Miracle-dz/UniFAR-FTUs huggingface dataset:
High Throughput qPCR Expression Profiling of Circulating MicroRNAs Reveals Minimal Sex- and Sample Timing-Related Variation in Plasma of Healthy Volunteers

MicroRNAs are a class of small non-coding RNA that regulate gene expression at a post-transcriptional level. MicroRNAs have been identified in various body fluids under normal conditions and their stability as well as their dysregulation in disease opens up a new field for biomarker study. However, diurnal and day-to-day variation in plasma microRNA levels, and differential regulation between males and females, may affect biomarker stability. A QuantStudio 12K Flex Real-Time PCR System was used to profile plasma microRNA levels using OpenArray in male and female healthy volunteers, in the morning and afternoon, and at four time points over a one month period. Using this system we were able to run four OpenArray plates in a single run, the equivalent of 32 traditional 384-well qPCR plates or 12,000 data points. Up to 754 microRNAs can be identified in a single plasma sample in under two hours. 108 individual microRNAs were identified in at least 80% of all our samples which compares favourably with other reports of microRNA profiles in serum or plasma in healthy adults. Many of these microRNAs, including miR-16-5p, miR-17-5p, miR-19a-3p, miR-24-3p, miR-30c-5p, miR-191-5p, miR-223-3p and miR-451a are highly expressed and consistent with previous studies using other platforms. Overall, microRNA levels were very consistent between individuals, males and females, and time points and we did not detect significant differences in levels of microRNAs. These results suggest the suitability of this platform for microRNA profiling and biomarker discovery and suggest minimal confounding influence of sex or sample timing. However, the platform has not been subjected to rigorous validation which must be demonstrated in future biomarker studies where large differences may exist between disease and control samples.
Improving QoS in High-Speed Mobility Using Bandwidth Maps

It is widely evidenced that location has a significant influence on the actual bandwidth that can be expected from Wireless Wide Area Networks (WWANs), e.g., 3G. Because a fast-moving vehicle continuously changes its location, vehicular mobile computing is confronted with the possibility of significant variations in available network bandwidth. While it is difficult for providers to eliminate bandwidth disparity over a large service area, it may be possible to map network bandwidth to the road network through repeated measurements. In this paper, we report results of an extensive measurement campaign to demonstrate the viability of such bandwidth maps. We show how bandwidth maps can be interfaced with adaptive multimedia servers and the emerging vehicular communication systems that use on-board mobile routers to deliver Internet services to the passengers. Using simulation experiments driven by our measurement data, we quantify the improvement in Quality of Service (QoS) that can be achieved by taking advantage of the geographical knowledge of bandwidth provided by the bandwidth maps. We find that our approach reduces the frequency of disruptions in perceived QoS for both audio and video applications in high-speed vehicular mobility by several orders of magnitude.
YmoA Negatively Regulates Expression of Invasin fromYersiniaenterocolitica

ABSTRACT inv encodes invasin, which is the primary invasion factor of Yersinia enterocolitica. inv expression in vitro is regulated in response to temperature, pH, and growth phase. In vitro, inv is maximally expressed at 26°C and repressed at 37°C at neutral pH but, when the pH of the media is adjusted to 5.5, levels of inv expression at 37°C are comparable to those at 26°C. A previous genetic screen for regulators of inv identified RovA, which was found to be required for activation of inv in vitro under all conditions tested as well as in vivo. Here we describe a screen that has identified a negative regulator of inv expression, ymoA. The ymoBA locus was identified by transposon mutagenesis as a repressor of inv expression in vitro at 37°C at neutral pH. This mutant shows increased inv expression at 37°C. The mutant can be fully complemented for inv expression by a plasmid expressing ymoA. These results indicate that YmoA plays a role in the negative regulation of inv.
An improved multi-label classification based on label ranking and delicate boundary SVM

In this paper, an improved multi-label classification is proposed based on label ranking and delicate decision boundary SVM. Firstly, an improved probabilistic SVM with delicate decision boundary is used as the scoring method to obtain a proper label rank. It can improve the probabilistic label rank by introducing the information of overlapped training samples into learning procedure. Secondly, a threshold selection related with input instance and label rank is proposed to decide the classification results. It can estimate an appropriate threshold for each testing instance according to the characteristics of instance and label rank. Experimental results on four multi-label benchmark datasets show that the proposed method improves the performance of classification efficiently, compared with binary SVM method and some existing well-known methods.
What, Where & How Many? Combining Object Detectors and CRFs

Computer vision algorithms for individual tasks such as object recognition, detection and segmentation have shown impressive results in the recent past. The next challenge is to integrate all these algorithms and address the problem of scene understanding. This paper is a step towards this goal. We present a probabilistic framework for reasoning about regions, objects, and their attributes such as object class, location, and spatial extent. Our model is a Conditional Random Field deﬁned on pixels, segments and objects. We deﬁne a global energy function for the model, which combines results from sliding window detectors, and low-level pixel-based unary and pairwise relations. One of our primary contributions is to show that this energy function can be solved efﬁciently. Experimental results show that our model achieves signiﬁcant improvement over the baseline methods on CamVid and PASCAL VOC datasets.
Thrombosis and antithrombotic therapy Anticoagulant vs . antiplatelet therapy in patients with cryptogenic stroke and patent foramen ovale : an individual participant data meta-analysis

CLINICAL RESEARCH Thrombosis and antithrombotic therapy
Catecholamines' Enhancement of Inducible Nitric Oxide Synthase-Induced Nitric Oxide Biosynthesis Involves CAT-1 and CAT-2A

Catecholamines enhance inducible nitric oxide synthase (iNOS) expression that results in nitric oxide (NO) overproduction in lipopolysaccharide (LPS)-stimulated macrophages. L-arginine transport mediated by cationic amino acid transporters (including CAT-1, CAT-2, CAT-2A, and CAT-2B) is crucial in regulating iNOS activity. We sought to assess the effects of catecholamines on L-arginine transport and CAT isozyme expression in stimulated macrophages. Confluent RAW264.7 cells were cultured with LPS with or without catecholamines (epinephrine or norepinephrine, 5 × 10−6 M) for 18 h. NO production, L-arginine transport, and enzyme expression were determined. Our data revealed that LPS co-induced iNOS, CAT-2, and CAT-2B expression, whereas CAT-1 and CAT-2A expression remained unaffected. Significant increases in NO production and L-arginine transport (approximately eight-fold and three-fold increases, respectively) were found in activated macrophages. Catecholamines significantly enhanced NO production and L-arginine transport (approximately 30% and 20% increases, respectively) in activated macrophages. Catecholamines also enhanced the expression of iNOS, CAT-1, and CAT-2A but not CAT-2 or CAT-2B in LPS-stimulated macrophages. Furthermore, the enhancement effects of catecholamines were inhibited by either dexamethasone or propranolol. We provide the first evidence to indicate that L-arginine transport in activated macrophages could be enhanced by catecholamines. Furthermore, this catecholamine-enhanced L-arginine transport might involve CAT-1 and CAT-2A but not CAT-2 or CAT-2B.
A Multi-Level Typology of Abstract Visualization Tasks

The considerable previous work characterizing visualization usage has focused on low-level tasks or interactions and high-level tasks, leaving a gap between them that is not addressed. This gap leads to a lack of distinction between the ends and means of a task, limiting the potential for rigorous analysis. We contribute a multi-level typology of visualization tasks to address this gap, distinguishing why and how a visualization task is performed, as well as what the task inputs and outputs are. Our typology allows complex tasks to be expressed as sequences of interdependent simpler tasks, resulting in concise and flexible descriptions for tasks of varying complexity and scope. It provides abstract rather than domain-specific descriptions of tasks, so that useful comparisons can be made between visualization systems targeted at different application domains. This descriptive power supports a level of analysis required for the generation of new designs, by guiding the translation of domain-specific problems into abstract tasks, and for the qualitative evaluation of visualization usage. We demonstrate the benefits of our approach in a detailed case study, comparing task descriptions from our typology to those derived from related work. We also discuss the similarities and differences between our typology and over two dozen extant classification systems and theoretical frameworks from the literatures of visualization, human-computer interaction, information retrieval, communications, and cartography.
Automatic detection of apnea and EEG arousals for sleep apnea syndrome

Sleep apnea/hypopnea syndrome (SAHS) is one of serious sleep disorders. EEG arousals due to the apnea hypopnea are frequently appeared in polysomnographic (PSG) record in patients with SAHS. In this study, the method for detecting EEG arousals and apnea interval in PSG record was proposed for supporting visual inspection to classify respiratory levels and EEG arousals. Two threshold values were established for classify the sleep apnea conditions and threshold values for detecting FFG arousals were then determined according to the results of apnea interval detection. Proposed automatic detection method will be an effective for diagnosis support system.
Rating Network Paths for Locality-Aware Overlay Construction and Routing

This paper investigates the rating of network paths, i.e., acquiring quantized measures of path properties such as round-trip time and available bandwidth. Compared to fine-grained measurements, coarse-grained ratings are appealing in that they are not only informative but also cheap to obtain. Motivated by this insight, we first address the scalable acquisition of path ratings by statistical inference. By observing similarities to recommender systems, we examine the applicability of solutions to a recommender system and show that our inference problem can be solved by a class of matrix factorization techniques. A technical contribution is an active and progressive inference framework that not only improves the accuracy by selectively measuring more informative paths, but also speeds up the convergence for available bandwidth by incorporating its measurement methodology. Then, we investigate the usability of rating-based network measurement and inference in applications. A case study is performed on whether locality awareness can be achieved for overlay networks of Pastry and BitTorrent using inferred ratings. We show that such coarse-grained knowledge can improve the performance of peer selection and that finer granularities do not always lead to larger improvements.
Novelty detection in time series data using ideas from immunology

Detecting anomalies in time series data is a problem of great practical interest in many manufacturing and signal processing applications. This paper presents a novelty detection algorithm inspired by the negative-selection mechanism of the immune system, which dis-criminates between self and other. Here self is de(cid:12)ned to be normal data patterns and non-self is any deviation exceeding an allowable variation. Experiments with this novelty detection algorithm are reported for two data sets - simulated cutting dynamics in a milling operation and a synthetic signal. The results of the experiments exhibiting the performance of the algorithm in detecting novel patterns are reported.
Mixed Collaborative and Content-Based Filtering with User-Contributed Semantic Features

We describe a recommender system which uses a unique combination of content-based and collaborative methods to suggest items of interest to users, and also to learn and exploit item semantics. Recommender systems typically use techniques from collaborative filtering, in which proximity measures between users are formulated to generate recommendations, or content-based filtering, in which users are compared directly to items. Our approach uses similarity measures between users, but also directly measures the attributes of items that make them appealing to specific users. This can be used to directly make recommendations to users, but equally importantly it allows these recommendations to be justified. We introduce a method for predicting the preference of a user for a movie by estimating the user's attitude toward features with which other users have described that movie. We show that this method allows for accurate recommendations for a sub-population of users, but not for the entire user population. We describe a hybrid approach in which a user-specific recommendation mechanism is learned and experimentally evaluated. It appears that such a recommender system can achieve significant improvements in accuracy over alternative methods, while also retaining other advantages.
Unfolding and Conformational Variations of Thrombin‐Binding DNA Aptamers: Synthesis, Circular Dichroism and Molecular Dynamics Simulations

Thrombin‐binding DNA aptamer (TBA), with a consensus 15‐base sequence: d(GGTTGGTGTGGTTGG), can fold into an antiparallel unimolecular G‐quadruplex structure that is necessary for its interaction with thrombin. For the first time, using steered molecular dynamics (SMD) simulations, we have successfully simulated the unfolding process of native TBA G‐quadruplex. The unfolding pathway proposed is in agreement with previously reported experimental NMR data. Moreover, the critical intermediate structure in the unfolding pathway, predicted by the NMR results, was identified. The structural characteristics of several TBA oligonucleotides modified with locked nucleoside (LNA) or 2′‐O‐methyl‐nucleoside (MNA) at different positions and number were also investigated by CD spectroscopy. An oligonucleotide substituted with either LNA or MNA at position 2 folds into a native‐like G‐quadruplex, while doubly substituted derivatives of TBA where LNA or MNA is incorporated at positions 11 and 14 are no longer able to form a G‐quadruplex. Starting from the same initial intermediate structure, we successfully overcame sampling limitations, and simulated the large conformational variations in structures of native TBA and modified TBAs by classic MD. Analysis of the models showed that inversion of the glycosyl orientation at position 14 contributes significantly to the disruption of G‐quadruplex formation in both of the di‐substituted modified TBA systems. Our calculations provide a simple and reliable theoretical model that can be used to investigate and predict the effects of the modifications of an oligonucleotide on the resultant G‐quadruplex structure. In addition, the computational protocol described can also be applied for other G‐quadruplex systems.
Psychometric evaluation of the Beck depression inventory in adults with social anxiety disorder

Although the Beck Depression Inventory (BDI) is commonly used to assess levels of depressive symptoms in socially anxious adults, its psychometric properties in this context have never been formally examined. Therefore, we examined the psychometrics of the BDI in a sample of adults with a principal diagnosis of social anxiety disorder (N = 113). The BDI exhibited good internal consistency and re‐test reliability. It also correlated significantly more strongly with other measures of depression than with measures of either social or non‐social anxiety. Thus, the BDI appears to be a valid tool for the assessment of depressive symptoms in adults with social anxiety disorder. Depression and Anxiety 14:145–148, 2001. © 2001 Wiley‐Liss, Inc.
Effect of the endogenous kappa opioid agonist dynorphin A(1-17) on cocaine-evoked increases in striatal dopamine levels and cocaine-induced place preference in C57BL/6J mice.

RATIONALE Effects of synthetic kappa opioid receptor agonists on cocaine-induced reward have been studied extensively in rats but relatively few studies have used the endogenous kappa agonist dynorphin A(1-17). OBJECTIVES Three studies were conducted to examine the effect of the natural sequence dynorphin on cocaine-induced increases in dopamine, on the formation of conditioned place preference and on increases in locomotor activity in C57BL/6 J mice. METHODS After implantation of guide cannulae into the caudate putamen, mice were allowed 4-5 days to recover from surgery. In the first study, dynorphin A (0, 1, 2, 4.4 nmol) was infused into the caudate putamen and dopamine levels were measured by in vivo microdialysis in that brain region. Then, the effect of dynorphin A (4.4 nmol) on increases in dopamine levels induced by 15 mg/kg cocaine i.p. was also measured with in vivo microdialysis. The third experiment examined the effect of dynorphin A (4.4 nmol) on conditioned place preference and locomotion induced by 15 mg/kg cocaine. RESULTS Dynorphin A significantly decreased basal dopamine levels in a dose-dependent manner by more than 60% at the highest dose, and this effect was completely blocked by pre-injection of the kappa-opioid receptor antagonist nor-BNI (10 mg/kg). The highest dose of dynorphin (4.4 nmol) blocked increases in dopamine levels, the formation of conditioned place preference and attenuated locomotion induced by 15 mg/kg cocaine. CONCLUSION The blockade of the cocaine-induced rise in striatal dopamine may contribute to both dynorphin's ability to prevent the development of cocaine-induced conditioned place preference and to attenuate the increase in locomotor activity.
Burden of common multiple-morbidity constellations on out-of-pocket medical expenditures among older adults.

PURPOSE On average, adults aged 60 years or older have 2.2 chronic diseases, contributing to the over 60 million Americans with multiple morbidities. We aimed to understand the financial implications of the most frequent multiple morbidities among older adults. DESIGN AND METHODS We analyzed Health and Retirement Study data, determining out-of-pocket medical expenses from 1998 and 2002 separately and examining differences in the impact of multiple-morbidity constellations on these expenses. We paid particular attention to the most common disease constellations - hypertension, arthritis, and heart disease. RESULTS An increasing prevalence of multiple morbidity (58% compared with 70% of adults had two or more chronic conditions in 1998 and 2002, respectively) was accompanied by escalating out-of-pocket expenditures (2,164 dollars in 1998, increasing by 104% to 3,748 dollars in 2002). Individuals with two, three, and four chronic conditions had health care expenditure increases of 41%, 85%, and 100%, respectively, over 4 years. Such patterns were particularly noticeable among the oldest old, those with higher educational attainment, and women, although having supplementary health insurance or Medicaid mitigated these expenses. Finally, there were significant differences in out-of-pocket expenditure levels among the multiple-morbidity combinations. IMPLICATIONS Increasing rates of multiple morbidities in conjunction with escalating health care costs and stable or declining incomes among elders warrant creative attention from providers, researchers, and policy makers. Further understanding how specific multiple-morbidity constellations impact out-of-pocket spending moves us closer to effective interventions to support vulnerable elders.
Implementation and Performance Analysis of Academic_MapReduce Algorithm (AcdMR)

In current scenario all organizations whether it is commercial, financial or educational, face the problem of maintaining and processing huge data. If we consider the case of educational institute, here thousands of student attendance records are generated per day. This will multiply per day, week and year as a result generates vast amount of data and subsequently increases the processing time apart from cost. In order to process the data efficiently, we have proposed MapReduce based algorithm (AcdMR) for processing of an academic data in our first paper. This work describes practical implementation of AcdMR and performance analysis based on distinct cluster configuration over dataset size (1GB, 5GB).
Efficient organization of large multidimensional arrays

Large multidimensional arrays are widely used in scientific and engineering database applications. The authors present methods of organizing arrays to make their access on secondary and tertiary memory devices fast and efficient. They have developed four techniques for doing this: (1) storing the array in multidimensional "chunks" to minimize the number of blocks fetched, (2) reordering the chunked array to minimize seek distance between accessed blocks, (3) maintaining redundant copies of the array, each organized for a different chunk size and ordering and (4) partitioning the array onto platters of a tertiary memory device so as to minimize the number of platter switches. The measurements on real data obtained from global change scientists show that accesses on arrays organized using these techniques are often an order of magnitude faster than on the unoptimized data. <<ETX>>
A Generic Social Capital Framework for Optimising Self-Organised Collective Action

Social capital has been defined as an attribute of individuals that facilitates cooperation to achieve mutual benefit, and enhances a group's capability to solve collective action problems. In this paper, we formalise a new computational framework for optimising self-organised collective action using electronic social capital. This framework comprises event handlers which update multivariate forms of social capital (trustworthiness, social network, and institutions), and a set of metrics over these forms that provide inputs to social decision-making processes. We implement an experimental multi-agent test bed where a number of agents iteratively play simultaneous n-player games, and use the social capital framework for their action-selection. Our results show that social capital optimises outcomes (in terms of long-term satisfaction and utility), reduces the complexity of decision making, and allows the system to scale to support self-organising collective action in 'large' groups.
An improved algorithm of FP - tree growth based on mapping

The basic philosophy of mining relevance rule algorithm in knowledge discovery is introduced. After analysis on obvious shortcoming of frequent Pattern tree (FP-tree) algorithm in relevance rule mining, an improved algorithm of FP - tree growth is proposed to enhance the performance of mining relevance rules based on mapping construction of FP-tree. The effectiveness of the method proposed here is demonstrated by the experiment result of an illustration simulation.