Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergence of Fluconazole Resistance in a Candida parapsilosis Strain That Caused Infections in a Neonatal Intensive Care Unit

ABSTRACT Candida parapsilosis is an increasing cause of bloodstream infections (BSIs) in neonatal intensive care units (NICUs). It has been a persistent problem in the NICU of Hospital for Children and Adolescents, Helsinki University Central Hospital, Helsinki, Finland, since 1987. Fluconazole prophylaxis has been used to control the problem. The number of new infections has, however, increased markedly since September 2000. We assessed fluconazole consumption and occurrence of all Candida species in the NICU from 1991 to 2002. C. parapsilosis bloodstream isolates obtained in the NICU from 1990 to 2002 (n = 26) were genotyped and their fluconazole susceptibility was defined. A low rate of C. parapsilosis BSIs was correlated with high rates of consumption of fluconazole. No emergence of Candida species with primary resistance to fluconazole was detected. However, genotyping with a complex DNA fingerprinting probe revealed that a single strain of C. parapsilosis with decreasing susceptibility to fluconazole was responsible for cross-infections that caused BSIs in the NICU over a 12-year period. The emergence of fluconazole resistance in that strain was observed after more than 10 years of fluconazole prophylaxis.
Stick-Breaking Policy Learning in Dec-POMDPs

Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often converge to maxima that are far from the optimal value. This paper represents the local policy of each agent using variable-sized FSCs that are constructed using a stick-breaking prior, leading to a new framework called decentralized stick-breaking policy representation (Dec-SBPR). This approach learns the controller parameters with a variational Bayesian algorithm without having to assume that the Dec-POMDP model is available. The performance of Dec-SBPR is demonstrated on several benchmark problems, showing that the algorithm scales to large problems while outperforming other state-of-the-art methods.
Behavioral Intervention Approaches to the Treatment and Management of Alzheimer's Disease: A Research Agenda

Alzheimer's disease (AD) is a progressive condition associated with a broad variety of clinical symptoms. These symptoms can, for the most part, be conceptualized as falling within three primary categories: (I) cognitive symptoms, (II) functional symptoms, and (III) behavioral (disturbance) symptoms. All of these symptom categories may potentially respond to specific behavioral intervention approaches to treatment and management. However, at this time, few behavioral approaches have convincingly been substantiated to be effective in treating any of the symptoms in any of these categories.
The Explanation, Assessment and Treatment of Child Sexual Abuse

Sexual offending against children remains a socially significant and complex problem. It is becoming increasingly evident that the psychological and emotional costs to victims and their families are profound, with many victims experiencing major difficulties in adjusting to the demands of adult life. In this paper I focus on a number of significant current issues associated with sexual offending and attempt to give a brief overview of these domains: the causes and types of sexual offending; assessment and risk evaluation; the principles of effective intervention; best practice treatment of sexual offenders; and a consideration of treatment effectiveness. I conclude by arguing that we also need to pay more attention to the relationship between normative theories and empirically validated therapeutic techniques. Any understanding of rehabilitation is underpinned by values and assumptions about the kind of lives available to offenders.
Parametric analysis of solar shading parameters in intermediate orientations located in desert climates

Improving daylighting performance for offices provides positive effects on well-being, productivity and energy savings. High exposure to direct solar radiation in desert climates has been addressed in the literature by the use of shading devices. A study of the effect of four parameters (screen rotation, depth and tilt, window-to-wall ratio) on daylighting of a generic office in the deserts of Cairo, Egypt was carried out in two intermediate orientations (SE and NE) through a parametric simulation approach using Rhino 3D, Grasshopper, SpeedSim-for-DIVA and Diva-forRhino. The newly approved IES metrics were used to evaluate the 1120 iterations with an exhaustive method. The use of mainly horizontal shading, downward tilting shades, high window-to-wall ratio (WWR), and large screen depths were suggested.
Simultaneous color consistency and depth map estimation for radiometrically varying stereo images

In this paper, we propose a new method that infers accurate depth maps and color-consistent images between radiometrically varying stereo images, simultaneously. In general, stereo matching and performing color consistency between stereo images are a chicken-and-egg problem. Color consistency enhances the performance of stereo matching, while accurate correspondences from stereo disparities improve color consistency between stereo images. We devise a new iterative framework in which these two processes can boost each other. For robust stereo matching, we utilize the mutual information-based method combined with the SIFT descriptor from which we can estimate the joint pdf in log-chromaticity color space. From this joint pdf, we can estimate a linear relationship between the corresponding pixels in stereo images. Using this linear relationship and the estimated depth maps, we devise a stereo color histogram equalization method to make color-consistent stereo images which conversely boost the disparity map estimation. Experimental results show that our method produces both accurate depth maps and color-consistent stereo images even for stereo images with severe radiometric differences.
A multiple-level genetic-fuzzy mining algorithm

In this paper, we propose a multiple-level genetic-fuzzy mining algorithm for mining membership functions and fuzzy association rule on multiple-concept levels. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The fitness value of each individual is then evaluated by the summation of large 1-itemsets of each item in different concept levels and the suitability of membership functions in the chromosome. After the GA process terminates, a better set of multiple-level fuzzy association rules can then be expected with a more suitable set of membership functions. Experimental results on a simulation dataset also show the effectiveness of the algorithm.
SOM based algorithm for video surveillance system parameter optimal selection

In automatic video surveillance systems, full time monitoring represents an important goal to achieve. The aim of the paper is to find a new methodology for video surveillance adaptive parameter regulation. The method presented is based on self organizing maps (SOM) which are used for parameter regulation purposes. Results shown prove how this method permits overall good performances to be achieved for all environments.
A Comment on "MABS: Multicast Authentication Based on Batch Signature&#x201D;

Recently, Zhou et al. proposed a multicast authentication protocol named MABS which employs an efficient cryptographic primitive called batch verification to authenticate an arbitrary number of data packets simultaneously. Three implementations were presented: MABS-RSA, MABS-BLS, and MABS-DSA. In this comment, we are concerned with the last implementation, which is claimed to be much more efficient than the others. Our particular interest also lies in the fact that MABS-DSA was designed to thwart a known attack against its underlying batch DSA primitive and is claimed to be with increased security. After a careful revisit of the involved arithmetic, however, we find that the real issue lies in protocol correctness rather than security; the algorithm of MABS-DSA actually does not hold as one would expect. More specifically, even if each of the data packets was signed by an honest sender and securely delivered to the receiver, verification of the batch of signatures will still almost always fail.
Collaborative Prediction for Multi-entity Interaction With Hierarchical Representation

With the rapid growth of Internet applications, there are more and more entities in interaction scenarios, and thus collaborative prediction for multi-entity interaction is becoming a significant problem. The state-of-the-art methods, e.g., tensor factorization and factorization machine, predict multi-entity interaction based on calculating the similarity among all entities. However, these methods are usually not able to reveal the joint characteristics of entities in the interaction. Besides, some methods may succeed in one specific application, but they can not be extended effectively for other applications or interaction scenarios with more entities. In this work, we propose a Hierarchical Interaction Representation (HIR) model, which models the mutual action among different entities as a joint representation. We generate the interaction representation of two entities via tensor multiplication, which is preformed iteratively to construct a hierarchical structure among all entities. Moreover, we employ several hidden layers to reveal the underlying properties of this interaction and enhance the model performance. After generating final representation, the prediction can be calculated using a variety of machine learning methods according to different tasks (i.e., linear regression for regression tasks, pair-wise ranking for ranking tasks and logistic regression for classification tasks). Experimental results show that our proposed HIR model yields significant improvements over the competitive compared methods in four different application scenarios (i.e., general recommendation, context-aware recommendation, latent collaborative retrieval and click-through rate prediction).
The CALO meeting speech recognition and understanding system

The CALO meeting assistant provides for distributed meeting capture, annotation, automatic transcription and semantic analysis of multiparty meetings, and is part of the larger CALO personal assistant system. This paper summarizes the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, question-answer pair identification, action item recognition, decision extraction, and summarization.
Competitive Processes in Cross-Situational Word Learning

Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple scales-both within and across trials/situations-learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process-level understanding of cross-situational learning.
Characterization of Bacterial and Fungal Community Dynamics by High-Throughput Sequencing (HTS) Metabarcoding during Flax Dew-Retting

Flax dew-retting is a key step in the industrial extraction of fibers from flax stems and is dependent upon the production of a battery of hydrolytic enzymes produced by micro-organisms during this process. To explore the diversity and dynamics of bacterial and fungal communities involved in this process we applied a high-throughput sequencing (HTS) DNA metabarcoding approach (16S rRNA/ITS region, Illumina Miseq) on plant and soil samples obtained over a period of 7 weeks in July and August 2014. Twenty-three bacterial and six fungal phyla were identified in soil samples and 11 bacterial and four fungal phyla in plant samples. Dominant phyla were Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes (bacteria) and Ascomycota, Basidiomycota, and Zygomycota (fungi) all of which have been previously associated with flax dew-retting except for Bacteroidetes and Basidiomycota that were identified for the first time. Rare phyla also identified for the first time in this process included Acidobacteria, CKC4, Chlorobi, Fibrobacteres, Gemmatimonadetes, Nitrospirae and TM6 (bacteria), and Chytridiomycota (fungi). No differences in microbial communities and colonization dynamics were observed between early and standard flax harvests. In contrast, the common agricultural practice of swath turning affects both bacterial and fungal community membership and structure in straw samples and may contribute to a more uniform retting. Prediction of community function using PICRUSt indicated the presence of a large collection of potential bacterial enzymes capable of hydrolyzing backbones and side-chains of cell wall polysaccharides. Assignment of functional guild (functional group) using FUNGuild software highlighted a change from parasitic to saprophytic trophic modes in fungi during retting. This work provides the first exhaustive description of the microbial communities involved in flax dew-retting and will provide a valuable benchmark in future studies aiming to evaluate the effects of other parameters (e.g., year-to year and site variability etc.) on this complex process.
Language-model look-ahead for large vocabulary speech recognition

Presents an efficient look-ahead technique which incorporates the language model knowledge at the earliest possible stage during the search process. This so-called language model look-ahead is built into the time-synchronous beam search algorithm using a tree-organized pronunciation lexicon for a bigram language model. The language model look-ahead technique exploits the full knowledge of the bigram language model by distributing the language model probabilities over the nodes of the lexical tree for each predecessor word. We present a method for handling the resulting memory requirements. The recognition experiments performed on the 20,000-word North American Business task (Nov. 1996) demonstrate that, in comparison with the unigram look-ahead, a reduction by a factor of 5 in the acoustic search effort can be achieved without loss in recognition accuracy.
Insights into Gene Expression Changes Impacting B-Cell Transformation: Cross-Species Microarray Analysis of Bovine Leukemia Virus Tax-Responsive Genes in Ovine B Cells

ABSTRACT Large-animal models for leukemia have the potential to aid in the understanding of networks that contribute to oncogenesis. Infection of cattle and sheep with bovine leukemia virus (BLV), a complex retrovirus related to human T-cell leukemia virus type 1 (HTLV-1), is associated with the development of B-cell leukemia. Whereas the natural disease in cattle is characterized by a low tumor incidence, experimental infection of sheep leads to overt leukemia in the majority of infected animals, providing a model for studying the pathogenesis associated with BLV and HTLV-1. TaxBLV, the major oncoprotein, initiates a cascade of events leading toward malignancy, although the basis of transformation is not fully understood. We have taken a cross-species ovine-to-human microarray approach to identify TaxBLV-responsive transcriptional changes in two sets of cultured ovine B cells following retroviral vector-mediated delivery of TaxBLV. Using cDNA-spotted microarrays comprising 10,336 human genes/expressed sequence tags, we identified a cohort of differentially expressed genes, including genes related to apoptosis, DNA transcription, and repair; proto-oncogenes; cell cycle regulators; transcription factors; small Rho GTPases/GTPase-binding proteins; and previously reported TaxHTLV-1-responsive genes. Interestingly, genes known to be associated with human neoplasia, especially B-cell malignancies, were extensively represented. Others were novel or unexpected. The results suggest that TaxBLV deregulates a broad network of interrelated pathways rather than a single B-lineage-specific regulatory process. Although cross-species approaches do not permit a comprehensive analysis of gene expression patterns, they can provide initial clues for the functional roles of genes that participate in B-cell transformation and pinpoint molecular targets not identified using other methods in animal models.
Ubiquitous ID: Standards for Ubiquitous Computing and the Internet of Things

In ubiquitous computing environments, many tiny computers cooperate, adapting their behaviors according to real-world contexts to provide flexible information services. These information services can't be realized without technology standardization in a broad area. In this article, we discuss the principle of ubiquitous computing technology standardization and introduce uID standards currently in development.
Optimising TCP over cognitive radio networks for trains

The Transmission Control Protocol (TCP) is a very popular transport protocol used by the majority of applications. However TCP is inefficient when used over mobile networks. Moreover mobile networks technology is moving towards a new emerging paradigm called Cognitive Radio (CR). Cognitive Radio brings its own share of problems for TCP however more importantly it brings the capability to gather more knowledge about its radio environment. TCP will face same problems, if not more, when used over mobile networks enabled with CR technology. However, these new CR capabilities open the door for better cross layer TCP optimisation techniques. This paper proposes a cross layer distributed mechanism using CR triggers and freeze TCP to optimise TCP over cognitive radio networks for trains. The results show that our mechanism can improve the link utilisation efficiency as compared to standard TCP.
A Preliminary Study of the Algicidal Mechanism of Bioactive Metabolites of Brevibacillus laterosporus on Oscillatoria in Prawn Ponds

The algae, Oscillatoria, is commonly found in prawn ponds and can lead to reduced productivity. We examined metabolites of the bacteria Brevibacillus laterosporus for algicidal qualities. To determine the possible algicidal mechanisms of these bioactive metabolites, different amounts of sterile filtrate of bacterial suspensions were added to cultures containing Oscillatoria. The dry weight, the concentrations of chlorophyll-a (chl-a), phycobiliprotein (PC, phycocyanin; APC, allophycocyanin; PE, phycoerythrin), and MDA (malondialdehyde) and the activities of SOD (superoxide dismutase), POD (peroxidase), and CAT (catalase) of algae were measured during the algicidal application. The results showed that lower concentrations of the sterile filtrate (addition ≤ 4 mL) accelerated the growth rate of Oscillatoria, but significant inhibition and lysis were observed with higher concentrations (addition ≥ 8 mL). In two trials (the additions were 8 mL and 10 mL, respectively), the algal dry weights were reduced by 26.02% and 45.30%, and the chl-a concentrations were decreased by 46.88% and 63.73%, respectively, after seven days. During the algicidal treatment, the concentrations of PC, APC, PE, and MDA and the activities of SOD, POD, and CAT were significantly increased in the early cultivation and declined quickly at later stages. Finally, the algae-lysing mechanism of the bioactive metabolites of the bacteria Brevibacillus laterosporus on Oscillatoria had been proposed.
InGaP/AlGaInP Quantum Well Discrete Mode Laser Diode Emitting at 689 nm

A discrete-mode laser diode, fabricated in the In0.6Ga0. 4P/AlGaInP multiple quantum well system, emitting a single mode at <inline-formula> <tex-math notation="LaTeX">$\lambda =689$ </tex-math></inline-formula> nm is reported. The laser has an ex-facet output power >10 mW at 30 °C and operates mode hop free in the temperature range 0 °C to 50 °C.
Medial Temporal Atrophy and Memory Dysfunction in Poststroke Cognitive Impairment-No Dementia

Background and Purpose It was recently reported that the prevalence of poststroke memory dysfunction might be higher than previously thought. Stroke may exist concomitantly with underlying Alzheimer's disease (AD), and so we determined whether post-stroke memory dysfunction indicates manifestation of underlying subclinical AD. Methods Of 1201 patients in a prospective cognitive assessment database, we enrolled subjects with poststroke amnestic vascular cognitive impairment-no dementia (aVCIND; n=48), poststroke nonamnestic vascular cognitive impairment-no dementia (naVCIND; n=50), and nonstroke amnestic mild cognitive impairment (aMCI; n=65). All subjects had cognitive deficits, but did not meet the criteria for dementia. A standardized neuropsychological test battery and magnetic resonance imaging were performed at least 90 days after the index stroke (mean, 473 days). Visual assessment of medial temporal atrophy (MTA) was used as a measure of underlying AD pathology. Results The MTA score was significantly lower in the naVCIND group (0.64±0.85, mean±SD) than in the aVCIND (1.10±1.08) and aMCI (1.45±1.13; p<0.01) groups. Multivariable ordinal logistic regression analysis revealed that compared with naVCIND, aVCIND [odds ratio (OR)=2.69; 95% confidence interval (CI)=1.21-5.99] and aMCI (OR=5.20; 95% CI=2.41-11.23) were significantly associated with increasing severity of MTA. Conclusions Our findings show that compared with poststroke naVCIND, the odds of having more-severe MTA were increased for poststroke aVCIND and nonstroke aMCI.
Common-mode EMI reduction for PFC converter

Interleaved multichannel power factor correction (PFC) converter is very popular today because of its ability to reduce input and output current ripples. In this paper, the common-mode (CM) electromagnetic interference noise emission model of the multichannel PFC converter is studied. A balance technique is introduced to reduce CM noise emission. To improve the high-frequency balance, a novel topology with coupled-inductor structure is proposed. Its CM noise model is derived and balance condition for minimizing CM noise is calculated. A two-channel PFC converter is built to verify the proposed balance technique. Simulation results show that CM noise can be reduced up to 20 dB between 150 kHz and 1 MHz.