Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in Molecular Serotyping and Subtyping of Escherichia coli

Escherichia coli plays an important role as a member of the gut microbiota; however, pathogenic strains also exist, including various diarrheagenic E. coli pathotypes and extraintestinal pathogenic E. coli that cause illness outside of the GI-tract. E. coli have traditionally been serotyped using antisera against the ca. 186 O-antigens and 53 H-flagellar antigens. Phenotypic methods, including bacteriophage typing and O- and H- serotyping for differentiating and characterizing E. coli have been used for many years; however, these methods are generally time consuming and not always accurate. Advances in next generation sequencing technologies have made it possible to develop genetic-based subtyping and molecular serotyping methods for E. coli, which are more discriminatory compared to phenotypic typing methods. Furthermore, whole genome sequencing (WGS) of E. coli is replacing established subtyping methods such as pulsed-field gel electrophoresis, providing a major advancement in the ability to investigate food-borne disease outbreaks and for trace-back to sources. A variety of sequence analysis tools and bioinformatic pipelines are being developed to analyze the vast amount of data generated by WGS and to obtain specific information such as O- and H-group determination and the presence of virulence genes and other genetic markers.
A Discounted Fuzzy Relational Clustering of Web Users’ Using Intuitive Augmented Sessions Dissimilarity Metric

Relational fuzzy clustering (RFC) algorithms prove very useful in Web user session clustering because Web user sessions may contain fuzzy, conflicting and imprecise information. Though RFC algorithms are very sensitive to cluster initialization and works only if the numbers of clusters are specified in advance. However, at all times, the prior initialization of a number of clusters is not feasible due to the dynamically evolving nature of user sessions. Therefore, estimating the number of clusters and initializing suitable cluster prototype are a significant performance bottleneck in this method. In this paper, the discounted fuzzy relational clustering (DFRC) algorithm is proposed to address the major constraint of RFC. The DFRC algorithm identifies Web user session clusters from Web server access logs, without initializing the number of clusters and prototypes of initial clusters. The DFRC algorithm works in two stages. In the first stage, DFRC automatically identifies the number of potential clusters based on the successively discounted potential density function value of each relational data and their respective centres. In the second stage, DFRC assigns fuzzy membership values to each data point and forms fuzzy clusters from the relational matrix. The DFRC algorithm is applied on an augmented session dissimilarity matrix obtained from a publicly accessed NASA Web server log data. The experimental results are evaluated using different fuzzy validity measures. The extensive experiments are performed to test the effect of various parameters, including accept/reject ratio and neighbourhood radius on the performance of DFRC algorithm. The results were also compared with fuzzy relational clustering algorithm using cluster quality measures. It is observed that the quality of generated clusters using DFRC is superior as compared with that of RFC.
Enhanced Light Output of GaN-Based Vertical-Structured Light-Emitting Diodes With Two-Step Surface Roughening Using KrF Laser and Chemical Wet Etching

A two-step roughening process that uses a KrF excimer laser and KOH chemical etching for the n-GaN layer surface of vertically structured GaN-based light-emitting diodes (VLEDs) to yield circular protrusions with hexagonal cones atop for light extraction enhancement is demonstrated. A possible mechanism of the formation of the circular protrusions commenced by laser irradiation with nonuniform etching rates at sites with various dislocation densities was investigated. An improvement in light output power of about 95% at 350-750 mA compared to that of flat VLEDs was obtained for the two-step roughened VLEDs, which is attributed to the increase in surface emission area and dimensions of roughness, and, in particular, the decrease in the n-GaN layer thickness.
Measurements of 3d occupancy from Cu L2,3 electron-energy-loss spectra of rapidly quenched CuZr, CuTi, CuPd, CuPt, and CuAu.

We used electron-energy-loss spectrometry to measure the intensities of the Cu white lines found at the onsets of the [ital L][sub 2] and [ital L][sub 3] absorption edges of rapidly quenched CuZr, CuTi, CuPd, CuPt, and CuAu. Enhancements of the white lines in the alloys as compared to elemental Cu were correlated with changes in the 3[ital d] occupancy of Cu atoms upon alloying. We found that the Cu atoms lost 0.2[plus minus]0.06 [ital d] electrons when alloyed with Ti or Zr, 0.0[sub [minus]0.0][sup +0.06] [ital d] electrons when alloyed with Au or Pd, and 0.03[sub [minus]0.03][sup +0.06][ital d] electrons when alloyed with Pt. We found no strong correlation between measured 3[ital d]- occupancy changes and heats of formation of the alloys, consistent with expectations that outer [ital s] electrons play an important role in the alloying of Cu with Pt, Pd, and Au.
Trichothecene Mycotoxins Trigger a Ribotoxic Stress Response That Activates c-Jun N-terminal Kinase and p38 Mitogen-activated Protein Kinase and Induces Apoptosis*

The trichothecene family of mycotoxins inhibit protein synthesis by binding to the ribosomal peptidyltransferase site. Inhibitors of the peptidyltransferase reaction (e.g.anisomycin) can trigger a ribotoxic stress response that activates c-Jun N-terminal kinase (JNK)/p38 mitogen-activated protein kinases, components of a signaling cascade that regulates cell survival in response to stress. We have found that selected trichothecenes strongly activate JNK/p38 kinases and induce rapid apoptosis in Jurkat T cells. Although the ability of individual trichothecenes to inhibit protein synthesis and activate JNK/p38 kinases are dissociable, both effects contribute to the induction of apoptosis. Among trichothecenes that strongly activate JNK/p38 kinases, induction of apoptosis increases linearly with inhibition of protein synthesis. Among trichothecenes that strongly inhibit protein synthesis, induction of apoptosis increases linearly with activation of JNK/p38 kinases. Trichothecenes that inhibit protein synthesis without activating JNK/p38 kinases inhibit the function (i.e. activation of JNK/p38 kinases and induction of apoptosis) of apoptotic trichothecenes and anisomycin. Harringtonine, a structurally unrelated protein synthesis inhibitor that competes with trichothecenes (and anisomycin) for ribosome binding, also inhibits the activation of JNK/p38 kinases and induction of apoptosis by trichothecenes and anisomycin. Taken together, these results implicate the peptidyltransferase site as a regulator of both JNK/p38 kinase activation and apoptosis.
Adaptive importance sampling simulation of queueing networks

In this paper, a method is presented for the efficient estimation of rare-event (overflow) probabilities in Jackson queueing networks using importance sampling. The method differs in two ways from methods discussed in most earlier literature: the change of measure is state-dependent, i.e., it is a function of the content of the buffers, and the change of measure is determined using a cross-entropy-based adaptive procedure. This method yields asymptotically efficient estimation of overflow probabilities of queueing models for which it has been shown that methods using a state-independent change of measure are not asymptotically efficient. Numerical results demonstrating the effectiveness of the method are presented as well.
Learning Hidden Markov Models with Hidden Markov Trees as Observation Distributions

Hidden Markov models have been found very useful for a wide range of applications in artificial intelligence. The wavelet transform arises as a new tool for signal and image analysis, with a special emphasis on nonlinearities and nonstationarities. However, learning models for wavelet coecients have been mainly based on fixed-length sequences. We propose a novel learning architecture for sequences analyzed on a short-term basis, but not assuming stationarity within each frame. Long-term dependencies are modeled with a hidden Markov model which, in each internal state, deals with the local dynamics in the wavelet domain using a hidden Markov tree. The training algorithms for all the parameters in the composite model are developed using the expectation-maximization framework. This novel learning architecture can be useful for a wide range of applications. We detail experiments with real data for speech recognition. In the results, recognition rates were better than the state of the art technologies for this task.
Playing hide and seek with a mobile companion robot

This paper addresses the problem of finding people in domestic environments utilizing a mobile robot. Companion robots, which should provide different services, must be able to robustly estimate the user's position. While detecting people in an upright pose is mainly solved, most of the users' various poses in living environments are hard to detect. We present a tracking framework that incorporates state-of-the-art detection modules, but also a novel approach for visually detecting the presence of people resting at previously known seating places in arbitrary poses. The method is based on a contextual color model of the respective place in the environment and a color model of the user's appearance. The system has been tested by evaluating the robot's capability to find the user in a 3-room apartment in a hide and seek scenario.
Solving High-Dimensional Multi-Objective Optimization Problems with Low Effective Dimensions

Multi-objective (MO) optimization problems require simultaneously optimizing two or more objective functions. An MO algorithm needs to find solutions that reach different optimal balances of the objective functions, i.e., optimal Pareto front, therefore, high dimensionality of the solution space can hurt MO optimization much severer than single-objective optimization, which was little addressed in previous studies. This paper proposes a general, theoretically-grounded yet simple approach ReMO, which can scale current derivative-free MO algorithms to the high-dimensional non-convex MO functions with low effective dimensions, using random embedding. We prove the conditions under which an MO function has a low effective dimension, and for such functions, we prove that ReMO possesses the desirable properties of optimal Pareto front preservation, time complexity reduction, and rotation perturbation invariance. Experimental results indicate that ReMO is effective for optimizing the high-dimensional MO functions with low effective dimensions, and is even effective for the high-dimensional MO functions where all dimensions are effective but most only have a small and bounded effect on the function value.
BioinformaticsUA: Concept Recognition in Clinical Narratives Using a Modular and Highly Efficient Text Processing Framework

Clinical texts, such as discharge summaries or test reports, contain a valuable amount of information that, if efficiently and effectively mined, could be used to infer new knowledge, possibly leading to better diagnosis and therapeutics. With this in mind, the SemEval-2014 Analysis of Clinical Text task aimed at assessing and improving current methods for identification and normalization of concepts occurring in clinical narrative. This paper describes our approach in this task, which was based on a fully modular architecture for text mining. We followed a pure dictionary-based approach, after performing error analysis to refine our dictionaries. We obtained an F-measure of 69.4% in the entity recognition task, achieving the second best precision over all submitted runs (81.3%), with above average recall (60.5%). In the normalization task, we achieved a strict accuracy of 53.1% and a relaxed accuracy of 87.0%.
A Three-Dimensional Feature Vector for Identification of Buried Landmines Using GPR Data #

Identification of shallowly buried landmines using ground penetrating radar (GPR) data is studied. Three kinds of features for target identification are proposed: (a) time interval between two pulses reflected from top and bottom sides of the objects, (b) normalized waveform correlation, and (c) dispersion of arrival time of target responses. Since the identification considered here is reduced to a classification problem of a desired target and other clutter objects, a support vector machine (SVM) is employed as a classifier. In order to evaluate the identification performance, we carry out a Monte Carlo simulation using dataset generated by a twodimensional finite difference time domain (FDTD) method. The results show that good identification performance is obtained, and thus we can confirm that the proposed features are useful for discrimination of landmines from confusing clutter objects.
Teaching and Learning about Writing in the Digital Media Culture: A Subjective Academic Narrative

What might we teach about/as writing in the digital media culture? In this paper, I survey some ideas of teaching and learning writing in the contemporary digital media culture. I look at how the creative possibilities presented by electronic deliveries are evident to students in their everyday lives, but have yet to be fully utilised in teaching and learning about writing. In utilising a conceptual framework following Gregory Ulmer’s ‘mystory’ (a scholarly story involving the personal, the socially learnt and the applied intellectual that is a combination of ‘my story’ and ‘mystery’) I put forward insights developed as a result of conceptualising and delivering online asynchronous virtual reality courses since 1995. This has led me to the development of the subjective academic narrative that recognises all academic discourse as personal (subjective), learned discourse academic) and a story (narrative) thus overcoming traditional linear patriarchal models of knowledge within the academy.
Static Removal of Redundant Loads

This paper presents a strong technique for removing redundant loads from programs. The first part of the paper describes an algorithm for detecting memory redundancies (based on Simpson’s SCC value numbering algorithm [34]) and two frameworks for subsequently removing the redundant operations from the code (one based on available expressions [14] and the other on lazy code motion [23, 17]). The redundancy detection algorithm integrates global value numbering for scalar values with that for memory-based values. Prior work handles these separately and can fail to detect redundancies that arise from interactions between scalar and memory values. We present experimental results on codes from MediaBench and SPEC2000 that show reductions of up to 40% in the number of dynamically executed loads. The results also show that integrating the handling of scalar and memory values allows the frameworks to detect more scalar redundancies. The second half of the paper presents a novel classification of known static and dynamic techniques for removing redundant memory operations. It looks at the difference between intraprocedural and interprocedural effects. It distinguishes between single and multiple dynamic instances of the same static instruction. We present simulation results that show the effective limits of what can be obtained in each category. Our study shows that many run-time memory redundancies exist in highly optimized code, even after applying the techniques shown in the first half of the paper. This suggests that more aggressive methods to detect and remove dynamic memory redundancies should be profitable.
Association of functional MMP-2 gene variant with intracranial aneurysms: case-control genetic association study and meta-analysis

Abstract Introduction: Abnormalities in Matrix Metalloproteinase (MMP) genes, which are important in extracellular matrix (ECM) maintenance and therefore arterial wall integrity are a plausible underlying mechanism of intracranial aneurysm (IA) formation, growth and subsequent rupture. We investigated whether the rs243865 C > T SNP (single nucleotide polymorphism) within the MMP-2 gene (which influences gene transcription) is associated with IA compared to matched controls. Materials and Methods: We conducted a case-control genetic association study, adjusted for known IA risk factors (smoking and hypertension), in a UK Caucasian population of 1409 patients with intracranial aneurysms (IA), and 1290 matched controls, to determine the association of the rs243865 C > T functional MMP-2 gene SNP with IA (overall, and classified as ruptured and unruptured). We also undertook a meta-analysis of two previous studies examining this SNP. Results: The rs243865 T allele was associated with IA presence in univariate (OR 1.18 [95% CI 1.04–1.33], p = .01) and in multi-variable analyses adjusted for smoking and hypertension status (OR 1.16 [95% CI 1.01-1.35], p = .042). Subgroup analysis demonstrated an association of the rs243865 SNP with ruptured IA (OR 1.18 [95% CI 1.03–1.34] p = .017), but, not unruptured IA (OR 1.17 [95% CI 0.97–1.42], p = .11). Conclusions: Our study demonstrated an association between the functional MMP-2 rs243865 variant and IAs. Our findings suggest a genetic role for altered extracellular matrix integrity in the pathogenesis of IA development and rupture.
A Prototype Emergency Resource Redistribution System ForDisease Control

The problem of logistics and resource management in disease control projects in the developing world can hardly be understated. One problem that arises is the occurance of regional imbalances in supply. Such imbalances can seriously compromise the eeectiveness of such programs. A prototype system is described, based on an order-based plan generator and a class of modern combinatorial optimisation techniques, which recommends an implementable plan for the redistribution of available resources to minimise such shortages for real-world situations.
Distributed Operating Systems: State-of-the-Art and Future Directions

Disk storage technology still suggests large central disks because of their better price/capacity ratio and their performance characteristics: Accessing a remote fast disk over a local network can be made faster than accessing a local slow disk. Similar arguments apply to keeping other resources in a central place. Examples are high-speed laser printers, phototypesetters, tape drives, and number crunchers.
Internalization of nerve growth factor by pheochromocytoma PC12 cells: absence of transfer to the nucleus

he intracellular distribution of 125I-labeled nerve growth factor (NGF) in rat pheochromocytoma PC12 cells was studied by quantitative electron microscopic (EM) autoradiography and by subcellular fractionation. PC12 cells were grown as monolayer cultures in medium supplemented with serum in the presence of 125I-NGF. EM autoradiography showed that 125I-NGF was localized at the plasma membrane and cytoplasmic compartments but did not accumulate in the nuclear chromatin or in the nuclear membrane compartment of cells analyzed after 1 hr and 1, 2, and 8 d of incubation with 125I-NGF. 125I-NGF also was not detected in nuclear subcellular fractions prepared from cells grown in serum-supplemented medium either in suspension for 1 d or in monolayer cultures for 1 to 8 d. In contrast, and in confirmation of the results of Yankner and Shooter (Yankner, B. A., and E. M. Shooter (1979) Pro. Natl. Acad. Sci. U. S. A. 76: 1269–1273), about 60% of the cell-bound 125I-NGF was found in the nuclear pellet after cell fractionation if the cells had been kept previously in suspension for 1 d in phosphate-buffered saline supplemented with 0.2% glucose, 0.1% bovine serum albumin, and 125I-NGF. The ultrastructure of PC12 cells grown under such conditions, however, revealed signs of varying degrees of damage. Autoradiography of the nuclear pellet from these cells showed the grains to be located mainly over damaged nuclei or over cell debris between nuclei. It is concluded that NGF, after binding to specific receptors at the plasma membrane, is transferred to membrane- confined cytoplasmic compartments but does not have to be transferred further to the nuclear membrane or to the nuclear chromatin as a prerequisite for its physiological action.
Phenomenon of intellectual entrepreneurship and emerging patterns of intellectual entrepreneurship in Latvia

Abstract The paper is a report on the work in progress on the broader exploratory study into intellectual entrepreneurship in the Baltics, which aims to explore the emerging patterns of intellectual entrepreneurship through experience of individuals. The findings are based on the qualitative interviews with 13 entrepreneurs, who fit Johansson et. al. (1999) and Konstantinov and Filonovich (2005) definition of intellectual entrepreneurs and whose businesses fit Schumpeter's (1934) definition of innovativeness. Based on the literature of intellectual entrepreneurship the emerging typology of intellectual entrepreneurship was developed to assist in selecting the sample. The participants of the study were divided into the following categories: entrepreneuring academics, entrepreneuring academic profesionals, entrepreneuring intellectuals and intuitive entrepreneurs. During the study three emerging patterns of IE in Latvia were identified, which can be called ‘an ideal one’, ‘professional one’ and a ‘forced on.
Psoriasis Regression Analysis of MHC Loci Identifies Shared Genetic Variants with Vitiligo

Psoriasis is a common inflammatory skin disease with genetic components of both immune system and the epidermis. PSOR1 locus (6q21) has been strongly associated with psoriasis; however, it is difficult to identify additional independent association due to strong linkage disequilibrium in the MHC region. We performed stepwise regression analyses of more than 3,000 SNPs in the MHC region genotyped using Human 610-Quad (Illumina) in 1,139 cases with psoriasis and 1,132 controls of Han Chinese population to search for additional independent association. With four regression models obtained, two SNPs rs9468925 in HLA-C/HLA-B and rs2858881 in HLA-DQA2 were repeatedly selected in all models, suggesting that multiple loci outside PSOR1 locus were associated with psoriasis. More importantly we find that rs9468925 in HLA-C/HLA-B is associated with both psoriasis and vitiligo, providing first important evidence that two major skin diseases share a common genetic locus in the MHC, and a basis for elucidating the molecular mechanism of skin disorders.
Enhance Genomic IR with Term Variation and Expansion: Experience of the IASL Group at Genomic Track 2005

The rapid increase of biomedical literature available on the web has made it increasingly difficult to find precise information. To implement an accurate biomedical information retrieval (IR) system, we must deal with the variants of biomedical terms carefully. In this paper, we focus on the generation of aliases, synonyms, acronyms, and lexical variants of such terms. In addition, we also propose a hyphen handling technique for processing hyphenated terms. We use the original terms/phrases, and expanded terms/phrases to construct an Indri query, and evaluate the effectiveness of various methods by two indicators: MAP, and recall. Our experiment results show that tackling hyphenation improves information retrieval significantly. In addition, synonym expansion also enhances IR performance when the focus of a query is identified. For a natural language query, deep semantic analysis and more knowledge-oriented expansion should be applied.
Mission as Good Neighbour: social policy of the Methodist Mission Northern in the 21st century

8 CHAPTER ONE: INTRODUCTION TO THESIS 9-13 1.1 Prologue 1.2 Literature Review 1.3 Methodology and Method 1.4 Data and Analysis 1.5 Future Policy Framework of the Methodist Mission Northern 1.6 Conclusion CHAPTER TWO: CONCEPT OF ‘GOOD NEIGHBOUR’ 14 27 2.