Miracle-dz/UniFAR-FTUs huggingface dataset:
Flt3 Ligand Preferentially Increases the Number of Functionally Active Myeloid Dendritic Cells in the Lungs of Mice

In the present study, we investigated the effects of in vivo Flt3L administration on the generation, phenotype, and function of lung dendritic cells (DCs) to evaluate whether Flt3L favors the expansion and maturation of a particular DC subset. Injection of Flt3L into mice resulted in an increased number of CD11c-expressing lung DCs, preferentially in the alveolar septa. FACS analysis allowed us to quantify a 19-fold increase in the absolute numbers of CD11c-positive, CD45R/B220 negative DCs in the lungs of Flt3L-treated mice over vehicle-treated mice. Further analysis revealed a 90-fold increase in the absolute number of myeloid DCs (CD11c positive, CD45R/B220 negative, and CD11b positive) and only a 3-fold increase of lymphoid DCs (CD11c positive, CD45R/B220 negative, and CD11b negative) from the lungs of Flt3L-treated mice over vehicle-treated mice. Flt3L-treated lung DCs were more mature than vehicle-treated lung DCs as demonstrated by a significantly higher percentage of cells expressing MHC class II, CD86, and CD40. Freshly isolated Flt3L lung DCs were not fully mature, because after an overnight culture they continued to increase accessory molecule expression. Functionally, Flt3L-treated lung DCs were more efficient than vehicle-treated DCs at stimulating naive T cell proliferation. Our data show that administration of Flt3L favors the expansion of myeloid lung DCs over lymphoid DCs and enhanced their ability to stimulate naive lymphocytes.
TCP Westwood : congestion control with faster recovery UCLA CSD TR # 200017

In this paper we propose a new version of the TCP protocol, called TCP Westwood, which enhances the performance of TCP window congestion control by using as feedback the end-to-end measurement of the bandwidth available along a TCP connection. The available bandwidth is estimated at the TCP source by measuring and low-pass filtering the returning rate of acknowledgments. The estimated bandwidth is then used to properly set the congestion window and the slow start threshold after a congestion episode, that is after a timeout or 3 duplicate acknowledgments. The rationale of this strategy is simple: TCP Westwood sets a slow start threshold and a congestion window which are consistent with the network capacity measured at the time congestion is experienced. In particular, TCP Westwood introduces a mechanism of faster recovery to avoid overly conservative reduction of the congestion window after a congestion episode by taking into account the end-to-end estimation of available bandwidth. The advantage of the proposed mechanism is that the TCP sender recovers faster after losses especially over connections with large round trip times, or running over wireless links where sporadic losses are due to unreliable links rather than congestion. The proposed modifications follow the end-to-end design principle of TCP. They require only slight modifications at the sender side and are backward-compatible. Simulation results show a considerable throughput increment in comparison with TCP Reno and TCP SACK over wired networks and even more over wireless networks.
Buffered Aloha with K-Exponential Backoff -- Part I: Stability and Throughput Analysis

This two-part paper series studies the performance of buffered Aloha networks with K-Exponential Backoff collision resolution algorithms. Part I focuses on stability and throughput analysis and Part II presents the delay analysis. In Part I, the buffered Aloha network is modeled as a multi-queue single-server system. We adopt a widely used approach in packet switching systems to decompose the multi-queue system into independent first-in-first-out (FIFO) queues, which are hinged together by the probability of success of head-of-line (HOL) packets. A unified method is devised to tackle the stability and throughput problems of K-Exponential Backoff with any cutoff phase K. We demonstrate that a network with K-Exponential Backoff can be stabilized if the retransmission factor q is properly selected. The stable region of q is characterized and illustrated via examples of Geometric Retransmission (K=1) and Exponential Backoff (K=infinity). With an increasing number of nodes n, we show that the stable region of Geometric Retransmission rapidly shrinks, and vanishes as n goes to infinity. In contrast, the stable region of Exponential Backoff does not vary with the network population n, implying that a stable throughput can be achieved in networks with Exponential Backoff even with an infinite number of nodes. All the analytical results presented in this paper series are verified by simulations.
Boosting Learning Algorithm for Pattern Recognition and Beyond

SUMMARY This paper discusses recent developments for pattern recognition focusing on boosting approach in machine learning. The statistical properties such as Bayes risk consistency for several loss functions are discussed in a probabilistic framework. There are a number of loss functions proposed for different purposes and targets. A unified derivation is given by a generator function U which naturally defines entropy, divergence and loss function. The class of U-loss functions associates with the boosting learning algorithms for the loss minimization, which includes AdaBoost and LogitBoost as a twin generated from Kullback-Leibler divergence, and the (partial) area under the ROC curve. We expand boosting to unsupervised learning, typically density estimation employing U-loss function. Finally, a future perspective in machine learning is discussed.
Low-Dose X-ray CT Reconstruction via Dictionary Learning

Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.
Exact Half-BPS Flux Solutions in M-theory III: Existence and rigidity of global solutions asymptotic to AdS(4) x S**7

The BPS equations in M-theory for solutions with 16 residual supersymmetries, SO(2,2) ? SO(4) ? SO(4) symmetry, and AdS4 ? S7 asymptotics, were reduced in [arXiv:0806.0605] to a linear first order partial differential equation on a Riemann surface with boundary, subject to a non-trivial quadratic constraint. In the present paper, suitable regularity and boundary conditions are imposed for the existence of global solutions. We seek regular solutions with multiple distinct asymptotic AdS4 ? S7 regions, but find that, remarkably, such solutions invariably reduce to multiple covers of the M-Janus solution found by the authors in [arXiv:0904.3313], suggesting rigidity of the half-BPS M-Janus solution. In particular, we prove analytically that no other smooth deformations away from the M-Janus solution exist, as such deformations invariably violate the quadratic constraint. These rigidity results are contrasted to the existence of half-BPS solutions with non-trivial 4-form fluxes and charges asymptotic to AdS7 ? S4. The results are related to the possibility of M2-branes to end on M5-branes, but the impossibility of M5-branes to end on M2-branes, and to the non-existence of half-BPS solutions with simultaneous AdS4 ? S7 and AdS7 ? S4 asymptotic regions.
Improved Bisector pruning for uncertain data mining

Uncertain data mining is well studied and very challenging task. This paper is concentrated on clustering uncertain objects with location uncertainty. Uncertain locations are described by probability density function (PDF). Number of uncertain objects can be very large and obtaining quality result within reasonable time is a challenging task. Basic clustering method is UK-means, in which all expected distances (ED) from objects to clusters are calculated. Thus UK-means is inefficient. To avoid ED calculations various pruning methods are proposed. The pruning methods are significantly more effective than UK-means method. In this paper, Improved Bisector pruning method is proposed as an improvement of clustering process.
GDNF Secreting Human Neural Progenitor Cells Protect Dying Motor Neurons, but Not Their Projection to Muscle, in a Rat Model of Familial ALS

Background Amyotrophic lateral sclerosis (ALS) is a fatal, progressive neurodegenerative disease characterized by rapid loss of muscle control and eventual paralysis due to the death of large motor neurons in the brain and spinal cord. Growth factors such as glial cell line derived neurotrophic factor (GDNF) are known to protect motor neurons from damage in a range of models. However, penetrance through the blood brain barrier and delivery to the spinal cord remains a serious challenge. Although there may be a primary dysfunction in the motor neuron itself, there is also increasing evidence that excitotoxicity due to glial dysfunction plays a crucial role in disease progression. Clearly it would be of great interest if wild type glial cells could ameliorate motor neuron loss in these models, perhaps in combination with the release of growth factors such as GDNF. Methodology/Principal Findings Human neural progenitor cells can be expanded in culture for long periods and survive transplantation into the adult rodent central nervous system, in some cases making large numbers of GFAP positive astrocytes. They can also be genetically modified to release GDNF (hNPCGDNF) and thus act as long-term ‘mini pumps’ in specific regions of the rodent and primate brain. In the current study we genetically modified human neural stem cells to release GDNF and transplanted them into the spinal cord of rats over-expressing mutant SOD1 (SOD1G93A). Following unilateral transplantation into the spinal cord of SOD1G93A rats there was robust cellular migration into degenerating areas, efficient delivery of GDNF and remarkable preservation of motor neurons at early and end stages of the disease within chimeric regions. The progenitors retained immature markers, and those not secreting GDNF had no effect on motor neuron survival. Interestingly, this robust motor neuron survival was not accompanied by continued innervation of muscle end plates and thus resulted in no improvement in ipsilateral limb use. Conclusions/Significance The potential to maintain dying motor neurons by delivering GDNF using neural progenitor cells represents a novel and powerful treatment strategy for ALS. While this approach represents a unique way to prevent motor neuron loss, our data also suggest that additional strategies may also be required for maintenance of neuromuscular connections and full functional recovery. However, simply maintaining motor neurons in patients would be the first step of a therapeutic advance for this devastating and incurable disease, while future strategies focus on the maintenance of the neuromuscular junction.
Pulp Regeneration by 3-dimensional Dental Pulp Stem Cell Constructs

Dental pulp regeneration therapy for the pulpless tooth has attracted recent attention, and clinical trial studies are underway with the tissue engineering approach. However, there remain many concerns, including the extended period for regenerating the dental pulp. In addition, the use of scaffolds increases the risk of inflammation and infection. To establish a basic technology for novel dental pulp regenerative therapy that allows transplant of pulp-like tissue, we attempted to fabricate scaffold-free 3-dimensional (3D) cell constructs composed of dental pulp stem cells (DPSCs). Furthermore, we assessed viability of these 3D DPSC constructs for dental pulp regeneration through in vitro and in vivo studies. For the in vitro study, we obtained 3D DPSC constructs by shaping sheet-like aggregates of DPSCs with a thermoresponsive hydrogel. DPSCs within constructs remained viable even after prolonged culture; furthermore, 3D DPSC constructs possessed a self-organization ability necessary to serve as a transplant tissue. For the in vivo study, we filled the human tooth root canal with DPSC constructs and implanted it subcutaneously into immunodeficient mice. We found that pulp-like tissues with rich blood vessels were formed within the human root canal 6 wk after implantation. Histologic analyses revealed that transplanted DPSCs differentiated into odontoblast-like mineralizing cells at sites in contact with dentin; furthermore, human CD31–positive endothelial cells were found at the center of regenerated tissue. Thus, the self-organizing ability of 3D DPSC constructs was active within the pulpless root canal in vivo. In addition, blood vessel–rich pulp-like tissues can be formed with DPSCs without requiring scaffolds or growth factors. The technology established in this study allows us to prepare DPSC constructs with variable sizes and shapes; therefore, transplantation of DPSC constructs shows promise for regeneration of pulpal tissue in the pulpless tooth.
The (betweenness) centrality of critical nodes and network cores

The betweenness centrality of a node is a measure related to the number of shortest path the node is involved with. It is, indeed, a measure of the importance of the node in the network, and in the recent years has been used intensively for network analysis. The major drawback of this measure is its high computational cost, and thus in the literature several works appeared providing ways of approximating it, thus presenting a trade off between accuracy and speed. The articulation points of a connected network are the nodes whose removal disconnects the network, and the critical nodes are the articulation points of the network core, i.e. the subset of the network obtained by repeatedly pruning the nodes of low (fixed) degree. In [1] Ausiello et al. showed that, in ten years of samples of the Autonomous System (AS) Network, the removal of a single critical node from the network was able to affect hundreds of nodes, that were no longer connected to the main part of the AS Network.
Radar signal reconstruction algorithm based on Complex Block Sparse Bayesian Learning

Sparse signal reconstruction is one core technology of Radar compressive sensing. Algorithms based on Sparse Bayesian Learning are the focus of study in recent years, because of their good reconstruction preferment. Signal reconstruction algorithm base Block Sparse Bayesian Learning (BSBL) frame is excellent for reconstructing signals of block sparse structure. It exploits not only the block sparse structure of signals, but also the correlation inside the block to improve the accuracy of reconstruction. But, classical BSBL algorithm is proposed for real signals. So, it cannot reconstruct complex signal directly. This paper extended the BSBL frame and reconstruction algorithm based on BSBL frame into complex number domain. The extended signal reconstruction algorithm based on Complex Block Sparse Bayesian Learning (CBSBL) frame can deal with complex signals directly. And then CBSBL algorithm was applied to reconstruct radar complex signals. Experimental results showed that the CBSBL algorithm is effective and efficient.
A novel high performance multi-modal approach for content based image retrieval

Retrieval of images based on visual features such as color, texture and shape have proven to have it's own set of limitations under different conditions. Various techniques have been implemented using these features like fuzzy color histogram, Tamura texture etc. In this paper we propose a novel method with highly accurate and retrieval efficient approach which will work on large image database with varied contents and background.
Dielectrophoresis-Based Discrimination of Bacteria at the Strain Level Based on Their Surface Properties

Insulator-based dielectrophoresis can be used to manipulate biological particles, but has thus far found limited practical applications due to low sensitivity. We present linear sweep three-dimensional insulator-based dielectrophoresis as a considerably more sensitive approach for strain-level discrimination bacteria. In this work, linear sweep three-dimensional insulator-based dielectrophoresis was performed on Pseudomonas aeruginosa PA14 along with six isogenic mutants as well as Streptococcus mitis SF100 and PS344. Strain-level discrimination was achieved between these clinically important pathogens with applied electric fields below 10 V/mm. This low voltage, high sensitivity technique has potential applications in clinical diagnostics as well as microbial physiology research.
The WHO EMF Project: Legitimating the Imaginary of Global Harmonization of EMF Safety Standards

An important topic for studies of STS and globalization are the ways “global” institutions create and attempt to implement health guidelines and safety standards to address risks associated with new technologies. In the following discussion this topic is examined through a case study of the activities of the World Health Organization’s Electric and Magnetic Field Project (WHO EMF Project). EMF exposures are associated with telecommunications and electrical infrastructure, most notably high voltage power-lines, radio and cell towers and the use of mobile telephones. The controversy over setting international safety standards and health guidelines has simmered for a number of decades. The mainstream “regulatory science” position has been that it is unlikely EMF’s constitute a significant health risk and therefore minimal regulatory intervention or significant precautionary considerations are required. A small but persistent stream of scientific studies nevertheless have continued to raise significant health concerns and have led to calls for regulators to build stronger precautionary approaches into EMF standards and guidelines. The WHO EMF project was established in 1996 in an attempt to discourage this situation leading to different nation states adopting strong precautionary approaches and developing a diversity of national EMF safety standards and health guidelines. In the following discussion I will explore the history of the WHO EMF Project and the strategies it has used to pursue its goal of global harmonization of EMF science, safety standards and guidelines. Three important strategies used by the WHO EMF Project have involved: appeals to technological determinism, developing bespoke models for sound science, and setting the boundaries between science and policy to attempt to exclude their opponents from the policy making arena.
Dealing with the video tidal wave: the relevance of expertise for video tagging

The vast amounts of video that need to be tagged preclude the exclusive use of professional indexers. Thus a significant amount of video will need to be tagged by non-experts. Are the tags created by experts demonstrably superior to those of non-experts, and when non-experts have to be used for tagging, is it better to rely on tags created by those who upload videos or on others who watch the videos? Two related studies were carried out, the first where domain experts and others tagged videos, and the second where experts and others rated the relevance of tags that had been assigned to videos in the first study. Expert tags were judged to be more relevant by both experts and non-experts, with non-expert viewers also creating significantly better tags than did owners (i.e., the people who uploaded the videos). While significant differences were observed, the mean overall judged relevance of tags was relatively low, even for experts. Thus there seems to be considerable scope for the use of tag recommendation systems and other tools that can make the tagging process more consistent.
A neural model of luminance-gated recurrent motion diffusion for 2D motion integration and segmentation

We propose a model of motion integration modulated by luminance information, which is able to explain the percept on a large class of motion stimuli facing the aperture problem. This model is related to other multi-layer architectures incorporating both feedforward, feedback and inhibitive lateral connections and is inspired by the motion processing cortical areas in the primate (V1, V2, MT). Our main contribution is to propose a new anisotropic integration model where motion diffusion through recurrent connectivity between layers working at different spatial scales is gated by the luminance distribution in the image. This simple model offers a competitive alternative to models based on a large set of cortical layers implementing specific form or motion features detectors. We demonstrate that the proposed approach produces results compatible with several psychophysical experiments concerning not only the resulting global motion percept but also the motion integration dynamics. It can also explain several properties of MT neurons regarding the dynamics of selective motion integration. As a whole, the paper affords an improved motion integration model which is numerically tractable and reproduces key aspect of cortical motion integration.
Advanced Saturation-based Model Checking of Well-formed Coloured Petri Nets

The failure of safety-critical embedded systems may have catastrophic consequences, therefore their development process requires a strong verification procedure to obtain a high confidence of correctness in the specification and implementation. Formal modelling and model checking provides a rigorous, mathematically precise verification method. Practical embedded systems are typically complex, distributed and asynchronous, thus they need expressive and compact formal models, and efficient model checking approaches. The saturation algorithm has an efficient iteration strategy. Combined with symbolic data structures, it can be used for state space generation and model checking of asynchronous systems. Coloured Petri nets are a good choice for modelling distributed and asynchronous systems, however their integration with saturation has not been solved in the past. In this paper we describe a new approach for applying saturation-based state space generation and model checking to coloured Petri nets. We demonstrate the performance of our new algorithm on the verification of a safety function used in the Reactor Protection System of a nuclear power plant.