Miracle-dz/UniFAR-FTUs huggingface dataset:
p53 mutation and locoregional treatment failure in head and neck squamous cell carcinoma.

BACKGROUND The p53 gene (also known as TP53) may be the most common genetic target involved in the malignant transformation of human cells. Direct sequence analysis has demonstrated that alteration of this gene occurs in approximately 45% of head and neck squamous cell carcinomas. The consequences of p53 mutations in these cancers with respect to tumor behavior and patient survival have not been rigorously determined. PURPOSE We evaluated the implications of p53 mutations in relation to the control of locoregional disease and overall survival following radiation therapy. METHODS Data from 110 consecutive patients with invasive disease who were treated with primary radiation therapy (given with curative intent) or with adjuvant radiation therapy (following complete surgical extirpation of gross disease) were included in the analysis. A 1.8-kilobase fragment of the p53 gene encompassing exons 5-9 was amplified from the DNA of stored (frozen) tumor specimens; the amplified DNA was cloned and sequenced by use of standard techniques. Overall survival and locoregional disease-free survival after the completion of radiation therapy were estimated by the Kaplan-Meier method; survival comparisons were made by use of the logrank test or proportional hazards regression models. Reported P values are two-sided. RESULTS Fortyeight (44%) of the 110 tumors had cells bearing p53 mutations. The risk of locoregional recurrence following either primary or adjuvant radiation therapy was significantly greater (i.e., the time to recurrence was shorter) for patients whose tumors contained mutant p53 genes (univariate model hazard ratio [HR] for p53 mutation versus wild-type = 2.2; 95% confidence interval [CI] = 1.2-4.1; P = .02). The presence of regional lymph node metastases (presence versus absence, HR = 2.0; 95% CI = 1.0-4.2; P = .05) and treatment type (primary radiation therapy versus surgery plus adjuvant radiation therapy, HR = 2.3; 95% CI = 1.2-4.3; P = .01) were also associated with greater risks of locoregional failure. The presence of p53 mutations and lymph node metastases and treatment with primary, as opposed to adjuvant, radiation therapy remained significant risk factors in multivariate regression analysis. No relationship was demonstrated between p53 status and overall survival (mutant versus wild-type, HR = 1.1; 95% CI = 0.6-2.1; P = .66); however, a relationship was shown for tumor stage and overall survival (stages III and IV [more advanced] versus stages I and II [less advanced], HR = 3.3; 95% CI = 1.0-10.8; P = .05). Mutation of the p53 gene was not associated with patient age, sex, tumor stage, primary tumor site, regional lymph node status, degree of tumor cell differentiation, or treatment method. CONCLUSIONS Mutation of the p53 gene is associated with an increased risk of locoregional failure in patients with invasive head and neck squamous cell carcinoma who are treated with radiation therapy.
Nonlinear sampled-data controller redesign via Lyapunov functions

Abstract We provide results for redesign of Lyapunov function based continuous time controllers for sampled-data implementation, using a particular form of the redesigned controller and the Taylor expansion of the sampled-data Lyapunov difference. We develop two types of redesigned controllers that (i) make the lower order terms (in T) in the series expansion of the Lyapunov difference with the redesigned controller more negative and (ii) make the terms in the Taylor expansions of the Lyapunov difference for the sampled-data system with the redesigned controller behave as close as possible to the respective values of the continuous-time system with the original controller. Simulation studies illustrate the performance of our controllers.
Mood classifiaction of lyrics using SentiWordNet

The text data being unstructured pose multiple research issues in document classification. Relevant feature extraction is the foremost problem in the preprocessing stage. SentiWordNet is an ontology that includes numeric scores related to the positive or negative aspects of the words. The work in this paper explores the use of SentiWordNet to extract sentiment features of the words in the song lyrics. The experiments are carried out on a collection of 185 lyrics each belonging to one of the four classes. Three classification algorithms namely, Naïve Bayesian (NB), k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) using six measures for attribute relevance analysis namely, Principal Component Analysis (PCA), Latent Semantic Analysis (LSA), Chi-Square (CS), Information Gain (IG), GINI Index (GI) and Gain Ratio (GR) have been applied to model the classifiers. The experiments examine the relevance of the sentiment features for classification. The ratio of the positive and negative scores, normalized ratio, and average of the positive and negative scores are three sentiment features. The experimental results indicate that the Naïve Bayesian classifier using the average of the positive and negative score as sentiment feature, and gain ratio as feature selection criteria achieve 78.27% accuracy based on top 10% of the features. The second best accuracy has been achieved by SVM-based classifiers using the average of the positive and negative score as sentiment feature and top 10% features applying all feature selection criteria except CS.
Creativity in Education: Teaching for Creativity Development

Creativity is essential to innovation, novelty, and sustenance. This research involves the study of creativity in education, specifically through the training of teachers and future teachers to apply theories of creativity in instructional design. Teacher Education students were exposed to creativity theory and conditioned to apply theory in developing learner creativity in lesson and project design. Creativity theories were included in an online course in cognition and critical thinking in education as foundational psychological frameworks to apply in educational practice and in the design of creative activity in the course. Participants studied and applied creativity frameworks in instruction and learning design in the form of lessons and projects. Lesson Designs were full-length lessons with applications of creativity theory. Project Designs were group projects incorporating creativity theory into an educational resource. Uses of creativity theory in lessons and projects were analyzed for understanding and application of theory.
Sparsifying Preconditioner for Pseudospectral Approximations of Indefinite Systems on Periodic Structures

This paper introduces the sparsifying preconditioner for the pseudospectral approximation of highly indefinite systems on periodic structures, which include the frequency-domain response problems of the Helmholtz equation and the Schrodinger equation as examples. This approach transforms the dense system of the pseudospectral discretization approximately into a sparse system via an equivalent integral reformulation and a specially designed sparsifying operator. The resulting sparse system is then solved efficiently with sparse linear algebra algorithms and serves as a reasonably accurate preconditioner. When combined with standard iterative methods, this new preconditioner results in small iteration counts. Numerical results are provided for the Helmholtz equation and the Schrodinger in both two and three dimensions to demonstrate the effectiveness of this new preconditioner.
Moving objects detection at an intersection by sequential background extraction

Recently, there are many traffic accidents in turning right at an intersection. They are mainly caused by a driver's oversight of pedestrians and motorcycles that are occluded by oncoming cars. Therefore a system is necessary to detect moving objects such as oncoming cars and pedestrians at an intersection, and warn a vehicle driver. This paper describes a technique for detecting moving objects in turning right at an intersection when vehicle is stopping. Moving objects are detected by Mixture of Gaussians (MoG). In addition, we distinguish cars from pedestrians using the difference of the area size and the aspect ratio of detected objects. The object which is classified as a pedestrian is tracked using Lucas-Kanade Tracker. If the detected cars and pedestrians overlap or a car completely obscures pedestrians, we perform the estimation of pedestrian's location by using the information on past frames. By doing this, it is possible to detect pedestrians that drivers are actually difficult to see. The performance of the proposed technique was examined employing car videos and satisfactory results were obtained.
Exo- and endoglycosidases revisited

Many glycosidases, which work as useful reagents for the studies of structures and functions of free and conjugated oligosaccharides, have been found and thoroughly purified. These enzymes are classified into exo- and endoglycosidases by their glycon specificities. Their usefulness and limits as reagents are explained in detail in this review. Endoglycosidases, which were originally found in the culture fluid of bacteria and in the extracts of plants, are now widely found in the mammals including humans. The physiological roles of these enzymes are discussed in relation to the oligosaccharides accumulated in the urine of patients with exoglycosidase deficiencies. Furthermore, PNGase is found to play important roles in the ER-associated degradation pathway of glycoproteins. Recent studies of the glycosidases in Bifidobacteria have revealed that GNB/LNB pathway, which uniquely exist in this bacteria, works for the expression of Bifidus factor activity of human milk oligosaccharides, an important topic in the baby nutrition. This interesting field will be introduced in detail in one section of this article.
Underwater Video Mosaics as Visual Navigation Maps

This paper presents a set of algorithms for the creation of underwater mosaics and illustrates their use as visual maps for underwater vehicle navigation. First, we describe the automatic creation of video mosaics, which deals with the problem of image motion estimation in a robust and automatic way. The motion estimation is based on a initial matching of corresponding areas over pairs of images, followed by the use of a robust matching technique, which can cope with a high percentage of incorrect matches. Several motion models, established under the projective geometry framework, allow for the creation of high quality mosaics where no assumptions are made about the camera motion. Several tests were run on underwater image sequences, testifying to the good performance of the implemented matching and registration methods. Next, we deal with the issue of determining the 3D position and orientation of a vehicle from new views of a previously created mosaic. The problem of pose estimation is tackled, using the available information on the camera intrinsic parameters. This information ranges from the full knowledge to the case where they are estimated using a self-calibration technique based on the analysis of an image sequence captured under pure rotation. The performance of the 3D positioning algorithms is evaluated using images for which accurate ground truth is available.
Randomized Ring-Partition Fingerprinting with Dithered Lattice Vector Quantization

Image fingerprinting is required to depend on one or several keys to ensure the content security. In this paper, we propose randomized ring-partition fingerprinting with dithered lattice vector quantization which achieves the security of image hashing by randomness. First, we extract features using randomized ring-partition with a secret key. Then, we perform dithered lattice vector quantization to quantize the features. Finally, we generate a fingerprint with 90 binary bits to represent an image. Experimental results show that the proposed method achieves significant improvements in image fingerprinting in terms of robustness, discriminability, runtime, and compact compared with state-of-the-art fingerprinting ones.
Energy efficiency of a parallel HEVC software decoder for embedded devices

In the context of fast adoption and deployment of recent video compression standard and thanks to recent high performance embedded processors, software video decoding can be performed in real time. But, it becomes among the most energy-intensive applications. Current embedded processors are based on multi-core architecture with advanced convenient features such as Dynamic Voltage Frequency Scaling (DVFS) in order to reduce their power consumption, allowing low power video decoding when no hardware decoding support is available for a given device. This paper deals with energy efficiency impact of different parallelization strategies of a software High Efficiency Video Coding (HEVC) decoder on multi-core ARM big.LITTLE processor. These strategies include the exploitation of data and task-level parallelism, as well as the use of different available DVFS policies.
Visual Place Categorization in Indoor Environments

This paper addresses the problem of visual place categorization, which aims at augmenting different locations of the environment visited by an autonomous robot with information that relates them to human-understandable concepts. We formulate the problem of visual place categorization in terms of energy minimization. To label visual observations with place categories we present a global image representation that is invariant to common changes in dynamic environments and robust against intra-class variations. To satisfy temporal consistency, a general solution is presented that incorporates statistical cues, without being restricted by constant and small neighbourhood radii, or being dependent on the actual path followed by the robot. A set of experiments on publicly available databases demonstrates the advantages of the presented system and show a significant improvement over available methods.
Citizen Science for Citizen Access to Law

This papers sits at the intersection of citizen access to law, legal informatics and plain language. The paper reports the results of a joint project of the Cornell University Legal Information Institute and the Australian National University which collected thousands of crowdsourced assessments of the readability of law through the Cornell LII site. The aim of the project is to enhance accuracy in the prediction of the readability of legal sentences. The study requested readers on legislative pages of the LII site to rate passages from the United States Code and the Code of Federal Regulations and other texts for readability and other characteristics. The research provides insight into who uses legal rules and how they do so. The study enables conclusions to be drawn as to the current readability of law and spread of readability among legal rules. The research is intended to enable the creation of a dataset of legal rules labelled by human judges as to readability. Such a dataset, in combination with machine learning, will assist in identifying factors in legal language which impede readability and access for citizens. As far as we are aware, this research is the largest ever study of readability and usability of legal language and the first research which has applied crowdsourcing to such an investigation. The research is an example of the possibilities open for enhancing access to law through engagement of end users in the online legal publishing environment for enhancement of legal accessibility and through collaboration between legal publishers and researchers.
The Meaning of Work among the Hard-Core Unemployed

O bviously, work does not have the same meaning for all people. For some, it is no more than a source of sustenance and security, while to others it is the means through which self-expression and status needs are attained. A review of studies concerned with the functions and meanings of work in the United States shows several common points of interest and findings (Friedmann and Havighurst, 1954; Morse and Weiss, 1955; Weiss and Kahn, 1960; Loether, 1964; Tausky, 1969). From these, a typology of six meanings of work was constructed as shown in Table 1. These meanings reflect he various facets of the work value system in the United States, which contains both instrumental and expressive components. Meanings one through four are expressive in nature, signifying strivings of man to express himself through work and gain satisfaction from the performance of his task. Meanings five and six are instrumental; work in these contexts is viewed as a means of accomplishing some external end rather than as an end in itself.
MV-IDX: indexing in multi-version databases

An index in a Multi-Version DBMS (MV-DBMS) has to reflect different tuple versions of a single data item. Existing approaches follow the paradigm of logically separating the tuple version data from the data item, e.g. an index is only allowed to return at most one version of a single data item (while it may return multiple data items that match a search criteria). Hence to determine the valid (and therefore visible) tuple version of a data item, the MV-DBMS first fetches all tuple versions that match the search criteria and subsequently filters visible versions using visibility checks. This involves I/O storage accesses to tuple versions that do not have to be fetched. In this vision paper we present the Multi-Version Index (MV-IDX) approach that allows index-only visibility checks which significantly reduce the amount of I/O storage accesses as well as the index maintenance overhead. The MV-IDX achieves significantly lower response times and higher transactional throughput on OLTP workloads.
Redundant filterbank precoders and equalizers. II. Blind channel estimation, synchronization, and direct equalization

For pt.I see ibid., vol.47, no.7, p.1988-2006 (1999). Transmitter redundancy introduced using finite impulse response (FIR) filterbank precoders offers a unifying framework for single- and multiuser transmissions. With minimal rate reduction, FIR filterbank transmitters with trailing zeros allow for perfect (in the absence of noise) equalization of FIR channels with FIR zero-forcing equalizer filterbanks, irrespective of the input color and the channel zero locations. Exploiting this simple form of redundancy, blind channel estimators, block synchronizers, and direct self-recovering equalizing filterbanks are derived in this paper. The resulting algorithms are computationally simple, require small data sizes, can be implemented online, and remain consistent (after appropriate modifications), even at low SNR colored noise. Simulations illustrate applications to blind equalization of downlink CDMA transmissions, multicarrier modulations through channels with deep fades, and superior performance relative to CMA and existing output diversity techniques relying on multiple antennas and fractional sampling.
Adeno-Associated Virus Type 2 Contains an Integrin α5β1 Binding Domain Essential for Viral Cell Entry

ABSTRACT Integrins have been implicated as coreceptors in the infectious pathways of several nonenveloped viruses. For example, adenoviruses are known to interact with αV integrins by virtue of a high-affinity arginine-glycine-aspartate (RGD) domain present in the penton bases of the capsids. In the case of adeno-associated virus type 2 (AAV2), which lacks this RGD motif, integrin αVβ5 has been identified as a coreceptor for cellular entry. However, the molecular determinants of AAV2 capsid-integrin interactions and the potential exploitation of alternative integrins as coreceptors by AAV2 have not been established thus far. In this report, we demonstrate that integrin α5β1 serves as an alternative coreceptor for AAV2 infection in human embryonic kidney 293 cells. Such interactions appear to be mediated by a highly conserved domain that contains an asparagine-glycine-arginine (NGR) motif known to bind α5β1 integrin with moderate affinity. The mutation of this domain reduces transduction efficiency by an order of magnitude relative to that of wild-type AAV2 vectors in vitro and in vivo. Further characterization of mutant and wild-type AAV2 capsids through transduction assays in cell lines lacking specific integrins, cell adhesion studies, and cell surface/solid-phase binding assays confirmed the role of the NGR domain in promoting AAV2-integrin interactions. Molecular modeling studies suggest that NGR residues form a surface loop close to the threefold axis of symmetry adjacent to residues previously implicated in binding heparan sulfate, the primary receptor for AAV2. The aforementioned results suggest that the internalization of AAV2 in 293 cells might follow a “click-to-fit” mechanism that involves the cooperative binding of heparan sulfate and α5β1 integrin by the AAV2 capsids.
Extracting local event information from micro-blogs for trip planning

This paper describes a method to extract local event information from the micro-blog service Twitter. Twitter holds innumerable user-posted short messages called tweets that cover various topics including local events. Our proposal is composed of three steps: 1) extract tweets related to local events from local tweets by the Support Vector Machine (SVM) approach, 2) identify and extract the venues, names and times of local events mentioned in the tweets by applying Conditional Random Fields (CRF), 3) use the venues and similarity of names to aggregate duplicate local event information. We implement the proposed method and confirm that it extracts local event information with higher precision than the conventional methods.
Wrapper approach for feature subset selection using GA

In this paper, wrapper based approach is presented to feature subset selection of ground penetrating radar (GPR) echo signal using genetic algorithm in conjunction with constructive learning algorithm. GPR echo signal inherently has the non- stationary characteristic and the target echo signal is deteriorated by the clutter. Firstly, WP-based preprocessing algorithm is used to clutter reduction and feature extraction. Then wrapper based approach is adopted to find optimal feature subset, and at meanwhile obtain the result of classification. Experiment result based on GPR landmine data shows the feasibility and advantage of the presented algorithm.
Hand-eye 3D pose estimation for a drawing robot

In order to draw pictures on a plane, it is important for a robot arm to know the pose of the end-effecter relative to the world frame which is settled on the workplace. Visual servoing is frequently used in the hand-eye configuration of robot arms. However, 3D pose is preferred for a drawing robot. It is not only required to obtain the three dimensional position of the end effecter so that the pen can touch the paper without drilling a hole, but also required to get the direction so that the pen is always perpendicular to the drawing plane. In this paper, we proposed a monocular camera vision system for a 6DOF drawing robotic arm to estimate 3D pose of the end effecter robustly. First, a coplanar asymmetric polygonal landmark, which is a rectangle with a corner cut off to remove rotation ambiguity, is introduced. Second, the corner points of the landmark are indirectly obtained by intersection of the edges obtained using RANSAC. After that, corner points are filtered by KALMAN filter to reduce detection errors. Third, the end effecter's three dimensional pose relative to the world frame is estimated through PnP algorithm and the kinematics of robotics arm. Experiments show that the 3D vision system has been successfully applied in our robotic arm to draw a circle. The stability and robustness of hand-eye is improved in real time drawing usage.