Miracle-dz/UniFAR-FTUs huggingface dataset:
Genipin-Crosslinked Chitosan Gels and Scaffolds for Tissue Engineering and Regeneration of Cartilage and Bone

The present review article intends to direct attention to the technological advances made since 2009 in the area of genipin-crosslinked chitosan (GEN-chitosan) hydrogels. After a concise introduction on the well recognized characteristics of medical grade chitosan and food grade genipin, the properties of GEN-chitosan obtained with a safe, spontaneous and irreversible chemical reaction, and the quality assessment of the gels are reviewed. The antibacterial activity of GEN-chitosan has been well assessed in the treatment of gastric infections supported by Helicobacter pylori. Therapies based on chitosan alginate crosslinked with genipin include stem cell transplantation, and development of contraction free biomaterials suitable for cartilage engineering. Collagen, gelatin and other proteins have been associated to said hydrogels in view of the regeneration of the cartilage. Viability and proliferation of fibroblasts were impressively enhanced upon addition of poly-l-lysine. The modulation of the osteocytes has been achieved in various ways by applying advanced technologies such as 3D-plotting and electrospinning of biomimetic scaffolds, with optional addition of nano hydroxyapatite to the formulations. A wealth of biotechnological advances and know-how has permitted reaching outstanding results in crucial areas such as cranio-facial surgery, orthopedics and dentistry. It is mandatory to use scaffolds fully characterized in terms of porosity, pore size, swelling, wettability, compressive strength, and degree of acetylation, if the osteogenic differentiation of human mesenchymal stem cells is sought: in fact, the novel characteristics imparted by GEN-chitosan must be simultaneously of physico-chemical and cytological nature. Owing to their high standard, the scientific publications dated 2010–2015 have met the expectations of an interdisciplinary audience.
Distributed Selfish Replication under Node Churn

In this paper the impact of node churn on the effectiveness of a distributed selfish replication group is investigated. In such a group, nodes cooperate in deciding which objects to store, so that the cost of providing content to their clientele be decreased, compared to that induce when operating in isolation. In order for a cooperation scheme to be sustainable, nodes should not be mistreated; i.e., their performance should never be lower than that in isolation. In this paper it is shown that node churn can introduce mistreatment to otherwise mistreatmentfree cooperation schemes. Finally, by properly modifying a previously described scheme that looses its mistreatment-free property under node churn, a scheme is developed that maintains the mistreatment-free property in a distributed selfish replication group in the presence of node churn.
Multilingual Summarisation and Sentiment Analysis Habilitation Thesis

Summarisation and sentiment analysis are the key NLP technologies which allow monitoring evolving content and opinions in huge amounts of textual data available on the web. Summarisa-tion can address the problem of information overload by extracting and presenting the main content and sentiment analysis can identify opinions expressed towards entities or events. Because there can be found so many opinions, it is needed to aggregate them and present to a user only the most important ones. And this is the case in which summarisation and sentiment analysis have to work together. Studying the problems in multiple languages, besides providing multilingual information access, opens new possibilities, like analysing disagreements in reporting across languages or producing more coherent summaries in the case of weakly covered languages. My research focussed mainly on news data, however, the attention is now shifting towards rising social media. This thesis describes the crossing paths of my research of summarisation and sentiment analysis in multilingual environment.
Temperature dependence of cell-cell fusion induced by the envelope glycoprotein of human immunodeficiency virus type 1

We investigated cell-cell fusion induced by the envelope glycoprotein of human immunodeficiency virus type 1 strain IIIB expressed on the surface of CHO cells. These cells formed syncytia when incubated together with CD4-positive human lymphoblastoid SupT1 cells or HeLa-CD4 cells but not when incubated with CD4-negative cell lines. A new assay for binding and fusion was developed by using fluorescent phospholipid analogs that were produced in SupT1 cells by metabolic incorporation of BODIPY-labeled fatty acids. Fusion occurred as early as 10 min after mixing of labeled SupT1 cells with unlabeled CHO-gp160 cells at 37 degrees C. When both the fluorescence assay and formation of syncytia were used, fusion of SupT1 and HeLa-CD4 cells with CHO-gp160 cells was observed only at temperatures above 25 degrees C, confirming recent observations (Y.-K. Fu, T.K. Hart, Z.L. Jonak, and P.J. Bugelski, J. Virol. 67:3818-3825, 1993). This temperature dependence was not observed with influenza virus-induced cell-cell fusion, which was quantitatively similar at both 20 and 37 degrees C, indicating that cell-cell fusion in general is not temperature dependent in this range. gp120-CD4-specific cell-cell binding was found over the entire 0 to 37 degrees C range but increased markedly above 25 degrees C. The enhanced binding and fusion were reduced by cytochalasins B and D. Binding of soluble gp120 to CD4-expressing cells was equivalent at 37 and 16 degrees C. Together, these data indicate that during gp120-gp41-induced syncytium formation, initial cell-cell binding is followed by a cytoskeleton-dependent increase in the number of gp120-CD4 complexes, leading to an increase in the avidity of cell-cell binding. The increased number of gp120-CD4 complexes is required for fusion, which suggests that the formation of a fusion complex consisting of multiple CD4 and gp120-gp41 molecules is a step in the fusion mechanism.
Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences

This paper studies the problem of learning from ambiguous supervision, focusing on the task of learning semantic correspondences. A learning problem is said to be ambiguously supervised when, for a given training input, a set of output candidates is provided with no prior of which one is correct. We propose to tackle this problem by solving a related unambiguous task with a label ranking approach and show how and why this performs well on the original task, via the method of task-transfer. We apply it to learning to match natural language sentences to a structured representation of their meaning and empirically demonstrate that this competes with the state-of-the-art on two benchmarks.
The Goal Matrix – A Model for Developing Shared Cognition in Teams

The purpose of this article is to present a model for developing shared cognition in teams, called the goal matrix. The theories and research behind the model is presented along with practical guidelines on how to use the model within a team. The model starts with the overall purpose of the team, why it exists in the first place. Then it defines an internal perspective, concerning team members and their roles, and an external perspective, concerning the stakeholders of the team. The purpose and the relationship between the internal and external perspective defines the context of the team. Further, the model describes three aspects of goals depending on the time horizon; process goals, future results and visions. The place and the time dimensions on goal achievement form six types of goals. These are internal standards and external standards, development goals and operative goals, guiding stars and vision.
Comparison of part-of-speech and automatically derived category-based language models for speech recognition

This paper compares various category-based language models when used in conjunction with a word-based trigram by means of linear interpolation. Categories corresponding to parts-of-speech as well as automatically clustered groupings are considered. The category-based model employs variable-length n-grams and permits each word to belong to multiple categories. Relative word error rate reductions of between 2 and 7% over the baseline are achieved in N-best rescoring experiments on the Wall Street Journal corpus. The largest improvement is obtained with a model using automatically determined categories. Perplexities continue to decrease as the number of different categories is increased, but improvements in the word error rate reach an optimum.
Model-based segmentation of medical x-ray images

This paper discusses the methods used to model the structure of x-ray images of the human body and the individual organs within the body. A generic model of a region is built up from x-ray images to aid in automatic segmentation. By using the ribs from a chest x-ray image as an example, it is shown how models of the different organs can be generated. The generic model of the chest region is built up by using a priori knowledge of the physical structure of the human body. The models of the individual organs are built up by using knowledge of the structure of the organs as well as other information contained within each image. Each image is unique and therefore information from the region surrounding the organs in the image has to be taken into account when adapting the generic model to individual images. Results showing the application of these techniques to x-ray images of the chest region, the labelling of individual organs, and the generation of models of the ribs are presented.
proceeding through 3 '-to-5 ' exosome-dependent decapping Regulated alpha-globin mRNA decay is a cytoplasmic event

The a-globin mRNA contains a C-rich stability element (CRE) in its 3 9 untranslated region (3 9 UTR) which is critical for the stability of this long-lived mRNA. A protein complex, termed the a-complex, forms on the CRE and has been shown to contribute to stabilization of the mRNA by at least two mechanisms, first by interacting with the poly(A)-binding protein (PABP) to prevent deadenylation, and second by protecting the mRNA from attack by an erythroid endoribonuclease. In this report, we demonstrate that the a-globin 3 9 UTR can confer stability on a heterologous mRNA in cells, and this stability is dependent on the a-complex. Moreover, the stability was exclusively detected with cytoplasmic mRNA, suggesting that the regulation of a-globin mRNA stability is a cytoplasmic event. An additional mechanism by which the a-complex can confer stability on an RNA in vitro was also identified and shown to involve inhibition of 3 9 to 59 exonucleolytic degradation. Furthermore, using an in vitro mRNA decay system, we were able to follow the demise of the a-globin RNA and demonstrate that the decay was initiated by deadenylation followed by 3 9-to-59 decay carried out by the exosome and ultimately hydrolysis of the residual cap structure.
The development of acoustic models for command and control arabic speech recognition system

The paper presents an acoustic training system for building acoustic models for a medium vocabulary speaker independent continuous speech recognition system. A speech database is constructed to train the acoustic models. The acoustic models are constructed, trained. A test set database is constructed to test the accuracy of the acoustic models, also 4 language models of two main types: bigram and context free grammar were built and used in tests. Our results show a 5.26 YO and 2.72 % word error rate for 1340 and 306 words bigram based language model respectively. Our results show also 0.19 % and 0.99% for 1340 and 306 words context free grammar based language models respectively.
Joint optimization of mapping and classifier using auxiliary coordinates

Dimensionality reduction (DR) is often used as a preprocessing step in classification, but usually one first fixes the DR mapping, possibly using label information, and then learns a classifier (a filter approach). Best performance would be obtained by optimizing the classification error jointly over DR mapping and classifier (a wrapper approach), but this is a difficult nonconvex problem, particularly with nonlinear DR. Using the method of auxiliary coordinates, we give a simple, efficient algorithm to train a combination of nonlinear DR and a classifier, and apply it to a RBF mapping with a linear SVM. This alternates steps where we train the RBF mapping and a linear SVM as usual regression and classification, respectively, with a closedform step that coordinates both. The resulting nonlinear low-dimensional classifier achieves classification errors competitive with the state-of-the-art but is fast at training and testing, and allows the user to trade off runtime for classification accuracy easily. We then study the role of nonlinear DR in linear classification, and the interplay between the DR mapping, the number of latent dimensions and the number of classes. When trained jointly, the DR mapping takes an extreme role in eliminating variation: it tends to collapse classes in latent space, erasing all manifold structure, and lay out class centroids so they are linearly separable with maximum
On the effects of varying filter bank parameters on isolated word recognition

The vast majority of commercially available isolated word recognizers use a filter bank analysis as the front end processing for recognition. It is not well understood how the parameters of different filter banks (e.g., number of filters, types of filters, filter spacing, etc.) affect recognizer performance. In this paper we present results of performance evaluation of several types of filter bank analyzers in a speaker trained isolated word recognition test using dialed-up telephone line recordings. We have studied both DFT (discrete Fourier transform) and direct form implementations of the filter banks. We have also considered uniform and nonuniform filter spacings. The results indicate that the best performance (highest word accuracy) is obtained by both a 15-channel uniform filter bank and a 13-channel nonuniform filter bank (with channels spacing along a critical band scale). The performance of a 7-channel critical band filter bank is almost as good as that of the two best filter banks. In comparison to a conventional linear predictive coding (LPC) word recognizer, the performance of the best filter bank recognizers was, on average, several percent worse than that of an eighth-order LPC-based recognizer. A discussion as to why some filter banks performed better than others, and why the LPC-based system did the best, is given in this paper.
Critical Roles of Transitional Cells and Na/K-ATPase in the Formation of Vestibular Endolymph

The mechanotransduction of vestibular sensory cells depends on the high endolymphatic potassium concentration ([K+]) maintained by a fine balance between K+ secretion and absorption by epithelial cells. Despite the crucial role of endolymph as an electrochemical motor for mechanotransduction, little is known about the processes that govern endolymph formation. To address these, we took advantage of an organotypic rodent model, which regenerates a genuine neonatal vestibular endolymphatic compartment, facilitating the determination of endolymphatic [K+] and transepithelial potential (Vt) during endolymph formation. While mature Vt levels are almost immediately achieved, K+ accumulates to reach a steady [K+] by day 5 in culture. Inhibition of sensory cell K+ efflux enhances [K+] regardless of the blocker used (FM1.43, amikacin, gentamicin, or gadolinium). Targeting K+ secretion with bumetanide partially and transiently reduces [K+], while ouabain application and Kcne1 deletion almost abolishes it. Immunofluorescence studies demonstrate that dark cells do not express Na-K-2Cl cotransporter 1 (the target of bumetanide) in cultured and young mouse utricles, while Na/K-ATPase (the target of ouabain) is found in dark cells and transitional cells. This global analysis of the involvement of endolymphatic homeostasis actors in the immature organ (1) confirms that KCNE1 channels are necessary for K+ secretion, (2) highlights Na/K-ATPase as the key endolymphatic K+ provider and shows that Na-K-2Cl cotransporter 1 has a limited impact on K+ influx, and (3) demonstrates that transitional cells are involved in K+ secretion in the early endolymphatic compartment.
Annotation of bacterial genomes using improved phylogenomic profiles

MOTIVATION Phylogenomic profiling is a large-scale comparative genomic method used to infer protein function from evolutionary information first described in a binary form by Pellegrini et al. (1999). Here, we propose improvements of this approach including the use of normalized Blastp bit scores, a normalization of the matrix of profiles to take into account the evolutionary distances between bacteria, the definition of a phylogenomic neighborhood based on continuous pairwise distances between genes and an original annotation procedure including the computation of a p-value for each functional assignment. RESULTS The method presented here increases the number of Ecocyc enzymes identified as being evolutionarily related by about 25% with respect to the original binary form (absent/present) method. The fraction of 'false' positives is shown to be smaller than 20%. Based on their phylogenomic relationships, genes of unknown function can then be automatically related to annotated genes. Each gene annotation predicted is associated with a p-value, i.e. its probability to be obtained by chance. The validity of this method was extensively tested on a large set of genes of known function using the MultiFun database. We find that 50% of 3122 function attributions that can be made at a p-value level of 10(-11) correspond to the actual gene annotation. The method can be readily applied to any newly sequenced microbial genome. In contrast to earlier work on the same topic, our approach avoids the use of arbitrary cut-off values, and provides a reliability estimate of the functional predictions in form of p-values.
Long-Term Interest Rates and Inflation: A Fisherian Approach

In recent years, Federal Reserve (Fed) policymakers have come to rely on long-term bond yields to measure the public’slong-term inflationary expectations. The long-term bond rate plays a central role in Goodfriend’s (1993) narrative account of Fed behavior, 1979‐1992, which links policyrelated movements in the federal funds rate to changes in the yield on long-term U.S. Treasury bonds. According to Goodfriend, Fed officials interpreted rapid increases in long-term bond rates as the product of rising inflationary expectations, reflecting a deterioration in the credibility of their fight against inflation. To restore that credibility, they responded by tightening monetary policy, that is, by raising the federal funds rate. Mehra (1995) presents statistical results that support Goodfriend’sview. Using an econometric model, he demonstrates that changes in long-term bond rates help explain movements in the federal funds rate during the 1980s. While these studies provide convincing evidence of a link between Fed policy and long-term bond rates, both start with the untested hypothesis that movements in such rates primarily reflect changes in long-term inflationary expectations. And while economic theory does identify expected inflation as one determinant of nominal bond yields, it suggests that there are other determinants as well. Using theory as a guide, this article seeks to measure the contribution each determinant makes in accounting for movements in longterm bond yields. By doing so, it attempts to judge the extent to which Fed policymakers are justified in using these bond yields as indicators of inflationary expectations.
Energy Efficiency in Cloud Software Architectures

Cloud-based software is often considered as providing a greener, more energy-efficient solution. At the same time, it introduces more complexity and demands for new investments in cloud services, technologies, and competencies for migration, maintenance, and evolution of the underlying software architectures. To understand better the implications of cloud software architectures on energy efficiency, in this paper we present the preliminary results of a systematic literature review that investigates what kind of software architectures for cloud service provisioning allow to achieve energy-efficient solutions.
Algorithms for Scheduling Runway Operations Under Constrained Position Shifting

The efficient operation of airports, and runways in particular, is critical to the throughput of the air transportation system as a whole. Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations of efficiency, safety, and equity among airlines. One approach to runway scheduling that arises from operational and fairness considerations is that of constrained position shifting (CPS), which requires that an aircraft's position in the optimized sequence not deviate significantly from its position in the first-come-first-served sequence. This paper presents a class of scalable dynamic programming algorithms for runway scheduling under constrained position shifting and other system constraints. The results from a prototype implementation, which is fast enough to be used in real time, are also presented.
Application of string kernel based support vector machine for malware packer identification

Packing is among the most popular obfuscation techniques to impede anti-virus scanners from successfully detecting malware. In this paper we propose a string-kernel-based support vector machine classifier to identify the packer that is used to create a given malware program. Our approach is featured by the following characteristics. First, the adoption of a string-kernel-based method bridges the gap between signature-based and machine-learning-base approaches. Second, the kernel function derived from the Levenshtein distance integrates important domain knowledge in the learning process. Then, application of support vector machine, a state-of-the-art classifier, enables an automated packer identification scheme with high generalization ability and time efficiency. Finally, selection of the code segment with the most essential packer relevant information further boosts the classification performance. Experiments on a dataset of 3228 binary programs composed of packed files created by 25 packers show that the proposed approach outperforms PEiD and previous machine-learning-based approaches in prediction accuracy with a large margin. This method can help to improve the scanning efficiency of anti-virus products and promote efficient back-end malware research.
Exact Markovian SIR and SIS epidemics on networks and an upper bound for the epidemic threshold

Exploiting the power of the expectation operator and indicator (or Bernoulli) random variables, we present the exact governing equations for both the SIR and SIS epidemic models on \emph{networks}. Although SIR and SIS are basic epidemic models, deductions from their exact stochastic equations \textbf{without} making approximations (such as the common mean-field approximation) are scarce. An exact analytic solution of the governing equations is highly unlikely to be found (for any network) due to the appearing pair (and higher order) correlations. Nevertheless, the maximum average fraction $y_{I}$ of infected nodes in both SIS and SIR can be written as a quadratic form of the graph's Laplacian. Only for regular graphs, the expression for the maximum of $y_{I}$ can be simplied to exhibit the explicit dependence on the spectral radius. From our new Laplacian expression, we deduce a general \textbf{upper} bound for the epidemic SIS threshold in any graph.