Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Natural Killer Cell Targeted Immunotherapies

Animal models have extensively contributed to our understanding of human immunobiology and to uncover the underlying pathological mechanisms occurring in the development of diseases. However, mouse models do not reproduce the genetic and molecular complexity inherent in human disease conditions. Human immune system (HIS) mouse models that are susceptible to human pathogens and can recapitulate human hematopoiesis and tumor immunobiology provide one means to bridge the interspecies gap. Natural killer cells are the founding member of the innate lymphoid cell family. They exert a rapid and strong immune response against tumor and pathogen-infected cells. Their antitumor features have long been exploited for therapeutic purposes in the context of cancer. In this review, we detail the development of highly immunodeficient mouse strains and the models currently used in cancer research. We summarize the latest improvements in adoptive natural killer (NK) cell therapies and the development of novel NK cell sources. Finally, we discuss the advantages of HIS mice to study the interactions between human NK cells and human cancers and to develop new therapeutic strategies.
Preparation and characterization of high‐molecular‐weight atactic poly(vinyl alcohol)/sodium alginate/silver nanocomposite by electrospinning

High-molecular-weight poly(vinyl alcohol) (PVA)/sodium alginate (SA)/ silver nanocomposite was successfully prepared via electrospinning technique. Water-based colloidal silver in a PVA/SA blend solution was directly mixing without any chemical and structural modifications into PVA/SA matrix to form an organic-metallic nanocomposite. The effect of the addition of silver colloidal solution on the PVA/SA/silver nanocomposite was investigated through a series of experiments varying molecular weight of PVA and electrospinning processing parameters such as concentration of PVA solution, PVA/SA blend ratio, applied voltage, and tip-to-collector distance. In the case of PVA with number-average degree of polymerization of 1700, by increasing the amount of SA in spinning solution, the morphology was changed from fine uniform fiber to beaded fiber or bead-on-string fiber structure. Increase of the amount of silver colloidal solution resulted in higher charge density on the surface of ejected jet during spinning, thus more electric charges carried by the electrospinning jet. As the charge density increased, the diameter of the nanocomposites became smaller. Transmission electron microscopy images showed that the dense silver nanoparticles were well separately dispersed in PVA/SA matrix. Energy-disperse X-ray analysis indicated that carbon, oxygen, natrium, and silver were the principle element of PVA/SA/silver nanocomposite. © 2009 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 47: 1916–1926, 2009
Novel SLC4A11 mutations in patients with recessive congenital hereditary endothelial dystrophy (CHED2)

Autosomal recessive congenital hereditary endothelial dystrophy (CHED2) is a severe and rare corneal disorder that presents at birth or shortly thereafter, characterized by corneal opacification and nystagmus. Recently the gene for CHED2 was identified and seven different mutations in the SLC4A11 gene were reported. Here, we report seven novel mutations and two previously identified mutations in families from India and the United Kingdom with recessive CHED. The novel changes include two nonsense (p.Trp240X; p.Gln800X) three missense (p.Glu143Lys; p.Cys386Arg; p.Arg755Trp) and two splice site mutations (c.2240+1G>A; c.2437‐1G>A). Interestingly, the c.2398C>T (p.Gln800X) and c.2437‐1G>A identified in two affected siblings represent the first compound heterozygous mutations in the SLC4A11 gene. © 2007 Wiley‐Liss, Inc.
Cross system personalization: enabling personalization across multiple systems:

The Web provides access to a wealth of information to a huge diverse user population on a global scale. One successful mechanism in dealing with this diversity of users is to personalize Web sites, services, and system content and customize for a specific user. Since this process currently occurs separately within each system, there are several drawbacks over an integrated approach. Cross system personalization (CSP) allows for sharing information across different information systems in a user-centric way and can overcome the aforementioned problems. Information about users, which is originally scattered across multiple systems, is combined to obtain maximum leverage and reuse. This book explains a principled approach towards achieving cross system personalization. We describe two approaches for CSP: semantic and learning-based, with a stronger emphasis on the learning approach. We also investigate the privacy and scalability aspects of CSP and provide solutions to these problems. Finally, we also explore in detail the aspect of robustness in recommender systems.
Interactions between species introduce spurious associations in microbiome studies

Microbiota contribute to many dimensions of host phenotype, including disease. To link specific microbes to specific phenotypes, microbiome-wide association studies compare microbial abundances between two groups of samples. Abundance differences, however, reflect not only direct associations with the phenotype, but also indirect effects due to microbial interactions. We found that microbial interactions could easily generate a large number of spurious associations that provide no mechanistic insight. Using techniques from statistical physics, we developed a method to remove indirect associations and applied it to the largest dataset on pediatric inflammatory bowel disease. Our method corrected the inflation of p-values in standard association tests and showed that only a small subset of associations is directly linked to the disease. Direct associations had a much higher accuracy in separating cases from controls and pointed to immunomodulation, butyrate production, and the brain-gut axis as important factors in the inflammatory bowel disease.
Keep it SIMPLEX: satisfying multiple goals with guarantees in control-based self-adaptive systems

An increasingly important concern of software engineers is handling uncertainties at design time, such as environment dynamics that may be difficult to predict or requirements that may change during operation. The idea of self-adaptation is to handle such uncertainties at runtime, when the knowledge becomes available. As more systems with strict requirements require self-adaptation, providing guarantees for adaptation has become a high-priority. Providing such guarantees with traditional architecture-based approaches has shown to be challenging. In response, researchers have studied the application of control theory to realize self-adaptation. However, existing control-theoretic approaches applied to adapt software systems have primarily focused on satisfying only a single adaptation goal at a time, which is often too restrictive for real applications. In this paper, we present Simplex Control Adaptation, SimCA, a new approach to self-adaptation that satisfies multiple goals, while being optimal with respect to an additional goal. SimCA offers robustness to measurement inaccuracy and environmental disturbances, and provides guarantees. We evaluate SimCA for two systems with strict requirements that have to deal with uncertainties: an underwater vehicle system used for oceanic surveillance, and a tele-assistance system for health care support.
Sentence Based Discourse Classification for Hindi Story Text-to-Speech (TTS) System

In this work, we have proposed an automatic discourse prediction model. It predicts the discourse information for a sentence. In this study, three discourse modes considered are descriptive, narrative and dialogue. The proposed model is developed using story corpus. The story corpus comprises of audio and its corresponding text transcription of short children stories. The development of this model entails two phases: feature extraction and classiﬁcation of the discourse. The feature extraction is carried out using ‘Word2Vec’ model. The classiﬁcation of discourse at sentence-level is explored by using Support Vector Machines (SVM), Convolu-tional Neural Network (CNN) and a combination of CNN-SVM. The main focus of this study is on the usage of CNN for developing the model because it has not been explored much for the problems related to text classiﬁcation. Experiments are carried out to ﬁnd the best model parameters (such as the number of the ﬁlter, ﬁlter-height, cross-validation number, dropout rate, and batch-size) for the CNN. The proposed model achieves its best accuracy 72.6% when support vector machine (SVM) is used for classiﬁcation and features are extracted from CNN (which is trained using the word2vec feature). This model can leverage the utilization of the discourse as a suprasegmental feature from the perspective
β8 Integrin Binds Rho GDP Dissociation Inhibitor-1 and Activates Rac1 to Inhibit Mesangial Cell Myofibroblast Differentiation*

αvβ8 integrin expression is restricted primarily to kidney, brain, and placenta. Targeted αvor β8 deletion is embryonic lethal due to defective placenta and brain angiogenesis, precluding investigation of kidney αvβ8 function. We find that kidney β8 is localized to glomerular mesangial cells, and expression is decreased in mouse models of glomerulosclerosis, suggesting that β8 regulates normal mesangial cell differentiation. To interrogate β8 signaling pathways, yeast two-hybrid and co-precipitation studies demonstrated β8 interaction with Rho guanine nucleotide dissociation inhibitor-1 (GDI). Selective β8 stimulation enhanced β8-GDI interaction as well as Rac1 (but not RhoA) activation and lamellipodia formation. Mesangial cells from itgb8-/- mice backcrossed to a genetic background that permitted survival, or gdi-/- mice, which develop glomerulosclerosis, demonstrated RhoA (but not Rac1) activity and α-smooth muscle actin assembly, which characterizes mesangial cell myofibroblast transformation in renal disease. To determine whether Rac1 directly modulates RhoA-associated myofibroblast differentiation, mesangial cells were transduced with inhibitory Rac peptide fused to human immunodeficiency virus-Tat, resulting in enhanced α-smooth muscle actin organization. We conclude that the β8 cytosolic tail in mesangial cells organizes a signaling complex that culminates in Rac1 activation to mediate wild-type differentiation, whereas decreased β8 activation shifts mesangial cells toward a RhoA-dependent myofibroblast phenotype.
Self-Explanatory Convex Sparse Representation for Image Classification

Sparse representation technique has been widely used in various areas of computer vision over the last decades. Unfortunately, in the current formulations, there are no explicit relationship between the learned dictionary and the original data. By tracing back and connecting sparse representation with the K-means algorithm, a novel variation scheme termed as self-explanatory convex sparse representation (SCSR) has been proposed in this paper. To be specific, the basis vectors of the dictionary are refined as convex combination of the data points. The atoms now would capture a notion of centroids similar to K-means, leading to enhanced interpretability. Sparse representation and K-means are thus unified under the same framework in this sense. Besides, an appealing property also emerges that the weight and code matrices both tend to be naturally sparse without additional constraints. Compared with the standard formulations, SCSR is easier to be extended into the kernel space. To solve the corresponding sparse coding sub problem and dictionary learning sub problem, block-wise coordinate descent and Lagrange multipliers are proposed accordingly. To validate the proposed algorithm, it is implemented in image classification, a successful applications of sparse representation. Experimental results on several benchmark data sets, such as UIUC-Sports, Scene 15, and Caltech-256 demonstrate the effectiveness of our proposed algorithm.
Common subtrees in related problems: A novel transfer learning approach for genetic programming

Transfer learning is a machine learning technique which has demonstrated great success in improving outcomes on a broad range of problems. However prior methods of transfer learning in Genetic Programming (GP) have tended to rely on random processes or meta-knowledge of the problem structure to facilitate selection of information for use in transfer. To address these issues, a non-random method for automatically finding relevant information for transfer between two source domain problems from the same problem domain based on common subtrees is proposed. This information is then utilised within a modular transfer learning framework, being added to the function set for a target problem prior to population initialisation. The performance of the proposed method is assessed using multiple benchmark problems from two distinct problem domains, namely symbolic regression and Boolean domain problems, and compared to standard GP and the-state-of-the-art transfer learning method for the given problems. The results show that the newly introduced method has either significantly outperformed, or achieved comparable performance to, the competitor methods on the problems of the two domains. We conclude that the proposed method demonstrates ability as a general transfer learning technique for GP and note some possible avenues for future research based off these results.
FtsK-Dependent Dimer Resolution on Multiple Chromosomes in the Pathogen Vibrio cholerae

Unlike most bacteria, Vibrio cholerae harbors two distinct, nonhomologous circular chromosomes (chromosome I and II). Many features of chromosome II are plasmid-like, which raised questions concerning its chromosomal nature. Plasmid replication and segregation are generally not coordinated with the bacterial cell cycle, further calling into question the mechanisms ensuring the synchronous management of chromosome I and II. Maintenance of circular replicons requires the resolution of dimers created by homologous recombination events. In Escherichia coli, chromosome dimers are resolved by the addition of a crossover at a specific site, dif, by two tyrosine recombinases, XerC and XerD. The process is coordinated with cell division through the activity of a DNA translocase, FtsK. Many E. coli plasmids also use XerCD for dimer resolution. However, the process is FtsK-independent. The two chromosomes of the V. cholerae N16961 strain carry divergent dimer resolution sites, dif1 and dif2. Here, we show that V. cholerae FtsK controls the addition of a crossover at dif1 and dif2 by a common pair of Xer recombinases. In addition, we show that specific DNA motifs dictate its orientation of translocation, the distribution of these motifs on chromosome I and chromosome II supporting the idea that FtsK translocation serves to bring together the resolution sites carried by a dimer at the time of cell division. Taken together, these results suggest that the same FtsK-dependent mechanism coordinates dimer resolution with cell division for each of the two V. cholerae chromosomes. Chromosome II dimer resolution thus stands as a bona fide chromosomal process.
Scalable network virtualization using FPGAs

Recent virtual network implementations have shown the capability to implement multiple network data planes using a shared hardware substrate. In this project, a new scalable virtual networking data plane is demonstrated which combines the performance efficiency of FPGA hardware with the flexibility of software running on a commodity PC. Multiple virtual router data planes are implemented using a Virtex II-based NetFPGA card to accommodate virtual networks requiring superior packet forwarding performance. Numerous additional data planes for virtual networks which require less bandwidth and slower forwarding speeds are implemented on a commodity PC server via software routers. Through experimentation, we determine that a throughput improvement of up to two orders of magnitude can be achieved for FPGA-based virtual routers versus a software-based virtual router implementation. Dynamic FPGA reconfiguration is supported to adapt to changing networking needs. System scalability is demonstrated for up to 15 virtual routers.
A shared-memory ACO+GA hybrid for combinatorial optimization

A novel hybrid algorithm combining the search capabilities of evolutionary genetic and artificial ant colony optimization algorithms through a common library of partial permutations is introduced. The two algorithms work independently in parallel to construct two different populations of individuals representing potential solutions. A shared memory containing variable size and partially incomplete permutations from above-average individuals of the two populations is used as the medium for information exchange between the two algorithms. The aim is to support the solution construction procedures of the two algorithms by knowledge incorporation through a shared external memory that contains experienced based knowledge gained through two different solution methods. Constructed solutions are also used to update the memory. The proposed approach is used for the solution of TSP and QAP for which the obtained results demonstrate that both the speed and solution quality are improved compared to the two individual component algorithms.
A hybrid multicast-unicast infrastructure for efficient publish-subscribe in enterprise networks

One of the main challenges in building a large scale publish subcribe infrastructure in an enterprise network, is to provide the subscribers with the required information, while minimizing the consumed host and network resources. Typically, previous approaches utilize either IP multicast or point-to-point unicast for efficient dissemination of the information. In this work, we propose a novel hybrid framework, which is a combination of both multicast and unicast data dissemination. Our hybrid framework allows us to take the advantages of both multicast and unicast, while avoiding their drawbacks. We investigate several algorithms for computing the best mapping of publishers' transmissions into multicast and unicast transport. Using extensive simulations, we show that our hybrid framework reduces consumed host and network resources, outperforming traditional solutions. To insure the subscribers interests closely resemble those of real-world settings, our simulations are based on stock market data and on recorded IBM WebShpere subscriptions.
A new model of numerical computer data and its application for construction of minimum-redundancy codes

Numerical computer data in many applications contain numerous leading and trailing zeros, and this data feature has been neglected in common source models and related data compression methods. A new approach to source modeling, consisting of the description of this data feature by probabilities p/sub ij/ of data values with i leading and j trailing zeros, is proposed. The entropy of this source model is proved to be less than the entropy of the common ones. Based on this model a few codes of different computational complexity are proposed. Experiments on typical computer files show that the coding algorithms based on the new source and data model considerably outperform existing data compression algorithms. >
Information Visualization of Multi-dimensional Cellular Automata using GPU Programming

We propose a method for generating all possible rules of multi-dimension Boolean cellular automata (CA). Based on an original encoding method and the programming of graphical processor units (GPU), this method allows us to visualize the CA information flow in real-time so that emerging behaviors can be easily identified. Algorithms of first and von Neumann neighborhood second degrees are detailed with their respective fragment shaders programs. As symmetrical CA rules are especially useful in many research fields, we propose an encoding technique to automatically derive their codes; we then apply this technique to identify the 4096 possible cases for surface CA. To show the efficiency of our model a set of converging global behaviors are listed and described. In the last part of the paper we present methods for developing Moore neighborhood in two and in three dimensions. Finally we discuss issues concerning computation and the visualization of non-Boolean and higher dimension CA.
On the Evaluation of Snippet Selection for Information Retrieval

In this paper we take a critical look at the evaluation method of WebCLEF 2007. The suitability of the evaluation method can be seen from two sides, namely from a participating system and a non participating system. A participant has the advantage that the evaluation is partly based upon his output. In this paper we will investigate if the size of the pool of snippets, the implementation of the evaluation method and the quality of the assessments is sufficient enough for reliable evaluation. Unfortunately we have to conclude that the evaluation is not suitable. Therefore some alternative evaluation methods will be discussed concluding in a recommendation to improve the evaluation of WebCLEF.
Cell Levels of a Terpenoid Glycoside ( Blumenin ) and Wall-Bound Phenolics in Some Cereal Mycorrhizas ’

Four cereals, Hordeum vulgare (barley), Triticum aestivum (wheat), Secale cereale (rye), and Avena safiva (oat), were grown in a defined nutritional medium with and without the arbuscular mycorrhizal fungus Glomus intraradices. Levels of soluble and cell wall-bound secondary metabolites in the roots of mycorrhizal and nonmycorrhizal plants were determined by high-performance liquid chromatography during the first 6 to 8 weeks of plant development. Whereas there was no difference in the levels of the cell wall-bound hydroxycinnamic acids, 4-coumaric and ferulic acids, there was a fungus-induced change of the soluble secondary root metabolites. The most obvious effect observed in all four cereals was the induced accumulation of a terpenoid glycoside. This compound was isolated and identified by spectroscopic methods (nuclear magnetic resonance, mass spectrometry) to be a cyclohexenone derivative, i.e. blumenol C 9-0(2’-D~-glucuronosyl)-~-glucoside. The level of this compound was found to be directly correlated with the degree of root colonization.
Hierarchical Genre Classification for Large Music Collections

The rapid progress in digital music distribution has lead to the creation of large collections of music. There is a need for content-based music classification methods to organize these collections automatically using a given genre taxonomy. To provide a versatile description of the music content, several kinds of features like rhythm, pitch or timbre characteristics are commonly used. Taking the highly dynamic nature of music into account, each of these features should be calculated up to several hundreds of times per second. Thus, a piece of music is represented by a complex object given by several large sets of feature vectors. In this paper, we propose a novel approach for the hierarchical classification of music pieces into a genre taxonomy. Our approach is able to handle multiple characteristics of music content and achieves a high classification accuracy efficiently, as shown in our experiments performed on a real world data set