Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Diverse Datasets Improves Developmental Enhancer Prediction

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology.
Towards high-performance word sense disambiguation by combining rich linguistic knowledge and machine learning approaches

Supervised word sense disambiguation (WSD) for truly polysemous words (in contrast to homonyms) is difficult for machine learning, mainly due to two problems: the lack of sense-tagged training data and the sparsity of the matrix of observed instances vs. features. At the same time, high accuracy is necessary for WSD to be beneficial for high-level applications, such as information retrieval, question answering, and machine translation. This paper addresses the above two problems through combining rich linguistic knowledge and machine learning methods. Our work has two major contributions. First, we propose and demonstrate empirically evidence that careful design and generation of linguistically motivated features help to alleviate the data sparseness inherent in WSD. We built a supervised WSD system by using a smoothed maximum entropy (MaxEnt) model and linguistically motivated features (e.g., the semantic categories of a verb's noun phrase (NP) arguments). With three specific enhancements to automatic feature generation, our system achieved the best published results in an evaluation using the SENSEVAL2 English verbs with fine-grained senses (64.6% accuracy; 16.7 senses on average). We then developed a WSD-based feature generation method to filter out semantic features associated with irrelevant senses of a verb's NP arguments, which improved the system performance on verbs whose senses rely heavily on their NP arguments. To generalize semantic features, we developed a new clustering algorithm that automatically acquires semantically coherent noun groups from large text corpora. Using semantic features associated with these noun groups improved our system's performance further. The second contribution of our work is showing the effectiveness of active learning in the creation of more labeled training data for supervised WSD. Our experiments showed that two uncertainty-based active learning methods, combined with the smoothed MaxEnt model, reduced the required training data by 1/2 to 3/4 when learning coarse-grained English verb senses, suggesting the high potential of active learning in reducing sense annotation effort and facilitating the development of a broad coverage high performance supervised WSD system.
Post-embedding Double-labeling of Antigen-retrieved Ultrathin Sections Using a Silver Enhancement-controlled Sequential Immunogold (SECSI) Technique

In electron microscopy, the post-embedding immunogold technique provides a high degree of resolution and the possibility of quantitation owing to the intrinsic characteristics of the colloidal gold marker. Application of this technique to the subcellular localization of multiple antigens by differential labeling using gold markers of different sizes, or to double labeling using the same primary antibody isotype with serial silver enhancement, has been reported. We have incorporated this double labeling technique into a modified procedure that produces excellent labeling and ultrastructural preservation, even after exposure of ultrathin sections large enough to cover a 300-μm-diameter single-hole grid to hot antigen retrieval solutions and prolonged labeling protocols.
Improved fast gauss transform and efficient kernel density estimation

Evaluating sums of multivariate Gaussians is a common computational task in computer vision and pattern recognition, including in the general and powerful kernel density estimation technique. The quadratic computational complexity of the summation is a significant barrier to the scalability of this algorithm to practical applications. The fast Gauss transform (FGT) has successfully accelerated the kernel density estimation to linear running time for low-dimensional problems. Unfortunately, the cost of a direct extension of the FGT to higher-dimensional problems grows exponentially with dimension, making it impractical for dimensions above 3. We develop an improved fast Gauss transform to efficiently estimate sums of Gaussians in higher dimensions, where a new multivariate expansion scheme and an adaptive space subdivision technique dramatically improve the performance. The improved FGT has been applied to the mean shift algorithm achieving linear computational complexity. Experimental results demonstrate the efficiency and effectiveness of our algorithm.
Dynamic Interpolation B-Tree: A New Access Method

The performance of Object-Oriented Database depends on the access method implemented in the data model. Dynamic Interpolation B-tree (DIB) is a new indexing technique supporting query processing in Object-Oriented Databases which is effective and efficient for multimedia databases. This is a new access method which supports range queries on Object-Oriented Databases. DIB supports inheritance and aggregation hierarchies. DIB has the structure of Dynamic Interpolation B-tree. Dynamic Interpolation B-tree consists of hashing and B-tree. Both hashing and B-tree are dynamic. DIB technique is compared with other techniques obtained from more traditional organizations. In this new technique all the operations are done efficiently. The result shows that the Dynamic Interpolation B-tree is significantly better than the traditional indexing methods over a wide range of parameters in terms of range of parameters, retrieval and update cost so that the storage overhead grows slowly with the number of indexed attributes.
An Appointment Based MAC Protocol for Wireless Sensor Networks

Several asynchronous duty cycle MAC protocols have been proposed for low power in the wireless sensor networks. However, these proposed MACs pay little attention on the performance degradation in many-to-one communications which is the most common traffic pattern in wireless sensor networks. In this paper, we propose AB-MAC, an appointment based MAC protocol to overcome the effect of channel contention of multiple senders. In AB-MAC, a fusion appointment scheme is proposed to enable scheduled batch transmission for multiple senders with low overhead. This scheme improves the transmission efficiency of the current asynchronous MACs in many-to-one traffic pattern. The experimental results show that, comparing to X-MAC which is a leading asynchronous MAC, AB-MAC can achieve better performance in terms of throughput, latency and energy efficiency.
Real‐time Rendering of Heterogeneous Translucent Objects with Arbitrary Shapes

We present a real‐time algorithm for rendering translucent objects of arbitrary shapes. We approximate the scattering of light inside the objects using the diffusion equation, which we solve on‐the‐fly using the GPU. Our algorithm is general enough to handle arbitrary geometry, heterogeneous materials, deformable objects and modifications of lighting, all in real‐time. In a pre‐processing step, we discretize the object into a regular 4‐connected structure (QuadGraph). Due to its regular connectivity, this structure is easily packed into a texture and stored on the GPU. At runtime, we use the QuadGraph stored on the GPU to solve the diffusion equation, in real‐time, taking into account the varying input conditions: Incoming light, object material and geometry. We handle deformable objects, provided the deformation does not change the topological structure of the objects.
Early Cognitive/Social Deficits and Late Motor Phenotype in Conditional Wild-Type TDP-43 Transgenic Mice

Frontotemporal Dementia (FTD) and amyotrophic lateral sclerosis (ALS) are two neurodegenerative diseases associated to mislocalization and aggregation of TAR DNA-binding protein 43 (TDP-43). To investigate in depth the behavioral phenotype associated with this proteinopathy, we used as a model transgenic (Tg) mice conditionally overexpressing human wild-type TDP 43 protein (hTDP-43-WT) in forebrain neurons. We previously characterized these mice at the neuropathological level and found progressive neurodegeneration and other features that evoke human TDP-43 proteinopathies of the FTD/ALS spectrum. In the present study we analyzed the behavior of mice at multiple domains, including motor, social and cognitive performance. Our results indicate that young hTDP-43-WT Tg mice (1 month after post-weaning transgene induction) present a normal motor phenotype compared to control littermates, as assessed by accelerated rotarod performance, spontaneous locomotor activity in the open field test and a mild degree of spasticity shown by a clasping phenotype. Analysis of social and cognitive behavior showed a rapid installment of deficits in social interaction, working memory (Y-maze test) and recognition memory (novel object recognition test) in the absence of overt motor abnormalities. To investigate if the motor phenotype worsen with age, we analyzed the behavior of mice after long-term (up to 12 months) transgene induction. Our results reveal a decreased performance on the rotarod test and in the hanging wire test, indicating a motor phenotype that was absent in younger mice. In addition, long-term hTDP-43-WT expression led to hyperlocomotion in the open field test. In sum, these results demonstrate a time-dependent emergence of a motor phenotype in older hTDP-43-WT Tg mice, recapitulating aspects of clinical FTD presentations with motor involvement in human patients, and providing a complementary animal model for studying TDP-43 proteinopathies.
Identification of diphtheria toxin receptor and a nonproteinous diphtheria toxin-binding molecule in Vero cell membrane

Two substances possessing the ability to bind to diphtheria toxin (DT) were found to be present in a membrane fraction from DT-sensitive Vero cells. One of these substances was found on the basis of its ability to bind DT and inhibit its cytotoxic effect. This inhibitory substance competitively inhibited the binding of DT to Vero cells. However this inhibitor could not bind to CRM197, the product of a missense mutation in the DT gene, and did not inhibit the binding of CRM197 to Vero cells. Moreover, similar levels of the inhibitory activity were observed in membrane fractions from DT-insensitive mouse cells, suggesting the inhibitor is not the DT receptor which is specifically present in DT-sensitive cells. The second DT-binding substance was found in the same Vero cell membrane preparation by assaying the binding of 125I-labeled CRM197. Such DT-binding activity could not be observed in membrane preparation from mouse L cells. From competition studies using labeled DT and CRM proteins, we conclude that this binding activity is due to the surface receptor for DT. Treatment of these substances with several enzymes revealed that the inhibitor was sensitive to certain RNases but resistant to proteases, whereas the DT receptor was resistant to RNase but sensitive to proteases. The receptor was solubilized and partially purified by chromatography on CM- Sepharose column. Immunoprecipitation and Western blotting analysis of the partially purified receptor revealed that a 14.5-kD protein is the DT receptor, or at least a component of it.
Towards scalable compositional analysis by refactoring design models

Automated finite-state verification techniques have matured considerably in the past several years, but state-space explosion remains an obstacle to their use. Theoretical lower bounds on complexity imply that all of the techniques that have been developed to avoid or mitigate state-space explosion depend on models that are "well-formed" in some way, and will usually fail for other models. This further implies that, when analysis is applied to models derived from designs or implementations of actual software systems, a model of the system "as built" is unlikely to be suitable for automated analysis. In particular, compositional, hierarchical analysis (where state-space explosion is avoided by simplifying models of subsystems at several levels of abstraction) depend on the modular structure of the model to be analyzed. We describe how as-built finite-state models can be refactored for compositional state-space analysis, applying a series of transformations to produce an equivalent model whose structure exhibits suitable modularity. The process is supported by a parser which can parse a subset of Promela syntax and transform Promela code into refactored state graphs.
Synthesis and Antimicrobial Evaluation of a New Series of Heterocyclic Systems Bearing a Benzosuberone Scaffold

A series of novel benzosuberone derivatives were synthesized and evaluated as antimicrobial agents by using substituted benzosuberone derivatives 1a,b as starting materials. Treatment of 1a,b with phenyl isothiocyanate in dimethylformamide was followed by treatment with cold HCl solution to afford the thioamides 4a,b, which was reacted with methyl iodide to obtain methylated products 5a,b. Cyclocondensation of 4a,b with chloroacetone 6 and phenacyl chloride 7 gave the corresponding thiophene derivatives 9a–c. Reaction of 4a,b with C-acetyl-N-arylhydrazonoyl chlorides 14a and 14b in boiling EtOH in the presence of triethylamine, afforded the corresponding 1,3,4-thiadiazoline derivatives 16a–d. The thioamides 4a,b were reacted with C-ethoxycarbonyl-N-arylhydrazonoyl chlorides 18a,b which afforded 1,3,4-thiadiazoline derivatives 19a–d. The benzosuberones 1a,b were treated with 3-mercaptopropanoic acid to give compounds 21a,b, which were cyclized to tricyclic thiopyran-4(5H)-one derivatives 22a,b. The latter compounds 22a,b were reacted with 3-mercaptopropanoic acid to give compounds 23a,b, which were cyclized tetracyclic ring systems 24a,b. Finally, compounds 24a,b were oxidized using hydrogen peroxide under reflux conditions to afford the oxidized form of the novel tetracyclic heterogeneous ring systems 25a,b. The newly synthesized compounds were screened for antimicrobial activities. The structures of new compounds were characterized by 1H-NMR, 13C-NMR, IR, and EI-MS.
A review of wavelet denoising in MRI and ultrasound brain imaging

There is a growing interest in using multiresolution noise filters in a variety of medical imaging applications. We review recent wavelet denoising techniques for medical ultrasound and for magnetic resonance images and discuss some of their potential applications in the clinical investigations of the brain. Our goal is to present and evaluate noise suppression methods based on both image processing and clinical expertise. We analyze two types of filters for magnetic resonance images (MRI): noise Suppression in magnitude MRI images and denoising blood oxygen level-dependent (BOLD) response in functional MRI images (fMRI). The noise distribution in magnitude MRI images is Rician, while the noise distribution in BOLD images has been recently shown to follow a Gaussian model well. We evaluate different methods based on signal to noise ratio improvement and based on the preservation of the shape of the activated regions in fMRI. A critical view on the problem of speckle filtering in ultrasound images is given where we discuss some of the issues that are overlooked in many speckle filters like the relevance of the "speckled texture", expert-defined features of interest and the reliability of the common speckle models. We analyze the use of multiresolution speckle filters to improve the automatic processing steps in the clinical research of non-cystic periventricular leukomalacia. In particular we apply speckle filters to ultrasound neonatal brain images and we evaluate the influence of the filtering on the effectiveness of the subsequent classification and segmentation of flares of affected tissue in comparison with the manual delineation of clinicians.
Smooth joint motion planning for high precision reconfigurable robot manipulators

The accuracy of reconfigurable robot manipulators is a critical aspect which prevents their diffused industrial adoption. This work presents a novel model for designing joint motion profiles, particularly suitable for the motion planning of modular robots with high accuracy requirements. The model generates smooth motion profiles, without sacrificing execution time and taking into account the different characteristics of each joint and the requirements of each production task. The proposed method has been tested across the most performing motion planning approaches found in the literature, providing up to 39% faster jerk-bounded trajectories. Moreover, the model is flexible to the generation of adapted trajectories when degrading phenomena occur over the time.
RemoAct: Portable Projected Interface with Hand Gesture Interaction

RemoAct is a wearable depth sensing and projection system that enables interaction on many surfaces. It makes interaction with the environment more intuitive through sharing and sending data with surrounding devices by applying certain gestures. This system offers a mobile and intuitive solution for interacting using a projected surface on habitual flat surfaces. Every user has their public and private areas, where the user can create tiles on the fly and share it with others and these public tiles are shown to other users through augmented reality. Interaction is made through hand gestures, finger tracking and hand tracking. This gives the user more freedom in movement. Different experiments were conducted to calculate the accuracy and RemoAct ran against different conventional methods to compare its accuracy, time and user experience. RemoAct takes less time for two users to draw one chart. As the system enables the users to work simultaneously, it reduces the needed time, short compared to successive drawing. For gesture recognition, accuracy reached 90-95\%. Object recognition and face identification accuracy varied with the variation of light.
Using Cognates in a French-Romanian Lexical Alignment System: A Comparative Study

This paper describes a hybrid French - Romanian cognate identification module. This module is used by a lexical alignment system. Our cognate identification method uses lemmatized, tagged and sentence-aligned parallel corpora. This method combines statistical techniques, linguistic information (lemmas, POS tags) and orthographic adjustments. We evaluate our cognate identification module and we compare it to other methods using pure statistical techniques. Thus, we study the impact of the used linguistic information and the orthographic adjustments on the results of the cognate identification module and on cognate alignment. Our method obtains the best results in comparison with the other implemented statistical methods.
A combined PET/CT scanner for clinical oncology.

UNLABELLED The availability of accurately aligned, whole-body anatomical (CT) and functional (PET) images could have a significant impact on diagnosing and staging malignant disease and on identifying and localizing metastases. Computer algorithms to align CT and PET images acquired on different scanners are generally successful for the brain, whereas image alignment in other regions of the body is more problematic. METHODS A combined PET/CT tomograph with the unique capability of acquiring accurately aligned functional and anatomical images for any part of the human body has been designed and built. The PET/CT scanner was developed as a combination of a Siemens Somatom AR.SP spiral CT and a partial-ring, rotating ECAT ART PET scanner. All components are mounted on a common rotational support within a single gantry. The PET and CT components can be operated either separately, or in combined mode. In combined mode, the CT images are used to correct the PET data for scatter and attenuation. Fully quantitative whole-body images are obtained for an axial extent of 100 cm in an imaging time of less than 1 h. When operated in PET mode alone, transmission scans are acquired with dual 137Cs sources. RESULTS The scanner is fully operational and the combined device has been operated successfully in a clinical environment. Over 110 patients have been imaged, covering a range of different cancers, including lung, esophageal, head and neck, melanoma, lymphoma, pancreas, and renal cell. The aligned PET and CT images are used both for diagnosing and staging disease and for evaluating response to therapy. We report the first performance measurements from the scanner and present some illustrative clinical studies acquired in cancer patients. CONCLUSION A combined PET and CT scanner is a practical and effective approach to acquiring co-registered anatomical and functional images in a single scanning session.
Inter-Ethnic Hostility and Mobility of Political Power: Changing Influences of Perceived Horizontal Inequalities

The purpose of this paper is to illuminate the origins of inter-ethnic hostility, which need to be addressed if the centripetalism institutions are to function properly. Using intention of ethnic voting as an indicator of inter-group discrimination, this paper tests several classic hypotheses adapted from social psychology based on the surveys conducted in 14 urban areas from six sub-Saharan African countries. Results show, among others, that horizontal inequalities, when perceived either advantageously or disadvantageously, actually increase the level of inter-group hostility, but that the malicious effect is context-dependent: a greedy tendency in terms of socio-economic group inequality is limited to non-minorities; minorities who think their own group is superior to others economically do not have stronger hostility towards others compared to those who do not see any socio-economic gap; perceived horizontal inequalities matter chiefly in the countries without any kind of political power mobility; hostility between groups is not associated with perceived horizontal inequalities in countries where experience of power sharing or change of government has convinced people that political change is not impossible.