Miracle-dz/UniFAR-FTUs huggingface dataset:
A NEW METHOD FOR THE GENERATION AND USE OF FOCUSED ULTRASOUND IN EXPERIMENTAL BIOLOGY

1. An efficient generator of focused ultrasound has been designed, built, and successfully operated. 2. The generator has been used to produce focal heating in the centers of paraffin blocks, and in a similar manner, focal areas of destruction were obtained deep in fresh liver tissue with minimal effects at the surface and no effects on the intervening tissue. 3. In animals, focused ultrasound of high intensity produced local cerebral changes as inferred from behavior disabilities and as demonstrated at autopsy. This local brain effect was achieved through intervening scalp, skull, and meninges. The resulting behavior disabilities disappeared in from 2 to 16 hours. 4. To date, it has not been possible to produce such brain changes without incidental injury to the skin and subcutaneous tissue lying at the base of the cone of radiation. 5. Improvements in generation and application of the focused supersonic beam are suggested whereby it should be possible to increase still further the focal effects in the brain, with a corresponding decrease or elimination of complicating surface injury.
Image-Difference Prediction: From Grayscale to Color

Existing image-difference measures show excellent accuracy in predicting distortions, such as lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one example of this is gamut mapping. This is partly because they either do not interpret chromatic information correctly or they ignore it entirely. We present an image-difference framework that comprises image normalization, feature extraction, and feature combination. Based on this framework, we create image-difference measures by selecting specific implementations for each of the steps. Particular emphasis is placed on using color information to improve the assessment of gamut-mapped images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.
SATTVA: SpArsiTy inspired classificaTion of malware VAriants

There is an alarming increase in the amount of malware that is generated today. However, several studies have shown that most of these new malware are just variants of existing ones. Fast detection of these variants plays an effective role in thwarting new attacks. In this paper, we propose a novel approach to detect malware variants using a sparse representation framework. Exploiting the fact that most malware variants have small differences in their structure, we model a new/unknown malware sample as a sparse linear combination of other malware in the training set. The class with the least residual error is assigned to the unknown malware. Experiments on two standard malware datasets, Malheur dataset and Malimg dataset, show that our method outperforms current state of the art approaches and achieves a classification accuracy of 98.55\% and 92.83\% respectively. Further, by using a confidence measure to reject outliers, we obtain 100\% accuracy on both datasets, at the expense of throwing away a small percentage of outliers. Finally, we evaluate our technique on two large scale malware datasets: Offensive Computing dataset (2,124 classes, 42,480 malware) and Anubis dataset (209 classes, 36,784 samples). On both datasets our method obtained an average classification accuracy of 77\%, thus making it applicable to real world malware classification.
PriVar: a toolkit for prioritizing SNVs and indels from next-generation sequencing data

UNLABELLED Next-generation sequencing has become a valuable tool for detecting mutations involved in Mendelian diseases. However, it is a challenge to identify the small subset of functionally important mutations from tens of thousands of rare variants in a whole exome/genome. Therefore, we developed a toolkit called PriVar, a systematic prioritization pipeline that takes into consideration calling quality of the variants, their predicted functional impact, known connection of the gene to the disease and the number of mutations in a gene, and inference from linkage analysis. AVAILABILITY Executable jar package is available at http://paed.hku. hk/uploadarea/yangwl/html/software.html.
Nitrogen Cycling Responses to Mountain Pine Beetle Disturbance in a High Elevation Whitebark Pine Ecosystem

Ecological disturbances can significantly affect biogeochemical cycles in terrestrial ecosystems, but the biogeochemical consequences of the extensive mountain pine beetle outbreak in high elevation whitebark pine (WbP) (Pinus albicaulis) ecosystems of western North America have not been previously investigated. Mountain pine beetle attack has driven widespread WbP mortality, which could drive shifts in both the pools and fluxes of nitrogen (N) within these ecosystems. Because N availability can limit forest regrowth, understanding how beetle-induced mortality affects N cycling in WbP stands may be critical to understanding the trajectory of ecosystem recovery. Thus, we measured above- and belowground N pools and fluxes for trees representing three different times since beetle attack, including unattacked trees. Litterfall N inputs were more than ten times higher under recently attacked trees compared to unattacked trees. Soil inorganic N concentrations also increased following beetle attack, potentially driven by a more than two-fold increase in ammonium (NH4 +) concentrations in the surface soil organic horizon. However, there were no significant differences in mineral soil inorganic N or soil microbial biomass N concentrations between attacked and unattacked trees, implying that short-term changes in N cycling in response to the initial stages of WbP attack were restricted to the organic horizon. Our results suggest that while mountain pine beetle attack drives a pulse of N from the canopy to the forest floor, changes in litterfall quality and quantity do not have profound effects on soil biogeochemical cycling, at least in the short-term. However, continuous observation of these important ecosystems will be crucial to determining the long-term biogeochemical effects of mountain pine beetle outbreaks.
Machine Translation, Linguistics, and Interlingua

An adequate, complete, and economical linguistic theory is necessary for MT and the question is whether a consistent use of the often unduly neglected dependency syntax, including a systematic description of topic and focus, cannot serve as a reliable base for the grammar of an interlingua, or of a set of interrelated interface structures.
Awareness and coordination in shared workspaces

Awareness of individual and group activities is critical to successful collaboration and is commonly supported in CSCW systems by active, information generation mechanisms separate from the shared workspace. These mechanisms pena~ise information providers, presuppose relevance to the recipient, and make access difficult, We discuss a study of shared editor use which suggests that awareness information provided and exploited passively through the shared workspace, allows users to move smoothly between close and loose collaboration, and to assign and coordinate work dynamically. Passive awareness mechanisms promise effective support for collaboration requiring this sort of behaviour, whilst avoiding problems with active approaches.
A Practical Multichannel Dereverberation Algorithm using Multichannel Dypsa and Spatiotemporal Averaging

Speech signals for hands-free telecommunication applications are received by one or more microphones placed at some distance from the talker. In an office environment, for example, unwanted signals such as reverberation and background noise from computers and other talkers will degrade the quality of the received signal. These unwanted components have an adverse effect upon speech processing algorithms and impair intelligibility. This paper demonstrates the use of the Multichannel DYPSA algorithm to identify glottal closure instants (GCIs) from noisy, reverberant speech. Using the estimated GCIs, a spatiotemporal averaging technique is applied to attenuate the unwanted components. Experiments with a microphone array demonstrate the dereverberation and noise suppression of the spatiotemporal averaging method, showing up to a 5 dB improvement in segmental SNR and 0.33 in normalized Bark spectral distortion score.
Expanding the chemical scope of RNA:methyltransferases to site-specific alkynylation of RNA for click labeling

This work identifies the combination of enzymatic transfer and click labeling as an efficient method for the site-specific tagging of RNA molecules for biophysical studies. A double-activated analog of the ubiquitous co-substrate S-adenosyl-l-methionine was employed to enzymatically transfer a five carbon chain containing a terminal alkynyl moiety onto RNA. The tRNA:methyltransferase Trm1 transferred the extended alkynyl moiety to its natural target, the N2 of guanosine 26 in tRNAPhe. LC/MS and LC/MS/MS techniques were used to detect and characterize the modified nucleoside as well as its cycloaddition product with a fluorescent azide. The latter resulted from a labeling reaction via Cu(I)-catalyzed azide-alkyne 1,3-cycloaddition click chemistry, producing site-specifically labeled RNA whose suitability for single molecule fluorescence experiments was verified in fluorescence correlation spectroscopy experiments.
Mediterranean spotted fever in Algerian children.

Mediterranean spotted fever (MSF) is a potentially life-threatening zoonosis caused by Rickettsia conorii and transmitted by the brown dog tick Rhipicephalus sanguineus [1]. Although MSF is one of the oldest recognised rickettsioses, there have been few reports about this rickettsiosis in North Africa [1,2]. The present work on MSF cases occurring in children is a subset of a larger study conducted within all age categories.
Incrementally Learning Time-Varying Half Planes

We present a distribution-free model for incremental learning when concepts vary with time. Concepts are caused to change by an adversary while an incremental learning algorithm attempts to track the changing concepts by minimizing the error between the current target concept and the hypothesis. For a single half-plane and the intersection of two half-planes, we show that the average mistake rate depends on the maximum rate at which an adversary can modify the concept. These theoretical predictions are verified with simulations of several learning algorithms including back propagation.
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow

This paper addresses the problem of image segmentation by means of active contours, whose evolution is driven by the gradient flow derived from an energy functional that is based on the Bhattacharyya distance. In particular, given the values of a photometric variable (or of a set thereof), which is to be used for classifying the image pixels, the active contours are designed to converge to the shape that results in maximal discrepancy between the empirical distributions of the photometric variable inside and outside of the contours. The above discrepancy is measured by means of the Bhattacharyya distance that proves to be an extremely useful tool for solving the problem at hand. The proposed methodology can be viewed as a generalization of the segmentation methods, in which active contours maximize the difference between a finite number of empirical moments of the ldquoinsiderdquo and ldquooutsiderdquo distributions. Furthermore, it is shown that the proposed methodology is very versatile and flexible in the sense that it allows one to easily accommodate a diversity of the image features based on which the segmentation should be performed. As an additional contribution, a method for automatically adjusting the smoothness properties of the empirical distributions is proposed. Such a procedure is crucial in situations when the number of data samples (supporting a certain segmentation class) varies considerably in the course of the evolution of the active contour. In this case, the smoothness properties of the empirical distributions have to be properly adjusted to avoid either over- or underestimation artifacts. Finally, a number of relevant segmentation results are demonstrated and some further research directions are discussed.
The role of Interleukin-17A and Interleukin-17E in multiple myeloma patients

Summary Background Tumor growth in multiple myeloma (MM) is regulated by the cytokine networks which are produced by myeloma cells and the microenvironment of the bone marrow. Interleukin-17 (IL-17) is implicated in the increased angiogenesis in the bone marrow of MM. Recent studies reported elevated levels of interleukin 17A (IL-17A) in the sera of patients with advanced stages according to Durie-Salmon classification. Material/Methods We compared the concentration of IL-17A and IL-17E in the blood serum of 34 newly diagnosed MM patients with healthy subjects’ sera. We also evaluated the concentration of IL-17A and IL-17E in the blood serum of MM patients and the relation to the percentage of plasma cells and other clinical parameters. The concentration of IL-17E and IL-17A of healthy subjects and patients with MM was assessed by enzyme-linked immunosorbent assay (ELISA). Results Our data confirm that IL-17A and IL-17E serum levels were significantly higher in all MM patients and also in patients with advanced stage compared with healthy subjects. We found the correlation between serum levels of IL-17A in MM patients and percentage of plasma cells. Our results also showed that if serum levels of IL-17E were higher in MM patients, the percentage of plasma cells and beta-2-microglobulin levels were lower. Conclusions The IL-17 family of cytokines may suppress or promote tumor growth. There seems to be some balance between the effects of IL-17A and IL-17E. The role of increased levels of IL-17E needs further investigation to understand its role in the pathobiology of MM.
Potential of RADARSAT-2 data for operational sea ice monitoring

Synthetic aperture radar (SAR) data from RADARSAT-1 are an important operational data source for several ice centres around the world. Whereas RADARSAT-1 is only capable of acquiring data at a single polarization, RADARSAT-2 will be capable of acquiring dual-polarization data in many wide-swath modes (most importantly ScanSAR) and fully polarimetric SAR data in a narrow 25-km swath mode. In this paper, we consider the ice information requirements for operational sea ice monitoring at the Canadian Ice Service and the potential for RADARSAT-2 to meet those requirements. Primary parameters are ice-edge location, ice concentration, and stage of development; secondary parameters include leads, ice thickness, ice topography and roughness, ice decay, and snow properties. Iceberg detection is included as an additional ice information requirement. The dual and fully polarimetric modes of RADARSAT-2 are expected to enhance our ability to measure these parameters. For ice operations, the dual-polarization data are expected to be most useful, as they will provide the required wide coverage in a number of modes. Although ScanSAR is the recommended mode of operation, the properties of other modes are also discussed. To illustrate the expected improvements from polarimetry, we review the conclusions of past work and add some new results using ENVISAT ASAR and simulated RADARSAT-2 data.
A Neighbor Coverage-Based Probabilistic Rebroadcast for Reducing Routing Overhead in Mobile Ad Hoc Networks

Due to high mobility of nodes in mobile ad hoc networks (MANETs), there exist frequent link breakages which lead to frequent path failures and route discoveries. The overhead of a route discovery cannot be neglected. In a route discovery, broadcasting is a fundamental and effective data dissemination mechanism, where a mobile node blindly rebroadcasts the first received route request packets unless it has a route to the destination, and thus it causes the broadcast storm problem. In this paper, we propose a neighbor coverage-based probabilistic rebroadcast protocol for reducing routing overhead in MANETs. In order to effectively exploit the neighbor coverage knowledge, we propose a novel rebroadcast delay to determine the rebroadcast order, and then we can obtain the more accurate additional coverage ratio by sensing neighbor coverage knowledge. We also define a connectivity factor to provide the node density adaptation. By combining the additional coverage ratio and connectivity factor, we set a reasonable rebroadcast probability. Our approach combines the advantages of the neighbor coverage knowledge and the probabilistic mechanism, which can significantly decrease the number of retransmissions so as to reduce the routing overhead, and can also improve the routing performance.
A Visual Query Language for Complex-Value Databases

In this paper, a visual language, VCP, for queries on complex-value databases is proposed. The main strength of the new language is that it is purely visual: (i) It has no notion of variable, quantification, partiality, join, pattern matching, regular expression, recursion, or any other construct proper to logical, functional, or other database query languages and (ii) has a very natural, strong, and intuitive design metaphor. The main operation is that of copying and pasting in a schema tree. We show that despite its simplicity, VCP precisely captures complex-value algebra without powerset, or equivalently, monad algebra with union and difference. Thus, its expressive power is precisely that of the language that is usually considered to play the role of relational algebra for complex-value databases.
Research of 3D Model for Information Retrieval Methods Based on Semantic Tree

The paper borrowed researching achievements in text retrieval field, describe the semantics of single model by using annotation, and present the semantics of 3D models by using semantic tree, based on the semantic similarity between the retrieval terms and semantic tree nodes with WordNet, and return the model that has strong semantic correlation. A flexible return policy is proposed to filter the representative model of the semantic relevant nodes so that the users can further optimize the retrieval results. According to the experimental results, the method of 3D model retrieval method based-on semantic tree proposed in this paper can improve the efficiency of information retrieval, which is valuable in theory as well as in application.
Statistical analysis of multi-channel detection using data from airborne AESA radar

We investigate the ground clutter homogeneity and target detection performance using airborne multi-channel AESA (Active Electronically Scanned Array) radar data from flight trials over the southern part of Sweden. The data sets consisted of clutter returns from both urban and rural areas. Multivariate normality tests indicate a significant difference between these two environments, and particularly the urban clutter does not fit the Gaussian signal model. Furthermore, we expose differences in terms of homogeneity as well. As homogeneity measure we employed the commonly used generalized inner product (GIP), and compared this to a homogeneity measure based on projection in the eigenspace of the covariance matrix. The focus of our interest was to examine the importance of screening the secondary data from non-homogeneities, when using adaptive target detection in AESA radar systems. To evaluate the non-homogeneity detection (NHD) performance we employed a synthetic target scheme. Our evaluation showed negligible improvement from NHD preprocessing, regardless of method, and hence we conclude that the clutter in our data sets is structurally homogeneous. However, in dense target scenarios it is crucial to screen for target contamination in secondary data, as we demonstrate on real data.
The mannose receptor functions as a high capacity and broad specificity antigen receptor in human dendritic cells

Dendritic cells, in contrast to B lymphocytes, must be able to efficiently internalize a diverse array of antigens for processing and loading onto major histo‐compatibility complex (MHC) class II molecules. Here we characterize the mannose receptor pathway in dendritic cells and show that mannose receptor‐mediated uptake of antigens results in a ∼ 100‐fold more efficient presentation to T cells, as compared to antigens internalized via fluid phase. Immunocytochemistry as well as subcellular fractionation revealed the localization of the mannose receptor and MHC class II molecules in distinct subcellular compartments. The mannose receptor thus functions in rapid internalization and concentration of a variety of glycosylated antigens that become available for processing and presentation. This may contribute to the unique capacity of dendritic cells to generate primary T cell responses against infectious agents.
Adaptive tests for association analysis of rare variants

In anticipation of the availability of next‐generation sequencing data, there has been increasing interest in association analysis of rare variants (RVs). Owing to the extremely low frequency of a RV, single variant‐based analysis and many existing tests developed for common variants may not be suitable. Hence, it is of interest to develop powerful statistical tests to assess association between complex traits and RVs with sequence data. Recently, a pooled association test based on variable thresholds (VT) was proposed and shown to be more powerful than some existing tests (Price et al. [2010] Am J Hum Genet 86:832–838). In this study, we generalize the VT test of Price et al. in several aspects. We propose a general class of adaptive tests that covers the VT test of Price et al. as a special case. In particular, we show that some of our proposed adaptive tests may substantially improve the power over the pooled association tests, including the VT test of Price et al., especially so in the presence of many neutral RVs and/or of causal RVs with opposite association directions, in which cases most of the existing pooled association tests suffer from significant loss of power. Our proposed tests are also general and flexible with the ability to incorporate weights on RVs and to adjust for covariates. Genet. Epidemiol. 2011. © 2011 Wiley‐Liss, Inc. 35:381‐388, 2011
K‐ras and p53 mutations in stage I gallbladder carcinoma with an anomalous junction of the pancreaticobiliary duct

An anomalous junction of the pancreaticobiliary duct (AJPBD) was thought to be an important risk factor for gallbladder carcinoma in Japan. In this report, we compared K‐ras and p53 mutations in Stage I gallbladder carcinomas (GC) of patients with AJPBD with those in patients without AJPBD.