Miracle-dz/UniFAR-FTUs huggingface dataset:
Unexpected allelic heterogeneity and spectrum of mutations in Fowler syndrome revealed by next‐generation exome sequencing

Protein coding genes constitute approximately 1% of the human genome but harbor 85% of the mutations with large effects on disease‐related traits. Therefore, efficient strategies for selectively sequencing complete coding regions (i.e., “whole exome”) have the potential to contribute our understanding of human diseases. We used a method for whole‐exome sequencing coupling Agilent whole‐exome capture to the Illumina DNA‐sequencing platform, and investigated two unrelated fetuses from nonconsanguineous families with Fowler Syndrome (FS), a stereotyped phenotype lethal disease. We report novel germline mutations in feline leukemia virus subgroup C cellular‐receptor‐family member 2, FLVCR2, which has recently been shown to cause FS. Using this technology, we identified three types of genetic abnormalities: point‐mutations, insertions‐deletions, and intronic splice‐site changes (first pathogenic report using this technology), in the fetuses who both were compound heterozygotes for the disease. Although revealing a high level of allelic heterogeneity and mutational spectrum in FS, this study further illustrates the successful application of whole‐exome sequencing to uncover genetic defects in rare Mendelian disorders. Of importance, we show that we can identify genes underlying rare, monogenic and recessive diseases using a limited number of patients (n=2), in the absence of shared genetic heritage and in the presence of allelic heterogeneity. Hum Mutat 31:1–6, 2010. © 2010 Wiley‐Liss, Inc.
Model-based Single-Channel Dereverberation in Noisy Acoustical Environments

This paper illustrates a new system for recovering clean speech signals from noisy acoustical environments using one microphone. At the beginning of this paper, we propose an assump-tion that the background noise is comprised of reverberant noise and direct-path noise. And a novel late reverberant spectral variance (LRSV) estimator is generated referring to this assump-tion, which can be used in the noisy acoustical environments. What’s more, the shape parameters of this LRSV estimator are updated by taking some frames of previous late reverberation into account. At last, a new spectral process system is developed to help making the LRSV estimator more efﬁcient. The experimental results show the beneﬁts of our new system when it is used to reduce the interference in both noise-free and noisy acoustical environments.
Solving Overlay Mismatching of Unstructured P2P Networks using Physical Locality Information

This paper addresses overlay topology mismatching problem by presenting "AM", an effective way to measure nodes' relative distance and a technique called "CTAG" to construct topology-aware Gnutella overlay. The approach excels in sending no extra messages to probe the target nodes, avoiding the extra traffic and delay time, while keeping the necessary accuracy
Recognizing Human Actions Using Key Poses

In this paper, we explore the idea of using only pose, without utilizing any temporal information, for human action recognition. In contrast to the other studies using complex action representations, we propose a simple method, which relies on extracting “key poses” from action sequences. Our contribution is two-fold. Firstly, representing the pose in a frame as a collection of line-pairs, we propose a matching scheme between two frames to compute their similarity. Secondly, to extract “key poses” for each action, we present an algorithm, which selects the most representative and discriminative poses from a set of candidates. Our experimental results on KTH and Weizmann datasets have shown that pose information by itself is quite effective in grasping the nature of an action and sufficient to distinguish one from others.
A Framework for the Automated Drawing of Data Structure Diagrams

Data structure diagrams are two-dimensional figures made up of lines that aim to pictorially indicate the interrelationships of the elements of a data structure. The various rules and factors of aesthetics that go into the way data structure diagrams are drawn are collected together. The various subjective factors are formulated into computable objectives and numeric parameters. These are distilled from a large number of data structure drawings found in various textbooks. The rules used have not reached a level of acceptance comparable to that of the relevant rules in engineering graphics. The internal architecture of a (sub)system that helps draw data structure diagrams is outlined. >
Multi-level gene/MiRNA feature selection using deep belief nets and active learning

Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].
The Effects of Property Taxes and Local Public Spending on Property Values: An Empirical Study of Tax Capitalization and the Tiebout Hypothesis

The purpose of this paper is to present some empirical findings on a problem for which we presently possess only the scantest of evidence: the effects of local public budgets on property values in the community. There do exist several studies of the incidence of property taxes, the mainstay of local revenue systems in the United States, but in nearly all cases these studies are based on assumptions concerning the degree to which the tax on various components of property is capitalized. We have, however, little hard empirical evidence indicating whether property taxes are in fact capitalized and, if so, to what extent.1 This deficiency might not seem very serious if we had a single, compelling theory of the shifting and incidence of property taxes, a theory which suggested a definite solution to the problem. The truth, however, is that the theory of the shifting of property taxes points to a wide range of possibilities: under some circumstances the whole of the tax may be reflected in a reduced rental income (and hence lower property values) for landlords, while in other situations the tax may result primarily in increased rents to tenants, with little impact on the market value of property. Some years ago in this journal, Tiebout (1956) developed a formal model involving consumer location in accord with preferences for local public goods and services. He suggested that at least at a theoretical level we can envision a system in which we get something resembling a market solution to the production and consumption of local public goods. Very simply, Tiebout's world is one in which the consumer "shops" among different
FAT-miner: mining frequent attribute trees

Data that can conceptually be viewed as tree structures abounds in domains such as bio-informatics, web logs, XML databases and multi-relational databases. Besides structural information such as nodes and edges, tree structured data also often contains attributes, that represent properties of nodes. Current algorithms for finding frequent patterns in structured data, do not take these attributes into account, and hence potentially useful information is neglected. We present FAT-miner, an algorithm for frequent pattern discovery in tree structured data with attributes. To illustrate the applicability of FAT-miner, we use it to explore the properties of good and bad loans in a well-known multi-relational financial database.
Large Scale Transductive SVMs

We show how the concave-convex procedure can be applied to transductive SVMs, which traditionally require solving a combinatorial search problem. This provides for the first time a highly scalable algorithm in the nonlinear case. Detailed experiments verify the utility of our approach. Software is available at http://www.kyb. tuebingen.mpg. de/bs/people/fabee/transduction.html .
Analysis of Transconductance $(g_{m})$ in Schottky-Barrier MOSFETs

This paper experimentally investigates the unique behavior of transconductance (gm) in the Schottky-barrier metal-oxide-semiconductor field-effect transistors (SB-MOSFETs) with various silicide materials. When the Schottky-barrier height (SBH) or a scaling parameter is not properly optimized, a peculiar shape of gm is observed. Thus, gm can be used as a novel metric that exhibits the transition of the carrier injection mechanisms from a thermionic emission (TE) to thermally assisted tunneling (TU) in the SB-MOSFETs. When the local maximum point of gm is observed, it can be expected that an incomplete transition occurs between TE and TU in SB-MOSFETs. When a dopant-segregation (DS) technique is implemented in the SB-MOSFETs, however, the carrier injection efficiency from the source to the channel is significantly improved, although the SBH is not minimized. As a consequence, the peculiar shape of the gm disappears, i.e., a complete transition from TE to TU can be enabled by the DS technique.
Exploration of Cytochrome P450-Related Interactions between Aflatoxin B1 and Tiamulin in Broiler Chickens

Poultry may face simultaneous exposure to aflatoxin B1 (AFB1) and tiamulin (TIA), given mycotoxin contamination and antibiotic use. As both mycotoxins and antibiotics can affect cytochrome P450 enzymes (CYP450), our study aimed to explore their interaction. We developed UHPLC-MS/MS methods for the first-time determination of the interaction between TIA and AFB1 in vitro and in vivo in broiler chickens. The inhibition assay showed the half maximal inhibitory concentration (IC50) values of AFB1 and TIA in chicken liver microsomes are more than 7.6 μM, indicating an extremely weak inhibitory effect on hepatic enzymes. Nevertheless, the oral TIA pharmacokinetic results indicated that AFB1 significantly increased the area under the plasma concentration-time curve (AUClast) of TIA by 167% (p < 0.01). Additionally, the oral AFB1 pharmacokinetics revealed that TIA increased the AUClast and mean residence time (MRT) of AFB1 by 194% (p < 0.01) and 136%, respectively. These results suggested that the observed inhibition may be influenced by other factors, such as transport. Therefore, it is meaningful to further explore transport and other enzymes, involved in the interaction between AFB1 and TIA. Furthermore, additional clinical studies are necessary to thoroughly assess the safety of co-exposure with mycotoxins and antibiotics.
The Perceived Threat in Adults with Leukemia Undergoing Hematopoietic Stem Cell Transplantation

Background: Leukemia and hematopoietic stem cell transplantation (HSCT) create physical, psychological, social, and spiritual distresses in patients. Understanding this threatening situation in adults with leukemia undergoing HSCT will assist health care professionals in providing holistic care to the patients. Objectives: The aim of the present study was exploring the perceived threat in adults with leukemia undergoing HSCT. Patients and Methods: This article is part of a longitudinal qualitative study which used the grounded theory approach and was conducted in 2009-2011. Ten adults with acute leukemia scheduled for HSCT were recruited from the Hematology–Oncology Research Center and Stem Cell Transplantation, Shariati Hospital in Tehran, Iran. A series of pre-transplant and post-transplant in-depth interviews were held in the hospital’s HSCT wards. Totally, 18 interviews were conducted. Three written narratives were also obtained from the participants. The Corbin and Strauss approach was used to analyze the data. Results: Perceived threat was one of the main categories that emerged from the data. This category included four subcategories, "inattention to the signs and symptoms", "doubt and anxiety", "perception of danger and time limitation" and "change of life conditions", which occurred in linear progression over time. Conclusion: Suffering from leukemia and experiencing HSCT are events that are uniquely perceived by patients. This threatening situation can significantly effect perception of patients and cause temporary or permanent alterations in patients' lives. Health care professionals can help these patients by deeper understanding of their experiences and effective interventions.
Limited-magnitude error-correcting Gray codes for rank modulation

We construct Gray codes over permutations for the rank-modulation scheme, which are also capable of correcting errors under the infinity-metric. These errors model limited-magnitude or spike errors, for which only single-error-detecting Gray codes are currently known. Surprisingly, the error-correcting codes we construct achieve better asymptotic rates than that of presently-known constructions not having the Gray property. We also cast the problem of improving upon these results into the context of finding a certain type of auxiliary codes in the symmetric group of even orders.
Augmenting text document by on-line learning of local arrangement of keypoints

We propose a technique for text document tracking over a large range of viewpoints. Since the popular SIFT or SURF descriptors typically fail on such documents, our method considers instead local arrangement of keypoints. We extends Locally Likely Arrangement Hashing (LLAH), which is limited to fronto-parallel images: We handle a large range of viewpoints by learning the behavior of keypoint patterns when the camera viewpoint changes. Our method starts tracking a document from a nearly frontal view. Then, it undergoes motion, and new configurations of keypoints appear. The database is incrementally updated to reflect these new observations, allowing the system to detect the document under the new viewpoint. We demonstrate the performance and robustness of our method by comparing it with the original LLAH.
Network traffic reduction in six degree-of-freedom haptic, telementoring systems

This paper introduces a haptic data reduction and transmission technique to reduce the packet rate in six Degree-of-Freedom (6-DoF) haptic-enabled telementoring systems. The presented method relies on the limitations of human haptic perception (i.e. the Just Noticeable Differences) with respect to a user's hand position and orientation in order to reduce the number of packets transmitted without compromising transparency. A haptic prediction model is exploited to further reduce the amount of haptic packets transmitted, and to improve the reconstruction of data samples on the receiver side. Several distance metrics are also discussed to evaluate the acuity of human haptic perception when data reduction is performed in 6-DoF settings. Psychophysical experiments validate the effectiveness of the suggested algorithm as great haptic data reduction is achieved (up to 96%), while preserving the overall quality of the telementoring environment.
A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular Network

In the era of mobile big data, smart mobile devices have become an integral part of our daily life, which brings many benefits to the digital society. However, their popularity and relatively lax security make them vulnerable to various cyber threats. Traditional network traffic analysis techniques utilizing pattern matching and regular expressions matching algorithms are becoming insufficient for mobile big data. Network traffic anomaly detection is an effective method to replace traditional methods. Network traffic anomaly detection can solve many new challenges brought by future network and protect the security of network. In this article, we propose a streaming network framework for mobile big data, referred to as SNMDF, which provides massive data traffic collection, processing, analysis, and updating functions, to cope with the tremendous amount of data traffic. In particular, by analyzing the specific characteristics of anomaly traffic data from flow and user behavior, our proposed SNMDF demonstrates its capability to offer real data-based advice to address new challenges for future wireless networks from the viewpoints of operators. Tested by real mobile big data, SNMDF has proven its efficiency and reliability. Furthermore, SNMDF is accessed for the digital twin of the space Internet, which validates that it can be generalized to other environments with massive data traffic or big data.
Web services selection for distributed composition of multimedia content

Growing numbers of pervasive devices are gaining access to the Internet. However, much of the existing rich multimedia content cannot be handled by mobile client devices with limited communication, processing, storage and display capabilities. In this paper, we propose new ways to enhance the universal access to multimedia content through Web Services and Semantic Web concepts. A semantic-based personalized delivery concept is drafted that makes use of these emerging technologies together with rather classical multimedia transcoding ideas. Instead of large, monolithic portal applications designed for multi-purpose adaptation and a single-source delivery, we propose to shift multimedia adaptation functionality to a portfolio of adequately selected Web Services. Web Services accessible through standard interfaces that allow for multimedia format conversion and composition can allow for a more flexible, application-independent adaptation and thus ease multimedia service provisioning essentially.
Wedge: Splitting Applications into Reduced-Privilege Compartments

Software vulnerabilities and bugs persist, and so exploits continue to cause significant damage, particularly by divulging users' sensitive data to miscreants. Yet the vast majority of networked applications remain monolithically structured, in stark contravention of the ideal of least-privilege partitioning. Like others before us, we believe this state of affairs continues because today's operating systems offer isolation primitives that are cumbersome. We present Wedge, a system well suited to the splitting of complex, legacy, monolithic applications into fine-grained, least-privilege compartments. Wedge consists of two synergistic parts: OS primitives that create compartments with default-deny semantics, which force the programmer to make compartments' privileges explicit; and Crowbar, a pair of run-time analysis tools that assist the programmer in determining which code needs which privileges for which memory objects. By implementing the Wedge system atop Linux, and applying it to the SSL-enabled Apache web server and the OpenSSH login server, we demonstrate that Wedge allows fine-grained compartmentalization of applications to prevent the leakage of sensitive data, at acceptable performance cost. We further show that Wedge is powerful enough to prevent a subtle man-in-the-middle attack that succeeds on a more coarsely privilege-separated Apache web server.
Discrimination of modulation type (amplitude modulation or frequency modulation) with and without background noise.

These experiments compare the ability to detect amplitude modulation (AM) and frequency modulation (FM) with the ability to discriminate AM from FM, using 10-Hz sinusoidal modulation of a 1000-Hz carrier. Initially, psychometric functions were measured for the detection of AM and FM alone, using a two-alternative forced-choice (2AFC) task. In experiment 1, pairs of values of AM and FM were selected that would be equally detectable, and psychometric functions were measured for the discrimination of AM from FM, again in a 2AFC task. Values of d' for discriminating AM from FM were always lower than the values of d' for detection of the AM or FM. When the detectability of the AM and FM was low (d' = 0.66), two subjects were essentially unable to discriminate AM from FM. This was true both for stimuli presented in quiet and for stimuli presented with continuous noise chosen to mask either the lower or the upper side of the excitation pattern. In experiment 2, subjects were again required to discriminate AM from FM, but the AM depth was fixed within a block of trials, while the FM depth was varied across trials. The discriminability of AM from FM did not show distinct minima at specific FM depths. Again, this was true both for stimuli presented in quiet and for stimuli presented with continuous noise chosen to mask either the lower or the upper side of the excitation pattern. This result suggests that the discrimination of AM from FM was not based on monitoring just one side of the excitation pattern of the carrier. (ABSTRACT TRUNCATED AT 250 WORDS)
Using Argumentative Zones for Extractive Summarization of Scientific Articles

Information structure, i.e the way speakers construct sentences to present new information in the context of old, can capture rich linguistic information about the discourse structure of scientific documents. Information structure has been found useful for important Natural Language Processing (NLP) tasks, such as information retrieval and extraction. Since scientific articles typically follow a certain discourse structure describing the prior work, problem being solved, methods used, and so forth, it could also be useful for summarization of these articles. In this work we focus on a scheme of information structure called Argumentative Zoning (AZ), and investigate whether its categories could support extractive text summarization in a scientific domain. We develop a summarization system that uses AZ categories (i) as features and (ii) in the final sentence selection process. We evaluate the system directly as well as using task-based evaluation. The results show that AZ can support both full document and customized summarization. We report a statistically significant improvement in summarization performance against a competitive baseline that uses journal section labels instead of AZ information. TITLE AND ABSTRACT IN MANDARIN
Putative early manifestations of bipolar II disorder emerge later in the initial prodrome than manifestations hypothesized to be unrelated: an exploratory study

Aim: Symptom instances characterized as episodic or chronic, and as exaggerated responses to life events or inexplicable (e.g. mood swings, irritability/aggressiveness), may be the most likely early manifestations of the first episode of bipolar II disorder (BD‐II). Assuming that symptoms that emerge late in the prodrome, to a larger extent, are early manifestations of the disorder itself, we explore the time of onset and the duration of instances classified as ‘likely’, ‘possible’ and ‘unlikely’ early manifestations.