Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic segmentation of rodent spinal cord diffusion MR images

MRI, is a key tool for noninvasive spinal cord lesion analysis; however, accurate, quantitative methods for this analysis are lacking. A new, multistep, multidimensional approach, utilizing the classification expectation maximization algorithm, is proposed for MRI segmentation of spinal cord tissues. Diffusion tensor imaging is used to generate multiple images of each spinal slice, with different diffusion direction weightings. The maximum likelihood tissue classifications are then jointly estimated to produce a binary classification image, corresponding to voxels containing either spinal cord or background. Edge detection is employed to find a nonparametric curve encapsulating the entire spinal cord. The algorithm is evaluated using data from in vivo diffusion tensor imaging of control and injured mouse spinal cords. The algorithm is shown to remain accurate for whole spinal cord, white matter, and hemorrhage segmentation in the presence of significant injury. The results of the method are shown to be at least on par with expert manual segmentation. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.
Spread of NDM-1-Producing Enterobacteriaceae in a Neonatal Intensive Care Unit in Istanbul, Turkey

ABSTRACT Twenty-two consecutive carbapenem-resistant enterobacterial isolates were recovered from patients hospitalized between January and April 2013 in different units at a university hospital in Istanbul, Turkey. These were Klebsiella pneumoniae isolates producing the carbapenemases OXA-48, NDM-1, and KPC-2, Enterobacter cloacae isolates producing NDM-1, and Escherichia coli isolates producing OXA-48. Most of the OXA-48-producing K. pneumoniae and all the NDM-1-producing E. cloacae were clonally related. The NDM-1-producing E. cloacae isolates recovered from a single neonatal intensive care unit corresponded to a single cluster, highlighting the spread of that clone in that setting.
Network Mixture Models: an Introduction and Application to Protein Interactomes

Human Protein Interactomics represents a crucial research area in systems biology, and several new data sources and methods have been recently proposed. In our work, we refer to data integration and network inference domains, and with focus on group selection and modularity refinement. Our reference dataset is Ulitsky, Karp and Shamir (http://acgt.cs.tau.ac.il/clean), which integrates various sources, BIND, HPRD, BioGRID, and includes interactions detected from small-scale experiments, thus listing 7, 385 nodes and 23, 462 links. Concerning network inference, we explore graph mining techniques from both a deterministic and a probabilistic standpoint. It is widely adopted the application of the former methods to protein interactomes, of which we provide two examples, but it is definitely harder to see the application of probabilistic methods. Comparative examples are provided here. We look first at corenessand community-based grouping techniques, but then also at statistical mixture modelling with the goal of detecting human protein sub-interactomes. We suggest a novel way to select and prioritize information otherwise usually extracted under connectivity-based principles. A graph can be represented by an adjacency matrix (AM) embedding the interaction and non-interaction structure of the observed graph. The result for the case of undirected graphs is a binary AM. We show that the AM can be efficiently sampled to deal better with the inherent sparsity property of biological networks, and then proceed by adopting mixture models under flexible statistical assumptions.
An Indicator Measuring Underlying Economic Activity in Greece

Using a state space formulation developed by Stock and Watson and Garratt and Hall we construct an indicator, which then is interpreted as a measure of the underlying economic activity of the Greek economy. The chief novelty of the paper is that the underlying model is calibrated, rather than estimated, using sample information. Our approach is more flexible than the original one, in that it provides the possibility to cope with outlying observations and to evaluate particular shocks affecting the economy using judgmental interventions. The new indicator could be very helpful for short run policy analysis signalling emerging economic problems.
Effects of Intercalation on the Hole Mobility of Amorphous Semiconducting Polymer Blends

Fullerenes have been shown to intercalate between the side chains of many crystalline and semicrystalline polymers and to affect the properties of polymer:fullerene bulk heterojunction solar cells. Here we present the first in-depth study of intercalation in an amorphous polymer. We study blends of the widely studied amorphous polymer poly(2-methoxy-5-(3′,7′-dimethyloctyloxy)-p-phenylene vinylene) (MDMO-PPV) with a variety of molecules using photoluminescence measurements, scanning electron microscopy, and space-charge limited current mobility measurements. The blends with elevated hole mobilities exhibit complete photoluminescence quenching and show no phase separation in a scanning electron microscope. We conclude that intercalation occurs in MDMO-PPV:fullerene blends and is responsible for the increase in the MDMO-PPV hole mobility by several orders of magnitude when it is blended with fullerenes, despite the dilution of the hole-conducting polymer with an electron acceptor.
Acupuncture treatment for chronic knee pain: a systematic review.

OBJECTIVES To evaluate the effects of acupuncture on pain and function in patients with chronic knee pain. METHODS Systematic review and meta-analysis of randomized controlled trials of adequate acupuncture. Computerized databases and reference lists of articles were searched in June 2006. Studies were selected in which adults with chronic knee pain or osteoarthritis of the knee were randomized to receive either acupuncture treatment or a control consisting of sham (placebo) acupuncture, other sham treatments, no additional intervention (usual care), or an active intervention. The main outcome measures were short-term pain and function, and study validity was assessed using a modification of a previously published instrument. RESULTS Thirteen RCTs were included, of which eight used adequate acupuncture and provided WOMAC outcomes, so were combined in meta-analyses. Six of these had validity scores of more than 50%. Combining five studies in 1334 patients, acupuncture was superior to sham acupuncture for both pain (weighted mean difference in WOMAC pain subscale score = 2.0, 95% CI 0.57-3.40) and for WOMAC function subscale (4.32, 0.60-8.05). The differences were still significant at long-term follow-up. Acupuncture was also significantly superior to no additional intervention. There were insufficient studies to compare acupuncture with other sham or active interventions. CONCLUSIONS Acupuncture that meets criteria for adequate treatment is significantly superior to sham acupuncture and to no additional intervention in improving pain and function in patients with chronic knee pain. Due to the heterogeneity in the results, however, further research is required to confirm these findings and provide more information on long-term effects.
ARS2 Is a Conserved Eukaryotic Gene Essential for Early Mammalian Development

ABSTRACT Determining the functions of novel genes implicated in cell survival is directly relevant to our understanding of mammalian development and carcinogenesis. ARS2 is an evolutionarily conserved gene that confers arsenite resistance on arsenite-sensitive Chinese hamster ovary cells. Little is known regarding the function of ARS2 in mammals. We report that ARS2 is transcribed throughout embryonic development and is expressed ubiquitously in mouse and human tissues. The mouse ARS2 protein is predominantly localized to the nucleus, and this nuclear localization is ablated in ARS2-null embryos, which in turn die around the time of implantation. After 24 h of culture, ARS2-null blastocysts contained a significantly greater number of apoptotic cells than wild-type or heterozygous blastocysts. By 48 h of in vitro culture, null blastocysts invariably collapsed and failed to proliferate. These data indicate ARS2 is essential for early mammalian development and is likely involved in an essential cellular process. The analysis of data from several independent protein-protein interaction studies in mammals, combined with functional studies of its Arabidopsis ortholog, SERRATE, suggests that this essential process is related to RNA metabolism.
Static profiling of the worst-case in real-time programs

With the increasing performance demand in real-time systems it becomes more and more relevant to provide feedback to engineers and programmers, but also software development tools, on the performance-relevant code parts of a real-time program. So far, the information provided to programmers through tools was limited to an estimation of the worst-case execution time (WCET) and its associated worst-case execution path (WCEP). However, these metrics only provide partial information. Only those code parts that are on one of the WCEPs are indicated to the programmer. No information is provided for all other code parts. To give an accurate view covering the entire code base, tools in the spirit of standard program profiling tools are required. This work proposes an efficient approach to compute worst-case timing information for all code parts of a program using a complementary metric, called criticality. Every statement of a real-time program is assigned a criticality value, expressing how critical the respective code is with respect to the global WCET. This gives an accurate view to programmers how close the worst execution path passing through a specific part of a real-time program is to the global WCEP. We formally define the criticality metric and investigate some of its properties with respect to dominance in control-flow graphs. Exploiting some of those properties, we propose an algorithm that reduces the overhead of computing the metric to cover complete real-time programs.
Empirical Methods for Compound Splitting

Compounded words are a challenge for NLP applications such as machine translation (MT). We introduce methods to learn splitting rules from monolingual and parallel corpora. We evaluate them against a gold standard and measure their impact on performance of statistical MT systems. Results show accuracy of 99.1% and performance gains for MT of 0.039 BLEU on a German-English noun phrase translation task.
The Antnet Routing Algorithm-A Modified Version

Antnet is an agent based routing algorithm that is influenced from the unsophisticated and individual ant’s emergent behaviour. Ants (software agents) are used in antnet to collect information and to update the probabilistic distance vector routing table entries. Modified antnet algorithm has been introduced, which improve the throughput and average delay. Results shows that by detecting and dropping 0.5% of packets routed through the non-optimal routes the average delay per packet decreased and network throughput can be increased. The effect of the traffic fluctuations has been limited with the boundaries introduced in this paper and the number of ants in the network has been limited with the current throughput of the network at any given time.
Data stream management system: Tools for live stream handling & their application on trivial network analysis problems

This paper presents handling and analysis of network packets using data stream management system tool TelegraphCQ. The number of tools for analyzing data traffic in the Internet is continuously increasing, because there is an increasing need in many different application domains. The high volume data that flows within a network requires one to rethink the fundamental architecture of a DBMS for this application area. The huge amount of data that has to be managed and analyzed together with the fact that many different analysis tasks are performed over a small set of different network trace formats, motivates us to study whether data stream management systems (DSMSs) might be useful to develop network traffic analysis tools . We will see that there are many tools available for live stream handling-each with its advantages & disadvantages.
Role of HtrA in Growth and Competence of Streptococcus mutans UA159

ABSTRACT We report here that HtrA plays a role in controlling growth and competence development for genetic transformation in Streptococcus mutans. Disruption of the gene for HtrA resulted in slow growth at 37°C, reduced thermal tolerance at 42°C, and altered sucrose-dependent biofilm formation on polystyrene surfaces. The htrA mutant also displayed a significantly reduced ability to undergo genetic transformation. A direct association between HtrA and genetic competence was demonstrated by the increased expression of the htrA gene upon exposure to competence-stimulating peptide. The induction of htrA gradually reached a maximum at around 20 min, suggesting that HtrA may be involved in a late competence response. Complementation of the htrA mutation in a single copy on the chromosome of the mutant could rescue the defective growth phenotypes but not transformability, apparently because a second gene, spo0J, immediately downstream of htrA, also affects transformation. The htrA and spo0J genes were shown to be both individually transcribed and cotranscribed and probably have a functional connection in competence development. HtrA regulation appears to be finely tuned in S. mutans, since strains containing multiple copies of htrA exhibited abnormal growth phenotypes. Collectively, the results reveal HtrA to be an integral component of the regulatory network connecting cellular growth, stress tolerance, biofilm formation, and competence development and reveal a novel role for the spo0J gene in genetic transformation.
Design Space Exploration for Frequency Synchronization of BPSK/QPSK Bursts

Abstract. Frequency synchronisation is a vital part of every inner receiver for wireless communication. In this paper we present different implementation alternatives for non data aided frequency estimation of BPSK/QPSK bursts with respect to implementation complexity and communications performance. Results with regard to different quantization levels, varying burstlengths, frequency offsets and modulation indices for different signal to noise ratios are presented. Implementation results are based on XILINX Virtex II Pro FPGA devices.
Kiefer Wolfowitz Algorithm is Asymptotically Optimal for a Class of Non-Stationary Bandit Problems

We consider the problem of designing an allocation rule or an"online learning algorithm"for a class of bandit problems in which the set of control actions available at each time $s$ is a convex, compact subset of $\mathbb{R}^d$. Upon choosing an action $x$ at time $s$, the algorithm obtains a noisy value of the unknown and time-varying function $f_s$ evaluated at $x$. The"regret"of an algorithm is the gap between its expected reward, and the reward earned by a strategy which has the knowledge of the function $f_s$ at each time $s$ and hence chooses the action $x_s$ that maximizes $f_s$. For this non-stationary bandit problem set-up, we consider two variants of the Kiefer Wolfowitz (KW) algorithm i) KW with fixed step-size $\beta$, and ii) KW with sliding window of length $L$. We show that if the number of times that the function $f_s$ varies during time $T$ is $o(T)$, and if the learning rates of the proposed algorithms are chosen"optimally", then the regret of the proposed algorithms is $o(T)$, and hence the algorithms are asymptotically efficient.
Regulation of two germin-like protein genes during plum fruit development

Germin-like proteins (GLPs) have several proposed roles in plant development and defence. Two novel genes (Ps-GLP1 and 2) encoding germin-like protein were isolated from plum (Prunus salicina). Their regulation was studied throughout fruit development and during ripening of early and late cultivars. These two genes exhibited similar expression patterns throughout the various stages of fruit development excluding two important stages, pit hardening (S2) and fruit ripening (S4). During fruit development until the ripening phase, the accumulation of both Ps-GLPs is related to the evolution of auxin. However, during the S2 stage only Ps-GLP1 is induced and this could putatively be in a H2O2-dependent manner. On the other hand, the diversity in the Ps-GLPs accumulation profile during the ripening process seems to be putatively due to the variability of endogenous auxin levels among the two plum cultivars, which consequently change the levels of autocatalytic ethylene available for the fruit to co-ordinate ripening. The effect of auxin on stimulating ethylene production and in regulating Ps-GLPs transcripts was also investigated. These data, supported by their localization in the extracellular matrix, suggest that auxin is somehow involved in the regulation of both transcripts throughout fruit development and ripening.
The dilemma of the dilemma: Getting education issues out of their dilemmas

This study seeks to introduce a new lens through which applied linguistics might be better conceived of. Importantly enough, this lens has already found itself in a good many disciplines, but it can hardly ever be seen in language teaching. To this end, this paper has taken a stance akin to that of Larsen-Freeman, when for the first time chaos/complexity was introduced into second language acquisition. Thus, after reasoning the usefulness and meaningfulness of social dilemmas in applied linguistics, different conundrums in applied linguistics are viewed from a social dilemmas’ lens, particularly through that of prisoner’s dilemma. Importantly, instead of putting forward some answers to the dilemmas in applied linguistics, the study focuses on what exactly the problems are. Therefore, this very new lens gives us the chance to dissect the problems clearly, and subsequently some insights might be gained.
Engineering DFS-Based Graph Algorithms

Depth-rst search (DFS) is the basis for many efcient graph algorithms. We introduce general techniques for efcient implementations of DFS-based graph algorithms and exemplify them on three algorithms for computing strongly connected components. The techniques lead to speed-ups by a factor of two to three compared to the implementations provided by LEDA and BOOST. We have obtained similar speed-ups for biconnected components algorithms. We also compare the graph data types of LEDA and BOOST.
Applying ILP-based Techniques to Natural Language Information Extraction: An Experiment in Relational Learning

In complex and context-rich domains, inductive logic programming (ILP) has some advantages over propositional, or feature-based, machine learning algorithms. The feature-based systems require that the examples be reduced to a nite, manageable set of features. Development of such a set of features can require signi cant representation engineering and may still exclude important contextual information. A rst order logic representation can represent a richer set of features and more easily capture contextual information. ILP also allows the use of background knowledge, and the resulting rules are often more comprehensible. The comprehensibility of symbolic rules makes it easier for the system developer to understand and verify the resulting system and perhaps even edit the learned knowledge [Cohen, 1996].
Chiral cosmic strings in supergravity

We consider F and D-term cosmic strings formed in supersymmetric theories. Supersymmetry is broken inside the string core, but restored outside. In global SUSY, this implies the existence of goldstino zero modes, and the string potentially carries fermionic currents. We show that these zero modes do not survive the coupling to gravity, due to the super Higgs mechanism. Therefore the superconductivity and chirality properties are different in global and local supersymmetry. For example, a string formed at the end of D-term inflation is chiral in supergravity but non-chiral in global SUSY.
Frequency-domain supply current macro-model

In order to perform block level analysis of the on-chip power distribution network, a high-level model is required that captures the dependence of the current waveform drawn by a logic block, per cycle, on its input vector pair. We present a frequency domain macro-modeling technique for capturing this dependence. The macro-model is based on estimating the Discrete Cosine Transform (DCT) of the current waveform and then taking the inverse transform to estimate the time domain current waveform.
Realistic simulation of cardiac magnetic resonance studies modeling anatomical variability, trabeculae, and papillary muscles

Simulated magnetic resonance imaging brain studies have been generated for over a decade. Despite their useful potential, simulated cardiac studies are only emerging. This article focuses on the realistic simulation of cardiac magnetic resonance imaging datasets. The methodology is based on the XCAT phantom, which is modified to increase realism of the simulated images. Modifications include the modeling of trabeculae and papillary muscles based on clinical measurements and published data. To develop and evaluate our approach, the clinical database included 40 patients for anatomical measurements, 10 patients for papillary muscle modeling, and 10 patients for local gray value statistics. The virtual database consisted of 40 digital voxel phantoms. Histograms from different tissues were obtained from the real datasets and compared with histograms of the simulated datasets with the Chi‐square dissimilarity metric (χ2) and Kullback‐Leibler divergence. For the original phantom, χ2 values averaged 0.65 ± 0.06 and Kullboek‐Leibler values averaged 0.69 ± 0.38. For the modified phantom, χ2 values averaged 0.34 ± 0.12 and Kullboek‐Leibler values averaged 0.32 ± 0.15. The proposed approach demonstrated a noticeable improvement of the local appearance of the simulated images with respect to the ones obtained originally. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.
Combinatorial effect of various features extraction on computer aided detection of pulmonary nodules in X-ray CT images

In this paper, we propose a new method for computer aided detection of pulmonary nodules in X-ray CT images to reduce false positive rate under high true positive rate conditions. An essential part of the method is to extract and combine two novel and effective features from the original CT images: One is orientation features of nodules in a region of interest (ROI) extracted by a Gabor filter, while the other is variation of CT values of the ROI in the direction along body axis. By using the extracted features, pattern recognition techniques can then be used to discriminate between nodule and non-nodule images. Simulation results show that discrimination performance using the proposed features is extremely improved compared to that of the conventional method.