Miracle-dz/UniFAR-FTUs huggingface dataset:
A microplate indicator-based method for determining the susceptibility of multidrug-resistant Mycobacterium tuberculosis to antimicrobial agents.

CONTEXTE: Laboratoire de Reference du Programme de lutte contre la tuberculose de la Province de Buenos Aires, Hopital Dr Cetrangolo, Argentine. OBJECTIF: Nous avons developpe une technique colorimetrique basee sur les microplaques (M-MTT) afin d'obtenir une methode rapide et peu couteuse pour determiner la concentration minimale inhibitrice (CMI) de differents medicaments agissant sur Mycobacterium tuberculosis multiresistant (TB-MR). SCHEMA: Nous avons etudie 101 isolats cliniques. Les medicaments ont ete places sur une plaque a microtitres et plusieurs dilutions de 2 en 2 ont ete pratiquees in situ. La kanamycine, la capreomycine, l'ethionamide, l'acide para-aminosalicylique et la clarithromycine ont ete testes a des concentrations variant de 8,0 a 0,25 μg/ml, la cycloserine a 60.0-1.9 μg/ml, la clofazimine de 3,0 a 0,10 μg/ml, la levofloxacine de 4,0 a 0,13 μg/ml et la rifabutine de 1,0 a 0,13 μg/ml. Pour la visualisation de la croissance cellulaire et de la viabilite, on a utilise l'indicateur general 3-(4,5-dimethylthiazol-2-yl)2,5 diphenyl bromure de tetrazolium (MTT) a une concentration de 5,0 mg/ml. Comme gold standard, on a utilise la methode des proportions dans le milieu de Middle-brook 7H11. RESULTATS: Grâce a M-MTT, les CMI ont ete obtenues en moyenne apres 8 jours et ont ete bien correlees avec celles obtenues par la methode des proportions. Dans nos conditions de travail, le cout total de la determination de la CMI de neuf medicaments a ete de 5 US$. CONCLUSION: Nous concluons que la M-MTT pourrait etre utilisee comme une technologie simple, rapide et peu couteuse pour tester la sensibilite des souches de bacilles TB-MR a l'egard de plusieurs medicaments de seconde ligne et alternatifs, avec comme objectif principal l'orientation des regimes ulterieurs de traitement.
The Hebrew FrameNet Project

We present the Hebrew FrameNet project, describe the development and annotation processes and enumerate the challenges we faced along the way. We have developed semi-automatic tools to help speed the annotation and data collection process. The resource currently covers 167 frames, 3,000 lexical units and about 500 fully annotated sentences. We have started training and testing automatic SRL tools on the seed data.
Neural Decoding for Motorand CommunicationProstheses

Neural prostheses, which are also termed brain–machine and brain–computer interfaces, offer the potential to substantially increase the quality of life for people suffering from motor disorders, including paralysis and amputation. Such devices translate electrical neural activity from the brain into control signals for guiding paralyzed upper limbs, prosthetic arms, and computer cursors. Several research groups have now demonstrated that monkeys (e.g., Serruya et al., 2002; Taylor et al., 2002; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Mulliken et al., 2008; Velliste et al., 2008) and humans (e.g., Kennedy et al., 2000a; Leuthardt et al., 2004; Wolpaw and McFarland, 2004b; Hochberg et al., 2006; Kim et al., 2008; Schalk et al., 2008) can learn to move computer cursors and robotic arms to various target locations simply by activating neural populations that participate in natural arm movements. Figure 7.1 illustrates the basic operating principle of neural prosthetic systems. Neural activity from various arm movement–related brain regions (e.g., Wise et al., 1997) is electronically processed to create control signals for enacting the desired movement. Non invasive sensors can collect neural signals representing the average activity of many neurons. When invasive permanently implanted arrays of electrodes are employed, as depicted, it is possible to use waveform shape differences to discriminate individual neurons (e.g., Lewicki, 1998; Santhanam et al., 2004). After determining how each neuron responds before and during a movement, which is typically accomplished by correlating arm movements made during a behavioral task with
Approximate server selection algorithms in content distribution networks

Server selection is an important function in any replication-based infrastructure, aiming at redirecting client requests to the "best" server according to some predefined metrics. Previous research work has mainly focused on client-side redirection schemes, where the client is responsible for the server selection process. Furthermore, previous work has shown that client probing techniques perform significantly better in discovering the "best" server, compared to hop- or RTT-based schemes. Client probing, however, is not very scalable, since the number of clients and servers in the network will be very large. In this paper, we propose a novel technique to transform the server selection problem into a problem of optimal routing, which enables us to shift the redirection process from the client to the server-side. In particular, we consider the environment of a content distribution network (CDN), and propose a flexible framework that can be used to optimize the server selection process, according to various metrics and/or policies. Using trace-driven simulations, we show that the proposed method can improve significantly the response time of HTTP requests while keeping the control overhead at a very low level.
Polymorphisms in the TNFA and IL6 Genes Represent Risk Factors for Autoimmune Thyroid Disease

Background Autoimmune thyroid disease (AITD) comprises diseases including Hashimoto's thyroiditis and Graves' disease, both characterized by reactivity to autoantigens causing, respectively, inflammatory destruction and autoimmune stimulation of the thyroid-stimulating hormone receptor. AITD is the most common thyroid disease and the leading form of autoimmune disease in women. Cytokines are key regulators of the immune and inflammatory responses; therefore, genetic variants at cytokine-encoding genes are potential risk factors for AITD. Methods Polymorphisms in the IL6-174 G/C (rs1800795), TNFA-308 G/A (rs1800629), IL1B-511 C/T (rs16944), and IFNGR1-56 T/C (rs2234711) genes were assessed in a case-control study comprising 420 Hashimoto's thyroiditis patients, 111 Graves' disease patients and 735 unrelated controls from Portugal. Genetic variants were discriminated by real-time PCR using TaqMan SNP genotyping assays. Results A significant association was found between the allele A in TNFA-308 G/A and Hashimoto's thyroiditis, both in the dominant (OR = 1.82, CI = 1.37–2.43, p-value = 4.4×10−5) and log-additive (OR = 1.64, CI = 1.28–2.10, p-value = 8.2×10−5) models. The allele C in IL6-174 G/C is also associated with Hashimoto's thyroiditis, however, only retained significance after multiple testing correction in the log-additive model (OR = 1.28, CI = 1.06–1.54, p-value = 8.9×10−3). The group with Graves' disease also registered a higher frequency of the allele A in TNFA-308 G/A compared with controls both in the dominant (OR = 1.85, CI = 1.19–2.87, p-value = 7.0×10−3) and log-additive (OR = 1.69, CI = 1.17–2.44, p-value = 6.6×10−3) models. The risk for Hashimoto's thyroiditis and Graves' disease increases with the number of risk alleles (OR for two risk alleles is, respectively, 2.27 and 2.59). Conclusions This study reports significant associations of genetic variants in TNFA and IL6 with the risk for AITD, highlighting the relevance of polymorphisms in inflammation-related genes in the etiopathogenesis of AITD.
Learning to Recommend Descriptive Tags for Questions in Social Forums

Around 40% of the questions in the emerging social-oriented question answering forums have at most one manually labeled tag, which is caused by incomprehensive question understanding or informal tagging behaviors. The incompleteness of question tags severely hinders all the tag-based manipulations, such as feeds for topic-followers, ontological knowledge organization, and other basic statistics. This article presents a novel scheme that is able to comprehensively learn descriptive tags for each question. Extensive evaluations on a representative real-world dataset demonstrate that our scheme yields significant gains for question annotation, and more importantly, the whole process of our approach is unsupervised and can be extended to handle large-scale data.
Differentially expressed genes in MHC-compatible rat strains that are susceptible or resistant to experimental autoimmune uveitis.

PURPOSE Experimental autoimmune uveitis (EAU) is an established model for immune-mediated human uveitis. Although several genes from major histocompatibility complex (MHC) loci have been shown to play a role in uveitis, little is known about the role of non-MHC genes in the pathogenesis of EAU. Several non-MHC genes have been implicated in the pathogenesis of various autoimmune diseases. The primary objective of this study was to identify the non-MHC genes involved in the pathogenesis of EAU, to identify potential drug targets and possibly to target their protein products for immunotherapy. METHODS EAU was induced in the susceptible (Lewis; LEW) or resistant (Fischer 344; F344) rats that have identical MHC class II haplotype. Draining lymph node cells were obtained during the innate and adaptive phase of the immune response, and the pattern of gene expression was evaluated using microarray technology. Differentially expressed genes were validated at mRNA and protein levels using various methods. RESULTS Susceptibility to EAU was associated with an increased expression of numerous non-MHC genes such as Th1-type cytokines and chemokines, antiapoptotic factors, hormones, and neurotransmitters and a downregulation of selected adhesion molecules. In this study a combined genetic-genomic approach was used to identify different patterns of gene expression associated with the sensitization and effector phase of EAU pathogenesis. CONCLUSIONS The data demonstrate that the differential expression of several non-MHC genes is associated with the mechanism of uveitis.
FormTester: Effective Integration of Model-Based and Manually Specified Test Cases

Whilst Model Based Testing (MBT) is an improvement over manual test specification, the leap from it to MBT can be hard. Only recently MBT tools for Web applications have emerged that can recover models from existing manually specified test cases. However, there are further requirements for supporting both MBT and manually specified tests. First, we need support for the generation of test initialization procedures. Also, we want to identify areas of the system that are not testable due to defects. We present Form Tester, a new MBT tool addressing these limitations. An evaluation with real Web applications shows that Form Tester helps to reduce the time spent on developing test cases.
Preemptive flow management in future SDNized wireless networks

The emerging Software-Defined Networking (SDN) concept aims at simplifying network management and control. SDN is envisioned as one of the core technologies for future wireless networks. However, such networks have inherent challenges, in particular mobility management (MM) and network selection, which require careful design in a centrally controlled SDNized network. In this paper, we propose a proactive flow management solution based on an algorithm that learns users' mobility behavior and proactively processes the handover to reduce the connection delays. The proposed approach was evaluated on a testbed that employs OpenDaylight controller, Mininet emulator, and a custom-built control interface. The results of experiments on the testbed verify the convergence and scalability of the learning algorithm, and showcase the potential of proactive flow setup by the SDN controller in improving the handover delays.
On the parallel solution of parabolic equations

We propose new parallel algorithms for the solution of linear parabolic problems. The first of these methods is based on using polynomial approximation to the exponential. It does not require solving any linear systems and is highly parallelizable. The two other methods proposed are based on Padé and Chebyshev approximations to the matrix exponential. The parallelization of these methods is achieved by using partial fraction decomposition techniques to solve the resulting systems and thus offers the potential for increased time parallelism in time dependent problems. We also present experimental results from the Alliant FX/8 and the Cray Y-MP/832 vector multiprocessors.
Identification of performance limitations in control

Non-minimum phase zeros and poles of a process put upper and lower constraints on the bandwidth of a closed loop system. It is thus of great interest to be able to identify these quantities. In this contribution it is shown that non-minimum phase zeros and unstable poles can be identified using high order models without the standard o(n) (n is the model order) variance penalty for over modeling. An asymptotic, in the model order and the number of data, expression for the variance of non-minimum phase zeros is derived. This result shows that the problem of determining the performance limits of a system from experimental data is considerably easier than identifying the complete system. As a direct application of this an optimal experiment design problem for identification of performance limits is posed and solved.
Estudio del gen YKT6 y miRNAs asociados en la liberación de exosomas en pacientes quirúrgicos de cáncer de pulmón

En los ultimos anos los exosomas han emergido como importantes mediadores en el desarrollo de cancer, demostrandose su utilidad como biomarcadores para diagnostico y pronostico de pacientes oncologicos. Los exosomas son vesiculas extracelulares de entre 30-100nm de diametro de origen endosomal que pueden favorecer diferentes procesos importantes en el progreso tumoral, como la modulacion del microambiente tumoral, inmunosupresion y preparacion del nicho premetastatico. El estudio de la biogenesis y liberacion de los exosomas se ha centrado principalmente en los mecanismos ESCRT y de proteinas Rab, postulandose tambien la importancia de las proteinas SNARE en estos procesos. YKT6 es una proteina SNARE que se ha descrito como necesaria para la liberacion de exosomas en diferentes tipos celulares. Sin embargo el papel que podria tener en la liberacion de exosomas en cancer de pulmon no se habia investigado. El trabajo realizado nos ha permitido investigar la importancia de YKT6 en el proceso de liberacion de exosomas en lineas celulares, sus mecanismos de regulacion mediante microRNAs (miRNAs), asi como tambien evaluar el posible papel pronostico en pacientes de cancer de pulmon de celula no pequena. En el estudio in vitro con la linea celular A549 de cancer de pulmon observamos que la inhibicion de YKT6 producia una disminucion drastica en la liberacion de exosomas. Ademas, identificamos y validamos miR-34a, miR-134, miR-135a y miR-135b como miRNAs capaces de regular la expresion del ARNm de YKT6. El efecto de la sobreexpresion de miR-134 y miR-135b, los miRNAs que mas efectivamente disminuian la expresion de YKT6 tambien provocaron una disminucion en la liberacion de exosomas en las celulas A549. En los pacientes de cancer de pulmon de celula no pequena observamos que la expresion de YKT6 estaba infraexpresada y que en contraste la expresion de los miRNAs miR-134 y miR-135b se encontraban sobrexpresados en tejido tumoral respecto al tejido normal. Aunque en el tejido tumoral no se pudo apreciar ninguna correlacion negativa entre los miRNAs y la expresion de YKT6, en el tejido normal si que observamos la correlacion negativa significativa entre miR-135b y la expresion de YKT6. El analisis de la expresion de YKT6 en las muestras tumorales de los pacientes resulto en la clasificacion de los pacientes segun niveles de expresion alto o bajo de YKT6, observandose un peor pronostico en el grupo de pacientes con alta expresion en terminos de una menor supervivencia global y supervivencia libre de enfermedad. Estos resultados indican que el estudio de los procesos de liberacion de exosomas y sus posibles mecanismos de liberacion, en este caso YKT6 a traves de miRNAs, pueden ser de utilidad en cancer.
Ultra-high resolution optical coherence tomography for monitoring tear meniscus volume in dry eye after topical cyclosporine treatment

Purpose This study was designed to determine the ability of ultra-high-resolution optical coherence tomography (UHR-OCT) to monitor tear meniscus volume after dry eye treatment with Restasis® (0.05% cyclosporine). Methods UHR-OCT was used to image the upper and lower tear menisci (height and volume) in 14 consecutive dry eye patients at baseline. The treatment group was initiated on daily Restasis and measurements were repeated at 1 and 2 months while on treatment. All measurements were performed immediately after a blink. Results The baseline tear meniscus volumes were 0.40 ± 0.19 μL (upper) and 0.43 ± 0.18 μL (lower). In the treatment group, tear meniscus heights showed significant increases of both upper (post-hoc test with Bonferroni correction: P = 0.003) and lower (P = 0.0003) tear menisci, after 1 month on Restasis. The tear meniscus volumes in the treatment group after 1 month of Restasis showed significant increases of both upper (P = 0.007) and lower (P = 0.007) tear menisci. At 2 months, the increase of the tear meniscus was still evident in all measured variables compared with baseline (P < 0.05). Conclusion UHR-OCT offers a promising tool for monitoring dry eye treatment. With this method, a measurable increase in tear meniscus volume was evident after 1 month of treatment with Restasis in dry eye patients.
Writer Code Based Adaptation of Deep Neural Network for Offline Handwritten Chinese Text Recognition

Recently, we propose deep neural network based hidden Markov models (DNN-HMMs) for offline handwritten Chinese text recognition. In this study, we design a novel writer code based adaptation on top of the DNN-HMM to further improve the accuracy via a customized recognizer. The writer adaptation is implemented by incorporating the new layers with the original input or hidden layers of the writer-independent DNN. These new layers are driven by the so-called writer code, which guides and adapts the DNN-based recognizer with the writer information. In the training stage, the writer-aware layers are jointly learned with the conventional DNN layers in an alternative manner. In the recognition stage, with the initial recognition results from the first-pass decoding with the writer-independent DNN, an unsupervised adaptation is performed to generate the writer code via the cross-entropy criterion for the subsequent second-pass decoding. The experiments on the most challenging task of ICDAR 2013 Chinese handwriting competition show that our proposed adaptation approach can achieve consistent and significant improvements of recognition accuracy over a highperformance writer-independent DNN-HMM based recognizer across all 60 writers, yielding a relative character error rate reduction of 23.62% in average.
A Secure Sketch for Set Reconciliation Soren Johnson Professor

In this paper we present an implementation of an algorithm for creating a secure sketch for the set difference as defined in [1]. This sketch is a short, efficiently computable description of a set such that, given a second set that differs only in a small percentage of elements, the original can be reconstructed. However, this sketch is secure in the sense that in the absence of such a similar set, as little information as possible can be obtained about the elements of the original. This work has applications in cryptography and database synchronization.
Robust Non-Rigid Point Set Registration Using Spatially Constrained Gaussian Fields

Estimating transformations from degraded point sets is necessary for many computer vision and pattern recognition applications. In this paper, we propose a robust non-rigid point set registration method based on spatially constrained context-aware Gaussian fields. We first construct a context-aware representation (e.g., shape context) for assignment initialization. Then, we use a graph Laplacian regularized Gaussian fields to estimate the underlying transformation from the likely correspondences. On the one hand, the intrinsic manifold is considered and used to preserve the geometrical structure, and a priori knowledge of the point set is extracted. On the other hand, by using the deterministic annealing, the presented method is extended to a projected high-dimensional feature space, i.e., reproducing kernel Hilbert space through a kernel trick to solve the transformation, in which the local structure is propagated by the coarse-to-fine scaling strategy. In this way, the proposed method gradually recovers much more correct correspondences, and then estimates the transformation parameters accurately and robustly when facing degradations. Experimental results on 2D and 3D synthetic and real data (point sets) demonstrate that the proposed method reaches better performance than the state-of-the-art algorithms.
ASX: An Object-Oriented Framework for Developing Distributed Applications

The ADAPTIVE Service eXecutive (ASX) is a highly modular and extensible object-oriented framework that simplifies the development and configuration of distributed applications on shared memory multi-processor platforms. This paper describes the structure and functionality of the ASX framework's object-oriented architecture. In addition, the paper presents the results of performance experiments conducted using ASX-based implementations of connection-oriented and connectionless protocols from the TCP/IP protocol family. These experiments measure the performance impact of alternative methods for parallelizing communication protocol stacks. Throughout the paper, examples are presented to indicate how the use of object-oriented techniques facilitate application extensibility, component reuse, and performance enhancement.
Explicit Max Margin Input Feature Selection for Nonlinear SVM using Second Order Methods

Incorporating feature selection in nonlinear SVMs leads to a large and challenging nonconvex minimization problem, which can be prone to suboptimal solutions. We use a second order optimization method that utilizes eigenvalue information and is less likely to get stuck at suboptimal solutions. We devise an alternating optimization approach to tackle the problem efficiently, breaking it down into a convex subproblem, corresponding to SVM optimization, and a nonconvex subproblem for feature selection. Importantly, we show that a naive approach, which implicitly maximizes the margin, can be susceptible to saddle point solutions. We propose a new explicit margin maximization method, which overcomes the drawbacks of the implicit approach. To avoid being trapped at saddle points, we first introduce an auxiliary variable and perform alternating optimization in the extended space, and then subsequently, to improve solution quality, we relax the use of geometric margin and maximize the functional margin in the feature selection subproblem. We demonstrate how this technique can also be applied to 1-norm linear programming SVMs. Experiment results show the explicit margin approach works effectively in the presence of class noise and many irrelevant features, consistently outperforming leading filter, wrapper, and other embedded nonlinear feature selection methods.