previous work using computational modeling we studied how the integration depends on dendritic conductances. We found that the transient A‐type potassium channel KA was able to selectively suppress input of high synchronicity. In recent years, compartmentalization of dendritic integration has been shown. We were therefore interested to study the influence of localization and pattern of synaptic input over the dendritic tree of the CA1 pyramidal neuron. We find that the selective suppression increases when synaptic inputs are placed on oblique dendrites further out from the soma. The suppression also increases along the radial axis from the apical trunk out to the end of oblique dendrites. We also find that the KA channel suppresses the occurrence of dendritic spikes. Moreover, recent studies have shown interaction between synaptic inputs. We therefore studied the influence of apical tuft input on the integration studied above. We find that excitatory input provides a modulatory influence reducing the capacity of KA to suppress synchronized activity, thus facilitating the excitatory drive of oblique dendritic input. Conversely, inhibitory tuft input increases the suppression by KA providing a larger control of oblique depolarizing factors on the CA1 pyramidal neuron in terms of what constitutes the most effective level of synchronicity. Furthermore, we show that the selective suppression studied above depends on the conductance of the KA channel. KA, as several other potassium channels, is modulated by several neuromodulators, for instance acetylcholine and dopamine, both of which have been discussed in relation to learning and memory. We suggest that dendritic conductances and their modulatory systems may be part of the regulation of processing of information, in particular for how network synchronicity affects learning and memory. © 2012 Wiley Periodicals, Inc.
Multiple Feature Selectoin for Pattern Recognition Using ID3 Ensemble System

In pattern recognition, feature selection is a quite important process for constructing practical systems. However, because there are many features as candidates to recognize object, it is difficult to select appropriate features for pattern recognition systematically. the previous research proposed a pattern recognition method using the ensemble system based on fuzzy classifier for multiple feature selection. However, this method can not apply for the problems with many input vectors because it takes a lot of time for learning when the number of the input vector increases. in this study, an attempt is made to overcome the problem by introducing ID3 (Iterative Dichotomizer 3) with classifiers consisting of many feature vectors. ID3 constructs a decision tree for multiple feature selection with the results obtained from classifiers based on each feature. Therefore, it is possible to select appropriate features applied many input vectors. Several benchmark problems are presented to demonstrate the efficiency and applicability of the proposed method.
Improved delay-dependent stability criteria for linear systems with time-varying delay

This paper is concerned with the delay-dependent stability and robust stability for uncertain systems with time-varying delay. Through constructing a general form of Lyapunov-Krasovskii functional, and using slack matrices and the convex combination condition in the calculation, the delay-dependent stability criteria are derived in terms of linear matrix inequalities without applying any bounding techniques. Numerical examples are given to illustrate the improvement on the conservatism of the delay bound over some existing results.
Data aggregation in Wireless Sensor Network: An integer linear programming formulation for energy optimization

Wireless Sensor Network is huge number of sensor nodes disposed randomly in severe field to gather data. It monitors physical phenominan changes such as temperature, pressure, humidity, solar radiation, ambident light extra. Sensor nodes are small in size, having less energy, less communication power, less cost and multi functional nodes. This paper we firstly discuss about different types types of network, protocols extra i.e Network such as Direct network, hierarchical network etc and protocols like LEACH, PEGASIS. Next in this paper we deployed sensor nodes randomly in the grid network, We focus on data aggregation in different type of situation known as critical zone, in critical zone(critical grid) senor nodes are heavily loaded with information. Our objective is to maximize the data generation rate of sensor nodes. To got this objective we form different integer linear programming formulation. A optimization tool such as CPLEX IBM ILOG used for optimization and to show critical grids aggregation used MATLAB.
Excitotoxic activation of the NMDA receptor results in inhibition of calcium/calmodulin kinase II activity in cultured hippocampal neurons

Neurotoxic effects of excitatory amino acids have been implicated in various neurological disorders, and have been utilized for excitotoxic models of delayed neuronal cell death. The excitotoxic glutamate- induced, delayed neuronal cell death also results in inhibition of calcium/calmodulin-dependent kinase II (CaM kinase II). In this report, we characterized the glutamate-induced inhibition of CaM kinase II in relation to loss of intracellular calcium regulation and delayed neuronal cell death. Glutamate (500 microM for 10 min), but not KCl (50 mM), exposure resulted in a significant inhibition of CaM kinase II activity. The inhibition of CaM kinase II activity was observed immediately following excitotoxic glutamate exposure and present at every time point measured. Glutamate-induced inhibition of kinase activity and delayed neuronal cell death was dependent upon both the activation of the NMDA glutamate receptor subtype and the presence of extracellular calcium. The relationship between inhibition of CaM kinase II activity and loss of intracellular calcium regulation was also examined. Experimental conditions which resulted in significant neuronal cell death and inhibition of CaM kinase II activity also resulted in a long-term loss of intracellular calcium regulation. Thus, inhibition of CaM kinase II activity occurred under experimental conditions which resulted in loss of neuronal viability and loss of neuronal calcium regulation. Since the glutamate-induced inhibition of CaM kinase II activity preceded neuronal cell death, the data support the hypothesis that inhibition of CaM kinase II activity may play a significant role in excitotoxicity-dependent, delayed neuronal cell death.
Efficient hidden surface removal for objects with small union size

Abstract Let S be a set of n non-intersecting objects in space for which we want to determine the portions visible from some viewing point. We assume that the objects are ordered by depth from the viewing point (e.g., they are all horizontal and are viewed from infinity from above). In this paper we give an algorithm that computes the visible portions in time O((U(n)+k)log2n), where U(n') is a super-additive bound on the maximal complexity of the union of (the projections on a viewing plane of) any n' objects from the family under consideration, and k is the complexity of the resulting visibility map. The algorithm uses O(U(n)log n) working storage. The algorithm is useful when the objects are ‘fat’ in the sense that the union of the projection of any subset of them has small (i.e., subquadratic) complexity. We present three applications of this general technique: (i) For disks (or balls in space) we have U(n)=O(n), thus the visibility map can be computed in time O((n+k)log2n). (ii) For ‘fat’ triangles (where each internal angle is at least some fixed θ degrees) we have U(n)=O(n log log n) and the algorithm runs in time O((n log log n+k)log2n. (iii)The method also applies to computing the visibility map for a polyhedral terrain viewed from a fixed point, and yields an O((nα(n)+k)log n) algorithm.
The Achievement of Electricity Competitive Reforms: A Governance structure problem?

Building on Oliver Williamson’s original analysis, the contributors introduce new ideas, different perspectives and provide tools for better understanding changes in the approach to regulation, the reform of public utilities, and the complex problems of governance. They draw largely upon a transaction cost approach, highlighting the challenges faced by major economic sectors and identifying critical flaws in prevailing views on regulation. Deeply rooted in sector analysis, the book conveys a central message of new institutional economics: that theory should be continuously confronted by facts, and reformed or revolutionized accordingly.
Using Fuzzy Analytic Hierarchy Process and Balanced Scorecard for Commercial Bank Performance Assessment

Rapid growth in financial markets, increasing volatility and globalization are the main reasons of growing competition among commercial banks. And commercial bank performance is very importance for society and economy of country. So bank regulatory commission must find and help the commercial bank which is in trouble by evaluate their performance. Conventional evaluation models for commercial bank performance are generally tended to use some financial date such as ROA, ROE etc. However those indicators are not enough to evaluate the commercial bank performance as a whole and canpsilat give the advice to solve problem. Balanced scorecard can be used to overcome this problem by integrating all aspects of the commercial banks. At the same time, ranking the bank performance is very complex and uncertain. Besides some aspects of balanced scorecard can only be described in vague languages for most of the times by experts. So fuzzy analytic hierarchy process and balanced scorecard are applied to help bank regulatory commission to rank the commercial bank performance in this paper.
Storage Management in RDBMS

Storage management is important to the performance of DBMS. This paper gives a comprehensive overview of storage management in relational DBMS. The storage management is divided into three levels (logical, physical in-memory, and physical on-disk) and discussed separately. The logical level caches logical units (tuples or index values) of the DBMS based on the logical information to improve buffer pool usage efficiency. The physical in-memory level caches physical pages directly in the buffer pool to reduce disk accesses. Many algorithms have been designed for the general buffer or for the buffer pool of DBMS. Complex algorithms can achieve better performance than simple LRU or CLOCK algorithms. However, the overhead is high and the advantage diminishes when the buffer is big. The physical on-disk level borrows many techniques from file systems and storage systems. Because of the excellent potential write performance of log-structured organization, the Log-structured File System and related topics are discussed. Each level has its advantage and limitation for further improving performance. To achieve better performance of storage management systems of DBMS, all these three levels should be considered.
Spatial filter adaptation based on geodesic-distance for motor EEG classification

The non-stationarity inherent across sessions recorded on different days poses a major challenge for practical electroencephalography (EEG)-based Brain Computer Interface (BCI) systems. To address this issue, the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel approach to compute the variations between labelled training data and a batch of unlabelled test data based on the geodesic-distance of the discriminative subspaces of EEG data on the Grassmann manifold. Subsequently, spatial filters can be updated and features that are invariant against such variations can be obtained using a subset of training data that is closer to the test data. Experimental results show that the proposed adaptation method yielded improvements in classification performance.
Discourse and citation analysis with concept-matching

A hydropneumatic tank control for a water supply system having a closed water tank (10), an air compressor (17) and a water pump (21, 22). A plurality of pressure switches (PS18, PS23, PS25, PS26) at the air compressor detect air pressure threshholds in the tank and a plurality of water level detector electrodes (32, 33, 34, 35) detect water level threshholds in the tank. A control causes the water pump to fill the tank to a pressure level or a water level at the operator's option. A pressure switch (PS2) in a tank adapter assembly (20) shuts down the system if an over pressure condition is detected. The compressor is housed in a waterproof housing (41) and cooled by a blower (48) passing air through the housing with thermostats (TH14, TH15, TH16) functioning to protect the compressor from overheating. A baffle 52 prevents the cooling air from recirculating through the housing.
A fast asymptotically efficient algorithm for blind separation of a linear mixture of block-wise stationary autoregressive processes

We propose a novel blind source separation algorithm called Block AutoRegressive Blind Identification (BARBI). The algorithm is asymptotically efficient in separation of instantaneous linear mixtures of blockwise stationary Gaussian autoregressive processes. A novel closed-form formula is derived for a Cramér Rao lower bound on elements of the corresponding Interference-to-Signal Ratio (ISR) matrix. This theoretical ISR matrix can serve as an estimate of the separation performance on the particular data. In simulations, the algorithm is shown to be applicable in blind separation of a linear mixture of speech signals.
Distributed Asynchronous Stochastic Dual Coordinate Ascent without Duality

In this paper, we propose new Distributed Asynchronous Dual -Free Coordinate Ascent method (Asy-df SDCA), and provide the proof of conver gence rate for two cases: the individual loss is convex and the individual loss is non-convex but its expected loss is convex. Stochastic Dual Coordinate Ascent (SDCA) model is a popular method and often has better performances than stoch asti gradient descent methods in solving regularized convex loss minimization pr oblems. Dual-Free Stochastic Dual Coordinate Ascent method is a variation of S DCA, and can be applied to non-convex problem when its dual problem is meani ngless. We extend Dual-Free Stochastic Dual Coordinate Ascent method to the d istributed mode with considering the star network in this paper.
Immunogenicity of targeted lentivectors

To increase the safety and possibly efficacy of HIV-1 derived lentivectors (LVs) as an anti-cancer vaccine, we recently developed the Nanobody (Nb) display technology to target LVs to antigen presenting cells (APCs). In this study, we extend these data with exclusive targeting of LVs to conventional dendritic cells (DCs), which are believed to be the main cross-presenting APCs for the induction of a TH1-conducted antitumor immune response. The immunogenicity of these DC-subtype targeted LVs was compared to that of broad tropism, general APC-targeted and non-infectious LVs. Intranodal immunization with ovalbumin encoding LVs induced proliferation of antigen specific CD4+ T cells, irrespective of the LVs' targeting ability. However, the cytokine secretion profile of the restimulated CD4+ T cells demonstrated that general APC targeting induced a similar TH1-profile as the broad tropism LVs while transduction of conventional DCs alone induced a similar and less potent TH1 profile as the non-infectious LVs. This observation contradicts the hypothesis that conventional DCs are the most important APCs and suggests that the activation of other APCs is also meaningful. Despite these differences, all targeted LVs were able to stimulate cytotoxic T lymphocytes, be it to a lesser extent than broad tropism LVs. Furthermore this induction was shown to be dependent on type I interferon for the targeted and non-infectious LVs, but not for broad tropism LVs. Finally we demonstrated that the APC-targeted LVs were as potent in therapy as broad tropism LVs and as such deliver on their promise as safer and efficacious LV-based vaccines.
Markov chain Monte Carlo sampling of a normal distribution from an exponential distribution

The following is a simple example to show two important properties of a Markov chain Monte Carlo (MCMC) sampler and to illustrate the basic functionality of the method and issues relating to it’s usage. A Markov chain is a series where the realisation of the next element in the series, Y , is dependent only on the current state, X, and occurs with probability, P (Y |X). So the even number series forms a Markov chain but the Fibonacci sequence does not. A particle trajectory is a Markov chain but the formation of a polymer is not. MCMC is a technique for sampling from space that might be difficult to sample from because it does not have an analytical form or it may be difficult to integrate, in which case a normalising constant and a probability density function (pdf) cannot be found. The elements might be a numerical result from Monte Carlo sampling within a multivariate system, which can not be sampled directly, but are generated from other sampled variables. MCMC enables independent sampling of a target distribution, f , by using samples from a integrable function, f ∗ in a similar fashion to importance sampling. The first important property of MCMC samplers is that the weighting function need not be know in advance but is effectively generated by a stochastic process. Samples are taken from the distribution f ∗ and accepted or rejected depending on the relative probabilities of the new state compared to the old state in the target distribution, f and the relative probabilities of selection in the integrable distribution f. Consequently, the second important property of MCMC samplers is that the probability of a state need never be known to better than a normalising constant. Hastings [1] introduces a general form for an MCMC sampler that is stationary which requires that given a sample from the target distribution, f , subsequent samples will remain in f . The probability of an arbitrary transition is given by P (Y |X) = Q(Y |X)α(X,Y ) where,
Growth and Turning Properties of Adult Glial Cell–Derived Neurotrophic Factor Coreceptor &agr;1 Nonpeptidergic Sensory Neurons

Abstract An overlapping population of adult primary sensory neurons that innervate the skin express the glial cell–derived neurotrophic factor coreceptor &agr;1 (GFR&agr;1), the lectin IB4, and the “regenerative brake” phosphatase and tensin homolog deleted on chromosome 10. Using an adapted turning and growth assay, we analyzed the growth cone behavior of adult immunoselected GFR&agr;1 sensory neurons. These neurons had less robust baseline growth and reluctant responsiveness to individual growth factors but responded to synergistic types of input from glial cell–derived neurotrophic factor, hepatocyte growth factor, a phosphatase and tensin homolog deleted on chromosome 10 inhibitor, or a downstream Rho kinase inhibitor. Hepatocyte growth factor and the phosphatase and tensin homolog deleted on chromosome 10 inhibitor were associated with growth cone turning. A gradient of protein extracted from skin samples, a primary target of GFR&agr;1 axons, replicated the impact of synergistic support. Within the skin, glial cell–derived neurotrophic factor was expressed within epidermal axons, indicating an autocrine role accompanying local hepatocyte growth factor synthesis. Taken together, our findings identify unique growth properties and plasticity of a distinct population of epidermal axons that are relevant to neurologic repair and skin reinnervation.
Exploration of Image Search Results Quality Assessment

Image retrieval plays an increasingly important role in our daily lives. There are many factors which affect the quality of image search results, including chosen search algorithms, ranking functions, and indexing features. Applying different settings for these factors generates search result lists with varying levels of quality. However, no setting can always perform optimally for all queries. Therefore, given a set of search result lists generated by different settings, it is crucial to automatically determine which result list is the best in order to present it to users. This paper aims to solve this problem and makes four main innovations. First, a preference learning model is proposed to quantitatively study and formulate the best image search result list identification problem. Second, a set of valuable preference learning related features is proposed by exploring the visual characters of returned images. Third, a query-dependent preference learning model is further designed for building a more precise and query-specific model. Fourth, the proposed approach has been tested on a variety of applications including re-ranking ability assessment, optimal search engine selection, and synonymous query suggestion. Extensive experimental results on three image search datasets demonstrate the effectiveness and promising potential of the proposed method.
Postgenomic futures: translations across the machine-nature border in systems biology

Abstract This article discusses the production of new “postgenomic” knowledges that aim to be more ecological and “wholistic” than the reductionist genetics of the last forty years. It examines systems biology and, briefly, developmental systems theory, which are two approaches that attempt to model complexities in biology. System biological metaphors and languages have been in part taken from engineering models of automobiles, airplanes and robots and then applied to complex living systems. Systems biology is only the most recent example and perhaps an excellent case in which to study this movement back and forth across the machine-living organism border in Euro-American biology to track how what we know to be nature and machine is constituted. This article argues for a careful analysis of this historical production specifically around the question of what is lost in translation at these border crossings and their potential consequences.