Miracle-dz/UniFAR-FTUs huggingface dataset:
Overexpressed or intraperitoneally injected human transferrin prevents photoreceptor degeneration in rd10 mice

Purpose Retinal degeneration has been associated with iron accumulation in age-related macular degeneration (AMD), and in several rodent models that had one or several iron regulating protein impairments. We investigated the iron concentration and the protective role of human transferrin (hTf) in rd10 mice, a model of retinal degeneration. Methods The proton-induced X-ray emission (PIXE) method was used to quantify iron in rd10 mice 2, 3, and 4 weeks after birth. We generated mice with the β-phosphodiesterase mutation and hTf expression by crossbreeding rd10 mice with TghTf mice (rd10/hTf mice). The photoreceptor loss and apoptosis were evaluated by terminal deoxynucleotidyl transferase dUTP nick end labeling in 3-week-old rd10/hTf mice and compared with 3-week-old rd10 mice. The neuroprotective effect of hTf was analyzed in 5-day-old rd10 mice treated by intraperitoneal administration with hTf for up to 25 days. The retinal hTf concentrations and the thickness of the outer nuclear layer were quantified in all treated mice at 25 days postnatally. Results PIXE analysis demonstrated an age-dependent iron accumulation in the photoreceptors of rd10 mice. The rd10/hTf mice had the rd10 mutation, expressed high levels of hTf, and showed a significant decrease in photoreceptor death. In addition, rd10 mice intraperitoneally treated with hTf resulted in the retinal presence of hTf and a dose-dependent reduction in photoreceptor degeneration. Conclusions Our results suggest that iron accumulation in the retinas of rd10 mutant mice is associated with photoreceptor degeneration. For the first time, the enhanced survival of cones and rods in the retina of this model has been demonstrated through overexpression or systemic administration of hTf. This study highlights the therapeutic potential of Tf to inhibit iron-induced photoreceptor cell death observed in degenerative diseases such as retinitis pigmentosa and age-related macular degeneration.
A model for sequential evolution of ligands by exponential enrichment (SELEX) data

A Systematic Evolution of Ligands by EXponential enrichment (SELEX) experiment begins in round one with a random pool of oligonucleotides in equilibrium solution with a target. Over a few rounds, oligonucleotides having a high affinity for the target are selected. Data from a high throughput SELEX experiment consists of lists of thousands of oligonucleotides sampled after each round. Thus far, SELEX experiments have been very good at suggesting the highest affinity oligonucleotide, but modeling lower affinity recognition site variants has been difficult. Furthermore, an alignment step has always been used prior to analyzing SELEX data. We present a novel model, based on a biochemical parametrization of SELEX, which allows us to use data from all rounds to estimate the affinities of the oligonucleotides. Most notably, our model also aligns the oligonucleotides. We use our model to analyze a SELEX experiment containing double stranded DNA oligonucleotides and the transcription factor Bicoid as the target. Our SELEX model outperformed other published methods for predicting putative binding sites for Bicoid as indicated by the results of an in-vivo ChIP-chip experiment.
The Lam/Mpi Checkpoint/Restart Framework: System-Initiated Checkpointing

As high performance clusters continue to grow in size and popularity, issues of fault tolerance and reliability are becoming limiting factors on application scalability. To address these issues, we present the design and implementation of a system for providing coordinated checkpointing and rollback recovery for MPI-based parallel applications. Our approach integrates the Berkeley Lab BLCR kernel-level process checkpoint system with the LAM implementation of MPI through a defined checkpoint/restart interface. Checkpointing is transparent to the application, allowing the system to be used for cluster maintenance and scheduling reasons as well as for fault tolerance. Experimental results show negligible communication performance impact due to the incorporation of the checkpoint support capabilities into LAM/MPI.
A novel LDA-based approach for motor bearing fault detection

Early detection of abnormalities for electrical motors is a key point to reduce economic losses caused by unscheduled maintenance and shutdown time. In this context, health monitoring and fault diagnosis are crucial tasks to be performed. We introduce a novel Linear Discriminant Analysis (LDA) based algorithm to deal with fault data dimension reduction and fault detection issues. In particular the algorithm, namely Δ-LDA, is designed to overcome the problem of a between-class scatter matrix trace very close to zero. Indeed, if the information of the expected value is not sufficient to discriminate the classes, we propose the use of the difference of covariance matrices. A performance comparison with other conventional methods, e.g. principal component analysis and classical LDA, is proposed. In particular experimental results show that the proposed algorithm improves the classification accuracy if the classes are overlapped, and gives comparable results in the remaining scenarios.
STAR: Self-Tuning Aggregation for Scalable Monitoring

We present STAR, a self-tuning algorithm that adaptively sets numeric precision constraints to accurately and efficiently answer continuous aggregate queries over distributed data streams. Adaptivity and approximation are essential for both robustness to varying workload characteristics and for scalability to large systems. In contrast to previous studies, we treat the problem as a workload-aware optimization problem whose goal is to minimize the total communication load for a multi-level aggregation tree under a fixed error budget. STAR's hierarchical algorithm takes into account the update rate and variance in the input data distribution in a principled manner to compute an optimal error distribution, and it performs cost-benefit throttling to direct error slack to where it yields the largest benefits. Our prototype implementation of STAR in a large-scale monitoring system provides (1) a new distribution mechanism that enables self-tuning error distribution and (2) an optimization to reduce communication overhead in a practical setting by carefully distributing the initial, default error budgets. Through extensive simulations and experiments on a real network monitoring implementation, we show that STAR achieves significant performance benefits compared to existing approaches while still providing high accuracy and incurring low overheads.
Clinical and Technical Overview of Preimplantation Genetic Diagnosis for Fragile X Syndrome: Experience at the University Hospital Virgen del Rocio in Spain

Fragile X syndrome (FXS) accounts for about one-half of cases of X-linked intellectual disability and is the most common monogenic cause of mental impairment. Reproductive options for the FXS carriers include preimplantation genetic diagnosis (PGD). However, this strategy is considered by some centers as wasteful owing to the high prevalence of premature ovarian failure in FXS carriers and the difficulties in genetic diagnosis of the embryos. Here we present the results of our PGD Program applied to FXS, at the Department of Genetics, Reproduction and Fetal Medicine of the University Hospital Virgen del Rocío in Seville. A total of 11 couples have participated in our PGD Program for FXS since 2010. Overall, 15 cycles were performed, providing a total of 43 embryos. The overall percentage of transfers per cycle was 46.67% and the live birth rate per cycle was 13.33%. As expected, these percentages are considerably lower than the ones obtained in PGD for other pathologies. Our program resulted in the birth of 3 unaffected babies of FXS for 2 of the 11 couples (18.2%) supporting that, despite the important drawbacks of PGD for FXS, efforts should be devoted in offering this reproductive option to the affected families.
Kraft Pulping Properties of Acacia mearnsii and Eucalyptus grandis Grown in Zimbabwe

SYNOPSIS Pulpwood samples from 8-year old Acacia mearnsii and Eucalyptus grandis plantations grown in Zimbabwe were evaluated for kraft pulping, bleaching and papermaking properties. The possibility of co-pulping the two species was also investigated. A. mearnsii had higher chip basic density than E. grandis, and required more active alkali to produce less pulp. At about the same Kappa number, the A. mearnsii pulp had lower strength properties but higher opacity. Pulps of both species could be bleached to an acceptable brightness. The results demonstrated that the conventional prejudice against the use of A. mearnsii as pulpwood in Zimbabwe may be undeserved. The species has superior pulpwood productivity and can be successfully co-pulped with E. grandis, the preferred hardwood pulpwood species in Zimbabwe.
Regulation of denitrification at the cellular level: a clue to the understanding of N2O emissions from soils

Denitrifying prokaryotes use NOx as terminal electron acceptors in response to oxygen depletion. The process emits a mixture of NO, N2O and N2, depending on the relative activity of the enzymes catalysing the stepwise reduction of NO3− to N2O and finally to N2. Cultured denitrifying prokaryotes show characteristic transient accumulation of NO2−, NO and N2O during transition from oxic to anoxic respiration, when tested under standardized conditions, but this character appears unrelated to phylogeny. Thus, although the denitrifying community of soils may differ in their propensity to emit N2O, it may be difficult to predict such characteristics by analysis of the community composition. A common feature of strains tested in our laboratory is that the relative amounts of N2O produced (N2O/(N2+N2O) product ratio) is correlated with acidity, apparently owing to interference with the assembly of the enzyme N2O reductase. The same phenomenon was demonstrated for soils and microbial communities extracted from soils. Liming could be a way to reduce N2O emissions, but needs verification by field experiments. More sophisticated ways to reduce emissions may emerge in the future as we learn more about the regulation of denitrification at the cellular level.
Learn to adapt based on users' feedback

Adaptive and personalized behavior is becoming essential and desirable in Human-Robot Interactive systems. We are interested in adaptive robots that learn from interaction traces (previous interactions with users). Our proposal is based on types of interactions where users express their level of satisfaction through feedback. Indeed, depending on the situation of interaction and the user himself, the robot behavior should adjust, and therefore can be judged, differently. From interaction traces (including robot actions and users' feedback), we aim to extract adaptation rules that give the dependencies between certain attributes of the interaction situation and/or the user profile, and the level of user satisfaction. We propose two learning algorithms to learn these adaptation rules. The first algorithm is direct, certain and optimal but slow to converge. The second is able to detect the importance of certain attributes in the adaptation process. It generalizes adaptation rules on unknown situations and to first time users, which makes it an approach with risk. We detail in this paper, our proposed model, both learning algorithms, and an evaluation of the learned rules from both algorithms by simulations and through a scenario with real users.
Spatially aware handhelds for high-precision tangible interaction with large displays

While touch-screen displays are becoming increasingly popular, many factors affect user experience and performance. Surface quality, parallax, input resolution, and robustness, for instance, can vary with sensing technology, hardware configurations, and environmental conditions. We have developed a framework for exploring how we could overcome some of these dependencies, by leveraging the higher visual and input resolution of small, coarsely tracked mobile devices for direct, precise, and rapid interaction on large digital displays. The results from a formal user study show no significant differences in performance when comparing four techniques we developed for a tracked mobile device, where two existing touch-screen techniques served as baselines. The mobile techniques, however, had more consistent performance and smaller variations among participants, and an overall higher user preference in our setup. Our results show the potential of spatially aware handhelds as an interesting complement or substitute for direct touch-interaction on large displays.
Purification of a factor which provides a costimulatory signal for gamma interferon production

A protein factor which induces high levels of gamma interferon (IFN-gamma) in resting splenic nonadherent cells was isolated from the sera of mice with generalized inflammation caused by endotoxic shock. The factor was highly purified by ammonium sulfate precipitation followed by ion-exchange column chromatography on DEAE-Sepharose, molecular sieving on Ultrogel AcA 44, and hydrophobic column chromatography with phenyl-Sepharose. It was further purified to apparent homogeneity by polyacrylamide gel electrophoresis. It induced IFN-gamma production in a dose-dependent manner in the presence of interleukin-2, monoclonal anti-CD3 antibody (anti-CD3 MAb), or concanavalin A (ConA) in spleen cells deprived of plastic plate- and nylon wool-adherent cells. Anti-CD3 MAb induced the highest level of production of the three. The factor, interleukin-2, anti-CD3 MAb, or ConA alone induced a trace of or no detectable IFN-gamma in these cells. The factor also exhibited an accessory function during proliferation in these cells in the presence of a suboptimal dose of ConA. However, the factor failed to stimulate IFN-gamma production when staphylococcal enterotoxin A, a superantigenic T-cell mitogen, was employed. Treatment with pronase or heat abolished these activities. These studies confirm the existence of a soluble protein factor which is able to exhibit a novel accessory function in IFN-gamma production in resting T or natural killer cells. It will be of interest to compare this factor with the recently cloned human natural killer stimulatory factor (NKSF/IL-12).
I-HASTREAM: Density-Based Hierarchical Clustering of Big Data Streams and Its Application to Big Graph Analytics Tools

Big Data Streams are very popular at now, as stirred-up by a plethora of modern applications such as sensor networks, scientific computing tools, Web intelligence, social network analysis and mining tools, and so forth. Here, the main research issue consists in how to effectively and efficiently extract useful knowledge from (streaming) big data, in order to support innovative big data analytics platforms. To this end, clustering analysis is a well-known tool for extracting knowledge from big data streams, as also confirmed by recent trends in active literature. A special applicative case is represented by so-called graph-shaped data (big) streams, which are produced by graph sources providing both structure-and content-oriented knowledge. On top of such sources, big graph analytics is a leading scientific area to be considered. At the convergence of these emerging topics, in this paper we provide the following contributions: (i) I-HASTREAM, a novel density-based hierarchical clustering algorithm for evolving big data streams that founds on it predecessor, namely HASTREAM, (ii) the architecture of a big graph analytics engine that embeds I-HASTREAM in its core layer.
Opposite Effects of Diabetes on Nephrotoxic and Ischemic Acute Tubular Necrosis

Abstract The effect of streptozotocin-induced diabetes mellitus on two different models of acute tubular necrosis (ATN), was studied: (i) the nephrotoxic model of HgCl2-induced ATN and (ii) the ischemic model of renal artery clamping for 60 min. Induction of ATN with HgCl2 in normal rats decreased CrCl from 0.67 ± 0.05 to 0.1 ± 0.019 ml/min (P < 0.001) after 24 hr, and it deteriorated further to 0.03 ± 0.013 ml/min after 48 hr; whereas, in the diabetic rats, HgCl2 decreased CrCl from 0.98 ± 0.11 only to 0.31 ± 0.037 ml/min (P < 0.0001), but CrCl recovered to 0.50 ± 0.08 ml/min after 48 hr. Bilateral clamping of renal arteries for 60 min in control and diabetic rats extremely decreased CrCl in both groups. Twenty-four hours after clamping, two of nine rats from the diabetic group died, whereas none from the control group died. Forty-eight hours after clamping, all nine rats from the diabetic group died, whereas only two rats from the control group died, and in the four surviving rats CrCl recovered slightly. Our study shows that streptozotocin-induced diabetes could not confer a general protection against ATN. It was protective against a nephrotoxic insult but aggravated the ischemic insult. An attempt to reconcile these discrepant effects is made in the Discussion.
Formation of an Internal Model of Environment Dynamics During Upper Limb Reaching Movements: A Fuzzy Approach

This paper presents the development and simulation of a fuzzy logic based learning mechanism to emulate human motor learning. In particular, fuzzy inference was used to develop an internal model of a novel dynamic environment experienced during planar reaching movements with the upper limb. A dynamic model of the human arm was developed and a fuzzy if-then rule base was created to relate trajectory movement and velocity errors to internal model update parameters. An experimental simulation was performed to compare the fuzzy system's performance with that of human subjects. It was found that the dynamic model behaved as expected, and the fuzzy learning mechanism created an internal model that was capable of opposing the environmental force field to regain a trajectory closely resembling the desired ideal.
PERFORMANCE ANALYSIS OF WLAN BY COMPARATIVE STUDY OF VARIOUS ATTRIBUTES USING OPNET SIMULATOR

Analysis of wireless LAN is very important because by using LAN sharing capability of network we share large data at far distances. In this paper we are analyzing various parameters and set their value for optimized performance of network. We are taking various value of data rate, analyze for different segment size and fragmentation threshold. Further we increase no. of nodes and analyze the network for same parameters. At the end we compare the simulation results and get final conclusion. We are analyzing these all performance for OLSR routing protocol which is used for WLAN.
Implementing Neonatal Screening for Haemoglobinopathies in the Netherlands

Background The birth prevalence of severe haemoglobinopathies such as sickle cell disease (SCD) in the Netherlands has been estimated to be at least 50 newborns per year. Neonatal screening for SCD was added to the Dutch screening programme in January 2007. We here evaluated three high performance liquid chromatography (HPLC) systems for application in neonatal screening for haemoglobinopathies, and present the results of a subsequent pilot screening programme. Methods The Variant NewBorn Screening (Vnbs) HPLC system (Bio-Rad) was validated by analysing 131 blood samples and blood mixtures. Subsequently, the performance of the G7 (Tosoh BioScience) and Ultra (Primus Corporation) was compared with the Vnbs. The three HPLC analysers were tested in a pilot screening programme on 21,969 dried blood spot samples from the routine Dutch neonatal screening programme. Results The pilot screening resulted in 188 abnormal patterns. The three HPLC devices presented comparable within-and between-run precision and detected the abnormal samples similarly. The high throughput, sampling systems, presentation of results, and integration of the chromatograms, however, were different. Conclusion All three analysers detected the same abnormal haemoglobins satisfactorily, but integrated the chromatograms with variable imprecision. Comparison of the results suggested that the Bio-Rad Vnbs was the preferred system. However, software adjustments were required to improve the diagnostic potential of this device for screening for β- and α-thalassaemia.
A hybrid rule and machine learning based generic alerting platform for smart environments

Existing smart environment based alert solutions have adopted a relatively complex and tailored approach to supporting individuals. These solutions have involved sensor based monitoring, activity recognition and assistance provisioning. Traditionally they have suffered from a number of issues, rooted in scalability and performance, associated with complex activity recognition processes. This paper introduces a generic approach to realizing an alerting platform within a smart environment. The core concept of this approach is presented and placed within the context of related work. A description of the approach is provided, followed by an evaluation. This evaluation shows the approach offers reasonable accuracy, future work will increase accuracy.
Online Performance Projection for Clusters with Heterogeneous GPUs

We present a fully automated approach to project the relative performance of an OpenCL program over different GPUs. Performance projections can be made within a small amount of time, and the projection overhead stays relatively constant with the input data size. As a result, the technique can help runtime tools make dynamic decisions about which GPU would run faster for a given kernel. Usage cases of this technique include scheduling or migrating GPU workloads over a heterogeneous cluster with different types of GPUs.
Exploiting language models to recognize unseen actions

This paper addresses the problem of human action recognition. Typically, visual action recognition systems need visual training examples for all actions that one wants to recognize. However, the total number of possible actions is staggering as not only are there many types of actions but also many possible objects for each action type. Normally, visual training examples are needed for all actions of this combinatorial explosion of possibilities. To address this problem, this paper is a first attempt to propose a general framework for unseen action recognition in still images by exploiting both visual and language models. Based on objects recognized in images by means of visual features, the system suggests the most plausible actions exploiting off-the-shelf language models. All components in the framework are trained on universal datasets, hence the system is general, flexible, and able to recognize actions for which no visual training example has been provided. This paper shows that our model yields good performance on unseen action recognition. It even outperforms a state-of-the-art Bag-of-Words model in a realistic scenario where few visual training examples are available.
Using Neighborhood Beyond One Hop in Disruption-Tolerant Networks

Most disruption-tolerant networking (DTN) protocols available in the literature have focused on mere contact and intercontact characteristics to make forwarding decisions. Nevertheless, there is a world behind contacts: just because one node is not in contact with some potential destination, it does not mean that this node is alone. There may be interesting end-to-end transmission opportunities through other nearby nodes. Existing protocols miss such possibilities by maintaining a simple contact-based view of the network. In this paper, we investigate how the vicinity of a node evolves through time and whether such information can be useful when routing data. We observe a clear tradeoff between routing performance and the cost for monitoring the neighborhood. Our analyses suggest that limiting a node's neighborhood view to three or four hops is more than enough to significantly improve forwarding efficiency without incurring prohibitive overhead.