Miracle-dz/UniFAR-FTUs huggingface dataset:
Bacterial genotoxicity bioreporters

Ever since the introduction of the Salmonella typhimurium mammalian microsome mutagenicity assay (the ‘Ames test’) over three decades ago, there has been a constant development of additional genotoxicity assays based upon the use of genetically engineered microorganisms. Such assays rely either on reversion principles similar to those of the Ames test, or on promoter–reporter fusions that generate a quantifiable dose‐dependent signal in the presence of potential DNA damaging compounds and the induction of repair mechanisms; the latter group is the subject of the present review. Some of these assays were only briefly described in the scientific literature, whereas others have been developed all the way to commercial products. Out of these, only one, the umu‐test, has been fully validated and ISO‐ and OECD standardized. Here we review the main directions undertaken in the construction and testing of bacterial‐based genotoxicity bioassays, including the attempts to incorporate at least a partial metabolic activation capacity into the molecular design. We list the genetic modifications introduced into the tester strains, compare the performance of the different assays, and briefly describe the first attempts to incorporate such bacterial reporters into actual genotoxicity testing devices.
Data Locality in Graph Engines: Implications and Preliminary Experimental Results

The size of graphs has dramatically increased. Graph engines for a single machine have been emerged to process these graphs efficiently. However, existing engines have overlooked a data locality which is an imperative factor to improve the performance of these engines in the previous literature. In this paper, we show the importance of data locality with graph algorithms by running on graph engines based on a single machine.
A novel local nonlinear preferential attachment model for Wireless Sensor Networks

We propose a novel Wireless Sensor Networks (WSNs) evolution model based on complex network theory. In real WSNs, the energy, power and transmission radius of the sensor nodes are limited, so nodes only communicate with the nodes in local area network (LAN). We exploit this property by introducing a local nonlinear preferential attachment strategy into our model. The new model has stable degree distribution and the others statistical properties. By adjusting some parameter of this model, we achieved a variety of degree distributions, from the exponential distribution to the power-law distribution. Moreover, we find some suitable range of the parameter a and m would make the network model have optimal performance.
Enhanced eigenspace separation transform for classification

This paper presents Enhanced Eigenspace Separation Transform (EEST), a novel supervised dimensionality reduction technique for classification. EEST is motivated from the Eigenspace Separation Transform (EST). The criterion of EEST is to maximize the difference in the average lengths of vectors in the underlying two classes, and minimize the intra-class average distances, to improve the generalization capacity of a classifier. We propose upper bounds of the criterion, and a specific solution space to attain these bounds. Existence of such a solution is restricted, thereby we have considered the orthonormal space of the upper bounds in order to achieve better dimensionality reduction, and improve the generalization accuracy of the classifier. A simple Nearest Neighborhood (NN) classification approach is adopted for classification to highlight the novelty of the proposed scheme. Theoretical analysis of the proposed techniques is also carried out. Different synthetic data sets have been used to evaluate the advantages of EEST over EST. Extensive empirical studies are made; and the proposed method is compared with three closely related schemes using real-world data sets to verify the efficiency of the proposed method.
Online ngram-enhanced topic model for academic retrieval

Applying topic model to text mining has achieved a great success. However, state-of-art topic modeling methods still have potential to improve in academic retrieval field. In this paper, we propose an online unified topic model, which is ngram-enhanced. Our model discovers topics with unigrams as well as topical bigrams and is updated by an online inference algorithm with the new incoming data streams. On this basis, we combine our model into the query likelihood model and develop an integrated academic searching system. Experiment results on ACM collection show that our proposed methods outperform the existing approaches on document modeling and searching accuracy. Especially, we prove the efficiency of our system on academic retrieval problem.
Fluorescent Tobacco mosaic virus-Derived Bio-Nanoparticles for Intravital Two-Photon Imaging

Multi-photon intravital imaging has become a powerful tool to investigate the healthy and diseased brain vasculature in living animals. Although agents for multi-photon fluorescence microscopy of the microvasculature are available, issues related to stability, bioavailability, toxicity, cost or chemical adaptability remain to be solved. In particular, there is a need for highly fluorescent dyes linked to particles that do not cross the blood brain barrier (BBB) in brain diseases like tumor or stroke to estimate the functional blood supply. Plant virus particles possess a number of distinct advantages over other particles, the most important being the multi-valency of chemically addressable sites on the particle surface. This multi-valency, together with biological compatibility and inert nature, makes plant viruses ideal carriers for in vivo imaging agents. Here, we show that the well-known Tobacco mosaic virus is a suitable nanocarrier for two-photon dyes and for intravital imaging of the mouse brain vasculature.
Director Strings Revisited: A Generic Approach to the Efficient Representation of Free Variables in Higher-order Rewriting

The representation of free variables is crucial for the efficiency of the implementation of various kinds of rewrite systems. We give an innovative, although very natural, representation of variables abstract enough to fit in many different frameworks and more satisfactory from an operational perspective than usual representations. This work also provides a generalization of director strings for the λ-calculus.
Generic Convex Collision Detection using Support Mapping

A collision detection algorithm that is computationally efficient, numerically stable, and straightforward to implement is a valuable tool in any virtual environment. This includes the ability to determine accurate proximity information, such as, penetration depth, contact position, and separating normal. We explore the practical and scalable issues of support mapping for use in detecting contact information for convex shapes. While support mapping is a popular technique used in common algorithms, such as, GJK, EPA, and XenonCollide, we demonstrate how to implement an uncomplicated algorithm and identify pitfalls in three-dimensional space. We explore the scalable nature of the technique for use in massively parallel execution environments and emphasise trade-offs in terms of performance and accuracy to achieve consistent real-time frame-rates through optimisations.
Towards Structural Analysis of Audio Recordings in the Presence of Musical Variations

One major goal of structural analysis of an audio recording is to automatically extract the repetitive structure or, more generally, the musical form of the underlying piece of music. Recent approaches to this problem work well for music, where the repetitions largely agree with respect to instrumentation and tempo, as is typically the case for popular music. For other classes of music such as Western classical music, however, musically similar audio segments may exhibit significant variations in parameters such as dynamics, timbre, execution of note groups, modulation, articulation, and tempo progression. In this paper, we propose a robust and efficient algorithm for audio structure analysis, which allows to identify musically similar segments even in the presence of large variations in these parameters. To account for such variations, our main idea is to incorporate invariance at various levels simultaneously: we design a new type of statistical features to absorb microvariations, introduce an enhanced local distance measure to account for local variations, and describe a new strategy for structure extraction that can cope with the global variations. Our experimental results with classical and popular music show that our algorithm performs successfully even in the presence of significant musical variations.
Searching and Filtering Tweets: CSIRO at the TREC 2012 Microblog Track

Abstract : We report on the participation of the CSIRO team in the TREC 2012 Microblog Track. We participated with four automatic runs for the ad hoc search task and four automatic runs for the filtering task. In the ad hoc search task, we experiment with different pre-processing and query expansion techniques. Our most important finding is highlighting the value of systematic pre-processing of tweets and its impact on improving the effectiveness of search. In the filtering task, we apply different feature extraction and classification techniques. We demonstrate the potential of using SVM classifiers for filtering tweets for a given topic.
mutLBSgeneDB: mutated ligand binding site gene DataBase

Mutations at the ligand binding sites (LBSs) can influence protein structure stability, binding affinity with small molecules, and drug resistance in cancer patients. Our recent analysis revealed that ligand binding residues had a significantly higher mutation rate than other parts of the protein. Here, we built mutLBSgeneDB (mutated Ligand Binding Site gene DataBase) available at http://zhaobioinfo.org/mutLBSgeneDB. We collected and curated over 2300 genes (mutLBSgenes) having ∼12 000 somatic mutations at ∼10 000 LBSs across 16 cancer types and selected 744 drug targetable genes (targetable_mutLBSgenes) by incorporating kinases, transcription factors, pharmacological genes, and cancer driver genes. We analyzed LBS mutation information, differential gene expression network, drug response correlation with gene expression, and protein stability changes for all mutLBSgenes using integrated genetic, genomic, transcriptomic, proteomic, network and functional information. We calculated and compared the binding affinities of 20 carefully selected genes with their drugs in wild type and mutant forms. mutLBSgeneDB provides a user-friendly web interface for searching and browsing through seven categories of annotations: Gene summary, Mutated information, Protein structure related information, Differential gene expression and gene-gene network, Phenotype information, Pharmacological information, and Conservation information. mutLBSgeneDB provides a useful resource for functional genomics, protein structure, drug and disease research communities.
Louis Working Paper Series An Analytical Approach to Buffer-Stock Saving under Borrowing Constraints

The profession has been longing for closed-form solutions to consumption functions under uncertainty and borrowing constraints. This paper proposes an analytical approach to solving general-equilibrium bu¤er-stock saving models with both idiosyncratic and aggregate uncertainties as well as liquidity constraints. It is shown analytically that an individuals optimal consumption plan follows the rule of thumb: Consumption is proportional to a target level of wealth, with the marginal propensity to consume dependent on the state of the macroeconomy. I apply this method to address two long-standing puzzles in general equilibrium: the "excess smoothness" and "excess sensitivity" of consumption with respect to income changes. Some of my ndings sharply contradict the conventional wisdom.
ICT "bottlenecks" and the wealth of nations: a contribution to the empirics of economic growth

The bulk of ICT is made by weightless, implementable and infinitely reproducible knowledge products (such as software and databases) (Quah, 1999) which are vehicled by telephone lines, accessed through internet hosts and processed and exchanged through personal computers. The specification of the labour augmenting factor in the aggregate production function with proxies of the above mentioned factors crucially affecting the diffusion of (non rivalrous and almost non excludable) knowledge products provides interesting answers to some of the open issues in the existing growth literature. The most recent information, even though available for a limited period, shows that telephone lines, personal computers, mobile phones and internet hosts significantly affect levels and growth of income per worker across countries. The result is robust to changes in the econometric specification and in the estimation approach. Exogenous subsample splits find that the ICT-growth link is stronger in OECD countries and in countries with high financial freedom confirming that financial liberalisation plays a crucial role in stimulating ICT innovation and growth.
Trajectory planning for automated parking using multi-resolution state roadmap considering non-holonomic constraints

This paper presents a trajectory planning method for automated parking. The proposed method constructs a state roadmap in which each node contains not only position but also orientation information of the vehicle. The roadmap is constructed by dividing the orientation space in multiple resolutions considering the non-holonomic constraints of the vehicle and the collision-avoidance constraints between the vehicle and the boundary of the parking environment. Using the state roadmap, a complex parking trajectory composed of both forward and reverse motions can be computed with small online computation cost. The proposed method is evaluated in both numerical simulations and an experiment using an electric vehicle.
Dynamics of aminopyridine block of potassium channels in squid axon membrane

Aminopyridines (2-AP, 3-AP, and 4-AP) selectively block K channels of squid axon membranes in a manner dependent upon the membrane potential and the duration and frequency of voltage clamp pulses. They are effective when applied to either the internal or the external membrane surface. The steady-state block of K channels by aminopyridines is more complete for low depolarizations, and is gradually relieved at higher depolarizations. The K current in the presence of aminopyridines rises more slowly than in control, the change being more conspicuous in 3-AP and 4-AP than in 2-AP. Repetitive pulsing relieves the block in a manner dependent upon the duration and interval of pulses. The recovery from block during a given test pulse is enhanced by increasing the duration of a conditioning depolarizing prepulse. The time constant for this recovery is in the range of 10-20 ms in 3-AP and 4-AP, and shorter in 2-AP. Twin pulse experiments with variable pulse intervals have revealed that the time course for re-establishment of block is much slower in 3-AP and 4-AP than in 2-AP. These results suggest that 2-AP interacts with the K channel more rapidly than 3-AP and 4-AP. The more rapid interaction of 2-AP with K channels is reflected in the kinetics of K current which is faster than that observed in 3-AP or 4-AP, and in the pattern of frequency-dependent block which is different from that in 3-AP or 4-AP. The experimental observations are not satisfactorily described by alterations of Hodgkin-Huxley n-type gating units. Rather, the data are consistent with a simple binding scheme incorporating no changes in gating kinetics which conceives of aminopyridine molecules binding to closed K channels and being released from open channels in a voltage-dependent manner.
Consideration on the performance of kernel adaptive filters for the mixture of linear and non-linear environments

In this paper, we consider the characteristics of the kernel adaptive filters for the mixture of linear and non-linear environments. We first consider employing a linear kernel as one of the kernels in multi-kernel adaptive filters. It is pointed out that the convergence characteristics of the filter corresponding to the linear kernel is affected by the selection of the other kernels. Then, we propose a new structure which uses a linear and a multi-kernel adaptive filter simultaneously. In the proposed method, the a posteriori error of the linear filter is calculated before updating the multi-kernel adaptive filter. Then, the a posteriori error is used to calculate the update term for the multi-kernel adaptive filter. The effectiveness of the proposed method is confirmed by computer simulations.
Dark halo mergers and the formation of a universal profile

We argue that a universal density profile for dark matter haloes arises as a natural consequence of hierarchical structure formation: it is a fixed point in the process of repeated mergers. We present analytic and numerical arguments for the emergence of a particular form of the central cusp profile. At small radii, the density should vary as r−α, with α determined by the way in which the characteristic density of haloes scales with their mass. If small haloes are dense, then α is large. The mass–density relation can be related to the power spectrum of initial fluctuations, P(k), through ‘formation time’ arguments. Early structure formation leads to steep cusps. For P(k) ∼ kn we find α ≃ 3(3 + n)/(5 + n). The universal profile is generated by tidal stripping of small haloes as they merge with larger objects.
Multidimensional Data Mining by Means of Randomly Travelling Hyper-Ellipsoids

The present study presents a new approach to automatic data clustering and classification problems in large and complex databases and, at the same time, derives specific types of explicit rules describing each cluster. The method works well in both sparse and dense multidimensional data spaces. The members of the data space can be of the same nature or represent different classes. A number of N -dimensional ellipsoids are used for enclosing the data clouds. Due to the geometry of an ellipsoid and its free rotation in space the detection of clusters becomes very efficient. The method is based on genetic algorithms that are used for the optimization of location, orientation and geometric characteristics of the hyper-ellipsoids. The proposed approach can serve as a basis for the development of general knowledge systems for discovering hidden knowledge and unexpected patterns and rules in various large databases. Keywords—Classification, clustering, data minig, genetic algorithms.
A Study of Influences and Life Choices: African American Males From an Urban Pre-Trial Detention Center and the Navy

OF THE DISSERTATION A Study of Influences and Life Choices: African American Males from An Urban Pretrial Detention Center and the Navy by Patrick 0. Amwine University of North Florida Jacksonville, Florida Professor G. Pritchy Smith, Chairperson This study was designed to explore the influences, experiences, and disparate life choices of eight African American males from the Jacksonville's Pre-trial Detention Center and the U.S. Navy. The focus of this project was on the choices made by the participants and possible reasons for those choices. Specifically, the research question for this study is "How do some African American males from Jacksonville's Pre-trial Detention Center and the Navy describe their life experiences and the influences of these experiences on their choices? '' The framework for this project was a cross-case and cross-site study. The sites were the Jacksonville Pre-trial Detention Center and the Navy. There were four participants from the Jacksonville Pre-trial Detention Center and four from the Navy. The research yielded some interesting results. All of the participants took full responsibility for their choices. At no time did any of these young men blame anyone but themselves for the bad decisions they made.
Hemodynamic response magnetic resonance imaging: application for renal hemodynamic characterization.

BACKGROUND The clinical use of iodinated radiocontrast agents or gadolinium for renal perfusion imaging is limited in the presence of renal dysfunction. We have previously demonstrated the feasibility of hemodynamic response imaging (HRI), a functional magnetic resonance imaging (MRI) method combined with hypercapnia and hypercapnic-hyperoxia, for monitoring changes in liver perfusion and hemodynamics. The aim of the present study was to evaluate the utility of HRI for monitoring changes in renal perfusion and hemodynamics. METHODS Renal HRI maps were acquired during graded hypercapnia (95% air + 5% CO2) and hypercapnic-hyperoxia (95% O2 + 5% CO2) in control mice. The utility of HRI for monitoring changes in renal perfusion and oxygenation was evaluated using pharmacological inhibition of nitric oxide synthase and cycloxygenase as well as in rhabdomyolysis-induced acute kidney injury (AKI) in mice. HRI results were further interpreted using Doppler ultrasound (US). RESULTS Renal HRI maps revealed pronounced signal-intensity changes in response to both hypercapnia and hypercapnic-hyperoxia, reflecting intense vascular reactivity. These changes were significantly attenuated following the pharmacological intervention and during AKI, corresponding with hampered perfusion dynamics, as confirmed by Doppler US. CONCLUSIONS The applicability of the non-invasive HRI method suggests its potential use for the evaluation of renal perfusion and vascular reactivity, excluding the need for contrast-agent administration.
Hedging Strategies : Complete and Incomplete Systems of Markets

We are motivated by the latest statistical facts that weather directly affects about 20% of the U.S. economy and, as a result energy companies experience enormous potential losses due to weather that is colder or warmer than expected for a certain period of a year. Incompleteness and illiquidity of markets renders hedging the exposure using energy as the underlying asset impossible. We attempt to price and hedge a written European call option with an asset that is highly correlated with the underlying asset; still, a significant amount of the total risk cannot be diversified. Yet, our analysis begins by considering hedging in a complete markets system that can be utilised as a theoretical point of reference, relative to which we can assess incompleteness. The Black-Scholes Model is introduced and the Monte Carlo approach is used to investigate the effects of three hedging strategies adopted; Delta hedging, Static hedging and a Stop-Loss strategy. Next, an incomplete system of markets is assumed and the Minimal Variance approach is demonstrated. This approach results in a non-linear PDE for the option price. We use the actuarial standard deviation principle to modify the PDE to account for the unhedgeable risk. Based on the derived PDE, two additional hedging schemes are examined: the Delta hedging and the Stop-loss hedging. We set up a risk-free bond to keep track of any money injected or removed from the portfolios and provide comparisons between the hedging schemes, based on the Profit/Loss distributions and their main statistical features, obtained at expiry.