Miracle-dz/UniFAR-FTUs huggingface dataset:
Core and penumbral nitric oxide synthase activity during cerebral ischemia and reperfusion.

BACKGROUND AND PURPOSE The present studies examined the hypothesis that the distribution of cerebral injury after a focal ischemic insult is associated with the regional distribution of nitric oxide synthase (NOS) activity. METHODS Based on previous studies that certain anatomically well-defined areas are prone to become either core or penumbra after middle cerebral artery occlusion (MCAO), we measured NOS activity in these areas from the right and left hemispheres in a spontaneously hypertensive rat filament model. Four groups were studied: (1) controls (immediate decapitation); (2) 1.5 hours of MCAO with no reperfusion (R0); (3) 1.5 hours of MCAO with 0.5 hour of reperfusion (R0.5); and (4) 1.5 hours of MCAO with 24 hours of reperfusion (R24). Three groups of corresponding isoflurane sham controls were also included: 1.5 (S1.5) or 2 (S2.0) hours of anesthesia and 1.5 hours of anesthesia+24 hours of observation (S24). RESULTS Control core NOS activity for combined right and left hemispheres was 129% greater than penumbral NOS activity (P<0.05). Combined core NOS activity was also greater (P<0.05) in the three sham groups: 208%, 122%, and 161%, respectively. In the three MCAO groups, ischemic and nonischemic core NOS remained higher than penumbral regions (P<0.05). However, NOS activity was lower in the ischemic than in the nonischemic core in all three groups: R0 (29% lower), R0.5 (48%), and R24 (86%) (P<0.05). Addition of cofactors (10 micromol/L tetrahydrobiopterin, 3 micromol/L flavin adenine dinucleotide, and 3 micromol/L flavin mononucleotide) increased NOS activity in all groups and lessened the decrease in ischemic core and penumbral NOS. CONCLUSIONS Greater NOS activity in core regions could explain in part the increased vulnerability of that region to ischemia and could theoretically contribute to the progression of the infarct over time. The data also suggest that NOS activity during ischemia and reperfusion could be influenced by the availability of cofactors.
ATTRIBUTE SELECTION USING ARTIFICIAL NEURAL NETWORKS – A CASE STUDY OF ISCHEMIC HEART DISEASE

Attribute selection also called as feature selection is a preprocessing technique to select a set of features or subset of features from the available large collection of features. An artificial neural network is the simulation of a human brain which learns with experience. Efficiency of a model or a system in terms of cost, time and accuracy will greatly improve if proper features of a system are selected. This proposed method uses Artificial Neural Network for selecting the interesting or important features from the input layer of the network. A Multi Layer Perceptron Neural Network is used for selection of interesting features from a Ischemic heart data base with 712 patients. Initially the number of attributes was 17 and after feature selection the number of attributes was reduced to 12. All combination of features is attempted as Inputs of a Neural Network. When the input features become 12 the predicted accuracy during training is high as 87.36% using 10 – fold cross validation. Further removal of features lowers the accuracy and hence the interesting attributes selected for prediction is concluded to be as 12 for this Ischemic Heart Disease data set.
Soil moisture measurement using smart antennas

This review article presents an overview of the soil moisture measuring technique using microwave radiometry. Remote sensing of soil moisture is a promising field of research if accuracy can be achieved. The optimal frequency to estimate at this state using microwave radiometry is at L-band (~1.4GHz). In this article analysis of basics of soil microwave sensing, parameters responsible for radiation variation, soil dielectric properties and retrieval algorithm is discussed mainly. In the end discussion on synchronization of soil microwave radiation receiving by polarimetric radiometer, conclusions and future perspective has been presented.
DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment

MOTIVATION Many drug discovery projects fail because the underlying target is finally found to be undruggable. Progress in structure elucidation of proteins now opens up a route to automatic structure-based target assessment. DoGSiteScorer is a newly developed automatic tool combining pocket prediction, characterization and druggability estimation and is now available through a web server. AVAILABILITY The DoGSiteScorer web server is freely available for academic use at http://dogsite.zbh.uni-hamburg.de CONTACT rarey@zbh.uni-hamburg.de.
A Fuzzy Lattice Approach to Automated Multimodal Image Fusion

This paper presents a new technique for multimodal image fusion. Unlike most previous works on image fusion, this paper explores the use of fuzzy lattice theory in the fusion process. Our proposed image fusion algorithm involving infrared and visual images based on fuzzy lattice theory show better experimental result than the related research work. Finally the paper discusses several key topics for future research, including the applications of this technique to computer vision and other related fields.
THE EFFECT OF GAIN VARIATION IN IMPROVING LEARNING SPEED OF BACK PROPAGATION NEURAL NETWORK ALGORITHM ON CLASSIFICATION PROBLEMS

We proposed a method for improving the performance of the back propagation algorithm by introducing the adaptive gain of the activation function. In a ‘feed forward’ algorithm, the slope of the activation function is directly influenced by a parameter referred to as ‘gain’. In this paper, the influence of the adaptive gain on the learning ability of a neural network is analysed. Multi layer feed forward neural networks have been assessed. Physical interpretation of the relationship between the gain value and the learning rate and weight values is given. Instead of a constant ‘gain’ value, we proposed an algorithm to change the gain value adaptively for each node. The efficiency of the proposed method is verified by means of simulation on three classification problems. The results show that the proposed method significantly improves the learning speed of the general back-propagation algorithm.
Feature-based Localization Refinement of Players in Soccer using Plausibility Maps

In soccer, live acquisition of quantitative motion information such as distances covered by players or ball possession can only be performed by sophisticated automatic algorithms. The most challenging task in automatic acquisition of player movements arises in situations when players occlude each other. The main contribution of this work is a computer vision based localization of individual players in real-time with focus on crowded situations. The system automatically detects occlusions between players and performs an advanced refinement of the player localization. This is done by creating a plausibility map that assigns to each image pixel a confidence of being a player's center point. The plausibility map is based on the players' heights, the colors of their jerseys and the densities of their visual appearances in the image. Results confirm a significant improvement of the performance of tracking players in soccer scenarios.
High-Resolution Tip/Tilt Near-Infrared Imaging of Warm Ultraluminous Infrared Galaxies

We present high spatial resolution (FWHM≈0.″3-0.″5) H (1.6 μm) and K' (2.1 μm) images of a complete sample (12) of ultraluminous infrared galaxies (ULIGs) chosen to have "warm" mid-infrared colors (f25/f60 > 0.2) known to be characteristic of active galaxies. The extended underlying galaxy is detected in each system at H and K', as are tidal features and many of the star-forming knots seen at optical wavelengths. While some of these knots have considerable near-infrared excesses, we show that they are likely to have bolometric luminosities more similar to those of the extended starbursts with similar optical morphology seen in less far-infrared-luminous interacting systems. We find that each ULIG has increasing contributions at long wavelengths by a very compact source, which we identify as an active galactic nucleus (AGN). We show that the optical/near-infrared colors of these putative nuclei are much more extreme than in the most active starburst IRAS galaxies, yet are identical to those in "far-infrared-loud" quasars, which are in turn similar to optical quasars combined with large quantities of hot (≈800 K) dust. Half of the ULIGs have nuclei with dereddened near-infrared luminosities comparable to those of QSOs, while the other half have dereddened luminosities more similar to Seyfert galaxies, although this may be an effect of patchy extinction and scattering.
Similarities between location depth and regression

The location depth of Tukey (1975) is a multivariate generalization of rank, and leads to a multivariate median known as the Tukey median. Recently, Rousseeuw and Hubert (1999a) introduced a notion of depth in the regression setting. It provides thèrank' of any line (plane), rather than ranks of observations or residuals. In general, depth is an integer assigned to a candidate fit relative to a data set. Both in location and in regression, the depth of a fit can also be defined with regard to a population distribution. In this paper we explore the analogies between location and regression depth. We compute the depth functions at elliptical distributions. We compare the lower and upper bounds for the maximal depth. We also consider the robustness, the asymptotics and the computation of the deepest location and the deepest regression. Finally we introduce the notion of centrality, which is a more quantitative version of depth that leads to affine equivariant estimators of location and regression with 50% breakdown value.
Forensic Triage for Mobile Phones with DEC0DE

We present DEC0DE, a system for recovering information from phones with unknown storage formats, a critical problem for forensic triage. Because phones have myriad custom hardware and software, we examine only the stored data. Via flexible descriptions of typical data structures, and using a classic dynamic programming algorithm, we are able to identify call logs and address book entries in phones across varied models and manufacturers. We designed DEC0DE by examining the formats of one set of phone models, and we evaluate its performance on other models. Overall, we are able to obtain high performance for these unexamined models: an average recall of 97% and precision of 80% for call logs; and average recall of 93% and precision of 52% for address books. Moreover, at the expense of recall dropping to 14%, we can increase precision of address book recovery to 94% by culling results that don't match between call logs and address book entries on the same phone.
Memory Row Reuse Distance and its Role in Optimizing Application Performance

Continuously increasing dataset sizes of large-scale applications overwhelm on-chip cache capacities and make the performance of last-level caches (LLC) increasingly important. That is, in addition to maximizing LLC hit rates, it is becoming equally important to reduce LLC miss latencies. One of the critical factors that influence LLC miss latencies is row-buffer locality (i.e., the fraction of LLC misses that hit in the large buffer attached to a memory bank). While there has been a plethora of recent works on optimizing row-buffer performance, to our knowledge, there is no study that quantifies the full potential of row-buffer locality and impact of maximizing it on application performance. Focusing on multithreaded applications, the first contribution of this paper is the definition of a new metric called (memory) row reuse distance (RRD). We show that, while intra-core RRDs are relatively small (increasing the chances for row-buffer hits), inter-core RRDs are quite large (increasing the chances for row-buffer misses). Motivated by this, we propose two schemes that measure the maximum potential benefits that could be obtained from minimizing RRDs, to the extent allowed by program dependencies. Specifically, one of our schemes (Scheme-I) targets only intra-core RRDs, whereas the other one (Scheme-II) aims at reducing both intra-core RRDs and inter-core RRDs. Our experimental evaluations demonstrate that (i) Scheme-I reduces intra-core RRDs but increases inter-core RRDs; (ii) Scheme-II reduces inter-core RRDs significantly while achieving a similar behavior to Scheme-I as far as intra-core RRDs are concerned; (iii) Scheme-I and Scheme-II improve execution times of our applications by 17% and 21%, respectively, on average; and (iv) both our schemes deliver consistently good results under different memory request scheduling policies.
Pipelines for Image Matching and Recognition

A Typical Pipeline for Image Matching For an image matching scenario the goal is to match a given image into a database of one or more candidate images which may differ from the query image by virtue of 2D or 3D transforms. It is desired to know which of the database images share common scene elements with the query image and optionally to estimate the geometric transform which maps pixels in the query to each discovered image. Example applications include estimation of inter-frame camera motion in video, assembling image panoramas, and 3D estimation as in Microsoft Photosynth.
Load Balancing Distributed Inverted Files: Query Ranking

Search engines use inverted files as index data structures to speed up the solution of user queries. The index is distributed on a set of processors forming a cluster of computers and queries are received by a broker machine and scheduled for solution in the cluster. The broker must use a scheduling algorithm to assign queries to processors since the computations associated with the ranking of documents that form part of the solutions to queries can take a significant fraction of the total running time. The cost of this task can be highly variable and depends on the particular user preferences for words when formulating queries in a given period of time. Thus the scheduling algorithm must be able to cope efficiently with a highly dynamic and very large amount of jobs being assigned in an on-line manner to the processors. In this paper we evaluate a number of scheduling algorithms proposed in the literature in the context of scheduling queries on a search engine.
A data fusion approach for the analysis of azimuth ambiguities

In the context of sea and ocean monitoring from SAR images, this paper illustrates a study of azimuth ambiguities, together with a new approach suggested to solve the problem. The proposed method presents here the added value in application to coastal monitoring and ship detection. This work is developed in the context of the project1 funded by the Italian Space Agency. The data set for experimentation is made of COSMO-SkyMed images related to Ligurian Sea (Italy).
High performance human resource management in Ireland and the Netherlands

Does high performance human resource management deliver superior organisational performance and if so, how would this come about? Do these practices make a difference to employee work performance and cooperation? Could all companies, regardless of their context, benefit from these HR practices and what factors are associated with the take-up of high performance human resource management? In this study each of these questions is examined using a matched establishment survey in two European countries, Ireland and the Netherlands. Firstly, it tackles the question of whether high performance HR management effects are exerted at several levels of the company. Particular consideration is given to the relationships among the practices, drawing out the implications of this inter-play for mobilising employee performance. Secondly, the study explicates the role played by the company's business strategy as a condition under which improvements made by high performance HR management at the employee level can be translated into improvements at the production and corporate levels. Thirdly, the study discriminates between factors that facilitate or inhibit the diffusion of high performance human resource management practices. Finally, there is a discussion of the role played by the socio-economic and cultural contexts of the countries in which companies implement high performance human resource management.
An analysis of morphological changes in visual cortical neurons after knocking down scaffolding proteins of glutamate receptors

NMDA receptor subunit changes have an important implication in synaptic development, learning, memory, and neuronal disorders. Previous studies have suggested that two membrane-associated guanylate kinase (MAGUK) family scaffolding proteins, SAP102 and PSD-95, are involved in the switch from predominance of NMDA receptors rich in NR2B subunits to that of NMDA receptors rich in NR2A subunits. Normally, eye opening causes PSD-95 levels to increase at synapses and its interactions with NR2A to increase while its interactions with NR2B decrease. In order to identify the exact roles of the two MAGUKs, this study examined changes in dendritic morphology of mouse visual cortical neurons at postnatal day 15 induced by eye opening and knocking down each of the two MAGUKs with small inhibitory RNAs (siRNAs). The changes I found include smaller cell bodies, increased frequency of dendritic branching, and a decrease in the number of dendritic intersections with a radial sphere centered on the cell body. Since dendritic patterning is critical for neuronal information processing, these results suggest an important aspect of MAGUK functions in cortical development. Further studies including mice at different ages and mice with closed eyes will determine the roles of MAGUKs in ageand activity-dependent development of the visual cortical circuit. Thesis Supervisor: Martha Constantine-Paton Title: Professor of Biology and Brain and Cognitive Sciences
Mallampati test as a predictor of laryngoscopic view.

AIM To determine the accuracy of the modified Mallampati test for predicting difficult tracheal intubation. DESIGN A cross-sectional, clinical, observational, non-blinded study. A quality analysis of anesthetic care. SETTING Operating theatres and department of anesthesiology in a university hospital. MATERIAL AND METHODS Following the local ethics committee approval and patients' informed consent to anesthesia, all adult patients (> 18 yrs) presenting for any type of non-emergency surgical procedures under general anesthesia requiring endotracheal intubation were enrolled. Prior to anesthesia, Samsoon and Young's modification of the Mallampati test (modified Mallampati test) was performed. Following induction, the anesthesiologist described the laryngoscopic view using the Cormack-Lehane scale. Classes 3 or 4 of the modified Mallampati test were considered a predictor of difficult intubation. Grades 3 or 4 of the Cormack-Lehane classification of the laryngoscopic view were defined as impaired glottic exposure. The sensitivity, specificity, positive and negative predictive value, relative risk, likelihood ratio and accuracy of the modified Mallampati test were calculated on 2x2 contingency tables. RESULTS Of the total 1,518 patients enrolled, 48 had difficult intubation (3.2%). We failed to detect as many as 35.4% patients in whom glottis exposure during direct laryngoscopy was inadequate (sensitivity 0.646). Compared to the original article by Mallampati, we found lower specificity (0.824 vs. 0.995), lower positive predictive value (0.107 vs. 0.933), higher negative predictive value (0.986 vs. 0.928), lower likelihood ratio (3.68 vs. 91.0) and accuracy (0.819 vs. 0.929). CONCLUSION When used as a single examination, the modified Mallampati test is of limited value in predicting difficult intubation.
A New Approach Combined Fuzzy Clustering and Bayesian Networks for Modeling Gene Regulatory Networks

The appearance of microarray technologies makes it possible to simultaneously monitor the expression levels for tens of thousands of genes. Bayesian Network (BN) is an important approach for predicting gene regulatory networks from expression data. However, two fundamental problems greatly reduce the effectiveness of current BN methods. The first problem is much less samples than genes, it makes that to find a "best" network is very difficult. The second is the excessive computational time, the search space of possible Bayesian network is very large because genes are numerous. In this paper, we introduce a new method to learn Bayesian networks which combines fuzzy clustering algorithm to reduce the search space. From the view of systems biology, modularity and hierarchy are key features of biological networks. This allows us to gain global network by assembling local components. A local component is composed of genes with same function. Those genes may have same or very similar expression pattern. Besides, those inter-related genes may be bridge of different functional modules. Consequently, in a process of learning of local networks, the gap between the number of samples and genes is shortened, and the search space is reduced.Our approach is evaluated using artificial data and expression data measured during the yeast cell cycle. Results demonstrate that this approach can predict regulatory networks with significantly improved accuracy and reduced computational time compared with existing BN approaches.
Testing an integrated behavioural and biomedical model of disability in N-of-1 studies with chronic pain

Background: Previous research has supported an integrated biomedical and behavioural model explaining activity limitations. However, further tests of this model are required at the within-person level, because while it proposes that the constructs are related within individuals, it has primarily been tested between individuals in large group studies. We aimed to test the integrated model at the within-person level. Method: Six correlational N-of-1 studies in participants with arthritis, chronic pain and walking limitations were carried out. Daily measures of theoretical constructs were collected using a hand-held computer (PDA), the activity was assessed by self-report and accelerometer and the data were analysed using time-series analysis. Results: The biomedical model was not supported as pain impairment did not predict activity, so the integrated model was supported partially. Impairment predicted intention to move around, while perceived behavioural control (PBC) and intention predicted activity. PBC did not predict activity limitation in the expected direction. Conclusions: The integrated model of disability was partially supported within individuals, especially the behavioural elements. However, results suggest that different elements of the model may drive activity (limitations) for different individuals. The integrated model provides a useful framework for understanding disability and suggests interventions, and the utility of N-of-1 methodology for testing theory is illustrated.