Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypothyroidism in diabetes mellitus patients in Eastern Nepal

Context: The coexistence of diabetes mellitus (DM) with hypothyroidism is a known clinical observation. Aims: To estimate prevalence and co-relate that of hypothyroidism in patients with DM in relation to the age and sex, the lipid profile, body mass index visiting diabetes clinic and inpatients in B. P. Koirala Institute of Health Sciences. Settings and Design: The hospital-based descriptive study. Materials and Methods: Two hundred and seventy-one known or newly detected cases of DM aged more than 15 years were selected randomly from September 2012 to September 2013 and subjected to evaluation for thyroid function – clinically and biochemically and other relevant investigations were done. Statistical Analysis Used: For descriptive statistics mean, standard deviation, percentage, proportion were calculated. For inferential statistics following test were carried out at the level of significant 0.05 where confidence interval is 95%. The statistical operations were done through Statistical Package for the Social Sciences version 10. Results: Of 271 subjects, the prevalence of hypothyroidism (clinical and subclinical) in diabetics was, 4.05% (11/271) with females preponderance, of which 7 (30.4%) were clinically hypothyroid and 4 (17.4%) were subclinical hypothyroid. One (4.3%) patient had subclinical hyperthyroidism. The mean age at diagnosis of type 2 DM was 51–60 years. 8.69% of diabetics with primary hypothyroids were having morbid obesity. High-density lipoprotein among different thyroid status were statistically significant (P = 0.042). Conclusions: Hypothyroidism is not uncommon in diabetes, and we found body mass index, mean triglyceride and cholesterol levels were more in those diabetic patients having coexisting hypothyroidism.
Spike classification with multivariate t-distribution mixture model via improved Expectation-Maximization algorithm

Recent research has developed various methods in automatic spike classification, including Expectation-Maximization (EM) clustering based on multivariate t-distribution mixture models. In our study, we improved the EM iterative algorithm with a significantly better ascent gradient in the high-dimensional feature space of spikes. Our simulations showed that this improvement of the EM algorithm could reduce the computation time with no significant change in classification error. Applications of this new algorithm yielded better computation cost and a more robust performance in real experimental spike data analysis.
Observations on total body chloride in children.

The distribution of bromide has been used to measure total body chloride in 50 infants and children. Total chloride has been found to follow the differential growth equation. An index of extracellular water has been obtained and followed with the progress of age. It has been found that infants in early life have 40% more chloride per unit weight than adults. Total chloride/kg. shows a rapid proportionate decrease during the first year of life.
Learning Effective Word Embedding using Morphological Word Similarity

Deep learning techniques aim at obtaining high-quality distributed representations of words, i.e., word embeddings, to address text mining and nat-ural language processing tasks. Recently, efﬁcient methods have been proposed to learn word embeddings from context that captures both semantic and syntactic relationships between words. However, it is challenging to handle unseen words or rare words with insufﬁcient context. In this paper, inspired by the study on word recognition process in cognitive psychology, we propose to take advantage of seemingly less obvious but essentially important morphological word similarity to address these challenges. In particular, we introduce a novel neural network architecture that leverages both contextual information and morphological word similarity to learn word embeddings. Meanwhile, the learning architecture is also able to reﬁne the pre-deﬁned morphological knowledge and obtain more accurate word similarity. Experiments on an analogical reasoning task and a word similarity task both demonstrate that the proposed method can greatly enhance the effectiveness of word embeddings.
Simultaneous measurement of temperature and strain based on all-fiber Fabry-Perot sensor

An all-fiber Fabry-Perot (FP) interferometer (FPI) sensor which can simultaneously measure strain and temperature is proposed and experimentally demonstrated. The FPI sensor is composed of a cascaded-cavity structure with a hollow-core tube fiber cavity and a standard single-mode fiber (SMF) cavity. Different cross section areas and thermal-optic coefficients of the two cavities make them differently sensitive to temperature and strain. The individual interference spectrum based on the air cavity or silica cavity can be extracted by band-pass filtering the spectrum of the sensor in frequency domain. The temperature and strain sensitivity of each cavity are obtained by tracing the wavelength shifts of the interference spectra. Furthermore, the temperature-strain cross-talk is eliminated by solving a cofficient matrix equation composed by the temperature and strain sensitivities of the two FP cavities. The strain sensitivities of two FP cavities achieved are 1.173 nm/με and 1.86 nm/με. The temperature sensitivities of two FP cavities are 0.00574 nm/°C and 0.00549 nm/°C. The advantages of the proposed sensor including simultaneous measurement of temperature and strain, no temperature-strain cross-talk, simple fabrication, and all-fiber structure make it appropriate for the real sensing applications.
Development of soft magnetic alloy by nanomaterials

Equiatomic FeCo alloy which has the highest saturation magnetisation of any soft magnetic material is limited in application due to it being very brittle. The development of composite carbon nano-particulate filled alloys has achieved little or no overall benefit due to difficulties with agglomeration in the mixing process. In this research, improvement in mechanical and magnetic properties has been achieved in FeCo alloy composite with a very low fraction carbon nanotube (CNT) reinforcement prepared only by low energy ball milling, with notable improvement as compared to composites prepared previously by pre-mixing in dimethylformamide, followed by low energy ball milling. Significant improvement in hardness and magnetic properties have been obtained in FeCo alloy by reinforcement with a small addition of graphene nanoplatelet (GNP) alone and prepared by low energy ball milling. However, significant overlapping between the nanoplatelets deteriorated the strength and elongation. Inserting CNT among nanoplatelets was found to reduce the overlapping and enabled refinement of the microstructure, however, at the expense of some increase in porosity in the spark plasma sintered materials. The spark plasma sintering conditions have been optimised at relatively low sintering pressure by use of high temperature and controlling the heating rate and dwelling time to enable near-full densification in base FeCo alloy. By careful selection of sintering parameters, it was found to be possible to increase yield strength, elongation and suppress the intergranular fracture in FeCo alloy, combined with good magnetic properties. This optimised SPS process was developed following the above-described solvent dispersion studies and applied subsequently to following alloy-composite developments. The ultimate strength, yield strength and elongation were significantly improved by embedding GNP in flaked FeCo powder, which enabled uniform dispersion up to 6 vol. % in contrast to very poor dispersion in spherical powder. The increased coercivity from dispersion in flaked FeCo powder was reduced by quenching the composites from the disordered region, which led to further improvement in elongation. However, the highest tensile strength of the heat treated samples was obtained after quenching from the ordered region. Interface bonding was improved in CNT- and GNP- filled FeCo alloy composites by high energy ball milling, which increased the properties of the composites. However, significant damage to the structure was imparted by ball milling of carbonaceous nanomaterial when the time of ball milling was extended. By nature of their two-dimensional form, dispersion of GNPs was found to be far more challenging in FeCo alloy compared with the CNTs. However, the mechanical properties of the GNP composite exceeded those of their counterpart CNT composite even at a lower volume fraction
Why do we sleep?

Slow-wave sleep consists in slowly recurring waves that are associated with a large-scale spatio-temporal synchrony across neocortex. These slow-wave complexes alternate with brief episodes of fast oscillations, similar to the sustained fast oscillations that occur during the wake state. We propose that alternating fast and slow waves consolidate information acquired previously during wakefulness. Slow-wave sleep would thus begin with spindle oscillations that open molecular gates to plasticity, then proceed by iteratively 'recalling' and 'storing' information primed in neural assemblies. This scenario provides a biophysical mechanism consistent with the growing evidence that sleep serves to consolidate memories.
Potent leukocidal action of Escherichia coli hemolysin mediated by permeabilization of target cell membranes

The contribution of Escherichia coli hemolysin (ECH) to bacterial virulence has been considered mainly in context with its hemolytic properties. We here report that this prevalent bacterial cytolysin is the most potent leukocidin known to date. Very low concentrations (approximately 1 ng/ml) of ECH evoke membrane permeability defects in PMN (2-10 x 10(6) cells/ml) leading to an efflux of cellular ATP and influx of propidium iodide. The attacked cells do not appear to repair the membrane lesions. Human serum albumin, high density and low density lipoprotein, and IgG together protect erythrocytes and platelets against attack by even high doses (5-25 micrograms/ml) of ECH. In contrast, PMN are still permeabilized by ECH at low doses (50-250 ng/ml) in the presence of these plasma inactivators. Thus, PMN become preferred targets for attack by ECH in human blood and protein-rich body fluids. Kinetic studies demonstrate that membrane permeabilization is a rapid process, ATP-release commencing within seconds after application of toxin to leukocytes. It is estimated that membrane permeabilization ensues upon binding of approximately 300 molecules ECH/PMN. This process is paralleled by granule exocytosis, and by loss of phagocytic killing capacity of the cells. The recognition that ECH directly counteracts a major immune defence mechanism of the human organism through its attack on granulocytes under physiological conditions sheds new light on its possible role and potential importance as a virulence factor of E. coli.
Source localization with MEG data: A beamforming approach based on covariance thresholding

Reconstructing neural activities using non‐invasive sensor arrays outside the brain is an ill‐posed inverse problem since the observed sensor measurements could result from an infinite number of possible neuronal sources. The sensor covariance‐based beamformer mapping represents a popular and simple solution to the above problem. In this article, we propose a family of beamformers by using covariance thresholding. A general theory is developed on how their spatial and temporal dimensions determine their performance. Conditions are provided for the convergence rate of the associated beamformer estimation. The implications of the theory are illustrated by simulations and a real data analysis.
Local Edge Distributions for Detection of Salient Structure Textures and Objects

Automatic detection of regions of salient texture and objects is useful for analysis of remotely sensed imagery, such as for land cover classification, object detection, and change detection. Intuitively, the local edges on an image indicate spectral discontinuity and the existence of structure texture or objects. This letter explores a simple method for measuring the saliency of texture and objects based on the edge density and spatial evenness of the edge distribution in the local window of each pixel. This method generates a saliency map by computing the saliency index of each pixel. By segmenting the saliency map, the salient structure texture regions and the locations of objects can be extracted. The algorithm requires only the window size as the input parameter and is relatively simple to implement. Experiments using high-resolution images show its effectiveness and accuracy in the detection of salient structure texture regions, such as crops and residential areas, and man-made objects, such as airplanes, cars, etc.
Open and Closed Worlds for Overloading : a definition and support for coexistence

The type system of Haskell and some related systems are based on an open world approach for overloading. In an open world, the principal type of each overloaded symbol must be explicitly annotated (in Haskell, annotations occur in type class declarations) and a definition of an overloaded symbol is required to exist only when overloading is resolved. In a closed world, on the other hand, each principal type is determined according to the types of definitions that exist in the relevant context and, furthermore, overloading resolution for an expression considers only the context of the definition of its constituent symbols. In this paper we formally characterize open and closed worlds, and discuss their relative advantages. We present a type system that supports both approaches together, and compare the defined system with Haskell type classes extended with multi-parameter type classes and functional dependencies. We show in particular that functional dependencies are not necessary in order to support multi-parameter type classes, and present an alternative route.
How Women Think Robots Perceive Them - as if Robots were Men

In previous studies, we developed an empirical account of user engagement with software agents. We formalized this model, tested it for internal consistency, and implemented it into a series of software agents to have them build up an affective relationship with their users. In addition, we equipped the agents with a module for affective decision-making, as well as the capability to generate a series of emotions (e.g., joy and anger). As follow-up of a successful pilot study with real users, the current paper employs a non-naive version of a Turing Test to compare an agent’s affective performance with that of a human. We compared the performance of an agent equipped with our cognitive model to the performance of a human that controlled the agent in a Wizard of Oz condition during a speed-dating experiment in which participants were told they were dealing with a robot in bot h conditions. Participants did not detect any differences between the two conditions in the emotions the agent experienced and in the way he supposedly perceived the participants. As is, our model can be used for designing believable virtual agents or humanoid robots on the surface level of emotion expression.
Rank optimization of personalized search

Augmenting the global ranking based on the linkage structure of the Web is one of the popular approaches in data engineering community today for enhancing the search and ranking quality of Web information systems. This is typically done through automated learning of user interests and re-ranking of search results through semantic based personalization. In this paper, we propose a query context window (QCW) based framework for Selective uT ilization of search history in personal- ized leArning and re-Ranking (STAR). We conduct extensive experiments to compare our STAR approach with the popular directory-based search methods (e.g., Google Directory search) and the general model of most existing re-ranking schemes of personalized search. Our experimental results show that the proposed STAR framework can effectively capture user-specific query-dependent personalization and improve the accuracy of personalized search over existing approaches.
On the use of GSV-SVM for Speaker Diarization and Tracking

In this paper, we present the use of Gaussian Supervectors with Support Vector Machines classifiers (GSV-SVM) in an acoustic speaker diarization and a speaker tracking system, compared with a standard Gaussian Mixture Model system based on adapted Universal Background Models (GMM-UBM). GSVSVM systems (which share the adaptation step with the GMMUBM systems) are observed to have comparable performances: for acoustic speaker diarization, the GMM-UBM system outperforms the GSV-SVM system on ESTER2 data but the latter system works better in the speaker tracking system. In particular, the linear combination of two systems at the score level outperforms each individual system.
Entity Modeling in the MLS Relational Model

Previous proposals for a multilevel secure relational model have utilized syntactic integrity properties to control problems such as polyinstantiation, pervasive ambiguity, and proliferation of tuples due to updates. Although successive versions of these models have shown steady improvement, most thorny problems have been mitigated but not resolved. We believe that the major roadblock to progress has been that no effort to date has shown what a multilevel secure database means semantically; instead the focus has been on making syntactic adjustments to avoid problems. In this paper, we introduce a belief-based semantics for multilevel secure databases that supports the description of semantic multilevel secure entities, and argue for the generality of this semantics. We also present our syntax for multilevel secure databases, and show its relationship to the semantics. Our syntax is free of most problems of previous models, and is also simpler without sacrificing security or expressiveness. Permission to copy without fee all or part of this material is grantedprovided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 18th VLDB Conference Vancouver, British Columbia, Canada 1992
Construction and Screening of Metagenomic Libraries Derived from Enrichment Cultures: Generation of a Gene Bank for Genes Conferring Alcohol Oxidoreductase Activity on Escherichia coli

ABSTRACT Enrichment of microorganisms with special traits and the construction of metagenomic libraries by direct cloning of environmental DNA have great potential for identifying genes and gene products for biotechnological purposes. We have combined these techniques to isolate novel genes conferring oxidation of short-chain (C2 to C4) polyols or reduction of the corresponding carbonyls. In order to favor the growth of microorganisms containing the targeted genes, samples collected from four different environments were incubated in the presence of glycerol and 1,2-propanediol. Subsequently, the DNA was extracted from the four samples and used to construct complex plasmid libraries. Approximately 100,000 Escherichia coli strains of each library per test substrate were screened for the production of carbonyls from polyols on indicator agar. Twenty-four positive E. coli clones were obtained during the initial screen. Sixteen of them contained a plasmid (pAK101 to pAK116) which conferred a stable carbonyl-forming phenotype. Eight of the positive clones exhibited NAD(H)-dependent alcohol oxidoreductase activity with polyols or carbonyls as the substrates in crude extracts. Sequencing revealed that the inserts of pAK101 to pAK116 encoded 36 complete and 17 incomplete presumptive protein-encoding genes. Fifty of these genes showed similarity to sequenced genes from a broad collection of different microorganisms. The genes responsible for the carbonyl formation of E. coli were identified for nine of the plasmids (pAK101, pAK102, pAK105, pAK107 to pAK110, pAK115, and pAK116). Analyses of the amino acid sequences deduced from these genes revealed that three (orf12, orf14, and orf22) encoded novel alcohol dehydrogenases of different types, four (orf5, sucB, fdhD, and yabF) encoded novel putative oxidoreductases belonging to groups distinct from alcohol dehydrogenases, one (glpK) encoded a putative glycerol kinase, and one (orf1) encoded a protein which showed no similarity to any other known gene product.
MODELING PLASTIC DEFORMATION OF NANO/SUBMICRON-SIZED TUNGSTEN PILLARS UNDER COMPRESSION: A COARSE-GRAINED ATOMISTIC APPROACH

In this work, coarse-grained atomistic simulations via the concurrent atomistic-continuum (CAC) method are performed to investigate compressive deformation of nano-/submicron-sized pillars in body-centered cubic (BCC) tungsten. Two models with different surface roughness are considered. All pillars have the same height-to-diameter aspect ratio of 3, with the diameter ranging from 27.35 to 165.34 nm; as a result, the largest simulation cell contains 291,488 finite elements, compared to otherwise ≈ 687.82 million atoms in an equivalent full atomistic model. Results show that (i) a larger surface roughness leads to a lower yield stress and (ii) the yield stress of pillars with a large surface roughness scales nearly linearly with the diameter while that of pillars with smooth surfaces scales exponentially with the diameter, the latter of which agrees with experiments. The differences in the yield stress between the two models are attributed to their different plastic deformation mechanisms: in the case of large surface roughness, dislocation nucleation is largely localized near the ends of the pillars; and in pillars with smooth surfaces, dislocation avalanches in a more homogeneous manner are observed. This work, which is the first attempt to simulate BCC systems using the CAC method, highlights the significance of the surface roughness in uniaxial deformation of nano-/submicropillars.
Cyber situation awareness and teamwork

Cyber analysis is a complex task that requires the coordination of a large sociotechnical system of human analysts working together with technology. Adequate situation awareness of such a complex system requires more than aggregate situation awareness of individuals. Teamwork in the form of communication and information coordination is at the heart of team-level situation awareness. In this position paper, we report observations from previously conducted cognitive task analyses that suggest that teamwork is lacking in many cyber analysis organizations. Communication is ineffective, team roles are inconsistent across organizations, reward structures and selection may thwart collaboration, and the environment is conducive to individual work. Suggestions for improving teamwork in the cyber domain are offered.
Arabic sentiment analysis using WEKA a hybrid learning approach

Data has become the currency of this era and it is continuing to massively increase in size and generation rate. Large data generated out of organisations' e-transactions or individuals through social networks could be of a great value when analysed properly. This research presents an implementation of a sentiment analyser for Twitter's tweets which is one of the biggest public and freely available big data sources. It analyses Arabic, Saudi dialect tweets to extract sentiments toward a specific topic. It used a dataset consisting of 3000 tweets collected from Twitter. The collected tweets were analysed using two machine learning approaches, supervised which is trained with the dataset collected and the proposed hybrid learning which is trained on a single words dictionary. Two algorithms are used, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). The obtained results by the cross validation on the same dataset clearly confirm the superiority of the hybrid learning approach over the supervised approach.