Miracle-dz/UniFAR-FTUs huggingface dataset:
A Microfluidic Device with Integrated Sonication and Immunoprecipitation for Sensitive Epigenetic Assays

Epigenetic studies increasingly require analysis of a small number of cells that are of one specific type and derived from patients or animals. In this report, we demonstrate a simple microfluidic device that integrates sonication and immunoprecipitation (IP) for epigenetic assays, such as chromatin immunoprecipitation (ChIP) and methylated DNA immunoprecipitation (MeDIP). By incorporating an ultrasonic transducer with a microfluidic chamber, we implemented microscale sonication for both shearing chromatin/DNA and mixing/washing of IP beads. Such integration allowed highly sensitive tests starting with 100 cross-linked cells for ChIP or 500 pg of genomic DNA for MeDIP (compared to 106–107 cells for ChIP and 1–10 μg of DNA for MeDIP in conventional assays). The entire on-chip process of sonication and IP took only 1 h. Our tool will be useful for highly sensitive epigenetic studies based on a small quantity of sample.
A Stochastic Realization Approach to the Smoothing Problem

in France, where he worked on the time delays), linear time-varying systems, linear two-dimensional sys-mathematical theory of infinite-dimensional systems. At Georgia Tech, tems, and bilinear systems. with the theory of infinite-dimensional systems (including systems with and Phi Kappa Phi. Abstract-'Ihe pmpose of this paper is to develop a theory of smoothing for linite dimensional linear stochastic systems in the context of storhastic realization theory. "le basic idea is to embed the given stochastic system
Comments using Tree Structured Conditional Random Fields

The Internet provides a variety of ways for people to easily share, socialize, and interact with each other. One of the most popular platforms is the online blog. This causes a vast amount of new text data in the form of blog comments and opinions about news, events and products being generated everyday. However, not all comments are informative. Informative or high quality comments have great impact on the readers’ opinions about the original post content, such as the quality of the product discussed in the post, or the interpretation of a political event. Therefore, developing an efficient and effective mechanism to detect the most informative comments is highly desirable. For this purpose, sites like Slashdot, where users volunteer to rate comments based on their informativeness, can be a great resource to build such automated system using supervised machine learning techniques. Our research concerns building an automatic comment classification system leveraging this freely available valuable resources. Specifically, we discuss how informative comments in blogs can be detected using Conditional Random Fields (CRFs) [6]. Blog conversations typically have a tree-like structure in which an initial post is followed by comments, and each comment can be followed by other comments. In this work, we propose to use Tree-structured Conditional Random Fields (TCRFs) to capture the dependencies in a tree-like conversational structure. This is in contrast with previous work [1] in which results produced by linear-chain CRF models had to be aggregated heuristically. As an additional contribution, we present a new blog corpus consisting of conversations of different genres from 5 different blog websites.
Brain circuits involved in self-paced motion: the influence of 0.1 Hz waves

The neural mechanisms behind human voluntary motion are not fully characterized yet, in spite of numerous research studies. Slow ( 0.1 Hz) brain oscillations are known to have a powerful modulatory effect on several cognitive and physiological phenomena, including free movement. This study is based on fMRI data acquired from 25 young, healthy subjects. The tasks were: rest, self-paced motion, motion paced by a periodic 0.1 Hz stimulus. The temporal resolution was finer than standard fMRI protocols (TR=871 ms). After preprocessing, the signal from brain regions of interest was extracted, and functional connectivity was computed between brain regions using wavelet phase coherence. Complementarily, effective connectivity was measured using Granger causality. The final output was Phase-Locking (PL) and Granger Causality (GC) matrices reflecting inter-regional phase coherence and causal interactions, respectively, around 0.1 Hz. Using the GraphVar toolbox, inter-task and inter-group comparisons were performed. In inter-task comparisons PL matrices showed encouraging results unlike GC matrices. Pairs of regions for which PL differs significantly between rest and self-paced movement were identified. These include mainly the Postcentral gyrus, Putamen, the Anterior Cingulum, the Precentral gyrus, the Calcarine, the Lingual and the Insula (all in the left hemisphere). Topological changes in the brain wiring were identified across the tasks by computing the node degree and global efficiency. Inter-group comparisons took into account the inter movement interval and the coupling between BOLD and heart rate beatto-beat interval signals and showed changes in brain activity depending on the regularity of movement intervals and specific connectivity patterns for neural BOLD oscillations, respectively. This methodological approach allowed to make a contribution towards the characterization of the functional connectivity of brain circuits related to voluntary motor behavior.
Identification of informal settlement using Remote Sensing Images

One of the biggest obstacles for the growth in the developing countries is rapid increase in population. The growth of informal settlement is increasing day by day in the developing countries such as India. There is critical need to identify these informal settlements to serve many urbanization purposes such as improving city planning, identifying lack of basic facilities such as roads connectivity etc. The Remote Sensing (RS) satellite images are good source of information to analyze informal settlements. The high resolution RS images are already proven useful to identify the informal settlements. This work presents two approaches for the identification of informal settlement using RS images. The first approach is based on Histogram Comparison using Bhattacharyya Distance (HCBD), and the second approach uses Hausdorff distance approach based on Citation-KNN. Accuracy and execution time of both approaches are compared. It is observed that Citation-KNN approach provides 83% precision, 72% recall and takes 13 seconds to process the sample image, whereas HCBD provides 76% precision and 87% recall in 2 seconds.
TERRAIN AIDED UNDERWATER NAVIGATION – A DEEPER INSIGHT INTO GENERIC MONTE CARLO LOCALIZATION

This paper proposes a method for terrainaided navigation in underwater environments based on the simple means of a digitized seafloor map and sonar measurements. Since the depth information is highly non-linear and non-Gaussian, a family of probabilistic algorithms known as Monte Carlo Localization (MCL) is used for the estimation process. To increase the performance of MCL under limited computational power the sample size of the classical particle filter is adapted on-the-fly. Within this adaptation process the number of samples is increased if the state uncertainty is high and decreased if the density distribution is focused on a small area. This approach is based on a method known as KLD-sampling. As the performance of the filterand therefore the accuracy of the position estimationcan be limited by the noise levels of the system, a method is proposed to adapt the noise level over time. This adaptation is made with reference to the terrain excitation in the area of the observation. The performance of this method is presented in terms of the entropy propagation of the particle distribution.
Heterogeneity of spine density in pyramidal neurons of isocortex of mongoose, Herpestes edwardsii (É. Geoffroy Saint‐Hilaire 1818)

The characteristics of pyramidal neurons within six layers of Indian gray mongoose (Herpestes edwardsii) isocortex have been investigated using Golgi and Cresyl‐Violet methods. Pyramidal neurons and the cytoarchitecture of isocortex of mongoose were photographed with the help of computer aided Nikon eclipse 80i microscope whereas the lucida drawings were made by simple light microscope equipped with camera lucida. The cortical neurons exhibit marked regional differences in phenotype. The differences occur in morphology and distribution of spines within the cortical neurons not only among different species but also within an animal's brain. The present investigation aims at studying the features of pyramidal neurons and to find out the differences if any in distribution of spines in different layers (II–VI) as well as regions (Frontal, Temporal, Parietal, and Occipital) of isocortex of mongoose, which will provide information regarding importance of different layer and region. This piece of work embarks the findings that spine density shows inter‐regional as well as interlaminar variations within isocortex of mongoose indicating that pyramidal cells present in varied layer and region are not equally functional and there do exists differences in activity among layers and regions. Among regions, the Temporal region possessing highest spine density contributes more toward functioning of mongoose isocortex and might play significant role in predatory nature of mongoose because this region in mammals is associated with auditory, visual perception, and object recognition. Microsc. Res. Tech. 76:818–828, 2013. © 2013 Wiley Periodicals, Inc.
Role of Platform Providers in Service Networks: The Case of Salesforce.com App Exchange

As IT technology advanced, a new style of innovation emerged, in which a leading innovation company invites end-users to its open software service platform. With respect to this type of innovation, a lot of innovation studies were performed to understand the structure of the interaction among users and the platform provider from the perspective of network science. By concentrating only on the internal mechanisms among agents, the previous studies miss to consider innovation through collective intelligence. A platform provider plays an important role in the innovation. In this research, we investigate the structure of a service network with empirical data gathered from Salesforce.com App Exchange and discuss the role of a platform provider in innovation through collective intelligence. Our results suggest that the platform provider led the innovation in the initial period and, then, third party developers became gradually innovation leaders. Our findings are expected to re-orient the research focus from internal mechanisms to the role of platform providers.
Coal partial gasification studies applied to co-production of hydrogen and electricity

To search for the optimum working condition of coal partial gasification for co-production of hydrogen and electricity, a gasifier has been simulated and studied by the use of an Aspen Plus model. Oxygen-to-coal ratio, steam-to-coal ratio and steam temperature are optimization variables of the system. The optimization variables impact on syngas yield, syngas concentration, system exergy efficiency, HER and CO2 separation rate have been analyzed. It is found that the oxygen-to-coal ratio and the steam-to-coal ratio both have large influence on performances of gasifer and system and the steam temperature has the least influence.
AC power meter design for home electrical appliances

Monitoring power consumption of portable appliances is a step towards energy saving plans for any electrical equipment. This paper proposes a customizable power meter design employing voltage and current transformers, and Arduino. This meter is to monitor RMS voltage and current, real and apparent power, and power factor in real time. The voltage, the current, and the real power are calibrated with respect to a reference power meter. The resolutions are derived from analytical approach and the accuracies are evaluated from experiments in a variable of appliances and current transformers.
Review of Feature Extraction Techniques in Automatic Speech Recognition

Speech has evolved as a primary form of communication between humans. The advent of digital technology, gave us highly versatile digital processors with high speed, low cost and high power which enable researchers to transform the analog speech signals in to digital speech signals that can be scientifically studied. Achieving higher recognition accuracy, low word error rate and addressing the issues of sources of variability are the major considerations for developing an efficient Automatic Speech Recognition system. In speech recognition, feature extraction requires much attention because recognition performance depends heavily on this phase. In this paper, an effort has been made to highlight the progress made so far in the feature extraction phase of speech recognition system and an overview of technological perspective of an Automatic Speech Recognition system are discussed.
Synchronous parsing of syntactic and semantic structures

We describe in this paper an approach for synchronous parsing of syntactic and semantic dependency structures that combines recent advances in the area to get a very high accuracy as well at the same time very good parsing times. The time for parsing, the time for training and the values of the memory footprint are to our knowledge the best results reported while the parsing accuracy are as high as the highest results reported in the 2008 shared task. The corpora used in the shared task are still different to the dependency structures of the Meaning-Text Theory. Therefore, we outline the adaption of the approach to the dependency structures of the Meaning-Text Theory
Deficits in conditioned fear extinction in obsessive-compulsive disorder and neurobiological changes in the fear circuit.

IMPORTANCE Obsessive-compulsive disorder (OCD) may be characterized by impaired self-regulation and behavioral inhibition. Elevated fear and anxiety are common characteristics of this disorder. The neurobiology of fear regulation and consolidation of safety memories have not been examined in this patient population. OBJECTIVE To examine the psychophysiological and neurobiological correlates of conditioned fear extinction in patients with OCD. DESIGN Cross-sectional, case-control, functional magnetic resonance imaging study. SETTING Academic medical center. PARTICIPANTS Twenty-one patients with OCD and 21 healthy participants. MAIN OUTCOMES AND MEASURES Skin conductance responses and blood oxygenation level-dependent responses. RESULTS The between-group difference noted in our psychophysiological measure (skin conductance responses) was during extinction recall: patients with OCD showed impaired extinction recall relative to control subjects. Regarding the functional magnetic resonance imaging data, patients with OCD showed significantly reduced activation in the ventromedial prefrontal cortex across training phases. Moreover, reduced activation in the patients with OCD was noted in the caudate and hippocampus during fear conditioning, as well as in the cerebellum, posterior cingulate cortex, and putamen during extinction recall. Contrary to our prediction, OCD symptom severity was positively correlated with the magnitude of extinction memory recall. Also contrary to our prediction, functional responses of the ventromedial prefrontal cortex were positively correlated with symptom severity, and functional responses of the dorsal anterior cingulate cortex were inversely correlated with symptom severity. CONCLUSIONS AND RELEVANCE As expected, our study showed that fear extinction and its neural substrates are impaired in patients with OCD. However, this study also yielded some surprising and unexpected results regarding the correlates between extinction capacity and its neural substrates and the severity of symptoms expressed in this disorder. Thus, our data report neural correlates of deficient fear extinction in patients with OCD. The negative correlations between fear extinction deficits and Yale-Brown Obsessive-Compulsive Scale symptoms in OCD suggest that there may be other factors, in addition to fear extinction deficiency, that contribute to the psychopathology of OCD.
Design of a Remotely Accessible PC based Temperature Monitoring System

An innovative data-acquisition circuit for temperature monitoring and control is designed and interfaced to printer port of a web server computer. Further, an interactive web application program has been developed and kept running in the server computer for controlling the operation of the data-acquisition circuit. Authenticated clients can access the web based instrumentation system through Internet / Intranet.
Language Processing Infrastructure in the XLike Project

This paper presents the linguistic analysis tools and its infrastructure developed within the XLike project. The main goal of the implemented tools is to provide a set of functionalities for supporting some of the main objectives of XLike, such as enabling cross-lingual services for publishers, media monitoring or developing new business intelligence applications. The services cover seven major and minor languages: English, German, Spanish, Chinese, Catalan, Slovenian, and Croatian. These analyzers are provided as web services following a lightweight SOA architecture approach, and they are publically callable and are catalogued in META-SHARE.
External Governance and Debt Agency Costs of Family Firms

We investigate the impact of family blockholders on the firm's debt agency costs under different investor protection environments. On one hand, families--through their undiversified investments, inter-generation presence, and reputation concerns--can mitigate debt agency costs. On the other hand, families--through their unique power position that can lead to private benefits extraction and higher bankruptcy risk--can exacerbate debt agency costs. The actual impact can go either way and what matters should be the creditors' protection environment. Using international bond issues from 1995 to 2000 for 1,072 international firms originating from 24 different countries, we find that family firms originating from low investor protection environments suffer from higher debt costs compared to non-family firms, while family firms originating from high investor protection environments benefit from lower debt costs compared to non-family firms. We find no impact from non-family blockholdings. These results are robust to various specifications and confirmed by an out-of-sample test using bonds issued by U.S. and foreign firms listed in the U.S. originating from 27 different countries.
Development, characterization, and in vivo assessment of mucoadhesive nanoparticles containing fluconazole for the local treatment of oral candidiasis

This study aimed to develop a suitable buccal mucoadhesive nanoparticle (NP) formulation containing fluconazole for the local treatment of oral candidiasis. The suitability of the prepared formulations was assessed by means of particle size (PS), polydispersity index, and zeta potential measurements, morphology analysis, mucoadhesion studies, drug entrapment efficiency (EE), in vitro drug release, and stability studies. Based on the optimum NP formulation, ex vivo drug diffusion and in vitro cytotoxicity studies were performed. Besides, evaluation of the antifungal effect of the optimum formulation was evaluated using agar diffusion method, fungicidal activity-related in vitro release study, and time-dependent fungicidal activity. The effect of the optimum NP formulation on the healing of oral candidiasis was investigated in an animal model, which was employed for the first time in this study. The zeta potential, mucoadhesion, and in vitro drug release studies of various NP formulations revealed that chitosan-coated NP formulation containing EUDRAGIT® RS 2.5% had superior properties than other formulations. Concerning the stability study of the selected formulation, the formulation was found to be stable for 6 months. During the ex vivo drug diffusion study, no drug was found in receptor phase, and this is an indication of local effect. The in vitro antifungal activity studies showed the in vitro efficacy of the NP against Candida albicans for an extended period. Also, the formulation had no cytotoxic effect at the tested concentration. For the in vivo experiments, infected rabbits were successfully treated with local administration of the optimum NP formulation once a day. This study has shown that the mucoadhesive NP formulation containing fluconazole is a promising candidate with once-a-day application for the local treatment of oral candidiasis.
Preprocessing and Feature Sets for Robust Face Recognition

Many face recognition algorithms have been developed over the past few years but the problem remains challenging, especially for images taken under uncontrolled lighting conditions. We show that the robustness of several popular linear subspace methods and of Local Binary Patterns (LBP) can be substantially improved by including a very simple image preprocessing stage based on gamma correction, Difference of Gaussian filtering and robust variance normalization. LBP proves to be the best of these features, and we improve on its performance in two ways: by introducing a 3-level generalization of LBP, Local Ternary Patterns (LTP), and by using an image similarity metric based on distance transforms of LBP image slices. We give experimental results on two face recognition sets chosen for their difficult lighting conditions: version 1 of the Face Recognition Grand Challenge experiment 4, and the full Yale-B dataset.
Analysis of Pull-In Instability of Geometrically Nonlinear Microbeam Using Radial Basis Artificial Neural Network Based on Couple Stress Theory

The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS.