Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of infectious factors on dendritic cell apoptosis

Pathogens can have a negative influence on dendritic cells (DCs), causing their apoptosis, which prevents active presentation of foreign antigens. It results in a state of immunosuppression which makes the body susceptible to secondary infections. Infected immature DCs have lower expression of co-stimulatory and adhesion molecules, reduced ability to secrete cytokines and an inhibited maturation process and are incapable of effective antigen presentation and activation of T-lymphocytes. In some cases, the ability of DCs to undergo rapid apoptosis is important for the body defense, which is probably because of DCs’ ability to cross-present and cooperate with other cells. Apoptotic bodies released from the infected DCs are phagocytosed by other DCs, which then stimulate the effector cells and present antigens more efficiently than infected cells. The aim of this article is to review how the DCs respond to viral and bacterial factors and which biochemical mechanisms are responsible for their apoptosis.
Clustering Biological Annotations and Gene Expression Data to Identify Putatively Co-regulated Biological Processes

MOTIVATION Functional profiling is a key step of microarray gene expression data analysis. Identifying co-regulated biological processes could help for better understanding of underlying biological interactions within the studied biological frame. RESULTS We present herein an original approach designed to search for putatively co-regulated biological processes sharing a significant number of co-expressed genes. An R language implementation named "FunCluster" was built and tested on two gene expression data sets. A discriminatory functional analysis of the first data set, related to experiments performed on separated adipocytes and stroma vascular fraction cells of human white adipose tissue, highlighted the prevalent role of nonadipose cells in the synthesis of inflammatory and immunity molecules in human adiposity. On the second data set, resulting from a model investigating insulin coordinated regulation of gene expression in human skeletal muscle, FunCluster analysis spotlighted novel functional classes of putatively co-regulated biological processes related to protein metabolism and the regulation of muscular contraction. AVAILABILITY Supplementary information about the FunCluster tool is available on-line at http://corneliu.henegar. info/FunCluster.htm.
Approximation algorithms for independent set problems on hypergraphs

This thesis deals with approximation algorithms for the Maximum Independent Set and the Minimum Hitting Set problems on hypergraphs. As a hypergraph is a generalization of a graph, the question is whether the best known approximations on graphs can be extended to hypergraphs. We consider greedy, local search and partitioning algorithms. We introduce a general technique, called shrinkage reduction, that reduces the worst case analysis of certain algorithms on hypergraphs to their analysis on ordinary graphs. This technique allows us to prove approximation ratios for greedy and local search algorithms for the Maximum Weak Independent Set problem on weighted and unweighted bounded-degree hypergraphs. For the weighted case we improve bounds using a simple partitioning algorithm. We also consider two variations of the max-greedy algorithms for the Maximum Strong Independent Set problem. We describe an SDP-based approach for the Maximum Weak Independent Set problem on bounded-degree hypergraphs. Our approach is to use semidefinite technique to sparsify a given hypergraph and then apply combinatorial algorithms to find a large independent set in the resulting sparser instance. Using this approach we obtain the first performance ratio that is sublinear in terms of the maximum or average degree of the hypergraph. We extend this result to the weighted case and give a similar bound in terms of the average weighted degree in a hypergraph, matching the best bounds known for the special case of graphs. We present several randomized and deterministic algorithms for the Maximum Weak Independent Set problem in a semi-streaming model. All our semi-streaming algorithms require only one pass over the stream and most of them resemble on-line algorithms in maintaining a feasible solution at all times. We introduce the on-line minimal space semi-streaming model and give lower and upper bounds for deterministic and randomized algorithms in this model. Nalgunarreiknirit fyrir ohað mengi i ofurnetum
Role of Counsellors in the Cultivation of Innovative Talents from Students' Perspective

Innovative talent training as an immense and complex system engineering matters the universities, governments and the whole social environment. Taking students' perspective as the entry point, this paper focuses on the functions the university Counsellors play in the cultivation of innovative talents from students' perspective. The purpose is to clarify to what degree students have so far understood the cultivation of innovative talents, and what’s the role of university counsellor in fostering the innovative talents from the students’ perspective. A questionnaire survey conducted among students show that it is important for university counsellors to participate in the innovative talent cultivation. With reference to pertinent literature, we build a framework system for the cultivation of innovative talents in colleges and universities, based on which, a thorough analysis is made on the role the university Counsellors should play in cultivating the innovative talents.
Maximum entropy production and the strength of boundary layer exchange in an atmospheric general circulation model

Boundary layer turbulence plays a central role in determining the strength of the overall atmospheric circulation by affecting the intensity of exchange of heat, mass, and momentum at the Earth's surface. It is often parameterized using the bulk formula, in which the von‐Karman parameter plays a critical role. Here we conducted a range of sensitivity simulations with an atmospheric general circulation model in which we modified the strength of boundary layer turbulence by varying the von‐Karman parameter. These simulations show that the maximum of entropy production associated with boundary layer dissipation is consistent with the observed value of the von‐Karman parameter of 0.4 and maximizes the planetary rate of entropy production with the global radiative temperature being close to its minimum value. Additional sensitivity simulations were conducted with an increased concentration of atmospheric carbon dioxide, which affects the relative radiative forcing of tropical vs. polar regions. We find that the global climate sensitivity is more‐or‐less independent of the assumed strength of boundary layer turbulence in our idealized setup. The difference in climate sensitivities of tropical and polar regions is at a minimum at a climatic state of MEP.
Excitation of oscillation modes by tides in close binaries: constraints on stellar and orbital parameters

The parameter space favourable for the resonant excitation of free oscillation modes by dynamic tides in close binary components is explored using qualitative considerations to estimate the order of magnitude of the tidalforce and the frequency range covered by the tidally induced oscillations. The investigation is valid for slowly rotating stars with masses in the interval between 2 and 20 M O . , and an evolutionary stage ranging from the beginning to the end of the main sequence. Oscillation modes with eigenfrequencies of the order of five times the inverse of the dynamical time-scale τ d y n of the star, i.e. the lowest-order p-modes, the f-mode and the lowest-order g + -modes, are found to be outside the favourable parameter space since their resonant excitation requires orbital eccentricities that are too high for the binary to stay detached when the components pass through the periastron of their relative orbit. Resonances between dynamic tides and g + -modes with frequencies of the order of half of the inverse of the dynamical time-scale of the star on the other hand are found to be favourable for orbital periods up to ∼200τ d y n , provided that the binary mass ratio q is larger than 1/3, and the orbital eccentricity e is larger than ∼0.25. This favourable range comes down to orbital periods of up to 5-12 d in the case of 2-20 M O . zero-age main-sequence binary components, and orbital periods of up to 21-70 d in the case of terminal main-sequence binary components.
Staggered flows: An application layer's way to avoid incast problem

In today's datacenter networks, TCP protocol is still used in most of the network traffic. On the other hand, TCP protocol can also result in severe goodput collapse in a high bandwidth and low latency datacenter environment. One of the problems is called incast problem. In order to efficiently utilize the bandwidth of distributed systems such as datacenter environments, some traffic control mechanisms should be very necessary. In this paper, we discussed about an application layer control mechanism to improve the performance of certain traffic pattern which may cause incast problem. The main idea is to make the data flows be staggered and transferred in serialized manner. We conducted both simulation-based and real-machine-based experiments. We find out that staggered flows are able to avoid incast problem and to make the performance better in most cases.
Scalable hybrid stream and hadoop network analysis system

Collections of network traces have long been used in network traffic analysis. Flow analysis can be used in network anomaly discovery, intrusion detection and more generally, discovery of actionable events on the network. The data collected during processing may be also used for prediction and avoidance of traffic congestion, network capacity planning, and the development of software-defined networking rules. As network flow rates increase and new network technologies are introduced on existing hardware platforms, many organizations find themselves either technically or financially unable to generate, collect, and/or analyze network flow data. The continued rapid growth of network trace data, requires new methods of scalable data collection and analysis. We report on our deployment of a system designed and implemented at the University of Kentucky that supports analysis of network traffic across the enterprise. Our system addresses problems of scale in existing systems, by using distributed computing methodologies, and is based on a combination of stream and batch processing techniques. In addition to collection, stream processing using Storm is utilized to enrich the data stream with ephemeral environment data. Enriched stream-data is then used for event detection and near real-time flow analysis by an in-line complex event processor. Batch processing is performed by the Hadoop MapReduce framework, from data stored in HBase BigTable storage. In benchmarks on our 10 node cluster, using actual network data, we were able to stream process over 315k flows/sec. In batch analysis were we able to process over 2.6M flows/sec with a storage compression ratio of 6.7:1.
Topic Identification for Speech Without ASR

Modern topic identification (topic ID) systems for speech use automatic speech recognition (ASR) to produce speech transcripts, and perform supervised classification on such ASR outputs. However, under resource-limited conditions, the manually transcribed speech required to develop standard ASR systems can be severely limited or unavailable. In this paper, we investigate alternative unsupervised solutions to obtaining tokenizations of speech in terms of a vocabulary of automatically discovered word-like or phoneme-like units, without depending on the supervised training of ASR systems. Moreover, using automatic phoneme-like tokenizations, we demonstrate that a convolutional neural network based framework for learning spoken document representations provides competitive performance compared to a standard bag-of-words representation, as evidenced by comprehensive topic ID evaluations on both single-label and multi-label classification tasks.
Analysing Balance Loss in VR Interaction with HMDs

Immersive virtual reality (VR) has been used in different fields such as training, educational programs, entertainment, psychological treatments, and rehabilitation. Despite its broad utilization, some issues limit its application such as the loss of balance. Balance is disturbed because visual stimuli received from the virtual scenario are not in harmony with perceived stimuli by the proprioception and vestibular systems that remain in contact with the real environment. With the increasing popularity and accessibility of high-quality VR systems, concerns have been raised about the propensity of VR to induce balance loss. Balance is essential for safe use of VR experience and its loss can result in severe injury. In this work, we present a methodology and the necessary tools to quantify the influence of VR on the user’s balance and assess risk of falls during VR interaction. By means of an experiment making use of an Oculus Rift and a MS Kinect Sensor, we observe, quantify and compare the effect of VR scenes with different levels of danger on the balance of users, as well as the effect of visual and auditory warnings of balance loss. Results suggest that auditory signs were not effective in warning users about risk of fall, and that the order which the scenes are presented to users affects their behavior. Users who were first presented to a more challenging scene proceeded more carefully and most of the time carried this behavior to the other less challenging scenes.
CONTENT-BASED IMAGE RETRIEVAL WITH SEMANTIC CLUSTERING AND SUPPORT VECTOR MACHINES

Many image content-based image retrieval(CBIR) systems are greatly enhanced by relevance feedback. This paper uses an approach that retains long-term relevance information in order to train support vector machines and semantically relate images that are very diverse in their low-level features. A semantic covering set is created with several query images, and the information in this set is used to accurately categorize untrained images using a semantic feature space and query semantic feature vectors. Efforts have been made to reduce the amount of relevance feedback compared to other semantic CBIR systems. Empirical results show fast results comparable to or exceeding other attempts while using less relevance feedback.
spatstat: An R Package for Analyzing Spatial Point Patterns

spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, model-fitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, spatial sampling regions of arbitrary shape, extra covariate data, and "marks" attached to the points of the point pattern. A unique feature of spatstat is its generic algorithm for fitting point process models to point pattern data. The interface to this algorithm is a function ppm that is strongly analogous to lm and glm. This paper is a general description of spatstat and an introduction for new users.
HORIZONTAL DIVERSIFICATION AND VERTICAL CONTRACTING: FIRM SCOE AND ASSET OWNERSHIP IN TAXI FLEETS.

This paper considers the vertical implications of horizontal diversification. Many studies have documented organizational problems following corporate diversification. We propose that selective vertical dis-integration – shifting asset ownership to agents – can mitigate rent-seeking and coordination failures in the diversified firm. We test this proposition in a particularly simple setting that allows us to isolate the effects of interest and control for the likely endogeneity of diversification: taxi fleets that diversify into the limousine, or black car, segment following a wave of entry deregulation in the early 1990s. The results show that taxi fleets are substantially more likely to use owner-operator drivers following diversification. Moreover, diversified fleets that use a greater share of owner operators are more productive than diversified fleets that own most of their vehicles. We interpret these findings as evidence that firms re-organize in response to the challenges of diversification, and that there are causal links between the horizontal and vertical boundaries of the fleet.
Inimunoaffinity Purification of Human 06-Alkylguanine-DNA Alkyltransferase Using Newly Developed Monoclonal Antibodies1

As well as repairing mutagenic lesions induced by simple methylating agents, O'-alkylguanine-DNA alkyltransferase repairs precursors of cytotoxic interstrand cross-links induced by chloroethylating anticancer drugs. Moreover, levels of the transferase correlate with cellular resist ance to these agents. Thus far, the human transferase has not been highly purified. In our quest to obtain the homogeneous protein we have pro duced four stable cloned hybridomas that secrete monoclonal antibodies against the alkyltransferase from human lymphoblasts. The specificity of these monoclonals was established by immunoblot analysis and immunoprecipitation. All these antibodies recognized the alkyltransferase only after its denaturation by sodium dodecyl sulfate. One of them, designated 19.2, was used in immunoaffinity chromatography to obtain the pure protein.
Evidence for a Hydrogenosomal-Type Anaerobic ATP Generation Pathway in Acanthamoeba castellanii

Diverse, distantly-related eukaryotic lineages have adapted to low-oxygen environments, and possess mitochondrion-related organelles that have lost the capacity to generate adenosine triphosphate (ATP) through oxidative phosphorylation. A subset of these organelles, hydrogenosomes, has acquired a set of characteristic ATP generation enzymes commonly found in anaerobic bacteria. The recipient of these enzymes could not have survived prior to their acquisition had it not still possessed the electron transport chain present in the ancestral mitochondrion. In the divergence of modern hydrogenosomes from mitochondria, a transitional organelle must therefore have existed that possessed both an electron transport chain and an anaerobic ATP generation pathway. Here, we report a modern analog of this organelle in the habitually aerobic opportunistic pathogen, Acanthamoeba castellanii. This organism possesses a complete set of enzymes comprising a hydrogenosome-like ATP generation pathway, each of which is predicted to be targeted to mitochondria. We have experimentally confirmed the mitochondrial localizations of key components of this pathway using tandem mass spectrometry. This evidence is the first supported by localization and proteome data of a mitochondrion possessing both an electron transport chain and hydrogenosome-like energy metabolism enzymes. Our work provides insight into the first steps that might have occurred in the course of the emergence of modern hydrogenosomes.
Mining Topological Relations from the Web

Topological relations between geographic regions are of interest in many applications. When the exact boundaries of regions are not available, such relations can be established by analysing natural language information from Web documents. In particular, we demonstrate how redundancy-based techniques can be used to acquire containment and adjacency relations, and how fuzzy spatial reasoning can be employed to maintain the consistency of the resulting knowledge base.
Dictionary Chinese Word Segmentation research a method combined with CRFs

The Word Segmentation of matching literal based on Chinese Dictionary cannot resolve the problem of segmenting ambiguity and combinatorial ambiguity. Based on the dictionary segmentation, this paper proposes a method of Dictionary Chinese Word Segmentation combined with CRFs. It is proved that this method can have better performance than CRFs segmentation and traditional dictionary segmentation.
An Apache Spark Implementation of Block Power Method for Computing Dominant Eigenvalues and Eigenvectors of Large-Scale Matrices

In this paper, we present an implementation of the block power method based on Spark, the big data processing framework, to approximate the dominant eigenvalues and eigenvectors of a large, sparse matrix in distributed computing environment. To take advantage of graph-parallel computation in Spark, we employ the property graph with specially defined vertex and edge types to represent the sparse matrix and the associated block matrix together. Graph operations are then performed on the constructed property graph to efficiently carry out the iteration and decomposition steps of the block power method in parallel. The numerical results on a Markov chain application for modeling stochastic luminal Calcium release site are provided to demonstrate the effectiveness and scalability of the block power method implementation.
Parents as consumers: a case study of parent satisfaction with the quality of schooling

Abstract In marketised school systems schools are financed by taxes and school fees, which effectively turns parents and children into consumers of education. This legitimises a claim to quality schooling. This paper deals with parent satisfaction with children’s schooling and is based on a case study carried out at a public primary school with a predominantly white learner population in a middle-class neighbourhood. Results indicate that although parents expressed appreciation for the school, they identified specific weaknesses. Recommendations were made regarding the timing of surveys, the degree of teacher buy-in to this endeavour and the importance of using open-ended questions to gain an indication of parent satisfaction.
Chemical composition of B-type supergiants in the OB 8, OB 10, OB 48, OB 78 associations of M 31

Absolute and dierential chemical abundances are presented for the largest group of massive stars in M 31 studied to date. These results were derived from intermediate resolution spectra of seven B-type supergiants, lying within four OB asso- ciations covering a galactocentric distance of 5 12 kpc. The results are mainly based on an LTE analysis, and we additionally present a full non-LTE, unified model atmosphere analysis of one star (OB 78-277) to demonstrate the reliability of the dier- ential LTE technique. A comparison of the stellar oxygen abundance with that of previous nebular results shows that there is an oset of between0:15 0:4 dex between the two methods which is critically dependent on the empirical calibration adopted for the R23 parameter with (O/H). However within the typical errors of the stellar and nebular analyses (and given the strength of dependence of the nebular results on the calibration used) the oxygen abundances determined in each method are fairly consis- tent. We determine the radial oxygen abundance gradient from these stars, and do not detect any systematic gradient across this galactocentric range. We find that the inner regions of M 31 are not, as previously thought, very "metal rich". Our abundances of C, N, O, Mg, Si, Al, S and Fe in the M 31 supergiants are very similar to those of massive stars in the solar neighbourhood.