Miracle-dz/UniFAR-FTUs huggingface dataset:
Dehydroepiandrosterone reverses chronic hypoxia/reoxygenation-induced right ventricular dysfunction in rats

Dehydroepiandrosterone (DHEA) prevents chronic hypoxia-induced pulmonary hypertension and associated right ventricle dysfunction in rats. In this animal model, reoxygenation following hypoxia reverses pulmonary hypertension but not right ventricle dysfunction. We thus studied the effect of DHEA on the right ventricle after reoxygenation, i.e. after a normoxic recovery phase secondary to chronic hypoxia in rats. Right ventricle function was assessed in vivo by Doppler echocardiography and in vitro by the isolated perfused heart technique in three groups of animals: control, recovery (21 days of hypoxia followed by 21 days of normoxia) and recovery DHEA (30 mg·kg−1 every 2 days during the recovery phase). Right ventricle tissue was assessed by optical and electron microscopy. DHEA abolished right ventricle diastolic dysfunction, as the echographic E wave remained close to that of controls (mean±sd 76.5±2.4 and 79.7±1.7 cm·s−1, respectively), whereas it was diminished to 40.3±3.7 in the recovery group. DHEA also abolished right ventricle systolic dysfunction, as shown by the inhibition of the increase in the slope of the pressure–volume curve in isolated heart. The DHEA effect was related to cardiac myocytes proliferation. In conclusion, DHEA prevents right ventricle dysfunction in this animal model by preventing cardiomyocyte alteration.
Prevalence odds ratio or prevalence ratio in the analysis of cross sectional data: what is to be done?

OBJECTIVES: To review the appropriateness of the prevalence odds ratio (POR) and the prevalence ratio (PR) as effect measures in the analysis of cross sectional data and to evaluate different models for the multivariate estimation of the PR. METHODS: A system of linear differential equations corresponding to a dynamic model of a cohort with a chronic disease was developed. At any point in time, a cross sectional analysis of the people then in the cohort provided a prevalence based measure of the effect of exposure on disease. This formed the basis for exploring the relations between the POR, the PR, and the incidence rate ratio (IRR). Examples illustrate relations for various IRRs, prevalences, and differential exodus rates. Multivariate point and interval estimation of the PR by logistic regression is illustrated and compared with the results from proportional hazards regression (PH) and generalised linear modelling (GLM). RESULTS: The POR is difficult to interpret without making restrictive assumptions and the POR and PR may lead to different conclusions with regard to confounding and effect modification. The PR is always conservative relative to the IRR and, if PR > 1, the POR is always > PR. In a fixed cohort and with an adverse exposure, the POR is always > or = IRR, but in a dynamic cohort with sufficient underlying follow up the POR may overestimate or underestimate the IRR, depending on the duration of follow up. Logistic regression models provide point and interval estimates of the PR (and POR) but may be intractable in the presence of many covariates. Proportional hazards and generalised linear models provide statistical methods directed specifically at the PR, but the interval estimation in the case of PH is conservative and the GLM procedure may require constrained estimation. CONCLUSIONS: The PR is conservative, consistent, and interpretable relative to the IRR and should be used in preference to the POR. Multivariate estimation of the PR should be executed by means of generalised linear models or, conservatively, by proportional hazards regression.
Route kernels for trees

Almost all tree kernels proposed in the literature match substructures without taking into account their relative positioning with respect to one another. In this paper, we propose a novel family of kernels which explicitly focus on this type of information. Specifically, after defining a family of tree kernels based on routes between nodes, we present an efficient implementation for a member of this family. Experimental results on four different datasets show that our method is able to reach state of the art performances, obtaining in some cases performances better than computationally more demanding tree kernels.
Analysis of the complete nucleotide sequence of the group IV RNA coliphage SP

We report the nucleotide sequence of the Group IV RNA bacteriophage SP. The entire sequence is 4276 nucleotides long. Four cistrons have been identified by comparison with the related Group III phage Q beta. The maturation protein contains 449 amino acids, the coat protein contains 131 amino acids, the read-through protein contains 330 amino acids and the replicase beta-subunit contains 575 amino acids. SP is 59 nucleotides longer than Q beta. We have analyzed both sequence and structural conservation between SP and Q beta and shown that the sequences for the coat and central region of the replicase are strongly conserved between the two genomes. We also show that the S and M replicase binding sites of Q beta are strongly conserved in SP. Interestingly, the base composition of SP and Q beta differ significantly from one another, and most of the differences can be accounted for by a strong preponderance of U in the third position of each codon of Q beta relative to SP. We also compare conserved hairpins associated with potential coat protein and replicase binding sites.
Statistical Analysis on Binary Images

1 Sum of Bernoulli Distributions Here are some basic statistical concepts needed to perform statistical analysis on binary images. Given a sample space S, a random variable X is a rule that assigns a number to each element of S. X is a function that maps element s ∈ S to a real number, i.e. X : S → R. Bernoulli random variable X takes values only 0 or 1. The probability distribution P of a random variable X is a function defined for every number x such that P (X = x) = P (s ∈ S : X(s) = x). The cumulative distribution function (cdf) of X is is defined as F (x) = P (X ≤ x) = ∑
Top-k Similarity Join in Heterogeneous Information Networks

As a newly emerging network model, heterogeneous information networks (HINs) have received growing attention. Many data mining tasks have been explored in HINs, including clustering, classification, and similarity search. Similarity join is a fundamental operation required for many problems. It is attracting attention from various applications on network data, such as friend recommendation, link prediction, and online advertising. Although similarity join has been well studied in homogeneous networks, it has not yet been studied in heterogeneous networks. Especially, none of the existing research on similarity join takes different semantic meanings behind paths into consideration and almost all completely ignore the heterogeneity and diversity of the HINs. In this paper, we propose a path-based similarity join (PS-join) method to return the top k similar pairs of objects based on any user specified join path in a heterogeneous information network. We study how to prune expensive similarity computation by introducing bucket pruning based locality sensitive hashing (BPLSH) indexing. Compared with existing Link-based Similarity join (LS-join) method, PS-join can derive various similarity semantics. Experimental results on real data sets show the efficiency and effectiveness of the proposed approach.
Geobiological Cycling of Gold: From Fundamental Process Understanding to Exploration Solutions

Microbial communities mediating gold cycling occur on gold grains from (sub)-tropical, (semi)-arid, temperate and subarctic environments. The majority of identified species comprising these biofilms are β-Proteobacteria. Some bacteria, e.g., Cupriavidus metallidurans, Delftia acidovorans and Salmonella typhimurium, have developed biochemical responses to deal with highly toxic gold complexes. These include gold specific sensing and efflux, co-utilization of resistance mechanisms for other metals, and excretion of gold-complex-reducing siderophores that ultimately catalyze the biomineralization of nano-particulate, spheroidal and/or bacteriomorphic gold. In turn, the toxicity of gold complexes fosters the development of specialized biofilms on gold grains, and hence the cycling of gold in surface environments. This was not reported on isoferroplatinum grains under most near-surface environments, due to the lower toxicity of mobile platinum complexes. The discovery of gold-specific microbial responses can now drive the development of geobiological exploration tools, e.g., gold bioindicators and biosensors. Bioindicators employ genetic markers from soils and groundwaters to provide information about gold mineralization processes, while biosensors will allow in-field analyses of gold concentrations in complex sampling media.
Datamining and Telemedicine Challenges and Opportunities

Of all the many interesting things possible to do on the Internet there is none as exciting and rewarding as writing a 50,000 word novel titled 'I want to Buy Google' for NanoWrimo. It is even more significant and relevant when it is connected with datamining and research to discover the etiology of an elusive disorder known to the medical fraternity as Autism. Research into the etiology, pathogenesis and treatment of Autism requires collection of huge amounts of data from many research centers across the globe. Data mining is ready for application in the healthcare sector because it is supported by three technologies that are now sufficiently mature: Massive data collection, Powerful multiprocessor computers and Data mining algorithms.
Traumatic Noise Activates Rho-Family GTPases through Transient Cellular Energy Depletion

Small GTPases mediate transmembrane signaling and regulate the actin cytoskeleton in eukaryotic cells. Here, we characterize the auditory pathology of adult male CBA/J mice exposed to traumatic noise (2–20 kHz; 106 dB; 2 h). Loss of outer hair cells was evident 1 h after noise exposure in the basal region of the cochlea and spread apically with time, leading to permanent threshold shifts of 35, 60, and 65 dB at 8, 16, and 32 kHz. Several biochemical and molecular changes correlated temporally with the loss of cells. Immediately after exposure, the concentration of ATP decreased in cochlear tissue and reached a minimum after 1 h while the immunofluorescent signal for p-AMPKα significantly increased in sensory hair cells at that time. Levels of active Rac1 increased, whereas those of active RhoA decreased significantly 1 h after noise attaining a plateau at 1–3 h; the formation of a RhoA-p140mDia complex was consistent with an activation of Rho GTPase pathways. Also at 1–3 h after exposure, the caspase-independent cell death marker, Endo G, translocated to the nuclei of outer hair cells. Finally, experiments with the inner ear HEI-OC1 cell line demonstrated that the energy-depleting agent oligomycin enhanced both Rac1 activity and cell death. The sum of the results suggests that traumatic noise induces transient cellular ATP depletion and activates Rho GTPase pathways, leading to death of outer hair cells in the cochlea.
Next generation optical-wireless converged network architectures

This article provides a comprehensive analysis on implementing the next-generation optical-wireless integration architectures. The different approaches to implementing a complete fixed-mobile converged network that ensures desired quality of service for various applications are explored. This discussion is specifically focused on LTE-10GEPON integration networks where passive optical networks are used as the backhaul to LTE. Special attention is given to address the issue of providing proper means to enable intercommunication between neighboring base stations, which is one of the crucial considerations in next-generation wireless networks. We propose potential 10GEPON-LTE converged network architectures and comparatively analyze the benefits gained from each of the integration architectures by elaborating on the operational and control structures. Our performance analysis provides insight into QoS-rich next-generation optical-wireless converged networks.
Three-dimensional adaptive sensing of people in a multi-camera setup

Sensing the presence and state of people is of paramount importance in assistive living environments. In this paper we utilise a set of fixed, calibrated cameras to model the bodies of people directly in three dimensions. An adaptive foreground segmentation algorithm is run per camera, providing evidence to be collected in 3D body blobs. A particle filter tracker allows monitoring the modelled bodies across time, offering estimations of their state by using hot-spots and body posture. We apply our system on fall detection and activity monitoring for the elderly, addressing both emergency and cognitive care.
A page fault equation for dynamic heap sizing

For garbage-collected applications, dynamically-allocated objects are contained in a heap. Programmer productivity improves significantly if there is a garbage collector to automatically de-allocate objects that are no longer needed by the applications. However, there is a run-time performance overhead in garbage collection, and this cost is sensitive to heap size H: a smaller H will trigger more collection, but a large H can cause page faults, as when H exceeds the size M of main memory allocated to the application. This paper presents a Heap Sizing Rule for how H should vary with M. The Rule can help an application trade less page faults for more garbage collection, thus reducing execution time. It is based on a heap-aware Page Fault Equation that models how the number of page faults depends on H and M. Experiments show that this rule outperforms the default policy used by JikesRVM's heap size manager. Specifically, the number of faults and the execution time are reduced for both static and dynamically changing M.
Volume Rendering of Time-Varying Scalar Fields on Unstructured Meshes

Volume Rendering of time-varying datasets is essential in several scientific applications. Due to the enormous amount of data involved, in datasets with static sampling regions it is common to consider only time-varying scalar fields (TVSFs). The use of Vector Quantization (VQ) to compress scalar fields has been shown to be quite effective when combined with texture-based volume rendering algorithms for structured grids. In this paper we discuss how to apply VQ to volume render unstructured grids (meshes of tetrahedra). We extended two of the fastest unstructured grid algorithms (both use programmable GPUs) to handle time-varying scalar fields, discuss advantages and disadavantages of each extension, and show results that allows us to interactively render meshes composed of nearly one million tetrahedra and several hundred time instances. CR Categories: I.3. 5 [Computer Graphics]: Computational Geometry and Object Modeling— Surface and object representations, geometric algorithms; I.3. 3 [Computer Graphics]: Picture/Image Generation
6th Ieee International Conference on Robot & Human Interactive Communication Issues in Human/robot Task Structuring and Teaching

Teaching a robot new skills may require that the teacher scaffolds the teaching experience appropriately. However , due to inherent assumptions made by a human teacher the scaffolding process may in some circumstances fail to effectively teach the robot. Here we illustrate this issue in two simple robot teaching exploratory studies and examine the assumptions made by the teacher when teaching the robot. In the first study the human teacher had to reason about robot perceived states in order to provide suitable teaching. In the second study the human teachers had to understand the perceptual constraints of the robot based on the instructions given beforehand by the experimenter and subsequently adapt the guidance given. The results suggest that although the two tasks are quite distinct in their level of complexity a common thread can be observed: people tend to underspecify their teaching. It seems that steps of the explanation are assumed to be known and skipped or not even considered at all. We reflect on the possibility that one of the major challenges in designing robots that are capable interaction partners in these teaching situations is to be able to make them communicate their internal state and current capabilities effectively. Furthermore, we also reflect on designing appropriate behavioral primitives for the robot, corresponding implications on the level of task description and for benefiting from human teaching.
GABAA Receptors in the Pontine Reticular Formation of C57BL/6J Mouse Modulate Neurochemical, Electrographic, and Behavioral Phenotypes of Wakefulness

Drugs that potentiate transmission at GABAA receptors are widely used to enhance sleep and to cause general anesthesia. The mechanisms underlying these effects are unknown. This study tested the hypothesis that GABAA receptors in the pontine reticular nucleus, oral part (PnO) of mouse modulate five phenotypes of arousal: sleep and wakefulness, cortical electroencephalogram (EEG) activity, acetylcholine (ACh) release in the PnO, breathing, and recovery time from general anesthesia. Microinjections into the PnO of saline (vehicle control), the GABAA receptor agonist muscimol, muscimol with the GABAA receptor antagonist bicuculline, and bicuculline alone were performed in male C57BL/6J mice (n = 33) implanted with EEG recording electrodes. Muscimol caused a significant increase in wakefulness and decrease in rapid eye movement (REM) and non-REM (NREM) sleep. These effects were reversed by coadministration of bicuculline. Bicuculline administered alone caused a significant decrease in wakefulness and increase in NREM sleep and REM sleep. Muscimol significantly increased EEG power in the delta range (0.5–4 Hz) during wakefulness and in the theta range (4–9 Hz) during REM sleep. Dialysis delivery of bicuculline to the PnO of male mice (n = 18) anesthetized with isoflurane significantly increased ACh release in the PnO, decreased breathing rate, and increased anesthesia recovery time. All drug effects were concentration dependent. The effects on phenotypes of arousal support the conclusion that GABAA receptors in the PnO promote wakefulness and suggest that increasing GABAergic transmission in the PnO may be one mechanism underlying the phenomenon of paradoxical behavioral activation by some benzodiazepines.
On the evolution of downlink physical layer in multi-antenna 3GPP LTE / LTE-A: A review

3GPP (Third Generation Partnership Project) started LTE (Long Term Evolution) as a project in 2004 and later it was introduced as the 4th generation of mobile communication standard in 3GPP Release 8. LTE uses some new technologies such as OFDMA (Orthogonal Frequency Division Multiple Access) with MU-MIMO (Multiuser Multiple Input Multiple Output) in the downlink and SC-FDMA (Single Carrier Frequency Division Multiple Access) in the uplink. The benefit of LTE is that it provides better performance in multimedia applications, higher data rates, improved system capacity and coverage, reduced latency, enhanced voice communication. To fulfill all this aim a better understanding of the physical layer of LTE is required. Therefore in this paper a holistic overview of the downlink physical layer of LTE been discussed.
Revising the Wordnet Domains Hierarchy: semantics, coverage and balancing

The continuous expansion of the multilingual information society has led in recent years to a pressing demand for multilingual linguistic resources suitable to be used for different applications. In this paper we present the WordNet Domains Hierarchy (WDH), a language-independent resource composed of 164, hierarchically organized, domain labels (e.g. Architecture, Sport, Medicine). Although WDH has been successfully applied to various Natural Language Processing tasks, the first available version presented some problems, mostly related to the lack of a clear semantics of the domain labels. Other correlated issues were the coverage and the balancing of the domains. We illustrate a new version of WDH addressing these problems by an explicit and systematic reference to the Dewey Decimal Classification. The new version of WDH has a better defined semantics and is applicable to a wider range of tasks.
Evaluation of lexical models for Hungarian Broadcast speech transcription and spoken term detection

In this paper, we re-evaluate morph (data-driven subword) and word lexical models used for large vocabulary continuous speech recognition of agglutinative languages. Since such speech recognition systems are applied mostly for information retrieval purposes we use evaluation metrics accordingly. Standard 3-gram language model with one million words vocabulary is used for words whereas statistical morph-based models are applied with smaller vocabularies and with higher order of n-gram models. Fostering real life applicability, the computational time and memory usage of the various approaches is kept below real-time and 1.5 GB, respectively. The lexical modeling approaches are tested on Hungarian Broadcast News and Broadcast Conversation speech. In our setup, although word-based models outperformed morph-based ones in terms of both word error rate and spoken term detection measures, a search-cascade of the word and morph approaches improved the latter results significantly.
Pedestrian Classification Using K-means and Random Decision Forests

In field of autonomous and intelligent vehicles, the goal of pedestrian classification is to reduce amount of accidents. The object classification accuracy depends on the type of classifier and the extracted object features used for classification. Support Vector Machines (SVM), is considered the most effective classifier for this task. However, it depends on a number of factors that require researchers to perform several modifications to obtain a good result with adequate performance. This study presents a promising alternative with fewer parameters, which works on large datasets, and reduced runtime. It also has the advantage of allowing the data analysis between every step of the algorithm. Differently from SVM, which can be considered as a black box approach, our method uses a k-means cluster technique combined with a radial basis function to transform data into a smaller and more relevant set, where the classification is performed using random decision forest. Experimental results show very satisfactory classification, efficient computational performance, simplicity of use, and reduced setup.
VHDL Modeling of Intrusion Detection & Prevention System (IDPS) A Neural Network Approach

The rapid development and expansion of World Wide Web and network systems have changed the computing world in the last decade and also equipped the intruders and hackers with new facilities for their destructive purposes. The cost of temporary or permanent damages caused by unauthorized access of the intruders to computer systems has urged different organizations to increasingly implement various systems to monitor data flow in their network. The systems are generally known as Intrusion Detection System (IDS). Our objective is to implement an artificial network approach to the design of intrusion detection and prevention system and finally convert the designed model to a VHDL (Very High Speed Integrated Circuit Hardware Descriptive Language) code. This feature enables the system to suggest proper actions against possible attacks. The promising results of the present study show the potential applicability of ANNs for developing practical IDSs.