Miracle-dz/UniFAR-FTUs huggingface dataset:
Compromised Influenza Virus-Specific CD8+-T-Cell Memory in CD4+-T-Cell-Deficient Mice

ABSTRACT The primary influenza A virus-specific CD8+-T-cell responses measured by tetramer staining of spleen, lymph node, and bronchoalveolar lavage (BAL) lymphocyte populations were similar in magnitude for conventional I-Ab+/+ and CD4+-T-cell-deficient I-Ab−/− mice. Comparable levels of virus-specific cytotoxic-T-lymphocyte activity were detected in the inflammatory exudate recovered by BAL following challenge. However, both the size of the memory T-cell pool and the magnitude of the recall response in the lymphoid tissues (but not the BAL specimens) were significantly diminished in mice lacking the CD4+ subset. Also, the rate of virus elimination from the infected respiratory tract slowed at low virus loads following challenge of naïve and previously immunized I-Ab−/− mice. Thus, though the capacity to mediate the CD8+-T-cell effector function is broadly preserved in the absence of concurrent CD4+-T-cell help, both the maintenance and recall of memory are compromised and the clearance of residual virus is delayed. These findings are consistent with mathematical models that predict virus-host dynamics in this, and other, models of infection.
Quiescent center initiation in the Arabidopsis lateral root primordia is dependent on the SCARECROW transcription factor

Lateral root formation is an important determinant of root system architecture. In Arabidopsis, lateral roots originate from pericycle cells, which undergo a program of morphogenesis to generate a new lateral root meristem. Despite its importance for root meristem organization, the onset of quiescent center (QC) formation during lateral root morphogenesis remains unclear. Here, we used live 3D confocal imaging to monitor cell organization and identity acquisition during lateral root development. Our dynamic observations revealed an early morphogenesis phase and a late meristem formation phase as proposed in the bi-phasic growth model. Establishment of lateral root QCs coincided with this developmental phase transition. QC precursor cells originated from the outer layer of stage II lateral root primordia, within which the SCARECROW (SCR) transcription factor was specifically expressed. Disrupting SCR function abolished periclinal divisions in this lateral root primordia cell layer and perturbed the formation of QC precursor cells. We conclude that de novo QC establishment in lateral root primordia operates via SCR-mediated formative cell division and coincides with the developmental phase transition. Summary: Live imaging reveals that the de novo establishment of organizing center cells in Arabidopsis lateral root primordia is dependent on SCARECROW expression and coincides with a developmental phase transition.
CubeQuery: Tangible Interface for Creating and Manipulating Database Queries

We demonstrate CubeQuery, a tangible user interface providing a physical way to both create and manipulate basic database queries. This interactive installation is designed for individual faceted browsing and allows users to explore contents of a database, i.e., a music library. While each tangible represents an individual search parameter of a search request, the physical arrangement of multiple tangibles permits the combination of search parameters by utilizing basic logical operators. Goal of this research is to explore the practicality of spatial arrangement of tangibles to ease the process of faceted browsing.
Throughput and Delay Performance of DSL Broadband Access with Cross-Layer Dynamic Spectrum Management

DSL broadband access suffers from crosstalk among different lines within the same cable bundle. Dynamic spectrum management (DSM) refers to a set of techniques to mitigate the impact of crosstalk leading to spectacular performance gains. DSM research has mainly aimed at physical layer performance metrics, such as data rates and transmit powers. However, for many applications higher-layer performance metrics, such as throughput and delay, may be much more important to improve user satisfaction. In this paper, we provide a cross-layer DSM framework to study throughput and delay performance by looking at scheduling and DSM together. We show how optimal scheduling can be combined with both optimal and suboptimal DSM and provide throughput-optimal scheduling algorithms which require only polynomial complexity. We analytically study the impact on delay performance of achieving throughput-optimality with suboptimal DSM compared to optimal DSM. We then present extensions that significantly improve delay performance by exploiting the specific structure of the problem, such as the temporal-spectral correlation property. Furthermore, we propose a second cross-layer DSM framework that achieves throughput-optimal scheduling with suboptimal DSM, but in addition also significantly reduces overall power consumption. Finally, we analyze and quantify the tradeoff between throughput, delay and power consumption for concrete DSL scenarios.
An integrated general purpose automated test environment

As software systems become more and more complex, both the complexity of the testing effort and the cost of maintaining the results of that effort increase proportionately. Most existing test environments lack the power and flexibility needed to adequately test significant software systems. The CONVEX Integrated Test Environment (CITE) is discussed as an answer to the need for a more complete and powerful general purpose automated software test system.
Building detection in complex scenes thorough effective separation of buildings from trees

Effective separation of buildings from trees is a major challenge in image-based automatic building detection. This paper presents a three-step method for effective separation of buildings from trees using aerial imagery and lidar data. First, it uses cues such as height to remove objects of low height such as bushes, and width to exclude trees with small horizontal coverage. The height threshold is also used to generate a ground mask where buildings are found to be more separable than in so-called normalized DSM. Second, image entropy and color information are jointly applied to remove easily distinguishable trees. Finally, an innovative rule-based procedure is employed using the edge orientation histogram from the imagery to eliminate false positive candidates. The improved building detection algorithm has been tested on different test areas and it is shown that the algorithm offers high building detection rate in complex scenes which are hilly and densely vegetated.
Nanogap Junctions and Carbon Nanotube Networks for Chemical Sensing and Molecular Electronics

Title of Document: NANOGAP JUNCTIONS AND CARBON NANOTUBE NETWORKS FOR CHEMICAL SENSING AND MOLECULAR ELECTRONICS Gokhan Esen, Doctor of Philosophy, 2006 Directed By: Associate Professor, Michael S. Fuhrer, Department of Physics This thesis work may be divided into two parts. The first part (chapters 2-7) focuses on the fabrication of a particular test structure, the electromigration (EM) formed metal nanogap junction, for studying the conduction through single molecules and for hydrogen sensing. The second part (chapters 8 and 9) focuses on carbon nanotube networks as electronic devices for chemical sensing. Chapters 2-4 discuss the formation of nanogap junctions in thin gold lines fabricated via feedback controlled electromigration. Using a feedback algorithm and experimenting on thin gold lines of different cross sections, I show that the feedback controls nanogap formation via controlling the temperature of the junction. Chapters 5 and 6 discuss the background and my experimental efforts towards fabricating superconducting electrodes for single molecule electronics research. Chapter 7 discusses the application of the techniques of chapters 2-4 to form palladium nanogaps via electromigration. I show that such devices can be used as hydrogen sensors, but suffer from slow response times (on the order of minutes). The results are discussed in the context of the in-plane stress buildup between the palladium metallization and the SiO2 substrate. The use of nanotube networks as chemical sensors is discussed in the second part of the thesis (chapters 8 and 9). I show measurements of the resistance and frequency-dependent (50 Hz 20 KHz) gate capacitance of carbon nanotube thin film transistors (CNT-TFTs) as a function of DC gate bias in ultra-high vacuum as well as low-pressure gaseous environments of water, acetone, and argon. The results are analyzed by modeling the CNT-TFT as an RC transmission line. I show that changes in the measured capacitance as a function of gate bias and analyte pressure are consistent with changes in the capacitive part of the transmission line impedance due to changes in the CNT film resistivity alone, and that the electrostatic gate capacitance of the CNT film does not depend on gate voltage or chemical analyte adsorption to within the resolution of my measurements. However, the resistance of the CNT-TFT is enormously sensitive to small partial pressure (< 10 Torr) of analytes, and the gate voltage dependence of the resistance changes upon analyte adsorption show analyte-dependent signatures. NANOGAP JUNCTIONS AND CARBON NANOTUBE NETWORKS FOR CHEMICAL SENSING AND MOLECULAR ELECTRONICS
Notes on the Space Requirements for Checking Satisfiability in Modal Logics

Recently, there has been growing attention to the space requirements of tableau methods (see for example (7), (1), (12)). We have proposed in (10) a method of reducing modal consequence rela- tions to the global and local consequence relation of (polymodal) K. The reductions used there did however not establish good time com- plexity bounds. In this note we shall use reduction functions to obtain rather sharp space bounds. These bounds can also be applied to show completeness of ordinary tableau systems, which in turn yield space bounds that are slightly dierent from the ones derived by applying the reduction functions alone. It has been shown by Hudelmaier ((7)) that satisfiability in S4 isO(n 2 logn)-
Alteration of postural responses to visual field motion in mild traumatic brain injury.

OBJECTIVE Balance deficits in individuals experiencing mild traumatic brain injury have been documented in numerous recent studies. However, long-lasting balance deficits and specific mechanisms causing these deficits have not been systematically examined. This article aimed to present empirical evidence showing destabilizing effects of visual field motion in concussed individuals up to 30 days postinjury. METHODS Sixty student athletes participated in the pilot (n = 12) and major experiments (n = 48) before injury. Eight of these 48 subjects who experienced mild traumatic brain injury in athletic events were tested again on Days 3, 10, and 30 after the incident. Postural responses to visual field motion were recorded using a virtual reality environment in conjunction with balance (AMTI force plate) and motion tracking (Flock of Birds) technologies. RESULTS The area of the center of pressure during upright stance did not change from Day 3 to 30 postinjury with respect to pre-injury status (P > .05). However, balance deficits induced by visual field motion were present up to 30 days postinjury. Destabilizing effect of visual field motion was observed via significant increase of the center of pressure data (P < .05) and reduced coherence value. CONCLUSION Our data suggest the presence of residual sensory integration dysfunction in concussed individuals at least 30 days postinjury and may indicate a lower threshold for brain reinjury.
Denial of Firewalling

Firewalls are critical security devices handling all traffic in and out of a network. When under heavy load of both malicious and legitimate traffic, firewalls may be overloaded and start discarding or permitting packets without checking firewall rules, which can cause huge revenue losses or security breaches. In this paper, we study Denial of Firewalling attacks, where attackers use well-crafted traffic to effectively overwhelm a firewall. We first investigate firewall implementation characteristics that can be exploited for such attacks while treating the firewall as a black box. We conducted our studies on a testbed with three popular firewall devices. Second, given a remote firewall, we propose methods for attackers to infer the implementation of the firewall. We develop firewall fingerprinting techniques based on firewall decisions on a sequence of TCP packets with unusual flags and machine learning techniques for inferring firewall implementation. Finally, we present methods that attackers can use to generate the traffic that can effectively overload an identified remote firewall. We show that some firewalls can be easily overloaded by a small volume of carefully crafted traffic.
Optimize the Large Scale SVM Social Spam Detection Model via Bistratal Reduction Method

Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method(BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters remained and saves them as the final result set. In the traditional reduction method, the grain-size is sigle. There is still a contradiction between compression and accuracy, and can't be solved perfectly. Bistratal reduction method changes the reduced intensity according to the number of redundant points remained. The experiments show that bistratal reduction method gives a higher compression ratio and accuracy. Apply the new method to the large scale SVM social spam detection model. The detection model speed up obviously.
Very-Low-Density Lipoprotein (VLDL)-Producing and Hepatitis C Virus-Replicating HepG2 Cells Secrete No More Lipoviroparticles than VLDL-Deficient Huh7.5 Cells

ABSTRACT In the plasma samples of hepatitis C virus (HCV)-infected patients, lipoviroparticles (LVPs), defined as (very-) low-density viral particles immunoprecipitated with anti-β-lipoproteins antibodies are observed. This HCV-lipoprotein association has major implications with respect to our understanding of HCV assembly, secretion, and entry. However, cell culture-grown HCV (HCVcc) virions produced in Huh7 cells, which are deficient for very-low-density lipoprotein (VLDL) secretion, are only associated with and dependent on apolipoprotein E (apoE), not apolipoprotein B (apoB), for assembly and infectivity. In contrast to Huh7, HepG2 cells can be stimulated to produce VLDL by both oleic acid treatment and inhibition of the MEK/extracellular signal-regulated kinase (ERK) pathway but are not permissive for persistent HCV replication. Here, we developed a new HCV cell culture model to study the interaction between HCV and lipoproteins, based on engineered HepG2 cells stably replicating a blasticidin-tagged HCV JFH1 strain (JB). Control Huh7.5-JB as well as HepG2-JB cell lines persistently replicated viral RNA and expressed viral proteins with a subcellular colocalization of double-stranded RNA (dsRNA), core, gpE2, and NS5A compatible with virion assembly. The intracellular RNA replication level was increased in HepG2-JB cells upon dimethyl sulfoxide (DMSO) treatment, MEK/ERK inhibition, and NS5A overexpression to a level similar to that observed in Huh7.5-JB cells. Both cell culture systems produced infectious virions, which were surprisingly biophysically and biochemically similar. They floated at similar densities on gradients, contained mainly apoE but not apoB, and were not neutralized by anti-apoB antibodies. This suggests that there is no correlation between the ability of cells to simultaneously replicate HCV as well as secrete VLDL and their capacity to produce LVPs.
Adaptive feedback cancellation in hearing aids using a sinusoidal near-end signal model

Acoustic feedback is a well-known problem in hearing aids, which is caused by the undesired acoustic coupling between the loudspeaker and the microphone. Acoustic feedback limits the maximum amplification that can be used in the hearing aid without making it unstable. The goal of adaptive feedback cancellation (AFC) is to adaptively model the feedback path and estimate the feedback signal, which is then subtracted from the microphone signal. The main problem in identifying the feedback path model is the correlation between the near-end signal and the loudspeaker signal, which is caused by the closed signal loop. A possible solution to this problem is to use the prediction error method (PEM)-based AFC with a linear prediction (LP) model for the near-end signal. In this paper, a modification to the PEM-based AFC is presented where the LP model is replaced by a sinusoidal near-end signal model. More specifically, it is shown that using frequency estimation techniques to estimate the sinusoidal near-end signal model improves the performance of the PEM-based AFC compared to using a LP model. Simulation results for a hearing aid scenario indicate a significant improvement in terms of misadjustment and maximum stable gain increase.
Generation and Characterization of rgs5 Mutant Mice

ABSTRACT Regulators of G-protein signaling (RGS) are involved in a wide variety of functions, including olfaction, vision, and cell migration. RGS5 has a perivascular expression pattern and was recently identified as a marker for brain pericytes. This suggests a role for RGS5 in vascular development and pericyte biology. We have created a mouse line which lacks the rgs5 gene and replaced it with a green fluorescent protein (GFP) reporter (rgs5GFP/GFP). The mice are viable and fertile and display no obvious developmental defects, and the vasculature appears to develop normally with proper pericyte coverage. Also, no differences were observed in the vasculature under pathological conditions, such as tumor growth and oxygen-induced retinopathy. The GFP expression in pericytes of rgs5GFP mice allows detection and sorting of these cells, thereby providing a valuable novel tool for pericyte research.
An ensemble of Weighted Support Vector Machines for Ordinal Regression

Instead of traditional (nominal) classification we investigate the subject of ordinal classification or ranking. An enhanced method based on an ensemble of Support Vector Machines (SVM's) is proposed. Each binary classifier is trained with specific weights for each object in the training data set. Experiments on benchmark datasets and synthetic data indicate that the performance of our approach is comparable to state of the art kernel methods for ordinal regression. The ensemble method, which is straightforward to implement, provides a very good sensitivity-specificity trade-off for the highest and lowest rank.
Changes in frailty conditions and phenotype components in elderly after hospitalization

ABSTRACT Objective: describing the changes in frailty conditions over the period of a year after hospital discharge, verifying predictive variables for changes in frailty conditions and frailty phenotype components according to worsening, improving and stable groups. Method: a longitudinal survey carried out with 129 elderly. A structured form for socioeconomic and health data, scales (Geriatric Depression Scale - short form, Katz scale, Lawton and Brody scale) and frailty phenotype according to Fried were used. Descriptive analysis and multinomial logistic regression model (p<0.05) were performed. Results: we found that 56.7% of older adults changed their condition from non-frail to pre-frail, with no changes from non-frail to frail. Deaths were found between frail and pre-frail elderly. In the worsening group, the increase in the number of morbidities was a predictor for exhaustion and/or fatigue, while in the improving group, increased dependence on instrumental activities of daily living was a predictor for weight loss, and reduced scores indicative of depression due to low level of physical activity. Conclusion: a greater percentage of changes from non-frail condition to pre-frail older adults were observed, and health variables were only predictive for frailty phenotype components.
Greedy forward selection algorithms to Sparse Gaussian Process Regression

This paper considers the basis vector selection issue invloved in forward selection algorithms to sparse Gaussian Process Regression (GPR). Firstly, we re-examine a previous basis vector selection criterion proposed by Smola and Bartlett [20], referred as loss-smola and give some new formulae to implement this criterion for the full-greedy strategy more efficiently in O(n2kmax) time instead of the original O(n2k2 max), where n is the number of training examples and kmax Lt n is the maximally allowed number of selected basis vectors. Secondly, in order to make the algorithm linearly scaling in n, which is quite preferable for large datasets, we present an approximate version loss-sun to loss-smola criterion. We compare the full greedy algorithms induced by the loss-sun and loss-smola criteria, respectively, on several medium-scale datasets. In contrast to loss-smola, the advantage associated with loss-sun criterion is that it could lead to an algorithm which scales as O(n2kmax) time and O(nkmax) memory if coupled with the sub-greedy scheme. Our criterion is similar to a matching pursuit approach, referred as loss-keert proposed very recently by Keerthi and Chu but with different motivations. Numerical experiments on a number of large-scale datasets have demonstrated that our proposed method is always better than loss-keert in both generalization performance and running time. Finally, we discuss the drawbacks of the sub-greedy strategy and present two approximate full-greedy strategies, which can be applied to all three basis vector selection criteria discussed in this paper.
Model and Objective Separation with Conditional Lower Bounds: Disjunction is Harder than Conjunction *

Given a model of a system and an objective, the model-checking question asks whether the model satisfies the objective. We study polynomial-time problems in two classical models, graphs and Markov Decision Processes (MDPs), with respect to several fundamental ω-regular objectives, e.g., Rabin and Streett objectives. For many of these problems the best-known upper bounds are quadratic or cubic, yet no super-linear lower bounds are known. In this work our contributions are two-fold: First, we present several improved algorithms, and second, we present the first conditional super-linear lower bounds based on widely believed assumptions about the complexity of CNF-SAT and combinatorial Boolean matrix multiplication. A separation result for two models with respect to an objective means a conditional lower bound for one model that is strictly higher than the existing upper bound for the other model, and similarly for two objectives with respect to a model. Our results establish the following separation results: (1) A separation of models (graphs and MDPs) for disjunctive queries of reachability and Büchi objectives. (2) Two kinds of separations of objectives, both for graphs and MDPs, namely, (2a) the separation of dual objectives such as Streett/Rabin objectives, and (2b) the separation of conjunction and disjunction of multiple objectives of the same type such as safety, Büchi, and coBüchi. In summary, our results establish the first model and objective separation results for graphs and MDPs for various classical ω-regular objectives. Quite strikingly, we establish conditional lower bounds for the disjunction of objectives that are strictly higher than the existing upper bounds for the conjunction of the same objectives.