Miracle-dz/UniFAR-FTUs huggingface dataset:
Mice with Hypomorphic Expression of the Sodium-Phosphate Cotransporter PiT1/Slc20a1 Have an Unexpected Normal Bone Mineralization

The formation of hydroxyapatite crystals and their insertion into collagen fibrils of the matrix are essential steps for bone mineralization. As phosphate is a main structural component of apatite crystals, its uptake by skeletal cells is critical and must be controlled by specialized membrane proteins. In mammals, in vitro studies have suggested that the high-affinity sodium-phosphate cotransporter PiT1 could play this role. In vivo, PiT1 expression was detected in hypertrophic chondrocytes of murine metatarsals, but its implication in bone physiology is not yet deciphered. As the complete deletion of PiT1 results in embryonic lethality at E12.5, we took advantage of a mouse model bearing two copies of PiT1 hypomorphic alleles to study the effect of a low expression of PiT1 on bone mineralization in vivo. In this report, we show that a 85% down-regulation of PiT1 in long bones resulted in a slight (6%) but significant reduction of femur length in young mice (15- and 30-day-old). However, despite a defect in alcian blue / alizarin red S and Von Kossa staining of hypomorphic 1-day-old mice, using X-rays micro-computed tomography, energy dispersive X-ray spectroscopy and histological staining techniques we could not detect differences between hypomorphic and wild-type mice of 15- to 300-days old. Interestingly, the expression of PiT2, the paralog of PiT1, was increased 2-fold in bone of PiT1 hypomorphic mice accounting for a normal phosphate uptake in mutant cells. Whether this may contribute to the absence of bone mineralization defects remains to be further deciphered.
A Buyer ’ s and User ’ s Guide to RF Coils

The RF coil is one of the primary factors determining the speed and image quality in MRI. Over the past 10 years there have been many important technical developments in RF coil design, particularly the rapid increase in the number of available RF channels. This has enabled the development of a host of accelerated acquisition methods which largely rely on the multiplicity of RF channels to significantly reduce scan times. Advances in RF coil design have also proven essential in unlocking the potential of ultra high field MRI. Currently a hot research area is parallel transmit, where RF is broadcast through a multitude of antennas promising to provide ever greater control over the RF excitation profile in the body. For someone considering the purchase of a new scanner or updating an existing scanner with state of the art RF coils, there are many factors to consider regarding the performance benefits of the multitude of RF coils presently available. In this presentation the basic principles determining RF coil performance and the practical trade-offs of various coil designs in the clinical setting will be explored.
Presenting a Hybrid Feature Selection Method Using Chi2 and DMNB Wrapper for E-Mail Spam Filtering

The growing volume of spam emails has resulted in the necessity for more accurate and efficient email classification system. The purpose of this research is presenting an machine learning approach for enhancing the accuracy of automatic spam detecting and filtering and separating them from legitimate messages. In this regard, for reducing the error rate and increasing the efficiency, the hybrid architecture on feature selection has been used. Features used in these systems, are the body of text messages. Proposed system of this research has used the combination of two filtering models, Filter and Wrapper, with Information Gain (IG) filter and Support Vector Machine (SVM) wrapper as feature selectors. In addition, MNB classifier, DMNB classifier, SVM classifier and Random Forest classifier are used for classification. Finally, the output results of this classifiers and feature selection methods are examined and the best design is selected and it is compared with another similar works by considering different parameters. The optimal accuracy of the proposed system is evaluated equal to 99%.
Detection of US Traffic Signs

This paper presents a comprehensive research study of the detection of US traffic signs. Until now, the research in Traffic Sign Recognition systems has been centered on European traffic signs, but signs can look very different across different parts of the world, and a system which works well in Europe may indeed not work in the US. We go over the recent advances in traffic sign detection and discuss the differences in signs across the world. Then we present a comprehensive extension to the publicly available LISA-TS traffic sign dataset, almost doubling its size, now with HD-quality footage. The extension is made with testing of tracking sign detection systems in mind, providing videos of traffic sign passes. We apply the Integral Channel Features and Aggregate Channel Features detection methods to US traffic signs and show performance numbers outperforming all previous research on US signs (while also performing similarly to the state of the art on European signs). Integral Channel Features have previously been used successfully for European signs, while Aggregate Channel Features have never been applied to the field of traffic signs. We take a look at the performance differences between the two methods and analyze how they perform on very distinctive signs, as well as white, rectangular signs, which tend to blend into their environment.
Profile-oriented algorithms teaching: A proposed methodology

One of the first challenges faced by students who are starting a Computer Science degree is to acquire skills which allow them solving real life problems through the use of algorithms. Such skills require the development of the ability to abstract as well as to apply algorithms development techniques. As a result of the difficulty in obtaining these competences, there are high failure and dropout rates in courses of related to this field of study. In order to solve this problem, we propose a methodology based on VARK questionnaire to define the students' profile and, consequently, what are their appropriate educational materials and tools.
Mining Aspects

The mining of existing assets is an important concern for software developers and organisations. The availability of tools which allow fine grained queries to be performed on these assets will improve productivity by allowing developers to locate and adapt assets efficiently. Mining of assets can take part at many different stages throughout the software development lifecycle. Typical assets for mining could include program code, designs, system architectures, specifications, algorithms and the like. Aspects are particularly suitable for mining as they allow system wide concerns such as synchronisation, logging, debugging and the like to be encapsulated into a single construct, making them ideal candidates for reuse. This paper discusses the increasing demand for tools that support the mining of existing assets with a special focus on Aspect-Oriented Software Development (AOSD).
Equality, Growth and Sustainability : Do They Mix?

The conference Equality, Growth and Sustainability – Do They Mix? was made possible through initiatives, joint efforts and collaboration between actors representing JAMI, VINNOVA, FAS, Thematic Group on Equality, NIKK, Tema Genus and Forum for Gender Studies and Equality at Linkoping University.The conference created an opportunity for academics, practitioners as well as policy makers to meet and discuss if and how questions of equality, growth and sustainability can be reconciled in organisations and implemented in general practice. 15 years after the Beijing Platform for Action the conference provided academics, policy makers and practitioners with an international and interdisciplinary forum for exchange.The purpose was to explore and disseminate knowledge and experiences on gender equality, gender mainstreaming, diversity management, growth and sustainability, inclusion and equal opportunities. The ambition was to discuss these key concepts and issues from an intersectional perspective, and in theory and practice, in order to rethink and find new ways to move on. The conference provided new research results, policy discussions and presented new ideas on how to advance the practical and theoretical work through collaborative/joint ventures between academia and society.The conference consituted a mix of presentations and discussions with the 165 participants, from all over the world. The interactive conference format contained eight interactive workshops and eleven parallell paper sessions, see appendix. The paper sessions contained academic paper presentetions while the workshops offered forums for listening practioners’ presentations and taking part in discussions.The four key note speakers, as well as the concluding panel, addressed and discussed the conference topics from their view points as policy makers, researchers and/or practitioners. Key note speakers included Ms Viviane Reding, Vice-President of the European Commission; Mieke Verloo, Professor of Comparative Politics and Inequality Issues at Radboud University Nijmegen; Jeff Hearn, Professor in Gender Studies with a focus on critical studies on men at Linkoping university and Tryggvi Hallgrimsson from the Center of Gender Equality, Iceland.The workshop was hosted by Linkoping University´s Forum for Gender Studies and Equality.Funding was provided by JAMI at Swedish Secretariat for Gender Research, Swedish Governmental Agency for Innovation Systems/VINNOVA, Swedish Council for Working Life and Social Research/FAS, Thematic Group on Equality, Faculty of Arts and Sciences at Linkoping University, Swedish Association of Local Authorities and Regions/SALAR/SKL, and Wenner-Gren Foundations.The majority of the paper authors have chosen to contribute to this document, which presents the proceedings from the conference.
Syntactic Graphs: A Representation for the Union of All Ambiguous Parse Trees

In this paper, we present a new method of representing the surface syntactic structure of a sentence. Trees have usually been used in linguistics and natural language processing to represent syntactic structures of a sentence. A tree structure shows only one possible syntactic parse of a sentence, but in order to choose a correct parse, we need to examine all possible tree structures one by one. Syntactic graph representation makes it possible to represent all possible surface syntactic relations in one directed graph (DG). Since a syntactic graph is expressed in terms of a set of triples, higher level semantic processes can access any part of the graph directly without navigating the whole structure. Furthermore, since a syntactic graph represents the union of all possible syntactic readings of a sentence, it is fairly easy to focus on the syntactically ambiguous points. In this paper, we introduce the basic idea of syntactic graph representation and discuss its various properties. We claim that a syntactic graph carries complete syntactic information provided by a parse forest---the set of all possible parse trees.
A new algorithm for solving data sparsity problem based-on Non negative matrix factorization in recommender systems

The “sparsity” challenge is a well-known problem in recommender systems. This issue relates to little information about each user or item in large data set. The purpose of this paper is pre-processing of data and using Non negative Matrix Factorization (NMF) method to improve this challenge. Since the original data are non negative, the algorithm based on NMF maintains positive effect of data on decomposition matrices and makes better prediction of original data in comparison to singular value decomposition (SVD) algorithm. Since the dimensions of data are very large, it offers a solution based on dimensionality reduction in which useful factors for selecting optimal dimensions (optimal `k') are extracted from the data matrix until appropriate approximation of the original data obtains from rank `k' matrices. Thus, the presented model not only selects the best factors from the original data but also, recommends appropriate values for the missing ratings and overcome sparsity problem. The results of experiments are evaluated with three metrics: RMSE1, NMAE2, and RE3. Results show that our approach leads to better prediction.
Bootstrapping Image Classification with Sample Evaluation

In this work, we look at the problem of multi-class image classification in a semisupervised learning framework. Given a small set of labeled images, and a much larger set of unlabeled images, we propose a semi-supervised learning method that combines bootstrapping with sample evaluation, to continuously update the learned models for each class. Bootstrapping involves using self-labeled images to re-train the learned models. To overcome the semantic drift that naive bootstrapping is prone to, we use additional sample evaluation methods based on the ideas of co-training and pairwise constraints, to determine whether or not a newly classified instance should be used for re-training. Experimental results show the usefulness of sample evaluation, when used in conjunction with bootstrapping. In particular, our method is able to achieve a 8% improvement in overall accuracy over baseline bootstrapping, on a 15 class subset of the SUN (Scene UNderstanding) dataset.
Monkey hippocampus and learning about spatially directed movements

Monkeys were given a series of problems to solve in which they had to learn whether to approach a given visual stimulus and make repeated contact with it or to withdraw from the stimulus and avoid making contact with it. The reward for the correct response in either case (approach or withdrawal) was food, which was always delivered in the same spatial location. This task requires the animal to learn in what spatial direction to move in relation to the visual stimuli, but it cannot be solved by learning the spatial relationships among stimuli in the environment. Transection of the fornix severely impaired the monkeys' learning ability in this task; bilateral ablation of the sulcus principalis did not. This result shows that the hippocampus is concerned with learning about spatially directed movement, rather than with the acquisition of maplike knowledge about the spatial relationships of stimuli in the environment.
Mechanism design for dynamic P2P streaming

In dynamic streaming, a user can dynamically choose from different versions of the same video. In P2P dynamic streaming, there is one P2P swarm for each version, and within a swarm, peers can share video chunks with each other, thereby reducing the server's bandwidth cost. Due to economy of scale, cooperation among peers can also reduce the per-peer content price. In this paper, we use cooperative game theory to dynamically assign each peer to a version. To maximally incentivize peer cooperation, we use mechanism design to develop pricing schemes that reflect content and bandwidth cost savings derived from peer cooperation. With this approach, each peer is assigned to a swarm that is commensurate with its upload contribution and the price it is willing to pay. We also develop and simulate a distributed dynamic P2P streaming algorithm, consisting of chunk scheduling, token-based accounting, and video version switching, to dynamically adjust each peer's video version based on the collaborative behaviors of all peers.
An Investigation Into The Effect of Channel Estimation and Interpolation Techniques In Long Term Evaluation System Through Realistic Channel Conditions

Channel estimation is an important technique at receiver’s end in order to combat the effect of noise or interference on the transmitted information. The channel estimation reduces the effect of the channel on signal and recovers the transmitted signal. These estimation techniques depend on the propagation model through which it is travelling. Estimation techniques LS, MMSE, PERFECT performs differently in different scenarios. Techniques like PERFECT and MMSE are complex but are good in performance, whereas LS has less complexity, but its performance is less than MMSE. This paper makes a number of contributions, as well as, deeply discusses the comparison of estimation technique in different realistic channel and further we analyse the effect of variation in interpolation scheme, and transmission diversity of antennas in order to make performance better.
Sonic hedgehog protein regulates fibroblast growth factor 8 expression in metanephric explant culture from BALB/c mice: Possible mechanisms associated with renal morphogenesis

The sonic hedgehog (SHH) morphogen regulates cell differentiation and controls a number of genes during renal morphogenesis. To date, the effects of SHH on fibroblast growth factors (Fgfs) in embryonic kidney development remain unclear. In the present study, explants of BALB/c mouse embryonic kidney tissues were used to investigate the role of exogenous SHH on Fgf8 and Fgf10 expression levels ex vivo. Ureteric bud branches and epithelial metanephric derivatives were used to determine the renal morphogenesis with Dolichos biflorus agglutinin or hematoxylineosin staining. mRNA expression levels were determined using reverse transcription-quantitative polymerase chain reaction, while the protein expression levels were examined using immunohistochemistry and western blot analysis. During the initial stages of metanephric development, low levels of SHH, Fgf8, and Fgf10 expression were observed, which were found to increase significantly during more advanced stages of metanephric development. In addition, exogenous SHH protein treatment increased the number of ureteric bud branches and enhanced the formation of nephrons. Exogenous SHH reduced the Fgf8 mRNA and protein expression levels, whereas cyclopamine (an SHH-smoothened receptor inhibitor) interfered with SHH-mediated downregulation of Fgf8 expression. By contrast, exogenous SHH protein was not found to modulate Fgf10 mRNA and protein expression levels. In conclusion, these results indicate that the modulatory effects of SHH on BALB/c mouse metanephric explant cultures may involve the regulation of Fgf8 expression but not Fgf10 expression, which provides evidence for the functional role of Fgf proteins in renal morphogenesis.
A learning approach to spam detection based on social networks

The massive increase of spam is posing a very serious threat to email which has become an important means of communication. Not only does it annoy users, but it also consumes much of the bandwidth of the Internet. Most spam filters in existence are based on the content of email one way or the other. While these anti-spam tools have proven very useful, they do not prevent the bandwidth from being wasted and spammers are learning to bypass them via clever manipulation of the spam content. A very different approach to spam detection is based on the behavior of email senders. In this paper, we propose a learning approach to spam sender detection based on features extracted from social networks constructed from email exchange logs. Legitimacy scores are assigned to senders based on their likelihood of being a legitimate sender. Moreover, we also explore various spam filtering and resisting possibilities.
Dynein light chain interaction with the peroxisomal import docking complex modulates peroxisome biogenesis in yeast

Summary Dynein is a large macromolecular motor complex that moves cargo along microtubules. A motor-independent role for the light chain of dynein, Dyn2p, in peroxisome biology in Saccharomyces cerevisiae was suggested from its interaction with Pex14p, a component of the peroxisomal matrix protein import docking complex. Here we show that cells of the yeast Yarrowia lipolytica deleted for the gene encoding the homologue of Dyn2p are impaired in peroxisome function and biogenesis. These cells exhibit compromised growth on medium containing oleic acid as the carbon source, the metabolism of which requires functional peroxisomes. Their peroxisomes have abnormal morphology, atypical matrix protein localization, and an absence of proteolytic processing of the matrix enzyme thiolase, which normally occurs upon its import into the peroxisome. We also show physical and genetic interactions between Dyn2p and members of the docking complex, particularly Pex17p. Together, our results demonstrate a role for Dyn2p in the assembly of functional peroxisomes and provide evidence that Dyn2p acts in cooperation with the peroxisomal matrix protein import docking complex to effect optimal matrix protein import.
Variational Bayesian View of Weighted Trace Norm Regularization for Matrix Factorization

Matrix factorization with trace norm regularization is a popular approach to matrix completion and collaborative filtering. When entries of the matrix are sampled non-uniformly (which is the case for collaborative prediction), a properly weighted correction to the trace norm regularization is known to improve the performance dramatically. While the weighted trace norm regularization has been rigorously studied, its generative counterpart is not known yet. In this paper we show that the weighted trace norm regularization emerges from variational Bayesian matrix factorization where variational distributions over factor matrices are restricted to be isotropic Gaussians with the common variance. We show that variational variance corresponds to the regularization parameter. Thus, the regularization parameter can be automatically learned by variational inference rather than cross-validation. Experiments on MovieLens and Netflix datasets confirm the variational Bayesian perspective of the weighted trace norm regularization, demonstrating that variational parameter learned by variational inference agrees with the value of the regularization parameter found by cross-validation.
Content-based image retrieval in medical applications for picture archiving and communication systems

Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select relevant images within PACS. Since textual descriptions insufficiently describe the great variety of details in medical images, content-based image retrieval (CBIR) is expected to have a strong impact when integrated into PACS. However, existing CBIR approaches usually are limited to a distinct modality, organ, or diagnostic study. In this state-of-the-art report, we present first results implementing a general approach to content-based image retrieval in medical applications (IRMA) and discuss its integration into PACS environments. Usually, a PACS consists of a DICOM image server and several DICOM-compliant workstations, which are used by radiologists for reading the images and reporting the findings. Basic IRMA components are the relational database, the scheduler, and the web server, which all may be installed on the DICOM image server, and the IRMA daemons running on distributed machines, e.g., the radiologists’ workstations. These workstations can also host the web-based front-ends of IRMA applications. Integrating CBIR and PACS, a special focus is put on (a) location and access transparency for data, methods, and experiments, (b) replication transparency for methods in development, (c) concurrency transparency for job processing and feature extraction, (d) system transparency at method implementation time, and (e) job distribution transparency when issuing a query. Transparent integration will have a certain impact on diagnostic quality supporting both evidence-based medicine and case-based reasoning.