Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical significance of basal-like breast cancer in Chinese women in Heilongjiang province.

BACKGROUND Our objective was to clarify the clinical and biological characteristics of basal-like breast cancer (BLBC) and non-basal-like breast cancer (TN3BKE) in Heilongjiang. METHODS We examined, by immunohistochemistry, expression of biological markers cytokeratin (CK) 5/6 and epidermal growth factor receptor (EGFR) and B cell specific moloney murine leukemia virus integration site 1( Bmi-1) in triple-negative breast cancer (TNBC). We studied the correlation between BLBC and several factors related to tumor progression, along with its prognostic value. RESULTS In the 229 cases of operable TNBC, BLBC was detected in 178 (77.7%) and TN3BKE- in 51 (22.2%). There was no significant difference in clinicopathological factors between them, However, BLBC was significantly associated with Bmi-1 expression (P=0.000) and shorter disease-free survival (DFS) (P = 0.045) and overall survival (OS) (P=0.041). CONCLUSIONS Compared with the non-basal group, patients with BLBC have a high expression of Bmi-1 and a poor prognosis.
Security checking in ABPMN

This paper addresses the security problem in ABPMN and proposed a new technique for increasing security level in ABPMN. Security is a major issue in every business process. Our aim is to provide better security features, which will remove the unauthorized access. ABPMN is a graphical representation for business process models that aims to showcase abstract notation of a process model. Meta data is used to describe the concepts of the abstract representation system. It introduces the new idea of an application chunk having an interface and body including their dependencies types. Application chunks are the nodes of our graph and dependencies are directed edges that are labeled by dependency types. Through dependencies concepts of a sequence, iteration, dependency types, choice, parallelism, instantaneous and long running processes priorities and preferences as well as deadlines are explained. Our proposed security technique will provide a secure key for each time user will logout. Thus unauthorized user cannot be access the account.
Single-incision laparoscopic management of a giant hepatic cyst

Large symptomatic hepatic cysts may warrant surgical management. Traditional multiport laparoscopic technique is typically preferred over open laparotomy, but the use of the single-incision laparoscopic approach for this diagnosis is not well documented. Here, we describe the case of a 68-year-old woman who underwent complete anterior wall fenestration, excision and cauterization of a simple hepatic cyst via a single-incision laparoscopic technique through an incision at the umbilicus. The objective of this case report is to document single-incision laparoscopy as a safe, feasible and cosmetically appealing approach for the management of a large hepatic cyst.
Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort

Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effort.
Tradition and Fashion in Consumer Choice: Bagging the Scottish Munros

Evidence is growing that in many markets consumers select not simply on the basis of the perceived attributes of products, but their preferences are modified by the behaviour of others. Economists have paid relatively little attention to such markets. We consider evidence from the activity of hill-walking. The Munros are a list of Scottish hills over 3000 feet in height. We analyse completions of both the Munros themselves, and the Munro Tops, a difficult and time-consuming extension of the Munros. The classic Bass diffusion model incorporates the imitation of others as a part of the behavioural rules used by consumers in making choice. We extend the Bass model to be able to apply it to long-term case studies where substantial changes over time in the population making choices at any given point have to be taken into account. The Munros are a particular illustration of this, but the extension can be used in other situations where such population changes are important. Our results show that Top completions are dominated by the ‘fashion’ component, suggesting there was a cohort among which Tops completions became fashionable, but which has not been sustained. Both our extended model and the standard model can account for this. In contrast, our extension is needed to explain why Munro completions have remained close to their peak level for a decade now, a fact for which the standard Bass model is unable to account.
NGS technologies for analyzing germplasm diversity in genebanks*

More than 70 years after the first ex situ genebanks have been established, major efforts in this field are still concerned with issues related to further completion of individual collections and securing of their storage. Attempts regarding valorization of ex situ collections for plant breeders have been hampered by the limited availability of phenotypic and genotypic information. With the advent of molecular marker technologies first efforts were made to fingerprint genebank accessions, albeit on a very small scale and mostly based on inadequate DNA marker systems. Advances in DNA sequencing technology and the development of high-throughput systems for multiparallel interrogation of thousands of single nucleotide polymorphisms (SNPs) now provide a suite of technological platforms facilitating the analysis of several hundred of Gigabases per day using state-of-the-art sequencing technology or, at the same time, of thousands of SNPs. The present review summarizes recent developments regarding the deployment of these technologies for the analysis of plant genetic resources, in order to identify patterns of genetic diversity, map quantitative traits and mine novel alleles from the vast amount of genetic resources maintained in genebanks around the world. It also refers to the various shortcomings and bottlenecks that need to be overcome to leverage the full potential of high-throughput DNA analysis for the targeted utilization of plant genetic resources.
Modeling clustered activity increase in amyloid-beta positron emission tomographic images with statistical descriptors

Background Amyloid-beta (Aβ) imaging with positron emission tomography (PET) holds promise for detecting the presence of Aβ plaques in the cortical gray matter. Many image analyses focus on regional average measurements of tracer activity distribution; however, considerable additional information is available in the images. Metrics that describe the statistical properties of images, such as the two-point correlation function (S2), have found wide applications in astronomy and materials science. S2 provides a detailed characterization of spatial patterns in images typically referred to as clustering or flocculence. The objective of this study was to translate the two-point correlation method into Aβ-PET of the human brain using 11C-Pittsburgh compound B (11C-PiB) to characterize longitudinal changes in the tracer distribution that may reflect changes in Aβ plaque accumulation. Methods We modified the conventional S2 metric, which is primarily used for binary images and formulated a weighted two-point correlation function (wS2) to describe nonbinary, real-valued PET images with a single statistical function. Using serial 11C-PiB scans, we calculated wS2 functions from two-dimensional PET images of different cortical regions as well as three-dimensional data from the whole brain. The area under the wS2 functions was calculated and compared with the mean/median of the standardized uptake value ratio (SUVR). For three-dimensional data, we compared the area under the wS2 curves with the subjects’ cerebrospinal fluid measures. Results Overall, the longitudinal changes in wS2 correlated with the increase in mean SUVR but showed lower variance. The whole brain results showed a higher inverse correlation between the cerebrospinal Aβ and wS2 than between the cerebrospinal Aβ and SUVR mean/median. We did not observe any confounding of wS2 by region size or injected dose. Conclusion The wS2 detects subtle changes and provides additional information about the binding characteristics of radiotracers and Aβ accumulation that are difficult to verify with mean SUVR alone.
The moderating effect of tourist’s involvement on the relationship between destination’s image and its future behavior

Building on the services-marketing literature, the present study develops an innovative model for evaluation of the effect of customer perceptions on the loyalty intentions of customer. The study then explores whether market heterogeneity affects this relationship by performing a latent cluster analysis in the tourism industry. Three major clusters of tourists emerge—according to the tourists’ involvement. The results show that there are significant differences among these segments in terms of the effects of a destination’s image on tourists’ intentions to return to a destination and their intentions to recommend it to friends and relatives.
Using Hypermedia as a Interdisciplinary Learning and Exploration Tool : a Case Study of the Development of Solar

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Pediatric neurodevelopmental treatment

ABSTRACT Over 70 years ago Dr. Karel Bobath and his wife Bertha Bobath began to craft the therapeutic intervention now known as neurodevelopmental treatment (NDT). This edition of Reviews, Tools, and Resources will highlight a historical review of research studies that have been completed, current websites, books, and blogs focusing on NDT.
Web Document Clustering Approaches Using K-Means Algorithm

The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure. In data mining K-means clustering algorithm is one of the efficient unsupervised learning algorithms to solve the well-known clustering problems. The disadvantage in k-means algorithm is that, the accuracy and efficiency is varied with the choice of initial clustering centers on choosing it randomly. So in this paper, less similarity based clustering method is proposed for finding the better initial centroids and to provide an efficient way of assigning the data points to suitable clusters with reduced time complexity. They mainly classified into three main categories: text- based, link-based and hybrid.
Color filter array demosaicking using joint bilateral filter

Bilateral filter has shown its outstanding performance in image denoising and other multimedia applications. In this paper, a new color interpolation technique named joint bilateral demosaicking is proposed. Considering the image gradient, an edge-sensing initialization step is performed. In addition, joint bilateral filter exploits the correlation between color channels with the information from initialization while preserving edges. Experimental results show the superiority of the proposed technique in suppressing demosaicking artifacts and producing more visually acceptable images.
Cluster-based support vector machines in text-independent speaker identification

Based on statistical learning theory, support vector machines (SVM) is a powerful tool for various classification problems, such as pattern recognition and speaker identification etc. However, training SVM consumes large memory and long computing time. This work proposes a cluster-based learning methodology to reduce training time and the memory size for SVM. By using k-means based clustering technique, training data at boundary of each cluster were selected for SVM learning. We also applied this technique to text-independent speaker identification problems. Without deteriorating recognition performance, the training data and time can be reduced up to 75% and 87.5% respectively.
Fully Automatic Skull-Stripping in 3D Time-of-Flight MRA Image Sequences

In this paper we present a robust skull-stripping method for the isolation of cerebral tissue in 3D Time-of-Flight (TOF) magnetic resonance angiographic images of the brain. 3D TOF images are often acquired in case of cerebral vascular diseases, because of their good blood-to-background-contrast. Skull-stripping is an essential preprocessing step towards a better segmentation as well as direct visualization of the vascular system. Our approach consists of three main steps. After preprocessing in order to reduce signal inhomogeneities and noise the first main step is the segmentation of the surrounding skull using a region growing approach. The second step is the automatic extraction of distinctive points at the border of the brain, based on the segmentation of the skull, which are then used as supporting points for a graph based contour extraction. The third step is a slicewise correction based on a non-linear registration in order to improve sub-optimal segmentation results. The method proposed was validated using 18 manually stripped datasets. The calculated similarity measures show that the proposed method leads to good segmentation results with only a few segmentation errors. At the same time the mean rate of vessel voxels included by the brain segmentation is 99.18%. In summary the procedure suggested allows a fast and fully automatic segmentation of the brain and is especially helpful as a preprocessing step towards an automatic segmentation of the vessel system or direct volume rendering.
Social network site usage, privacy concerns, and disclosure of autobiographical memories

Internet usage has become a big part of many people’s lives, and among the many uses Social Networking Sites (SNS) have become very prevalent. How privacy concerns, trust and control are affecting SNS usage and self-disclosure has become an important question for many people. I compiled a questionnaire and analyzed it for reliability. Then it was used to determine the relationship between these constructs and compared to the behavioral data that was collected from participant’s Facebook pages. The results showed that privacy concerns do not affect SNS usage or self-disclosure. Trust and control were significantly correlated with specific autobiographical memories, but trust was negatively correlated. Because the behavioral data was different than the self-report data or the expected results more research including a behavioral measure needs to be completed. Based on these results trust and control are linked with SNS usage and selfdisclosure, while privacy concerns are not affecting them. SOCIAL NETWORK SITE USAGE AND PRIVACY CONCERNS 6 Social network site usage, privacy concerns, and disclosure of autobiographical memories Internet usage has become an integral part of many people’s daily lives. The uses are abundant: jobs, research, games, starting new relationships, maintaining old relationships, etc. Social networking sites are a prevalent use of the internet. According to a survey conducted by Pew Research Center, roughly 85% of American adults use the internet and of these users about 67% use social networking sites (Pew Research Center, 2012). Social networking sites have many benefits including allowing individuals to maintain contact without having to meet face to face, which allows people to maintain old relationships and start new relationships without having to meet in person with these other people. On the other hand, there are also possible problems associated with social networking sites, such as breaches of privacy. The goal of this study is to determine how privacy concerns, trust, and control affect perceived versus actual social network site usage and self-disclosure.
Politics/matter: Governing Cape Town’s informal settlements

Through the lens of South Africa’s informal settlements, this paper explores the intersections between plans, practices and materiality. These three arenas are each presented as uniquely agentic, contributing to plural configurations. In doing so, this work questions a prevalent tendency to frame governance/government solely as relationships between state and non-state actors. By reintroducing the agency and power of matter and materiality, not as adjunct or background, but as a critical technology of government and/in place, this work contributes to a growing debate within the (emerging) urban socio-technical systems literature.
Tracking feature points: a new algorithm

A new algorithm is presented for feature point based motion tracking in long image sequences. Dynamic scenes with multiple, independently moving objects are considered in which feature points may temporarily disappear enter and leave the view field. The existing approaches to feature point tracking have limited capabilities in handling incomplete trajectories, especially when the number of points and their speeds are large, and trajectory ambiguities are frequent. The proposed algorithm was designed to efficiently resolve these ambiguities.
Stretching the Edges of SVM Traffic Classification With FPGA Acceleration

Analyzing the composition of Internet traffic has many applications nowadays, like tracking bandwidth-consuming applications or QoS-based traffic engineering. Even though many classification methods, such as Support Vector Machines (SVMs) have demonstrated their accuracy, the ever-increasing data rates encountered in networks are higher than existing implementations can support. As SVM has been proven to provide a high level of accuracy, and is challenging to implement at high speeds, we consider in this paper the design of a real-time SVM traffic classifier at hundreds of Gb/s to allow online detection of categories of applications. We show the limits of software implementation and offer a solution based on the massive parallelism and low-level network interface access of FPGA boards. We also improve this solution by testing algorithmic changes that dramatically simplify hardware implementation. We then find theoretical supported bit rates up to 473 Gb/s for the most challenging trace on a Virtex 5 FPGA, and confirm them through experimental performance results on a Combov2 board with a 10 Gb/s interface.
Development, validation, and factorial comparison of the McGill Self-Efficacy of Learners For Inquiry Engagement (McSELFIE) survey in natural science disciplines

ABSTRACT Sociocognitive theory [Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall; Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44, 1175–1184. doi:10.1037/0003-066x.44. 9.1175; Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248–287. doi:10.1016/0749-5978(91)90022-L] accords high importance to the mechanisms of human agency and how they are exercised through self-efficacy. In this paper, we developed and validated the McGill Self-Efficacy For Inquiry Engagement (McSELFIE) instrument with undergraduate students in natural science disciplines. We defined inquiry engagement as carrying out the practices of science (POS) that are supported by students’ personality characteristics (SPCs) and that result in achieving inquiry-learning outcomes (ILOs). Based on these theoretical perspectives, the McSELFIE is a 60-item, learner-focused survey that addresses three components that are theoretically important for engaging in scientific inquiry: (a) SPCs, (b) ILOs, and (c) POS. Evidence for construct and content validity were obtained by using experts’ judgments and confirmatory factor analysis with a sample of 110 undergraduate students enrolled in science disciplines. Internal consistency of the factors and instrument was also examined. The McSELFIE instrument is a reliable and valid instrument for measuring science undergraduate students’ self-efficacy for inquiry engagement. Matched pairs analyses were conducted among the instruments’ factors. Students reported the highest self-efficacy for openness, applying knowledge, and carrying out investigations. Students reported the lowest self-efficacy for extraversion, understanding metacognitive knowledge, and planning investigations. Theoretical and practical implications are discussed.
Scalable and Efficient MAC for Next-Generation Wireless Data Networks

The next-generation wireless technologies, e.g., IEEE 802.11n, 802.11s and 802.16, support much higher data rates and offer multirate capability at the physical layer. These advanced transmission technologies facilitate wireless networks to operate with a large number of stations in conference, enterprise, and campus settings. However, the state-of-the-art medium access control (MAC) designs do not scale well to higher physical-layer speeds and large user populations. In this paper, we propose a Token-coordinated random access MAC (TMAC), a scalable and efficient protocol for the emerging wireless LANs and mesh networks. TMAC takes a two-tier MAC design approach for centralized, coarse-grained channel regulation, and distributed fine-grained random access. The high-tier design organizes the large number of active stations into multiple token groups and allows only the stations in one group to contend for the channel at a time. It effectively manages the intensity of contention, rate of collisions, as well as the large contention window size, caused by a large user population. Stations within a token group access the channel following a low-tier paradigm which provides an adaptive fair share of the channel through a novel adaptive fairness model. This model effectively leverages rate-heterogeneity among stations and adaptively enables trade-offs between a minimum fair share for each station and maximizing aggregate system throughput. Our analysis and extensive simulations have demonstrated the scalability and efficiency of TMAC protocol with respect to a large user population and a wide range of physical-layer rates. More Specifically, TMAC exhibits a scalable performance and achieves approximately 300% throughput gain as users popularizes to 300 as compared with IEEE 802.11 MAC. It also attains 17.7 times throughput increase, instead of 10 times in the legacy MAC, as the physical-layer rate increases from 6Mb/s to 216Mb/s.
Topological solitons and folded proteins.

We argue that protein loops can be described by topological domain-wall solitons that interpolate between ground states which are the α helices and β strands. We present an energy function that realizes loops as soliton solutions to its equation of motion, and apply these solitons to model a number of biologically active proteins including 1VII, 2RB8, and 3EBX (Protein Data Bank codes). In all the examples that we have considered we are able to numerically construct soliton solutions that reproduce secondary structural motifs such as α-helix-loop-α-helix and β-sheet-loop-β-sheet with an overall root-mean-square-distance accuracy of around 1.0 A or less for the central α-carbons, i.e., close to the limits of current experimental accuracy.