Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of a Reporter Vector System for In Vivo Analysis of Promoter Activity in Propionibacterium freudenreichii

ABSTRACT A β-galactosidase reporter system for the analysis of promoter elements in Propionibacterium freudenreichii was designed. The pTD210 in vivo reporter vector was constructed using a promoterless lacZ gene from Bifidobacterium longum cloned into the pAMT1 plasmid. The utility of the pTD210 reporter vector was demonstrated by an investigation of six predicted promoters in P. freudenreichii. The system produced accurate and reproducible measurements that facilitated both promoter identification and the quantification of promoter activities.
A Novel Secure Steganography In Contourlet Domain

Steganography is a technique for secret data communication in an appropriate multimedia medium such as video, audio, image and text files. In this paper, a digital image steganography method provided by the Contourlet domain. In this approach contourlet coefficients within each block separately use a permutation genetic algorithm optimization for hiding the data as a mapping function. The optimal pixel adjustment process (OPAP) is applied after embedding the message to improve the hiding capacity with low distortions. The simulation results show that the quality factor in this method outperform other methods in Contourlet domain, moreover it has high resistance to important steganalysis methods in the Contourlet domain. Thus the proposed approach is also a secure method of steganography
Survey for Highly Pathogenic Avian Influenza from Poultry in Two Northeastern States, Nigeria

Highly pathogenic avian influenza (HPAI) is a major global zoonosis. It has a complex ecological distribution with almost unpredictable epidemiological features thus placing it topmost in the World Organization for Animal Health list A poultry diseases. Structured questionnaire survey of poultry farmer's knowledge, attitudes, and practices (KAP) in two Nigerian states revealed the presence of risk farming practices that may enable avian influenza high chance of introduction/reintroduction. There existed significant statistical association between farmer's educational levels and AI awareness and zoonotic awareness (P < 0.005). Poultry rearing of multiage and species (81%), multiple sources of stock (62%), inadequate dead-bird disposal (71%), and access to live bird markets (LBMs) (62%) constituted major biosecurity threats in these poultry farming communities. Haemagglutination inhibition (HI) test detected antibodies against H5 avian influenza (AI) in 8 of the 400 sera samples; rapid antigen detection test kit (RADTK) was negative for all the 400 cloaca and trachea swabs. These results and other poultry diseases similar to AI observed in this study could invariably affect avian influenza early detection, reporting, and control. We recommend strong policy initiatives towards poultry farmers' attitudinal change and increasing efforts on awareness of the implications of future HPAI outbreaks in Nigeria.
SystemC/TLM flow for SoC design and verification

As systems grow in complexity, their verification becomes a bottleneck on the design flow. In this paper we propose a top-down methodology to perform the complete flow from specifications to Register Transfer Level (RTL). Different abstraction levels such as Transaction Level Modeling (TLM) allows early system verification (with simulation or formal methods), reducing the risk of long redesign cycles. The methodology is validated by showing a case study.
Attentive processing improves object recognition

The human visual system can recognize several thousand object categories irrespective of their position and size. This combination of selectivity and invariance is built up gradually across several stages of visual processing. However, the recognition of multiple objects in cluttered visual scenes presents a difficult problem for human as well as machine vision systems. The human visual system has evolved to perform two stages of visual processing: a pre-attentive parallel processing stage, in which the entire visual field is processed at once and a slow serial attentive processing stage, in which a region of interest in an input image is selected for “specialized” analysis by an attentional spotlight. We argue that this strategy evolved to overcome the limitation of purely feed forward processing in the presence of clutter and crowding. Using a Bayesian model of attention along with a hierarchical model of feed forward recognition on a data set of real world images, we show that this two stage attentive processing can improve recognition in cluttered and crowded conditions.
Modulation of Neural Activity during Observational Learning of Actions and Their Sequential Orders

How does the brain transform perceptual representations of others' actions into motor representations that can be used to guide behavior? Here we used functional magnetic resonance imaging to record human brain activity while subjects watched others construct multipart objects under varied task demands. We find that relative to resting baseline, passive action observation increases activity within inferior frontal and parietal cortices implicated in action encoding (mirror system) and throughout a distributed network of areas involved in motor representation, including dorsal premotor cortex, pre-supplementary motor area, cerebellum, and basal ganglia (experiments 1 and 2). Relative to passive observation, these same areas show increased activity when subjects observe with the intention to subsequently reproduce component actions using the demonstrated sequential procedures (experiment 1). Observing the same actions with the intention of reproducing component actions, but without the requirement to use the demonstrated sequential procedure, increases activity in the same regions, although to a lesser degree (experiment 2). These findings demonstrate that when attempting to learn behaviors through observation, the observers' intentions modulate responses in a widely distributed network of cortical and subcortical regions implicated previously in action encoding and/or motor representation. Among these regions, only activity within the right intraparietal sulcus predicts the accuracy with which observed procedures are subsequently performed. Successful formation of motor representations of sequential procedures through observational learning is dependent on computations implemented within this parietal region.
Constant Row Maximizing Problem for Covering Arrays

A Covering Array denoted by CA(N;t,k,v) is a matrix of size N^k, where each tuple of t columns has at least one time each v^t combination of symbols. The CAs are combinatorial objects used for software testing and design of experiments in: biology, agriculture, medicine, etc. CAs can be constructed using heuristic algorithms, greedy search and algebraic procedures. The Hartman Style Rising Procedures (HSRP) are algebraic procedures to construct large CAs. These procedures create CAs using small CAs to construct large CAs. If the small $CA$s have many constant rows, the HSRP provides better large CAs. In this paper we present the constant Row Maximizing Problem (CMRP). We propose 4 distinct models to maximize the number of constant rows in a CA. The models were tested with binary and senary CAs. Our results give new upper bounds for large CAs.
Poster: Distributed Voronoi-based Acoustic Source Localization with Wireless Sensor Networks

This paper presents DiVA, a new acoustic source localization scheme that uses an ad-hoc network of microphone sensor nodes to produce an accurate estimate of the source's location. DiVA uses pairwise comparisons of sound detection timestamps between local Voronoi neighbors to identify the node closest to the acoustic source and then estimates the source's location. The scheme improves on the state of the art by effectively dealing with anchor nodes' position error, time stamp measurement error and time synchronization error in real world conditions. Through simulation and experimental evaluations, DiVA is shown to be more robust than existing solutions under different error conditions.
0 50 20 64 v 4 2 A pr 2 00 6 Gauge-invariant Non-spherical Metric Perturbations of Schwarzschild BlackHole Spacetimes

The theory of gauge-invariant non-spherical metric pertur bations of Schwarzschild black hole spacetimes is now well established. Yet, as diffe rent notations and conventions have been used throughout the years, the literature on the subjec t is often confusing and sometimes confused. The purpose of this paper is to review and collect t he relevant expressions related to the Regge-Wheeler and Zerilli equations for the odd and ev en-parity perturbations of a Schwarzschild spacetime. Special attention is paid to the f orm they assume in the presence of matter-sources and, for the two most popular conventions in the literature, to the asymptotic expressions and gravitational-wave amplitudes. Besides p ointing out some inconsistencies in the literature, the expressions collected here could serve as a quick reference for the calculation of the perturbations of Schwarzschild black hole space times driven by generic sources and for those approaches in which gravitational waves are extra cted from numerically generated spacetimes. PACS numbers: 04.30. Db, 04.40. Dg, 04.70. Bw,
Corporate Control, Corporate Power: A Twentieth Century Fund Study

List of tables and figures Foreword Preface 1. Corporate control: background and issues 2. Control and strategic position 3. Control of the large corporation: evolution and present status 4. Financial control of the large corporation 5. Government of the large corporation 6. The centralisation of corporate power 7. Power, responsibility, and conflicting imperatives Notes Index.
Pulse shaping for differential offset-QPSK

Pulse shaping is examined as a means to improve the performance of a differential offset quadrature phase-shift keying system in a bandwidth-constrained environment. Through optimization with respect to a composite Nyquist criterion, the derived pulse shapes have comparable performance to a pi/4-differential quadrature phase-shift keying in an additive white Gaussian noise (AWGN) channel and better performance in a hard-limited AWGN channel
Alternative Approaches to Estimate Returns to Scale in DEA-Models

: This paper presents a number of approaches to estimate returns to scale properties of general multiple-input multiple-output technologies. Both qualitative scale efficiency-properties and quantitative scale elasticity-properties are covered. The estimation approaches considered are based on data envelopment analysis (DEA)-models using primal production data on inputs and outputs. We propose an approach to estimate scale elasticity based on the primal efficiency problem. This alternative approach is derived as a direct approximation of the differentials of the efficiency measures in the scale elasticity definition. The different approaches to estimate scale efficiency and scale elasticity are exemplified and compared using a small dataset.
OCD-Like Behaviors Caused by a Neuropotentiating Transgene Targeted to Cortical and Limbic D1+ Neurons

To study the behavioral role of neurons containing the D1 dopamine receptor (D1+), we have used a genetic neurostimulatory approach. We generated transgenic mice that express an intracellular form of cholera toxin (CT), a neuropotentiating enzyme that chronically activates stimulatory G-protein (Gs) signal transduction and cAMP synthesis, under the control of the D1 promoter. Because the D1 promoter, like other CNS-expressed promoters, confers transgene expression that is regionally restricted to different D1+ CNS subsets in different transgenic lines, we observed distinct but related psychomotor disorders in different D1CT-expressing founders. In a D1CT line in which transgene expression was restricted to the following D1+ CNS regions—the piriform cortex layer II, layers II–III of somatosensory cortical areas, and the intercalated nucleus of the amygdala—D1CT mice showed normal CNS and D1+ neural architecture but increased cAMP content in whole extracts of the piriform and somatosensory cortex. These mice also exhibited a constellation of compulsive behavioral abnormalities that strongly resembled human cortical-limbic–induced compulsive disorders such as obsessive–compulsive disorder (OCD). These compulsive behaviors included episodes of perseverance or repetition of any and all normal behaviors, repetitive nonaggressive biting of siblings during grooming, and repetitive leaping. These results suggest that chronic potentiation of cortical and limbic D1+ neurons thought to induce glutamatergic output to the striatum causes behaviors reminiscent of those in human cortical-limbic–induced compulsive disorders.
Household Composition, Labor Markets, and Labor Demand: Testing for Separation in Agricultural Household Models

This paper tests the separation of farm labor supply and labor demand decisions, using the observation that household composition is an important determinant of farm labor use with nonseparation. After assessing the conditions under which the test has power against several alternatives, an empirical model is developed to test the proposition that farm employment is independent of family composition. The model is estimated on a data set from rural Java. The null hypothesis that farm labor allocation decisions are independent of household structure is not rejected. The results are robust to different specifications of the labor demand function. Copyright 1992 by The Econometric Society.
Intrusion Detection, Diagnosis, and Recovery with Self-Securing Storage (CMU-CS-02-140)

Self-securing storage turns storage devices into active parts of an intrusion survival strategy. From behind a thin storage interface (e.g., SCSI or CIFS), a self-securing storage server can watch storage requests, keep a record of all storage activity, and prevent compromised clients from destroying stored data. This paper describes three ways selfsecuring storage enhances an administrator’s ability to detect, diagnose, and recover from client system intrusions. First, storage-based intrusion detection offers a new observation point for noticing suspect activity. Second, post-hoc intrusion diagnosis starts with a plethora of normally-unavailable information. Finally, post-intrusion recovery is reduced to restarting the system with a pre-intrusion storage image retained by the server. Combined, these features can improve an organization’s ability to survive successful digital intrusions. We thank the members and companies of the PDL Consortium (including EMC, Hewlett-Packard, Hitachi, IBM, Intel, Network Appliance, Panasas, Seagate, Sun, and Veritas) for their interest, insights, feedback, and support. We thank IBM and Intel for hardware grants supporting our research efforts. This material is based on research sponsored by the Air Force Research Laboratory, under agreement number F49620-01-1-0433. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the U.S. Government. This work is partially funded by DARPA/ITO’s OASIS program (Air Force contract number F30602-99-2-0539-AFRL). Craig Soules is supported by a USENIX Fellowship. Garth Goodson is supported by an IBM Fellowship.
A mRNA-Responsive G-Quadruplex-Based Drug Release System

G-quadruplex-based drug delivery carriers (GDDCs) were designed to capture and release a telomerase inhibitor in response to a target mRNA. Hybridization between a loop on the GDDC structure and the mRNA should cause the G-quadruplex structure of the GDDC to unfold and release the bound inhibitor, anionic copper(II) phthalocyanine (CuAPC). As a proof of concept, GDDCs were designed with a 10-30-mer loop, which can hybridize with a target sequence in epidermal growth factor receptor (EGFR) mRNA. Structural analysis using circular dichroism (CD) spectroscopy showed that the GDDCs form a (3 + 1) type G-quadruplex structure in 100 mM KCl and 10 mM MgCl2 in the absence of the target RNA. Visible absorbance titration experiments showed that the GDDCs bind to CuAPC with Ka values of 1.5 × 105 to 5.9 × 105 M−1 (Kd values of 6.7 to 1.7 μM) at 25 °C, depending on the loop length. Fluorescence titration further showed that the G-quadruplex structure unfolds upon binding to the target RNA with Ka values above 1.0 × 108 M−1 (Kd values below 0.01 μM) at 25 °C. These results suggest the carrier can sense and bind to the target RNA, which should result in release of the bound drug. Finally, visible absorbance titration experiments demonstrated that the GDDC release CuAPC in response to the target RNA.
Design and implementation considerations for monitoring and evaluating information systems

The effective administration of information systems requires a well-defined methodology for evaluating system performance and analyzing system usage. This paper describes some of the more important design and implementation considerations relevant to monitoring and evaluating information systems. The general objectives of information system monitoring and evaluation are discussed, with a focus on system efficiency analysis and user interaction analysis. The potential for utilizing software monitoring mechanisms within information systems is highlighted and the detailed structure of a monitor record, generated from a representative software monitor, is presented.
Consensus filter based target-enclosing strategies for multiple nonholonomic vehicles

This paper deals with cooperative target-enclosing problems using consensus filter for multiple vehicle systems. Proposed strategies are based on consensus seeking algorithms and at least one vehicle only has to acquire the states of target-object. In addition, the measurement signals are made to be agreed using the consensus filters when each vehicle acquires the noisy signal of the states of the target-object. To analyze the enclosing problem, algebraic graph theory and matrix theory are utilized. Numerical simulations and experiments are carried out to demonstrate the effectiveness of proposed methods.
On the Prospects for Detection and Identification of Low-Frequency Oscillation Modes in Rotating B Type Stars

We study how rotation affects observable amplitudes of high-order g- and mixed r/g-modes and examine prospects for their detection and identification. Our formalism, which is described in some detail, relies on a nonadiabatic generalization of the traditional approximation. Numerical results are presented for a number of unstable modes in a model of SPB star, at rotation rates up to 250 km/s. It is shown that rotation has a large effect on mode visibility in light and in mean radial velocity variations. In most cases, fast rotation impairs mode detectability of g-modes in light variation, as Townsend (2003b) has already noted, but it helps detection in radial velocity variation. The mixed modes, which exist only at sufficiently fast rotation, are also more easily seen in radial velocity. The amplitude ratios and phase differences are strongly dependent on the aspect, the rotational velocity and on the mode. The latter dependence is essential for mode identification.
Task and motion planning using physics-based reasoning

For everyday manipulation tasks, the combination of task and motion planning is required regarding the need of providing the set of possible subtasks which have to be done and how to perform them. Since many alternative plans may exist, the determination of their feasibility and the identification of the best one is a great challenge in robotics. To address this, this paper proposes: a) a version of GraphPlan (one of the best current approaches to task planning) that has been modified to use ontological knowledge and to allow the retrieval of all possible plans; and b) a physics-based reasoning process that determines the feasibility of the resulting plans and an associated cost that allows to select the best one among them. The proposed framework has been implemented and is illustrated through an example.
S EPIA : Surface Span Extension to Syntactic Dependency Precision-based MT Evaluation

We present a new Machine Translation (MT) evaluation metric, SEPIA. SEPIA falls within the class of syntactically-aware evaluation metrics, which have been getting a lot of attention recently (Liu and Gildea, 2005; Owczarzak et al., 2007; Giménez and Màrquez, 2007). Specifically, S EPIA uses dependency representation but extends it to include surface span as a factor in the evaluation score. The dependency surface span is the surface distance between two words that are in a direct relationship in a dependency tree. The basic idea behind S EPIA is that long-distance dependencies should receive a greater weight in MT evaluation metrics than shortdistance dependencies. This is because we suspect that having more long-distance matches indicates a higher degree of grammaticality. In the rest of this document we describe the S EPIA metric and its variants, and the publicly available S EPIA package.
Machine Learning Algorithms with Co-occurrence Based Term Association for Text Mining

In this paper, an effective method for computing term association from a text corpus is presented. Two machine learning algorithms are employed to evaluate the effectiveness of the proposed method for text mining. The co-occurrence based term association method is to overcome the problem of lack of relationship between words for keyword based text mining and improve the performance of text mining. The experiments are conducted on the standard Reuter-21578 data set and 20 news group data set. Different number of associated terms are compared in the experiments. Experimental results show that the proposed method can achieve better results on different machine learning algorithms when measured by F measure.
BCG Vaccine-Induced Neuroprotection in a Mouse Model of Parkinson's Disease

There is a growing interest in using vaccination with CNS antigens to induce autoreactive T cell responses that home to damaged areas in the CNS and ameliorate neurodegenerative disease. Neuroprotective vaccine studies have focused on administering oligodendrocyte antigens or Copaxone® in complete Freund's adjuvant (CFA). Theoretical considerations, however, suggest that vaccination with a neuronal antigen may induce more robust neuroprotective immune responses. We assessed the neuroprotective potential of vaccines containing tyrosine hydroxylase (a neuronal protein involved in dopamine synthesis) or Copaxone® in CFA in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of Parkinson's disease. Surprisingly, we observed that the main beneficial factor in these vaccines was the CFA. Since the major immunogenic component in CFA is Mycobacterium tuberculosis, which closely related to the bacille Calmette-Guérin (BCG) that is used in human vaccines, we tested BCG vaccination in the MPTP mouse model. We observed that BCG vaccination partially preserved markers of striatal dopamine system integrity and prevented an increase in activated microglia in the substantia nigra of MPTP-treated mice. These results support a new neuroprotective vaccine paradigm in which general (nonself-reactive) immune stimulation in the periphery can limit potentially deleterious microglial responses to a neuronal insult and exert a neurorestorative effect in the CNS. Accordingly, BCG vaccination may provide a new strategy to augment current treatments for a wide range of neuropathological conditions.