Miracle-dz/UniFAR-FTUs huggingface dataset:
Conversion of the LIN-1 ETS protein of C . elegans from a sumoylated transcriptional repressor to a phosphorylated transcriptional activator

ARTICLE SUMMARY The LIN-1 ETS transcription factor plays a pivotal role in controlling cell fate decisions during development of the Caenorhabditis elegans vulva. Prior to activation of the RTK/Ras/ERK signaling pathway, LIN-1 functions as a SUMOylated transcriptional repressor that inhibits the primary vulval cell fate. Here we demonstrate that LIN-1 binds two proteins predicted to repress transcription: RAD-26 binds the SUMOylated amino-terminus and EGL-27 binds the unSUMOylated carboxy terminus. We propose a model in which LIN-1 recruits multiple transcriptional repressors to inhibit the primary vulval cell fate, and phosphorylation by ERK converts LIN-1 to a transcriptional activator that promotes the primary vulval cell fate. ABSTRACT The LIN-1 ETS transcription factor plays a pivotal role in controlling cell fate decisions during development of the Caenorhabditis elegans vulva. Prior to activation of the RTK/Ras/ERK signaling pathway, LIN-1 functions as a SUMOylated transcriptional repressor that inhibits vulval cell fates. Here we demonstrate using the yeast two-hybrid system that SUMOylation of LIN-1 mediates interactions with a protein predicted to be involved in transcriptional repression: the RAD-26 Mi-2β/CHD4 component of the nucleosome remodeling and histone deacetylation (NuRD) transcriptional repression complex. Genetic studies indicated that rad-26 functions to inhibit vulval cell fates in worms. Using the yeast two-hybrid system, we showed that the EGL-27/MTA1 component of the NuRD complex binds the carboxy-terminus of LIN-1 independent of LIN-1 SUMOylation. EGL-27 also binds UBC-9, an enzyme involved in SUMOylation, and MEP-1, a zinc finger protein previously shown to bind LIN-1. Genetic studies indicated that egl-27 inhibits vulval cell fates in worms. These results suggest that LIN-1 recruits multiple proteins that repress transcription via both the SUMOylated amino-terminus and the unSUMOylated carboxy-terminus. Assays in cultured cells showed that the carboxy-terminus of LIN-1 was converted to a potent transcriptional activator in response to active ERK. We propose a model in which LIN-1 recruits multiple transcriptional repressors to inhibit the 1° vulval cell fate, and phosphorylation by ERK converts LIN-1 to a transcriptional activator that promotes the 1° vulval cell fate. Leight et al.
Blood-Brain Barrier Disruption Induced by Chronic Sleep Loss: Low-Grade Inflammation May Be the Link

Sleep is a vital phenomenon related to immunomodulation at the central and peripheral level. Sleep deficient in duration and/or quality is a common problem in the modern society and is considered a risk factor to develop neurodegenerative diseases. Sleep loss in rodents induces blood-brain barrier disruption and the underlying mechanism is still unknown. Several reports indicate that sleep loss induces a systemic low-grade inflammation characterized by the release of several molecules, such as cytokines, chemokines, and acute-phase proteins; all of them may promote changes in cellular components of the blood-brain barrier, particularly on brain endothelial cells. In the present review we discuss the role of inflammatory mediators that increase during sleep loss and their association with general disturbances in peripheral endothelium and epithelium and how those inflammatory mediators may alter the blood-brain barrier. Finally, this manuscript proposes a hypothetical mechanism by which sleep loss may induce blood-brain barrier disruption, emphasizing the regulatory effect of inflammatory molecules on tight junction proteins.
Evaluation of the Hierarchical Temporal Memory as Soft Computing Platform and its VLSI Architecture

A large number of real world applications, like user support systems, can still not be performed easily by conventional algorithms in comparison with the human brain. Recently, such intelligence has often been reached by using probability based systems. This paper presents results on the implementation of one such user support system, namely an intention estimation information appliance system, on a Bayesian Network as well as Hierarchical Temporal Memory. The latter is a new and quite promising soft computing platform modelling the human brain, though currently only available as a software model. A second part of the paper therefore focuses on a possible VLSI architecture for Hierarchical Temporal Memory. Since it models the human brain, communication as well as memory are of high importance for this VLSI architecture.
Content-dependent spatio-temporal video watermarking using 3-dimensional discrete cosine transform

In this paper we propose a content-dependent spatio-temporal watermarking scheme for digital videos. Content dependency is achieved by incorporating the hash of the video sequence into the watermark. The video sequence is treated as a 3-dimensional spatio-temporal signal for the purposes of video hash computation and watermark embedding and detection. Our experiments show that the video hash algorithm has good discriminating power and robustness against various attacks. The watermark is also shown in the experiments to have good robustness against a variety of attacks, in particular when the watermark is copied from one video sequence to another.
Topic indexing of spoken documents based on optimized N-best approach

For topic indexing of spoken documents, the word error rate is hopefully decreased instead of the whole sentence error rate, so the center hypothesis among the N-best results is selected as the final output in speech recognition system. Then all spoken documents can be represented as vectors with high dimensions in vector space model, which can be combined with non-negative matrix factorization or singular value decomposition to map the vector space into semantic space. Experiment results show that optimized N-best approach is more suitable to the topic indexing system than one-best method. Combined with the non-negative matrix factorization, the correct topic indexing can achieve 98.1% in optimized N-best approach, which is 0.9% higher than the one-best approach under the same condition. When the semantic space is decreased to 10, there is about 11.1% difference between these two approaches. Furthermore, compared with singular value decomposition method, non-negative matrix factorization has the advantages of better performance, faster computation speed and less storage space.
Residual symptoms in patients with partial versus complete remission of a major depressive disorder episode: patterns of painful physical symptoms in depression

Objective The patterns of residual painful physical symptoms (PPS) and emotional symptoms among patients with partial remission (PR) or complete remission (CR) of a major depressive disorder (MDD) episode were compared. Methods This is a multicenter, cross-sectional, observational study. Patients who had originally been diagnosed with MDD, were treated with an antidepressant for 12 weeks for that episode, and achieved either PR or CR at study entry were enrolled in the study. Using the 17-item Hamilton Rating Scale for Depression (HAM-D17), PR was defined as a score of ≥8 and ≤18 and CR as a score of ≤7. Residual symptoms were assessed using the Brief Pain Inventory-Short Form (BPI-SF) and the HAM-D17. Results A total of 323 patients (CR =158, PR =165) were included in the study. Patients in the PR group had a higher mean (standard deviation) score in the HAM-D17 than those in the CR group (11.8 [3.1] and 4.4 [2.0], respectively). BPI-SF results showed that “at least moderate PPS” (score ≥3 on BPI-SF question 5) was significantly more prevalent among patients with PR than those with CR (37.0% vs 16.5%, respectively; odds ratio =3.04; P<0.001). Presence of pain (any severity) was also more prevalent among patients with PR than those with CR (54.5% vs 35.4%, respectively). The HAM-D17 results for individual items indicated that impaired work and activities, depressed mood, psychological and somatic anxiety, and general somatic symptoms were observed in at least 75% of patients with PR. Conclusion PR was associated with a higher prevalence of at least moderate PPS. Other residual symptoms commonly observed in patients with PR included typical core emotional symptoms (eg, loss of interest, depressed mood, and psychological anxiety). These results underline the importance of PPS, because PPS is clinically relevant for the patients but difficult to assess with the commonly used depression evaluation scale.
1 A Leap-supported , hybrid AR interface approach

We present a novel interface approach which combines 2D video-based AR with a partial voxel model allowing for more convincing interactions with 3D objects and worlds. It enables users in a hand-controlled interface (a) to interact with a virtual environment (VE) and at the same time (b) to allow for correct mutual occlusions between interacting fingers and the VE. A Leap motion controller is used to track the users' fingers and a webcam overlay allows for an augmented view. Our "VoxelAR" concept can be applied in modified ways to any video-see through AR system we demonstrate our approach in a physical rehabilitation application scenario. Our prototype implementation and our work-in-progress findings are presented. Author
A Method of Quantitative Analysis for Dynamic Cellular Morphological Change

In recent years, with the advancement of micro- scopic imaging technique, cellular morphological analysis has become one of the most important branches of biomedical image processing field. Cell image analysis, an assisting method of clinical information acquisition, has played an important role in facilitating the appropriate diagnosis and treatment against some serious diseases such as cancer, leukemia and etc. Early studies of this field focuses on still image information and mainly concerns either cell counting or the morphological characteristics of cell image. Furthermore, much effort has been devoted to analyzing the morphological change of cell image qualitatively, while less attention has been paid to analyzing it quantitatively which in fact is more effective, objective and accurate. Under this very circumstances, our work concentrates on analyzing the cellular morphological change by means of quantitative measurement, and a new method—circumference radial difference (Rdc )i s pro- posed. The basic idea is to measure the morphological change by estimating the radial differences from 360 different angles. Two sets of lymphocyte video database provided by the cooperation hospital are used to verify the proposed approach. Experimental results show that the proposed (Rdc) measurement can produce reasonably good agreement with subjective judgment by people.
Quantized Posterior Hashing : Efficient Posterior Exemplar Search Exploiting Class-Specific Sparsity Structures

This paper shows that exemplar-based speech processing using class-conditional posterior probabilities admits a highly effective search strategy relying on posteriors’ intrinsic sparsity structures. The posterior probabilities are estimated for phonetic and phonological classes using deep neural network (DNN) computational framework. Exploiting the class-specific sparsity leads to a simple quantized posterior hashing procedure to reduce the search space of posterior exemplars. To that end, small subset of quantized posteriors are regarded as representatives of the posterior space and used as hash keys to index buckets of similar exemplars. The k nearest neighbor (kNN) method is applied for posterior based classification problems. The phonetic posterior probabilities are used as exemplars for phoneme classification whereas the phonological posteriors are used as exemplars for higher level linguistic parsing. Experimental results demonstrate that posterior hashing improves the efficiency of kNN classification drastically. This work encourages the use of posteriors as discriminative exemplars appropriate for large scale speech classification tasks.
A S URVEY ON E NERGY E FFICIENT R OUTING P ROTOCOLS FOR MANET

With the popularization of Internet and formation of wireless technologies provide significant impact on Internet and Communication Technologies. These technologies have support of one of famous technique known as Adhoc Network.Adhoc Networks are assortment of mobile nodes connected by wireless links and also receiving attention in the scientific community. Energy Efficient is one of the key issues in MANETs because of highly dynamic and distributed nature of nodes. Especially energy efficient is most important because all the nodes are battery powered. Failure of one node may affect the entire network. If a node runs out of energy the probability of network partitioning will be increased. Since every mobile node has limited power supply, energy depletion has become one of the main threats to the lifetime of the mobile ad-hoc network. This article surveys and classifies the energy aware routing protocols proposed for MANETs. They minimize active communication energy required to transmit or receive packets and the inactive energy consumed when a mobile node stays idle even though it listens to the wireless medium for any possible communication requests from other nodes. Transmission power control approach and load distribution approach belong to the active communication energy, and sleep/power-down mode approach belongs to the inactive energy. While it is not clear that any particular algorithm or a class of algorithms is the best for all scenarios, each protocol has definite advantages/disadvantages and is well-suited for certain situations. The purpose of this paper is to facilitate the research efforts in combining the existing solutions to offer a more energy efficient routing mechanism.
Oxygen isotopic composition of carbon dioxide in the middle atmosphere

The isotopic composition of long-lived trace molecules provides a window into atmospheric transport and chemistry. Carbon dioxide is a particularly powerful tracer, because its abundance remains >100 parts per million by volume (ppmv) in the mesosphere. Here, we successfully reproduce the isotopic composition of CO2 in the middle atmosphere, which has not been previously reported. The mass-independent fractionation of oxygen in CO2 can be satisfactorily explained by the exchange reaction with O(1D). In the stratosphere, the major source of O(1D) is O3 photolysis. Higher in the mesosphere, we discover that the photolysis of 16O17O and 16O18O by solar Lyman-α radiation yields O(1D) 10–100 times more enriched in 17O and 18O than that from ozone photodissociation at lower altitudes. This latter source of heavy O(1D) has not been considered in atmospheric simulations, yet it may potentially affect the “anomalous” oxygen signature in tropospheric CO2 that should reflect the gross carbon fluxes between the atmosphere and terrestrial biosphere. Additional laboratory and atmospheric measurements are therefore proposed to test our model and validate the use of CO2 isotopic fractionation as a tracer of atmospheric chemical and dynamical processes.
A Compositional Neuro-Controller for Advanced Motor Control Tasks

Humans and animals developed a sophisticated motor control apparatus and there is much evidence that it has a modular structure. The modularity offers a range of benefits, e.g. ability to learn dissociable motion styles without interference and forgetting, fast adaptation and de-adaptation to changes conditions. However in robotics, building a controller that can efficiently incrementally learn new motion styles and provide switching between them is a formidable challenge. In this paper we address the problem by proposing a novel biologically inspired compositional neuro-controller. We have shown that the compositional controller is able to reproduce a set of trajectories more efficiently comparing to a simple controller, exploiting incremental learning benefits. Second, we have demonstrated that the proposed controller is able to learn different locomotion styles and switch between them in a simulated robot-snake.
Bibliography mapping with semantic social bookmarks

A semantically-rich and structured bibliography can be seen as a map of the publication space related to the research area. Social bookmarking applications have shown considerable advantage over traditional reference applications in creating and managing such “publication maps” by means of personalisation, recommendation and global availability. Advances in semantic web and linked data are increasing the need for means of guided semantic navigation that the existing bookmarking systems lack. As a solution, we consider a model of semantic bookmarking that is personalised, semantically rich and navigationally savvy. The emphasis is made on separating structural and semantic layers in order to improve navigation by maintaining and visualising structural relationships among the bookmarks.
The modeling and comparison of wireless network denial of service attacks

Mobile handhelds with wireless access are used in numerous safety critical applications. The wireless network protocols in use are vulnerable to a wide array of denial of service attacks. We propose a formal method for modeling semantic denial of service attacks against wireless network protocols. We then use our proposed model to find a new deadlock vulnerability in IEEE 802.11. The history of published denial of service attacks against wireless protocols indicates that formal methods can contribute to the construction of robust protocols.
Complementary Analysis of High-Order Association Patterns and Classification

To facilitate more meaningful interpretation considering the internal interdependency relationships between data values, a new form of high-order (multiple-valued) pattern known as Nested High-Order Pattern (or NHOP) is recently proposed. This pattern satisfies a consistent statistical criterion when the pattern is iteratively extracted. The general form of High-Order Pattern (HOP), that NHOP is a subtype, is a set of multiple associated values (identified as variable outcomes) extracted from a random N-tuple. The pattern is detected by statistical testing if the occurrence is significantly deviated from the expected according to a prior model or null hypothesis. Here we extend our work of NHOP to the classification task. The rationale is that, meaningful association patterns, involving multiple values jointly and at the same time predict classification, can reinforce the underlying regularity, and hence provide a better understanding of the data domain. In this paper, we propose a Classification method based on the Nested HighOrder Patterns (C-NHOP). In evaluating our method using 26 UCI benchmark datasets, the experiments show a highly competitive and interpretable result.
Shared relationship analysis: ranking set cohesion and commonalities within a literature-derived relationship network

MOTIVATION There is a general scientific need to be able to identify and evaluate what any given set of 'objects' (e.g. genes, phenotypes, chemicals, diseases) has in common. Whether it is to classify, expand upon or identify commonalities and functional groupings, informational needs can be diverse and the best source to identify relationships among a potentially heterogeneous set of objects is the scientific literature. RESULTS We first establish a network of related objects by their co-occurrence within MEDLINE records. A set of objects within this network can then be queried to identify shared relationships, and a method is presented to score their statistical relevance by comparing observed frequencies with what would be expected in a random network model. Using Gene Ontology (GO) categories, we demonstrate that this method enables a quantitative ranking of the 'cohesiveness' of a set of objects and, importantly, allows other objects related to this set to be identified and evaluated for their 'cohesion' to it. Supplemental information: A list of ranked genes related to each GO category analyzed can be found at http://innovation.swmed. edu/IRIDESCENT/GO_relationships. htm
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods

We propose a sparse method for scalable automated variational inference (AVI) in a large class of models with Gaussian process (GP) priors, multiple latent functions, multiple outputs and non-linear likelihoods. Our approach maintains the statistical efficiency property of the original AVI method, requiring only expectations over univariate Gaussian distributions to approximate the posterior with a mixture of Gaussians. Experiments on small datasets for various problems including regression, classification, Log Gaussian Cox processes, and warped GPs show that our method can perform as well as the full method under high sparsity levels. On larger experiments using the MNIST and the SARCOS datasets we show that our method can provide superior performance to previously published scalable approaches that have been handcrafted to specific likelihood models.
Affordable Affective Avatars. Persuasion, Emotions and Language(s)

Persuasive applications become vital. The rationale is: from effectiveness to persuasion, to credibility, to emotivity, to bio-mimetic avatars, to human-like expression of affective states. Economical restraints imply affordable characters, i.e. simulating emotions using traditional equipment. After summarizing the approach and the architecture described in previous papers – and the main software-engineering mechanisms – the paper focuses on perceptual interface affordability, based on implementing context-relevant affective features through usual devices and simple sensors. Emphasis is put on the rationale for blending speech and non-verbal communication in expressing emotions. The design-space dimensions are proposed accordingly. Among the conclusions: a) basic affective computing can be achieved using common configurations; b) due to their undemanding implementation, the agent-oriented mechanisms employed can be used as test-bed; c) the proportions of the language blend depend strongly on the intended use. Future work: a) affective computing functionality to existing persuasive avatars; b) other common devices (e.g., mobile
A parallel genetic algorithm for the graph partitioning problem

Genetic algorithms are stochastic search and optimization techniques which can be used for a wide range of applications. This paper addresses the application of genetic algorithms to the graph partitioning problem. Standard genetic algorithms with large populations suffer from lack of efficiency (quite high execution time). A massively parallel genetic algorithm is proposed, an implementation on a SuperNode® of Transputers® and results of various benchmarks are given. The parallel algorithm shows a superlinear speed-up, in the sense that when multiplying the number of processors by p, the time spent to reach a solution with a given score, is divided by kp (k>1). A comparative analysis of our approach with hill-climbing algorithms and simulated annealing is also presented. The experimental measures show that our algorithm gives better results concerning both the quality of the solution and the time needed to reach it.