Miracle-dz/UniFAR-FTUs huggingface dataset:
Roles of Ca2+, Hyperpolarization and Cyclic Nucleotide-Activated Channel Activation, and Actin in Temporal Synaptic Tagging

At crayfish neuromuscular junctions, cAMP increases transmitter released by action potentials by activating two effectors, hyperpolarization and cyclic nucleotide-activated channels (HCNCs) and a separate target that has been tentatively identified as exchange protein activated by cAMP (Epac). Intense electrical activity in the motor neuron induces a long-term facilitation (LTF) of transmitter release in which hyperpolarization from an electrogenic Na+-K+ exchanger activates HCNCs. The coupling of HCNCs to transmission involves actin. After LTF induction, cAMP further increases transmission in an HCNC-independent manner, activating the second target. This relaxation of the requirement for HCNC activation to enhance release is called temporal synaptic tagging. Tagging lasts at least 1 d but develops only in the 10 min period after electrical activity. The HCNCs are activated by the post-tetanic hyperpolarization occurring during this time. Both synaptic tagging and LTF induction depend on presynaptic Ca2+ accumulation during activity; both are blocked by EGTA-AM, and LTF is also prevented by stimulation in a low-[Ca2+] medium. Actin depolymerizers prevent induction of LTF and tagging, with little effect on HCNCs, whose sensitivity to cAMP and HCNC blockers is unaffected by tagging. Enhancement of actin polymerization can rescue tagging from HCNC block, suggesting that actin acts at a step after HCNC activation. These and other recent results suggest a model in which HCNC activation, followed by a process involving actin polymerization, acts cooperatively with [Ca2+] to induce tagging, after which only Epac activation is required for cAMP to further enhance transmission.
Gauge invariant theory of pion photoproduction with dressed hadrons

Based on an effective field theory of hadrons in which quantum chromodynamics is assumed to provide the necessary bare cutoff functions, a gauge-invariant theory of pion photoproduction with fully dressed nucleons is developed. The formalism provides consistent dynamical descriptions of {pi}N{r_arrow}{pi}N scattering and {gamma}N{r_arrow}{pi}N production mechanisms in terms of nonlinear integral equations for fully dressed hadrons. Defining electromagnetic currents via the gauging of hadronic n-point Green{close_quote}s functions, dynamically detailed currents for dressed nucleons are introduced. The dressed hadron currents and the pion photoproduction current are explicitly shown to satisfy gauge invariance in a self-consistent manner. Approximations are discussed that make the nonlinear formalism manageable in practice and yet preserve gauge invariance. This is achieved by recasting the gauge conditions for all contributing interaction currents as continuity equations with {open_quotes}surface{close_quotes} terms for the individual particle legs coming into or going out of the hadronic interaction region. General procedures are given that approximate any type of (global) interaction current in a gauge-invariance-preserving manner as a sum of single-particle {open_quotes}surface{close_quotes} currents. It is argued that these prescriptions carry over to other reactions, irrespective of the number or type of contributing hadrons or hadronic systems. {copyright} {ital 1997} {ital The American Physical Society}
A Similarity Measure for Unsupervised Semantic Disambiguation

This paper presents an unsupervised method for the resolution of lexical ambiguity of nouns. The method relies on the topological structure of the noun taxonomy of WordNet where a notion of semantic distance is defined. An unsupervised semantic tagger, based on the above measure, is evaluated over an hand-annotated portion of the British National Corpus and compared with a supervised approach based on the Maximum Entropy Model.
Color-Spatial Image Indexing and Applications

We propose a new image feature called the color correlogram as a generic color-spatial indexing tool to tackle various problems that arise in content-based image retrieval and video browsing. Informally speaking, a correlogram represents the spatial correlation of colors in an image. While the computing and storage costs of correlograms match those of histograms, the presence of spatial information makes the former more stable to tolerate large image appearance changes than the latter. This makes the correlogram very attractive for applications such as content-based image retrieval and cut detection. To validate this, we first show that the correlogram, used as an image feature, is scalable for image retrieval on very large image databases. Our experimental results on a database over 200,000 images suggest that the color correlogram is much more effective than the color histogram (and variants) with the same amount of information for these applications. We also propose a new distance metric called relative distance metric for comparing image feature vectors. It outperforms other distance functions in most cases and improves the performance of color histograms and histogram-based features. To further enhance the quality of retrieval, we then present two supervised learning methods--learning the query, learning the metric--and combine these learning methods with color correlograms. Our experiments show that these learning methods are quite effective with even a little effort from users. We also adapt the correlogram to handle the problems of image subregion querying, object localization and tracking. We propose the correlogram intersection for object detection and correlogram correction for object localization. These simple methods perform better than methods based on color histograms. Finally, we propose a method for hierarchical classification of images via supervised learning. This scheme uses correlogram as the low-level feature and performs feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages. All our experimental results suggest that the color correlogram can serve as a good generic indexing tool for various image and video processing applications. Thus, it promises to be a basic building block for efficient and effective schemes to retrieve images from say, the world-wide web.
Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances

Human Activity Recognition (HAR) is an important part of ambient intelligence systems since it can provide user-context information, thus allowing a greater personalization of services. One of the problems with HAR systems is that the labeling process for the training data is costly, which has hindered its practical application. A common approach is to train a general model with the aggregated data from all users. The problem is that for a new target user, this model can perform poorly because it is biased towards the majority type of users and does not take into account the particular characteristics of the target user. To overcome this limitation, a user-dependent model can be trained with data only from the target user that will be optimal for this particular user; however, this requires a considerable amount of labeled data, which is cumbersome to obtain. In this work, we propose a method to build a personalized model for a given target user that does not require large amounts of labeled data. Our method uses data already labeled by a community of users to complement the scarce labeled data of the target user. Our results showed that the personalized model outperformed the general and the user-dependent models when labeled data is scarce.
Maximizing the long-term integral influence in social networks under the voter model

We address the problem of discovering the influential nodes in social networks under the voter model, which allows multiple activations to the same node, by defining an integral influence maximization problem in a long term. We analyze the problem formulation and present an exact solution to the maximization problem. We also provide a sufficient condition for the convergence of the integral influence. We experimentally compare the exact solution with other heuristic algorithms in the aspects of quality and efficiency.
Toward Large Scale Integration: Building a MetaQuerier over Databases on the Web

The Web has been rapidly “deepened” by myriad searchable databases online, where data are hidden behind query interfaces. Toward large scale integration over this “deep Web,” we have been building the MetaQuerier system– for both exploring (to find) and integrating (to query) databases on the Web. As an interim report, first, this paper proposes our goal of the MetaQuerier for Web-scale integration– With its dynamic and ad-hoc nature, such large scale integration mandates both dynamic source discovery and on-thefly query translation. Second, we present the system architecture and underlying technology of key subsystems in our ongoing implementation. Third, we discuss “lessons” learned to date, focusing on our efforts in system integration, for putting individual subsystems to function together. On one hand, we observe that, across subsystems, the system integration of an integration system is itself non-trivial– which presents both challenges and opportunities beyond subsystems in isolation. On the other hand, we also observe that, across subsystems, there emerge unified insights of “holistic integration”– which leverage large scale itself as a unique opportunity for information integration.
Sensor in the Dark: Building Untraceable Large-Scale Honeypots Using Virtualization Technologies

A Honeypot is a system that aims to detect and analyze malicious attacks attempted on a network in an interactive manner. Because the primary objective of a honeypot is to detect enemies without being known to them, it is important to hide its existence. However, as several studies have reported, exploiting the unique characteristics of hosts working on a consecutive IP addresses range easily reveals the existence of honeypots. In fact, there exist some anti-honeypot tools that intelligently probe IP address space to locate Internet security sensors including honeypots. In order to tackle this problem, we propose a system called DarkPots, that consists of a large number of virtualized honeypots using unused and nonconsecutive IP addresses in a production network. DarkPots enables us to deploy a large number of honeypots within an active IP space used for a production network; thus detection is difficult using existing probing techniques. In addition, by virtually classifying the unused IP addresses into several groups, DarkPots enables us to perform several monitoring schemes simultaneously. This function is meaningful because we can adopt more than one monitoring schemes and compare their results in an operating network. We design and implement a prototype of DarkPots and empirically evaluate its effectiveness and feasibility by concurrently performing three independent monitoring schemes in a high-speed campus network. The system successfully emulated 7,680 of virtualized honeypots on a backbone link that carries 500 Mbps – 1 Gbps of traffic without affecting legitimate traffic. Our key findings suggest: (1) active and interactive monitoring schemes provide more in-depth insights of malicious attacks, compared to passive monitoring approach in a quantitative way, and (2) randomly distributed allocation of IP addresses has an advantage over the concentrated allocation in that it can collect more information from malwares. These features are crucial in monitoring the security threats.
Evaluation of linear regression for speaker adaptation in HMM-based articulatory movements estimation

Acoustic-to-articulatory inversion problem is usually studied in speaker-specific manner because both articulatory data and acoustic features contain speaker-specific components. This paper presents our work on speaker-adaptation training for this problem. We implement speaker adaptation in HMM-based acoustic-to-articulatory inversion mapping, and evaluate different combinatorial structures of the articulatory data and acoustic features. The HMM-based inversion mapping models are built with single-stream and multistream, independent clustering and shared clustering structures. The speaker adaptation is implemented in stream-independent structure and shared adaptation structure. The constrained maximum likelihood linear regression method is used for the speaker-adaptive transformation. The experimental results show that the sharing of the speaker-adaptive transformation of the articulatory feature stream and acoustic feature stream can improve the estimation accuracy in inversion mapping. The multi-stream system with shared clustering and shared adaptive transformation has the best result among all the tested structures.
Why individuals seek diverse opinions (or why they don't)

Fact checking has been hard enough to do in traditional settings, but, as news consumption is moving on the Internet and sources multiply, it is almost unmanageable. To solve this problem, researchers have created applications that expose people to diverse opinions and, as a result, expose them to balanced information. The wisdom of this solution is, however, placed in doubt by this paper. Survey responses of 60 individuals in the UK and South Korea and in-depth structured interviews of 10 respondents suggest that exposure to diverse opinions would not always work. That is partly because not all individuals equally value opinion diversity, and mainly because the same individual benefits from it only at times. We find that whether one looks for diverse opinions largely depends on three factors--one's prior convictions, emotional state, and social context.
Minimally Invasive Posterior Stabilization Improved Ambulation and Pain Scores in Patients with Plasmacytomas and/or Metastases of the Spine

Background. The incidence of spine metastasis is expected to increase as the population ages, and so is the number of palliative spinal procedures. Minimally invasive procedures are attractive options in that they offer the theoretical advantage of less morbidity. Purpose. The purpose of our study was to evaluate whether minimally invasive posterior spinal instrumentation provided significant pain relief and improved function. Study Design. We compared pre- and postoperative pain scores as well as ambulatory status in a population of patients suffering from oncologic conditions in the spine. Patient Sample. A consecutive series of patients with spine tumors treated minimally invasively with stabilization were reviewed. Outcome Measures. Visual analog pain scale as well as pre- and postoperative ambulatory status were used as outcome measures. Methods. Twenty-four patients who underwent minimally invasive posterior spinal instrumentation for metastasis were retrospectively reviewed. Results. Seven (29%) patients were unable to ambulate secondary to pain and instability prior to surgery. All patients were ambulating within 2 to 3 days after having surgery (P = 0.01). The mean visual analog scale value for the preoperative patients was 2.8, and the mean postoperative value was 1.0 (P = 0.001). Conclusion. Minimally invasive posterior spinal instrumentation significantly improved pain and ambulatory status in this series.
Interpersonal Dimensions of Goal Pursuit: Goal Support, Shared Goals, Communal Strength, and Generativity in Relationship to Self-Determination Theory

of a dissertation at the University of Miami. Dissertation supervised by Professor Blaine Fowers. No. of pages in text. (105) Self-Determination Theory (SDT) research on goal pursuit indicates that people with intrinsic goal pursuits experience greater well-being than those with extrinsic goal pursuits. Three nutriments have been suggested by SDT that facilitate intrinsic motivation: autonomy, competence and relatedness. These nutriments, considered social conditions by SDT, have been understudied. However, recent SDT research and the small literature on goal support in relationships suggest that social aspects of goal pursuit are quite relevant and warrant further investigation. This study examined interpersonal dimensions of goal pursuit including Goal Support, Shared Goals, Communal Strength and Generativity. This interpersonal cluster was examined in the context of “active involvement with others” in order to enrich our understanding of the link between goal pursuit and psychological well-being. This study was the first to explore these interpersonal dimensions of goal pursuit and well-being in the context of SDT. Correlations explored the relationships among the interpersonal dimensions and regression analyses were used to explore moderating effects the interpersonal dimensions had on the relationship between self-concordant (intrinsic/extrinsic) goal pursuit and well-being. Shared goal orientation was the only dimension to act as a moderator. Implications include continuing to identify the active role of others in one’s goal pursuits and adding further understanding to the relationship between goal pursuit and well-being.
dbSNP: the NCBI database of genetic variation

In response to a need for a general catalog of genome variation to address the large-scale sampling designs required by association studies, gene mapping and evolutionary biology, the National Center for Biotechnology Information (NCBI) has established the dbSNP database [S.T. Sherry, M.Ward and K. Sirotkin (1999) Genome Res., 9, 677-679]. Submissions to dbSNP will be integrated with other sources of information at NCBI such as GenBank, PubMed, LocusLink and the Human Genome Project data. The complete contents of dbSNP are available to the public at website: http://www.ncbi.nlm.nih.gov/SNP. The complete contents of dbSNP can also be downloaded in multiple formats via anonymous FTP at ftp://ncbi.nlm.nih.gov/snp/.
The Chemokine Receptor CCR2 Mediates the Binding and Internalization of Monocyte Chemoattractant Protein-1 along Brain Microvessels

Previous results from this laboratory revealed the presence of high-affinity saturable binding sites for monocyte chemoattractant protein-1 (MCP-1) along human brain microvessels (Andjelkovic et al., 1999; Andjelkovic and Pachter, 2000), which suggested that CC chemokine receptor 2 (CCR2), the recognized receptor for this chemokine, was expressed by the brain microvascular endothelium. To test the role of CCR2 directly in mediating MCP-1 interactions with the brain microvasculature, we assessed MCP-1 binding activity in murine brain microvessels isolated from wild-type mice and from CCR2 (−/−) mice engineered to lack this receptor. Results demonstrate that MCP-1 binding is greatly attenuated in microvessels prepared from CCR2 (−/−) mice compared with wild-type controls. Moreover, microvessels from wild-type mice exhibited MCP-1-induced downmodulation in MCP-1 binding and a recovery of binding activity that was not dependent onde novo protein synthesis. Furthermore, MCP-1 was shown to be internalized within wild-type microvessels, but not within microvessels obtained from CCR2 (−/−) mice, additionally demonstrating that CCR2 is obligatory for MCP-1 endocytosis. Last, internalization of MCP-1, but not transferrin, was observed to be inhibited by disruption of caveolae. Internalized MCP-1 also colocalized at some sites with caveolin-1, a major protein of caveolae, implying that this chemokine is endocytosed, in part, via nonclathrin-coated vesicles. These results prompt consideration that MCP-1 signals may be relayed across the blood–brain barrier by highly specialized interactions of this chemokine with its cognate receptor, CCR2, along brain microvascular endothelial cells.
Feasibility of Music and Hypnotic Suggestion to Manage Chronic Pain

Abstract The authors investigated the feasibility and possible effects of hypnotic suggestion and music for chronic pain. Ten people completed the 2-week intervention that consisted of daily listening to hypnotic suggestions combined with music. Averaged subjective pain intensity, pain bothersomeness, overall distress, anxiety, and depression decreased from baseline to endpoint. Participants rated pre- and postlistening pain intensity and pain bothersomeness decreased for each session. Information provided during end-of-study interviews indicated all participants were satisfied with treatment and felt they benefited from being in the study. Means and standard deviations are reported for outcome measures and a case study is provided. This preliminary study supports the use of a combined hypnotic suggestion and music intervention for chronic pain.
The AMP-Foot 2.0: Mimicking intact ankle behavior with a powered transtibial prosthesis

Almost all of the transtibial prostheses that are available on the market are purely passive devices. They store energy in an elastic element at the beginning of a step and release it at the end in order to move the body forward. The main problem with these prostheses is that only the energy that has been stored in the elastic element is used for the push-off, unlike for non-pathological ankles where the muscles provide extra energy. There are a few prostheses who use active components for this energy input. In this article, the authors propose a new design of an energy efficient, powered transtibial prosthesis to mimic intact ankle behaviour, the AMP-Foot 2.0. The main idea behind our research is to have the actuator work longer with a lower power rating while the produced energy is stored in elastic elements and released when needed for propulsion. The device is designed to provide 100% of push-off for a 75 kg subject walking at normal cadence on ground level.